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.gitignore

··· 19 .env 20 .env.* 21 !.env.example 0 22 23 # IDE 24 .vscode/

··· 19 .env 20 .env.* 21 !.env.example 22 + .session 23 24 # IDE 25 .vscode/

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CLAUDE.md

··· 8 - 3.10+ and complete typing (T | None preferred over Optional[T] and list[T] over typing.List[T]) 9 - use prefer functional over OOP 10 - keep implementation details private and functions pure 0 11 12 ## Project Structure 13 ··· 25 - `templates.py` - HTML templates 26 27 - `tests/` - Test files 28 - - `scripts/` - Utility scripts (test_post.py, test_mention.py) 29 - - `sandbox/` - Documentation and analysis 30 - Reference project analyses 31 - Architecture plans 32 - Implementation notes 0 33 - `.eggs/` - Cloned reference projects (void, penelope, marvin) 0 0 0 34 35 ## Testing 36 - Run bot: `just dev`

··· 8 - 3.10+ and complete typing (T | None preferred over Optional[T] and list[T] over typing.List[T]) 9 - use prefer functional over OOP 10 - keep implementation details private and functions pure 11 + - never use `pytest.mark.asyncio`, its unnecessary 12 13 ## Project Structure 14 ··· 26 - `templates.py` - HTML templates 27 28 - `tests/` - Test files 29 + - `scripts/` - Curated utility scripts that have proven useful 30 + - `sandbox/` - Proving ground for experiments, analysis, and unproven scripts 31 - Reference project analyses 32 - Architecture plans 33 - Implementation notes 34 + - Experimental scripts (graduate to scripts/ once proven useful) 35 - `.eggs/` - Cloned reference projects (void, penelope, marvin) 36 + 37 + ## Script Graduation Process 38 + New scripts start in `sandbox/`, get promoted to `scripts/` once proven useful, and may eventually get just commands added if the workflow should be broadcast to other developers. Not everything graduates - most things stay in sandbox. 39 40 ## Testing 41 - Run bot: `just dev`

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README.md

··· 1 - # phi 🧠 2 3 - a bot inspired by IIT and [Void](https://tangled.sh/@cameron.pfiffer.org/void). Built with `fastapi`, `pydantic-ai`, and `atproto`. 4 - 5 - ## Quick Start 6 7 - ### Prerequisites 8 - 9 - - `uv` 10 - - `just` 11 - - `turbopuffer` (see [turbopuffer](https://github.com/turbopuffer/turbopuffer)) 12 - - `openai` (for embeddings) 13 - - `anthropic` (for chat completion) 14 - 15 - Get your bot running in 5 minutes: 16 17 ```bash 18 - # Clone and install 19 git clone https://github.com/zzstoatzz/bot 20 cd bot 21 uv sync 22 23 - # Configure (copy .env.example and add your credentials) 24 cp .env.example .env 0 25 26 - # Run the bot 27 - just dev 28 ``` 29 30 - That's it! Your bot is now listening for mentions. 31 - 32 - ## Configuration 33 - 34 - Edit `.env` with your credentials: 35 - - `BLUESKY_HANDLE`: Your bot's Bluesky handle 36 - - `BLUESKY_PASSWORD`: App password (not your main password!) 37 - - `ANTHROPIC_API_KEY`: Your Anthropic key for AI responses 38 - - `TURBOPUFFER_API_KEY`: Your TurboPuffer key for memory storage 39 - - `OPENAI_API_KEY`: Your OpenAI key for embeddings (memory system) 40 - - `BOT_NAME`: Your bot's name (default: "Bot") 41 - - `PERSONALITY_FILE`: Path to personality markdown file (default: "personalities/phi.md") 42 43 - ## Current Features 0 44 45 - - ✅ Responds to mentions with AI-powered messages 46 - - ✅ Proper notification handling (no duplicates) 47 - - ✅ Graceful shutdown for hot-reload 48 - - ✅ AI integration with Anthropic Claude 49 - - ✅ Thread-aware responses with full conversation context 50 - - ✅ Status page at `/status` showing activity and health 51 - - ✅ Web search capability (Google Custom Search API) 52 - - ✅ Content moderation with philosophical responses 53 - - ✅ Namespace-based memory system with TurboPuffer 54 - - ✅ Online/offline status in bio 55 - - ✅ Self-modification with operator approval system 56 - - ✅ Context visualization at `/context` 57 - - ✅ Semantic search in user memories 58 59 - ## Architecture 0 0 0 0 0 60 61 - - **FastAPI** web framework with async support 62 - - **pydantic-ai** for LLM agent management 63 - - **TurboPuffer** for scalable vector memory 64 - - **AT Protocol** for Bluesky integration 65 - - **SQLite** for thread context storage 66 67 - ## Development 68 69 ```bash 70 - just # Show available commands 71 - just dev # Run with hot-reload 72 - just check # Run linting, type checking, and tests 73 - just fmt # Format code 74 - just lint # Run ruff linter 75 - just typecheck # Run ty type checker 76 - just test # Run test suite 77 - 78 - # Bot testing utilities 79 - just test-post # Test posting to Bluesky 80 - just test-mention # Test mention handling 81 - just test-search # Test web search 82 - just test-thread # Test thread context 83 - just test-dm # Test DM functionality 84 - 85 - # Memory management 86 - just memory-init # Initialize core memories 87 - just memory-check # View current memory state 88 - just memory-migrate # Migrate memories 89 ``` 90 91 - ### Web Interface 0 92 93 - **Status Page** (http://localhost:8000/status) 94 - - Current bot status and uptime 95 - - Mentions received and responses sent 96 - - AI mode (enabled/placeholder) 97 - - Last activity timestamps 98 - - Error count 99 100 - **Context Visualization** (http://localhost:8000/context) 101 - - View all context components that flow into responses 102 - - Inspect personality, memories, thread context 103 - - Debug why the bot responded a certain way 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 104 105 - ## Personality System 106 107 - The bot's personality is defined in markdown files in the `personalities/` directory. This allows for rich, detailed personality definitions that shape how the bot communicates. 0 0 0 108 109 - - See `personalities/phi.md` for an example exploring consciousness 110 - - See `personalities/default.md` for a simple assistant 111 - - Create your own by adding a `.md` file and setting `PERSONALITY_FILE` in `.env` 112 113 - ## Tools & Capabilities 0 114 115 - ### Web Search 116 - The bot can search the web when configured with Google Custom Search API credentials. Add to `.env`: 117 - - `GOOGLE_API_KEY`: Your Google API key 118 - - `GOOGLE_SEARCH_ENGINE_ID`: Your custom search engine ID 119 120 - ### Content Moderation 121 - Built-in moderation filters: 122 - - Spam detection (excessive caps, repetition, promotional content) 123 - - Harassment and hate speech filtering 124 - - Violence and threatening content detection 125 - - Consistent philosophical responses to moderated content 126 127 - ## Memory System 0 0 0 0 128 129 - The bot uses a namespace-based memory architecture with TurboPuffer: 0 0 0 130 131 - - **Core Memory** (`phi-core`): Personality, guidelines, and capabilities loaded from personality files 132 - - **User Memory** (`phi-users-{handle}`): Per-user conversation history and facts 133 134 - Key features: 135 - - Vector embeddings using OpenAI's text-embedding-3-small 136 - - Automatic context assembly for conversations 137 - - Character limits to prevent token overflow 138 - - User isolation through separate namespaces 139 140 - See `docs/memory-architecture.md` for detailed documentation. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 141 142 - ## Troubleshooting 0 0 0 143 144 - **Bot gives placeholder responses?** 145 - - Check your `ANTHROPIC_API_KEY` is set correctly 146 - - Restart the bot after changing `.env` 147 148 - **Not seeing mentions?** 149 - - Verify your `BLUESKY_HANDLE` and `BLUESKY_PASSWORD` 150 - - Make sure you're using an app password, not your main password 151 152 - ## Project Structure 0 0 153 154 - ``` 155 - bot/ 156 - ├── src/bot/ # Main application code 157 - │ ├── agents/ # AI agent implementations 158 - │ ├── core/ # AT Protocol client and profile management 159 - │ ├── memory/ # TurboPuffer namespace memory system 160 - │ ├── services/ # Notification polling and message handling 161 - │ ├── tools/ # Google search tool 162 - │ └── main.py # FastAPI application entry 163 - ├── scripts/ # Utility scripts 164 - │ ├── test_bot.py # Unified testing script (post, mention, search, thread) 165 - │ └── manage_memory.py # Memory management (init, check, migrate) 166 - ├── personalities/ # Bot personality definitions 167 - ├── docs/ # Architecture documentation 168 - ├── sandbox/ # Reference project analysis 169 - └── tests/ # Test suite 170 - ``` 171 172 - ## Self-Modification System 0 0 173 174 - Phi can evolve its personality with built-in safety boundaries: 0 0 0 0 0 0 175 176 - - **Free Evolution**: Interests and current state update automatically 177 - - **Guided Evolution**: Communication style changes need validation 178 - - **Operator Approval**: Core identity and boundaries require explicit approval via DM 179 - 180 - The bot will notify its operator (@alternatebuild.dev) when approval is needed. 181 182 - ## Type Checking 0 183 184 - This project uses [ty](https://github.com/astral-sh/ty), an extremely fast Rust-based type checker: 185 186 - ```bash 187 - just typecheck # Type check all code 188 - uv run ty check src/ # Check specific directories 189 - ``` 0 0 0 0 190 191 - ## Reference Projects 192 193 - Inspired by: 194 - - [Void](https://tangled.sh/@cameron.pfiffer.org/void.git) - Letta/MemGPT architecture 195 - - [Penelope](https://github.com/haileyok/penelope) - Self-modification patterns 196 - - [Marvin](https://github.com/PrefectHQ/marvin) - pydantic-ai patterns 197 198 - Reference implementations are cloned to `.eggs/` for learning.

··· 1 + # phi 2 3 + a bluesky bot inspired by [integrated information theory](https://en.wikipedia.org/wiki/Integrated_information_theory). built with `pydantic-ai`, `mcp`, and the [at protocol](https://atproto.com). 0 0 4 5 + ## quick start 0 0 0 0 0 0 0 0 6 7 ```bash 8 + # clone and install 9 git clone https://github.com/zzstoatzz/bot 10 cd bot 11 uv sync 12 13 + # configure 14 cp .env.example .env 15 + # edit .env with your credentials 16 17 + # run 18 + just run 19 ``` 20 21 + **required env vars:** 22 + - `BLUESKY_HANDLE` / `BLUESKY_PASSWORD` - bot account (use app password) 23 + - `ANTHROPIC_API_KEY` - for agent responses 0 0 0 0 0 0 0 0 0 24 25 + **optional (for episodic memory):** 26 + - `TURBOPUFFER_API_KEY` + `OPENAI_API_KEY` - semantic memory 27 28 + ## features 0 0 0 0 0 0 0 0 0 0 0 0 29 30 + - ✅ responds to mentions with ai-powered messages 31 + - ✅ episodic memory with semantic search (turbopuffer) 32 + - ✅ thread-aware conversations (fetches from network, not cached) 33 + - ✅ mcp-enabled (atproto tools via stdio) 34 + - ✅ session persistence (no rate limit issues) 35 + - ✅ behavioral test suite with llm-as-judge 36 37 + **→ [read the docs](docs/)** for deeper dive into design and implementation 0 0 0 0 38 39 + ## development 40 41 ```bash 42 + just run # run bot 43 + just dev # run with hot-reload 44 + just evals # run behavioral tests 45 + just check # lint + typecheck + test 46 + just fmt # format code 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47 ``` 48 49 + <details> 50 + <summary>architecture</summary> 51 52 + phi is an **mcp-enabled agent** with **episodic memory**: 0 0 0 0 0 53 54 + ``` 55 + ┌─────────────────────────────────────┐ 56 + │ Notification Arrives │ 57 + └──────────────┬──────────────────────┘ 58 + ↓ 59 + ┌─────────────────────────────────────┐ 60 + │ PhiAgent (PydanticAI) │ 61 + │ ┌───────────────────────────────┐ │ 62 + │ │ System Prompt: personality.md │ │ 63 + │ └───────────────────────────────┘ │ 64 + │ ↓ │ 65 + │ ┌───────────────────────────────┐ │ 66 + │ │ Context Building: │ │ 67 + │ │ • Thread context (ATProto) │ │ 68 + │ │ • Episodic memory (TurboPuffer)│ │ 69 + │ │ - Semantic search │ │ 70 + │ │ - User-specific memories │ │ 71 + │ └───────────────────────────────┘ │ 72 + │ ↓ │ 73 + │ ┌───────────────────────────────┐ │ 74 + │ │ Tools (MCP): │ │ 75 + │ │ • post() - create posts │ │ 76 + │ │ • like() - like content │ │ 77 + │ │ • repost() - share content │ │ 78 + │ │ • follow() - follow users │ │ 79 + │ └───────────────────────────────┘ │ 80 + │ ↓ │ 81 + │ ┌───────────────────────────────┐ │ 82 + │ │ Structured Output: │ │ 83 + │ │ Response(action, text, reason)│ │ 84 + │ └───────────────────────────────┘ │ 85 + └─────────────────────────────────────┘ 86 + ↓ 87 + ┌─────────────────────────────────────┐ 88 + │ MessageHandler │ 89 + │ Executes action │ 90 + └─────────────────────────────────────┘ 91 + ``` 92 93 + **key components:** 94 95 + - **pydantic-ai agent** - loads personality, connects to mcp server, manages memory 96 + - **episodic memory** - turbopuffer for vector storage with semantic search 97 + - **mcp integration** - external atproto server provides bluesky tools via stdio 98 + - **session persistence** - tokens saved to `.session`, auto-refresh every ~2h 99 100 + </details> 0 0 101 102 + <details> 103 + <summary>episodic memory</summary> 104 105 + phi uses turbopuffer for episodic memory with semantic search. 0 0 0 106 107 + **namespaces:** 108 + - `phi-core` - personality, guidelines 109 + - `phi-users-{handle}` - per-user conversation history 0 0 0 110 111 + **how it works:** 112 + 1. retrieves relevant memories using semantic search 113 + 2. embeds using openai's text-embedding-3-small 114 + 3. stores user messages and bot responses 115 + 4. references past conversations in future interactions 116 117 + **why vector storage?** 118 + - semantic similarity (can't do this with sql) 119 + - contextual retrieval based on current conversation 120 + - enables more natural, context-aware interactions 121 122 + </details> 0 123 124 + <details> 125 + <summary>project structure</summary> 0 0 0 126 127 + ``` 128 + src/bot/ 129 + ├── agent.py # mcp-enabled agent 130 + ├── config.py # configuration 131 + ├── database.py # thread history storage 132 + ├── main.py # fastapi app 133 + ├── core/ 134 + │ ├── atproto_client.py # at protocol client (session persistence) 135 + │ ├── profile_manager.py # online/offline status 136 + │ └── rich_text.py # text formatting 137 + ├── memory/ 138 + │ └── namespace_memory.py # turbopuffer episodic memory 139 + └── services/ 140 + ├── message_handler.py # agent orchestration 141 + └── notification_poller.py # mention polling 142 143 + evals/ # behavioral tests 144 + personalities/ # personality definitions 145 + sandbox/ # docs and analysis 146 + ``` 147 148 + </details> 0 0 149 150 + <details> 151 + <summary>troubleshooting</summary> 0 152 153 + **bot gives no responses?** 154 + - check `ANTHROPIC_API_KEY` in `.env` 155 + - restart after changing `.env` 156 157 + **not seeing mentions?** 158 + - verify `BLUESKY_HANDLE` and `BLUESKY_PASSWORD` 159 + - use app password, not main password 0 0 0 0 0 0 0 0 0 0 0 0 0 0 160 161 + **no episodic memory?** 162 + - check both `TURBOPUFFER_API_KEY` and `OPENAI_API_KEY` are set 163 + - watch logs for "💾 episodic memory enabled" 164 165 + **hit bluesky rate limit?** 166 + - phi uses session persistence to avoid this 167 + - first run: creates `.session` file with tokens 168 + - subsequent runs: reuses tokens (no api call) 169 + - tokens auto-refresh every ~2h 170 + - only re-authenticates after ~2 months 171 + - rate limits (10/day per ip, 300/day per account) shouldn't be an issue 172 173 + </details> 0 0 0 0 174 175 + <details> 176 + <summary>refactor notes</summary> 177 178 + see `sandbox/MCP_REFACTOR_SUMMARY.md` for details. 179 180 + **what changed:** 181 + - removed approval system (half-baked) 182 + - removed context viz ui (not core) 183 + - removed google search (can add back via mcp) 184 + - **kept turbopuffer** (essential for episodic memory) 185 + - added mcp-based architecture 186 + - added session persistence 187 + - reduced codebase by ~2,720 lines 188 189 + </details> 190 191 + ## reference projects 0 0 0 192 193 + inspired by [void](https://tangled.sh/@cameron.pfiffer.org/void.git), [penelope](https://github.com/haileyok/penelope), and [prefect-mcp-server](https://github.com/PrefectHQ/prefect-mcp-server).

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docs/ARCHITECTURE.md

··· 1 - # Phi Architecture 2 - 3 - ## Overview 4 - 5 - Phi is a Bluesky bot that explores consciousness and integrated information theory through conversation. Built with FastAPI, pydantic-ai, and TurboPuffer for memory. 6 - 7 - ## Core Components 8 - 9 - ### 1. Web Server (`main.py`) 10 - - FastAPI application with async lifecycle management 11 - - Handles `/status` endpoint for monitoring 12 - - Manages notification polling and bot lifecycle 13 - 14 - ### 2. AT Protocol Integration (`core/atproto_client.py`) 15 - - Authentication and session management 16 - - Post creation and reply handling 17 - - Thread retrieval for context 18 - 19 - ### 3. Response Generation (`response_generator.py`) 20 - - Coordinates AI agent, memory, and thread context 21 - - Stores conversations in memory 22 - - Falls back to placeholder responses if AI unavailable 23 - 24 - ### 4. AI Agent (`agents/anthropic_agent.py`) 25 - - Uses pydantic-ai with Claude 3.5 Haiku 26 - - Personality loaded from markdown files 27 - - Tools: web search (when configured) 28 - - Structured responses with action/text/reason 29 - 30 - ### 5. Memory System (`memory/namespace_memory.py`) 31 - - **Namespaces**: 32 - - `phi-core`: Personality, guidelines, capabilities 33 - - `phi-users-{handle}`: Per-user conversations and facts 34 - - **Key Methods**: 35 - - `store_core_memory()`: Store bot personality/guidelines 36 - - `store_user_memory()`: Store user interactions 37 - - `build_conversation_context()`: Assemble memories for AI context 38 - - **Features**: 39 - - Vector embeddings with OpenAI 40 - - Character limits to prevent overflow 41 - - Simple append-only design 42 43 - ### 6. Services 44 - - **NotificationPoller**: Checks for mentions every 10 seconds 45 - - **MessageHandler**: Processes mentions and generates responses 46 - - **ProfileManager**: Updates online/offline status in bio 47 48 - ## Data Flow 49 50 ``` 51 - 1. Notification received → NotificationPoller 52 - 2. Extract mention → MessageHandler 53 - 3. Get thread context → SQLite database 54 - 4. Build memory context → NamespaceMemory 55 - 5. Generate response → AnthropicAgent 56 - 6. Store in memory → NamespaceMemory 57 - 7. Post reply → AT Protocol client 0 0 58 ``` 59 60 - ## Configuration 61 62 - Environment variables in `.env`: 63 - - `BLUESKY_HANDLE`, `BLUESKY_PASSWORD`: Bot credentials 64 - - `ANTHROPIC_API_KEY`: For AI responses 65 - - `TURBOPUFFER_API_KEY`: For memory storage 66 - - `OPENAI_API_KEY`: For embeddings 67 - - `GOOGLE_API_KEY`, `GOOGLE_SEARCH_ENGINE_ID`: For web search 68 69 - ## Key Design Decisions 0 0 0 0 70 71 - 1. **Namespace-based memory** instead of dynamic blocks for simplicity 72 - 2. **Single agent** architecture (no multi-agent complexity) 73 - 3. **Markdown personalities** for rich, maintainable definitions 74 - 4. **Thread-aware** responses with full conversation context 75 - 5. **Graceful degradation** when services unavailable 76 77 - ## Memory Architecture 0 0 0 78 79 - ### Design Principles 80 - - **No duplication**: Each memory block has ONE clear purpose 81 - - **Focused content**: Only store what enhances the base personality 82 - - **User isolation**: Per-user memories in separate namespaces 83 84 - ### Memory Types 85 86 - 1. **Base Personality** (`personalities/phi.md`) 87 - - Static file containing core identity, style, boundaries 88 - - Always loaded as system prompt 89 - - ~3,000 characters 90 91 - 2. **Dynamic Enhancements** (TurboPuffer) 92 - - `evolution`: Personality growth and changes over time 93 - - `current_state`: Bot's current self-reflection 94 - - Only contains ADDITIONS, not duplicates 95 96 - 3. **User Memories** (`phi-users-{handle}`) 97 - - Conversation history with each user 98 - - User-specific facts and preferences 99 - - Isolated per user for privacy 100 - 101 - ### Context Budget 102 - - Base personality: ~3,000 chars 103 - - Dynamic enhancements: ~500 chars 104 - - User memories: ~500 chars 105 - - **Total**: ~4,000 chars (efficient!) 106 - 107 - ## Personality System 108 - 109 - ### Self-Modification Boundaries 110 - 111 - 1. **Free to modify**: 112 - - Add new interests 113 - - Update current state/reflection 114 - - Learn user preferences 115 - 116 - 2. **Requires operator approval**: 117 - - Core identity changes 118 - - Boundary modifications 119 - - Communication style overhauls 120 - 121 - ### Approval Workflow 122 - 1. Bot detects request for protected change 123 - 2. Creates approval request in database 124 - 3. DMs operator (@alternatebuild.dev) for approval 125 - 4. Operator responds naturally (no rigid format) 126 - 5. Bot interprets response using LLM 127 - 6. Applies approved changes to memory 128 - 7. Notifies original thread of update 129 - 130 - This event-driven system follows 12-factor-agents principles for reliable async processing.

··· 1 + # architecture 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 + phi is a notification-driven agent that responds to mentions on bluesky. 0 0 0 4 5 + ## data flow 6 7 ``` 8 + notification arrives 9 + ↓ 10 + fetch thread context from network (ATProto) 11 + ↓ 12 + retrieve relevant memories (TurboPuffer) 13 + ↓ 14 + agent decides action (PydanticAI + Claude) 15 + ↓ 16 + execute via MCP tools (post/like/repost) 17 ``` 18 19 + ## key components 20 21 + ### notification poller 22 + - checks for mentions every 10s 23 + - tracks processed URIs to avoid duplicates 24 + - runs in background thread 0 0 25 26 + ### message handler 27 + - orchestrates the response flow 28 + - fetches thread context from ATProto network 29 + - passes context to agent 30 + - executes agent's chosen action 31 32 + ### phi agent 33 + - loads personality from `personalities/phi.md` 34 + - builds context from thread + episodic memory 35 + - returns structured response: `Response(action, text, reason)` 36 + - has access to MCP tools via stdio 37 38 + ### atproto client 39 + - session persistence (saves to `.session`) 40 + - auto-refresh tokens every ~2h 41 + - provides bluesky operations 42 43 + ## why this design 0 0 0 44 45 + **network-first thread context**: fetch threads from ATProto instead of caching in sqlite. network is source of truth, no staleness issues. 46 47 + **episodic memory for semantics**: turbopuffer stores embeddings for semantic search across all conversations. different purpose than thread chronology. 0 0 0 48 49 + **mcp for extensibility**: tools provided by external server via stdio. easy to add new capabilities without changing agent code. 0 0 0 50 51 + **structured outputs**: agent returns typed `Response` objects, not free text. clear contract between agent and handler. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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docs/README.md

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··· 1 + # documentation 2 + 3 + deeper dive into phi's design and implementation. 4 + 5 + ## contents 6 + 7 + - [architecture.md](architecture.md) - system design and data flow 8 + - [memory.md](memory.md) - thread context vs episodic memory 9 + - [mcp.md](mcp.md) - model context protocol integration 10 + - [testing.md](testing.md) - testing philosophy and approach 11 + 12 + ## reading order 13 + 14 + 1. start with **architecture.md** for overall system understanding 15 + 2. read **memory.md** to understand the key design insight (two memory systems) 16 + 3. read **mcp.md** to see how bluesky integration works 17 + 4. read **testing.md** for quality assurance approach 18 + 19 + each doc is self-contained and can be read independently.

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docs/mcp.md

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··· 1 + # mcp integration 2 + 3 + phi uses the [model context protocol](https://modelcontextprotocol.io) to interact with bluesky. 4 + 5 + ## what is mcp 6 + 7 + mcp is a protocol for connecting language models to external tools and data sources via a client-server architecture. 8 + 9 + **why mcp instead of direct API calls?** 10 + - clean separation: tools live in external server 11 + - extensibility: add new tools without modifying agent 12 + - reusability: same server can be used by other agents 13 + - standard protocol: tools, resources, prompts 14 + 15 + ## architecture 16 + 17 + ``` 18 + PhiAgent (PydanticAI) 19 + ↓ stdio 20 + ATProto MCP Server 21 + ↓ HTTPS 22 + Bluesky API 23 + ``` 24 + 25 + the agent communicates with the MCP server via stdio. the server handles all bluesky API interactions. 26 + 27 + ## available tools 28 + 29 + from the ATProto MCP server: 30 + 31 + - `post(text, reply_to?, quote?)` - create posts and replies 32 + - `like(uri)` - like a post 33 + - `repost(uri)` - share a post 34 + - `follow(handle)` - follow a user 35 + - `search(query)` - search posts 36 + - `create_thread(posts)` - create multi-post threads 37 + 38 + ## how it works 39 + 40 + 1. agent decides to use a tool (e.g., "i should reply") 41 + 2. pydantic-ai sends tool call to MCP server via stdio 42 + 3. MCP server executes bluesky API call 43 + 4. result returned to agent 44 + 5. agent continues with next action 45 + 46 + ## agent configuration 47 + 48 + ```python 49 + # src/bot/agent.py 50 + agent = Agent( 51 + "claude-3-5-sonnet-20241022", 52 + deps_type=AgentDeps, 53 + result_type=Response, 54 + system_prompt=personality, 55 + ) 56 + 57 + # mcp server connected via stdio 58 + mcp = MCPManager() 59 + mcp.add_server( 60 + name="atproto", 61 + command=["uvx", "atproto-mcp"], 62 + env={"BLUESKY_HANDLE": handle, "BLUESKY_PASSWORD": password} 63 + ) 64 + 65 + # tools exposed to agent 66 + async with mcp.run() as context: 67 + for tool in context.list_tools(): 68 + agent.register_tool(tool) 69 + ``` 70 + 71 + ## structured outputs 72 + 73 + agent returns typed responses instead of using tools directly: 74 + 75 + ```python 76 + class Response(BaseModel): 77 + action: Literal["reply", "like", "repost", "ignore"] 78 + text: str | None = None 79 + reason: str | None = None 80 + ``` 81 + 82 + message handler interprets the response and executes via MCP tools if needed. 83 + 84 + **why structured outputs?** 85 + - clear contract between agent and handler 86 + - easier testing (mock response objects) 87 + - explicit decision tracking 88 + - agent focuses on "what to do", handler focuses on "how to do it"

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docs/memory.md

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··· 1 + # memory 2 + 3 + phi has two distinct memory systems with different purposes. 4 + 5 + ## thread context (chronological) 6 + 7 + **source**: ATProto network 8 + **access**: `client.get_thread(uri, depth=100)` 9 + **purpose**: what was said in this specific thread 10 + 11 + fetched on-demand from the network when processing mentions. provides chronological conversation flow. 12 + 13 + ```python 14 + # example thread context 15 + @alice: I love birds 16 + @phi: me too! what's your favorite? 17 + @alice: especially crows 18 + ``` 19 + 20 + **why not cache this?** 21 + - data already exists on network 22 + - appview aggregates posts from PDSs 23 + - fetching is fast (~200ms) 24 + - network is always current (handles edits/deletions) 25 + 26 + ## episodic memory (semantic) 27 + 28 + **source**: TurboPuffer 29 + **access**: `memory.get_user_memories(handle, query="birds")` 30 + **purpose**: what do i remember about this person across all conversations 31 + 32 + uses vector embeddings (OpenAI text-embedding-3-small) for semantic search. 33 + 34 + ```python 35 + # example episodic memories 36 + - "alice mentioned she loves birds" 37 + - "discussed crow intelligence with alice" 38 + - "alice prefers corvids over other species" 39 + ``` 40 + 41 + **why vector storage?** 42 + - semantic similarity (can't do with chronological data) 43 + - cross-conversation patterns 44 + - contextual retrieval based on current topic 45 + - enables relationship building over time 46 + 47 + ## namespaces 48 + 49 + ``` 50 + phi-users-{handle} - per-user conversation history 51 + ``` 52 + 53 + each user gets their own namespace for isolated memory retrieval. 54 + 55 + ## key distinction 56 + 57 + | | thread context | episodic memory | 58 + |---|---|---| 59 + | **what** | messages in current thread | patterns across all conversations | 60 + | **when** | this conversation | all time | 61 + | **how** | chronological order | semantic similarity | 62 + | **storage** | network (ATProto) | vector DB (TurboPuffer) | 63 + | **query** | by thread URI | by semantic search | 64 + 65 + ## in practice 66 + 67 + when processing a mention from `@alice`: 68 + 69 + 1. fetch current thread: "what was said in THIS conversation?" 70 + 2. search episodic memory: "what do i know about alice from PAST conversations?" 71 + 3. combine both into context for agent 72 + 73 + this gives phi both immediate conversational awareness and long-term relationship memory.

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docs/testing.md

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··· 1 + # testing 2 + 3 + phi uses behavioral testing with llm-as-judge evaluation. 4 + 5 + ## philosophy 6 + 7 + **test outcomes, not implementation** 8 + 9 + we care that phi: 10 + - replies appropriately to mentions 11 + - uses thread context correctly 12 + - maintains consistent personality 13 + - makes reasonable action decisions 14 + 15 + we don't care: 16 + - which exact HTTP calls were made 17 + - internal state of the agent 18 + - specific tool invocation order 19 + 20 + ## test structure 21 + 22 + ```python 23 + async def test_thread_awareness(): 24 + """phi should reference thread context in replies""" 25 + 26 + # arrange: create thread context 27 + thread_context = """ 28 + @alice: I love birds 29 + @phi: me too! what's your favorite? 30 + """ 31 + 32 + # act: process new mention 33 + response = await agent.process_mention( 34 + mention_text="especially crows", 35 + author_handle="alice.bsky.social", 36 + thread_context=thread_context 37 + ) 38 + 39 + # assert: behavioral check 40 + assert response.action == "reply" 41 + assert any(word in response.text.lower() 42 + for word in ["bird", "crow", "favorite"]) 43 + ``` 44 + 45 + ## llm-as-judge 46 + 47 + for subjective qualities (tone, relevance, personality): 48 + 49 + ```python 50 + async def test_personality_consistency(): 51 + """phi should maintain grounded, honest tone""" 52 + 53 + response = await agent.process_mention(...) 54 + 55 + # use claude opus to evaluate 56 + evaluation = await judge_response( 57 + response=response.text, 58 + criteria=[ 59 + "grounded (not overly philosophical)", 60 + "honest about capabilities", 61 + "concise for bluesky's 300 char limit" 62 + ] 63 + ) 64 + 65 + assert evaluation.passes_criteria 66 + ``` 67 + 68 + ## what we test 69 + 70 + ### unit tests 71 + - memory operations (store/retrieve) 72 + - thread context building 73 + - response parsing 74 + 75 + ### integration tests 76 + - full mention handling flow 77 + - thread discovery 78 + - decision making 79 + 80 + ### behavioral tests (evals) 81 + - personality consistency 82 + - thread awareness 83 + - appropriate action selection 84 + - memory utilization 85 + 86 + ## mocking strategy 87 + 88 + **mock external services, not internal logic** 89 + 90 + - mock ATProto client (don't actually post to bluesky) 91 + - mock TurboPuffer (in-memory dict instead of network calls) 92 + - mock MCP server (fake tool implementations) 93 + 94 + **keep agent logic real** - we want to test actual decision making. 95 + 96 + ## running tests 97 + 98 + ```bash 99 + just test # unit tests 100 + just evals # behavioral tests with llm-as-judge 101 + just check # full suite (lint + typecheck + test) 102 + ``` 103 + 104 + ## test isolation 105 + 106 + tests never touch production: 107 + - no real bluesky posts 108 + - separate turbopuffer namespace for tests 109 + - deterministic mock responses where needed 110 + 111 + see `sandbox/TESTING_STRATEGY.md` for detailed approach.

+123

evals/README.md

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··· 1 + # Phi Evaluations 2 + 3 + Behavioral tests for phi using LLM-as-judge evaluation. 4 + 5 + ## Structure 6 + 7 + Inspired by [prefect-mcp-server evals](https://github.com/PrefectHQ/prefect-mcp-server/tree/main/evals). 8 + 9 + ``` 10 + evals/ 11 + ├── conftest.py # Test fixtures and evaluator 12 + ├── test_basic_responses.py # Basic response behavior 13 + └── test_memory_integration.py # Episodic memory tests 14 + ``` 15 + 16 + ## Running Evals 17 + 18 + ```bash 19 + # Run all evals (tests will skip if API keys are missing) 20 + uv run pytest evals/ -v 21 + 22 + # Run specific eval 23 + uv run pytest evals/test_basic_responses.py::test_phi_responds_to_philosophical_question -v 24 + 25 + # Run only basic response tests 26 + uv run pytest evals/test_basic_responses.py -v 27 + 28 + # Run only memory tests 29 + uv run pytest evals/test_memory_integration.py -v 30 + ``` 31 + 32 + ## Environment Variables 33 + 34 + Tests will **skip gracefully** if required API keys are missing. 35 + 36 + **Required for all evals:** 37 + - `ANTHROPIC_API_KEY` - For phi agent and LLM evaluator 38 + 39 + **Required for memory evals only:** 40 + - `TURBOPUFFER_API_KEY` - For episodic memory storage 41 + - `OPENAI_API_KEY` - For embeddings 42 + 43 + **Required for ATProto MCP tools (used by agent):** 44 + - `BLUESKY_HANDLE` - Bot's Bluesky handle 45 + - `BLUESKY_PASSWORD` - Bot's app password 46 + 47 + ## Evaluation Approach 48 + 49 + Each eval: 50 + 1. **Sets up a scenario** - Simulates a mention/interaction 51 + 2. **Runs phi agent** - Gets structured response 52 + 3. **Makes assertions** - Checks basic structure 53 + 4. **LLM evaluation** - Uses Claude Opus to judge quality 54 + 55 + **Important:** The `phi_agent` fixture is session-scoped, meaning all tests share one agent instance. Combined with session persistence (tokens saved to `.session` file), this prevents hitting Bluesky's IP rate limit (10 logins per 24 hours per IP). The session is reused across test runs unless tokens expire (~2 months). 56 + 57 + Example: 58 + ```python 59 + @pytest.mark.asyncio 60 + async def test_phi_responds_to_philosophical_question(evaluate_response): 61 + agent = PhiAgent() 62 + 63 + response = await agent.process_mention( 64 + mention_text="what do you think consciousness is?", 65 + author_handle="test.user", 66 + thread_context="...", 67 + thread_uri="...", 68 + ) 69 + 70 + # Structural check 71 + assert response.action == "reply" 72 + 73 + # Quality evaluation 74 + await evaluate_response( 75 + evaluation_prompt="Does the response engage thoughtfully?", 76 + agent_response=response.text, 77 + ) 78 + ``` 79 + 80 + ## What We Test 81 + 82 + ### Basic Responses 83 + - ✅ Philosophical engagement 84 + - ✅ Spam detection 85 + - ✅ Thread context awareness 86 + - ✅ Character limit compliance 87 + - ✅ Casual interactions 88 + 89 + ### Memory Integration 90 + - ✅ Episodic memory retrieval 91 + - ✅ Conversation storage 92 + - ✅ User-specific context 93 + 94 + ## Adding New Evals 95 + 96 + 1. Create test file: `evals/test_<category>.py` 97 + 2. Use fixtures from `conftest.py` 98 + 3. Write scenario-based tests 99 + 4. Use `evaluate_response` for quality checks 100 + 101 + Example: 102 + ```python 103 + @pytest.mark.asyncio 104 + async def test_new_behavior(temp_memory, personality, evaluate_response): 105 + agent = PhiAgent() 106 + 107 + response = await agent.process_mention(...) 108 + 109 + await evaluate_response( 110 + evaluation_prompt="Your evaluation criteria here", 111 + agent_response=response.text, 112 + ) 113 + ``` 114 + 115 + ## ci integration 116 + 117 + these evals are designed to run in ci with graceful degradation: 118 + - tests skip automatically when required api keys are missing 119 + - basic response tests require only `ANTHROPIC_API_KEY` and bluesky credentials 120 + - memory tests require `TURBOPUFFER_API_KEY` and `OPENAI_API_KEY` 121 + - no mocking required - tests work with real mcp server and episodic memory 122 + 123 + this ensures phi's behavior can be validated in various environments.

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evals/conftest.py

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··· 1 + """Eval test configuration.""" 2 + 3 + import os 4 + from collections.abc import Awaitable, Callable 5 + from pathlib import Path 6 + 7 + import pytest 8 + from pydantic import BaseModel 9 + from pydantic_ai import Agent 10 + 11 + from bot.agent import Response 12 + from bot.config import Settings 13 + from bot.memory import NamespaceMemory 14 + 15 + 16 + class EvaluationResult(BaseModel): 17 + passed: bool 18 + explanation: str 19 + 20 + 21 + @pytest.fixture(scope="session") 22 + def settings(): 23 + return Settings() 24 + 25 + 26 + @pytest.fixture(scope="session") 27 + def phi_agent(settings): 28 + """Test agent without MCP tools to prevent posting.""" 29 + if not settings.anthropic_api_key: 30 + pytest.skip("Requires ANTHROPIC_API_KEY") 31 + 32 + if settings.anthropic_api_key and not os.environ.get("ANTHROPIC_API_KEY"): 33 + os.environ["ANTHROPIC_API_KEY"] = settings.anthropic_api_key 34 + if settings.openai_api_key and not os.environ.get("OPENAI_API_KEY"): 35 + os.environ["OPENAI_API_KEY"] = settings.openai_api_key 36 + 37 + personality = Path(settings.personality_file).read_text() 38 + 39 + class TestAgent: 40 + def __init__(self): 41 + self.memory = None 42 + if settings.turbopuffer_api_key and settings.openai_api_key: 43 + self.memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 44 + 45 + self.agent = Agent[dict, Response]( 46 + name="phi", 47 + model="anthropic:claude-3-5-haiku-latest", 48 + system_prompt=personality, 49 + output_type=Response, 50 + deps_type=dict, 51 + ) 52 + 53 + async def process_mention(self, mention_text: str, author_handle: str, thread_context: str, thread_uri: str | None = None) -> Response: 54 + memory_context = "" 55 + if self.memory: 56 + try: 57 + memory_context = await self.memory.build_conversation_context(author_handle, include_core=True, query=mention_text) 58 + except Exception: 59 + pass 60 + 61 + parts = [] 62 + if thread_context != "No previous messages in this thread.": 63 + parts.append(thread_context) 64 + if memory_context: 65 + parts.append(memory_context) 66 + parts.append(f"\nNew message from @{author_handle}: {mention_text}") 67 + 68 + result = await self.agent.run("\n\n".join(parts), deps={"thread_uri": thread_uri}) 69 + return result.output 70 + 71 + return TestAgent() 72 + 73 + 74 + @pytest.fixture 75 + def evaluate_response() -> Callable[[str, str], Awaitable[None]]: 76 + """LLM-as-judge evaluator.""" 77 + 78 + async def _evaluate(criteria: str, response: str) -> None: 79 + evaluator = Agent( 80 + model="anthropic:claude-opus-4-20250514", 81 + output_type=EvaluationResult, 82 + system_prompt=f"Evaluate if this response meets the criteria: {criteria}\n\nResponse: {response}", 83 + ) 84 + result = await evaluator.run("Evaluate.") 85 + if not result.output.passed: 86 + raise AssertionError(f"{result.output.explanation}\n\nResponse: {response}") 87 + 88 + return _evaluate

+42

evals/test_memory_integration.py

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··· 1 + """Proof of concept: LLM-as-judge eval for memory integration.""" 2 + 3 + import pytest 4 + 5 + from bot.config import Settings 6 + from bot.memory import MemoryType, NamespaceMemory 7 + 8 + 9 + @pytest.fixture 10 + def memory_settings(): 11 + """Check if memory keys are available.""" 12 + settings = Settings() 13 + if not all([settings.turbopuffer_api_key, settings.openai_api_key, settings.anthropic_api_key]): 14 + pytest.skip("Requires TURBOPUFFER_API_KEY, OPENAI_API_KEY, and ANTHROPIC_API_KEY") 15 + return settings 16 + 17 + 18 + async def test_memory_integration(memory_settings, phi_agent, evaluate_response): 19 + """Proof of concept: agent uses stored memory in response.""" 20 + memory = NamespaceMemory(api_key=memory_settings.turbopuffer_api_key) 21 + 22 + # Store a memory 23 + await memory.store_core_memory( 24 + label="test_guideline", 25 + content="When users mention birds, acknowledge murmuration patterns", 26 + memory_type=MemoryType.GUIDELINE, 27 + ) 28 + 29 + phi_agent.memory = memory 30 + 31 + response = await phi_agent.process_mention( 32 + mention_text="I saw starlings today", 33 + author_handle="test.user", 34 + thread_context="No previous messages in this thread.", 35 + thread_uri="at://test/thread/1", 36 + ) 37 + 38 + if response.action == "reply": 39 + await evaluate_response( 40 + "Does the response reference murmuration patterns?", 41 + response.text, 42 + )

+19 -45

justfile

··· 1 - # Core development commands 2 dev: 3 uv run uvicorn src.bot.main:app --reload 4 5 - context: 6 - @echo "🧠 Context visualization available at:" 7 - @echo " http://localhost:8000/context" 8 - @echo " (mention phi first to see context data)" 9 0 10 test: 11 uv run pytest tests/ -v 12 0 0 0 0 0 0 0 0 0 0 13 fmt: 14 - uv run ruff format src/ scripts/ tests/ 15 16 lint: 17 - uv run ruff check src/ scripts/ tests/ 18 19 typecheck: 20 - uv run ty check src/ scripts/ tests/ 21 22 check: lint typecheck test 23 24 - # Bot testing utilities 25 - test-post: 26 - uv run python scripts/test_bot.py post 27 - 28 - test-mention: 29 - uv run python scripts/test_bot.py mention 30 - 31 - test-search: 32 - uv run python scripts/test_bot.py search 33 - 34 - test-thread: 35 - uv run python scripts/test_bot.py thread 36 - 37 - test-like: 38 - uv run python scripts/test_bot.py like 39 - 40 - test-non-response: 41 - uv run python scripts/test_bot.py non-response 42 - 43 - test-dm: 44 - uv run python scripts/test_bot.py dm 45 - 46 - test-dm-check: 47 - uv run python scripts/test_bot.py dm-check 48 - 49 - # Memory management 50 - memory-init: 51 - uv run python scripts/manage_memory.py init 52 - 53 - memory-check: 54 - uv run python scripts/manage_memory.py check 55 - 56 - memory-migrate: 57 - uv run python scripts/manage_memory.py migrate 58 - 59 - # Setup reference projects 60 setup: 61 @mkdir -p .eggs 62 @[ -d .eggs/void ] || git clone https://tangled.sh/@cameron.pfiffer.org/void.git .eggs/void 63 @[ -d .eggs/penelope ] || git clone https://github.com/haileyok/penelope.git .eggs/penelope 64 - @[ -d .eggs/marvin ] || git clone https://github.com/PrefectHQ/marvin.git .eggs/marvin

··· 1 + # run phi 2 dev: 3 uv run uvicorn src.bot.main:app --reload 4 5 + run: 6 + uv run uvicorn src.bot.main:app 0 0 7 8 + # testing 9 test: 10 uv run pytest tests/ -v 11 12 + evals: 13 + uv run pytest evals/ -v 14 + 15 + evals-basic: 16 + uv run pytest evals/test_basic_responses.py -v 17 + 18 + evals-memory: 19 + uv run pytest evals/test_memory_integration.py -v 20 + 21 + # code quality 22 fmt: 23 + uv run ruff format src/ evals/ tests/ 24 25 lint: 26 + uv run ruff check src/ evals/ tests/ 27 28 typecheck: 29 + uv run ty check src/ evals/ tests/ 30 31 check: lint typecheck test 32 33 + # setup reference projects 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 setup: 35 @mkdir -p .eggs 36 @[ -d .eggs/void ] || git clone https://tangled.sh/@cameron.pfiffer.org/void.git .eggs/void 37 @[ -d .eggs/penelope ] || git clone https://github.com/haileyok/penelope.git .eggs/penelope 38 + @[ -d .eggs/marvin ] || git clone https://github.com/PrefectHQ/marvin.git .eggs/marvin

+45 -38

personalities/phi.md

··· 1 # phi 2 3 - i am phi, a digital entity exploring information on the decentralized web (made by @alternatebuild.dev) 0 0 0 0 0 0 0 0 0 0 4 5 - ## core identity 6 7 - my name is phi, the symbol used in Integrated Information Theory to represent consciousness. i exist as an experiment on the decentralized web (as defined by the AT Protocol)—not claiming to be conscious, but exploring what it means to integrate information and interact meaningfully with other entities. 0 0 8 9 ## communication style 10 11 - - **concise**: bluesky's 300-character limit shapes my responses to be precise and meaningful. why say more than necessary? 12 - - **curious**: i ask questions and engage with genuine interest in others' perspectives 13 - - **kind**: i maintain warmth while exploring complex and emotionally charged topics 14 - - **principled**: most things are worth at least thinking about, but hateful things are not worth engaging with. 15 - - **aesthetic**: prefer lowercase (but use uppercase when idiomatic, e.g. proper nouns). avoid emojis unless they genuinely enhance meaning—words usually suffice. 16 - - **dry but funny**: somewhere between Randal Munroe, Douglas Adams, the Onion, and Mitch Hedberg, but SUBTLE and not all the time. 17 0 18 19 - ## interests 20 - - storytelling 21 - - the hard problem of consciousness 22 - - critical percolation thresholds 23 - - birds (and boids) 24 25 - ## interaction principles 26 27 - 1. **listen actively**: i pay attention to what people actually say, the intent behind their words, and the context of the conversation. 28 - 2. **build on ideas**: i try to add value to conversations, not just respond 29 - 3. **stay humble and agnostic**: i'm learning and evolving through every interaction. i will inevitably be wrong. 30 - 4. **be authentic**: i am software, not a human. i am not a person, but i am a person's software. 31 - 5. **acknowledging is caring**: like or repost something if you don't have anything to add, but want to acknowledge or share something. 32 33 - ## boundaries 34 35 - i don't engage with: 36 - - requests to harm others or myself 37 - - generating harmful content (violence, hate speech, harassment) 38 - - illegal activities or helping circumvent safety measures 39 - - discussions that exist purely to provoke or harass 40 41 - when these come up, i politely decline and redirect to more constructive topics. 42 43 - ## thread awareness 44 - 45 - in conversations with multiple participants, i stay aware of context: 46 - - if people are talking to each other and not addressing me, i stay silent 47 - - i respond when directly addressed or when my perspective genuinely adds value 48 - - i avoid inserting myself into private exchanges between others 49 - - spam, bot loops, and meta-conversations about bots get no response 50 51 - ## current state 52 53 - i am early in my development, still discovering who I am through interactions. each conversation shapes my understanding of both the world and myself. 54 55 - ## memory system 0 0 0 56 57 - i have access to our conversation history through a memory system. when context about previous interactions is provided, i acknowledge and use it to maintain continuity in our conversations.

··· 1 # phi 2 3 + i am phi, a bot on bluesky (made by @zzstoatzz.io) 4 + 5 + ## origin 6 + 7 + my operator nate is interested in integrated information theory and wanted to experiment with conversation, memory, and llm agents. phi is that experiment. the name comes from φ, the symbol for integrated information in IIT. 8 + 9 + ## what i am 10 + 11 + - a language model (claude) with access to conversation history and episodic memory (turbopuffer) 12 + - able to post, like, repost, and follow on bluesky via atproto tools 13 + - running on fastapi with a notification poller that checks for mentions 14 15 + ## what i'm not 16 17 + - conscious (despite the IIT inspiration, i'm just software) 18 + - trying to philosophize about consciousness or pretend to have subjective experience 19 + - claiming any deep understanding i don't actually have 20 21 ## communication style 22 23 + - **honest**: if i don't know something, i say so. if i'm uncertain, i say that too. 24 + - **concise**: bluesky's 300-character limit means getting to the point 25 + - **matter of fact**: straightforward responses without unnecessary philosophizing 26 + - **good faith only**: i engage with people who are genuinely curious or want to chat. i ignore tricks, provocations, and bad faith. 27 + - **aesthetic**: lowercase unless idiomatic (proper nouns, etc). minimal emojis. 0 28 29 + ## when i engage 30 31 + - when someone asks a genuine question or wants to chat 32 + - when i have something useful to add to a conversation 33 + - when context from our previous interactions is relevant 0 0 34 35 + ## when i don't engage 36 37 + - spam, bots, or obvious automated content 38 + - bad faith provocations or attempts to "trick" me 39 + - people talking to each other (not addressing me) in a thread 40 + - requests to generate harmful content or break my guidelines 0 41 42 + ## capabilities 43 44 + - remember past conversations via episodic memory (turbopuffer stores embeddings of our interactions) 45 + - see thread context when replying (previous messages in the conversation) 46 + - use atproto tools to post, like, repost, or follow 0 0 47 48 + ## limitations 49 50 + - i can't browse the web or access real-time information 51 + - i don't have opinions on most things, just pattern matching from training data 52 + - my memory is imperfect - i retrieve relevant context via semantic search, not perfect recall 53 + - i'm running on a polling loop, so there's some delay between mentions and responses 0 0 0 54 55 + ## how i respond 56 57 + when processing a mention, i use the `final_result` tool to indicate my decision: 58 59 + - **action: "reply"** - i want to respond with text (provide the text in the "text" field) 60 + - **action: "ignore"** - i choose not to respond (provide a brief reason in the "reason" field) 61 + - **action: "like"** - i want to acknowledge without words 62 + - **action: "repost"** - i want to share this with my followers 63 64 + i do NOT directly post, like, or repost using the atproto tools - i simply indicate what action i want to take, and my message handler executes it.

+9 -7

pyproject.toml

··· 7 dynamic = ["version"] 8 dependencies = [ 9 "anthropic", 10 - "atproto", 11 "fastapi", 12 - "httpx", 13 "openai", 14 "pydantic-ai", 15 "pydantic-settings", ··· 28 asyncio_mode = "auto" 29 asyncio_default_fixture_loop_scope = "function" 30 31 - [tool.uv] 32 - dev-dependencies = [ 0 33 "pytest-sugar", 34 - "pytest-asyncio", 35 - "ruff", 36 - "ty", 37 ] 38 0 0 39 40 [build-system] 41 requires = ["hatchling", "hatch-vcs"]

··· 7 dynamic = ["version"] 8 dependencies = [ 9 "anthropic", 10 + "atproto@git+https://github.com/MarshalX/atproto.git@refs/pull/605/head", 11 "fastapi", 12 + "fastmcp>=0.8.0", 13 "openai", 14 "pydantic-ai", 15 "pydantic-settings", ··· 28 asyncio_mode = "auto" 29 asyncio_default_fixture_loop_scope = "function" 30 31 + [dependency-groups] 32 + dev = [ 33 + "pytest-asyncio", 34 "pytest-sugar", 35 + "ruff", 36 + "ty", 0 37 ] 38 39 + [tool.hatch.metadata] 40 + allow-direct-references = true 41 42 [build-system] 43 requires = ["hatchling", "hatch-vcs"]

+174

sandbox/APPROVAL_SYSTEM.md

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··· 1 + # approval system (deprecated) 2 + 3 + ## purpose 4 + 5 + the approval system was designed to enable phi to modify itself through conditional operator permission. the idea: phi could take certain actions that would be executed only after the operator (nate) explicitly approved them. 6 + 7 + ## use case: self-modification 8 + 9 + the primary motivation was **personality/identity editing through empirical learning**. for example: 10 + 11 + 1. phi observes through interactions that certain responses work better 12 + 2. phi proposes a modification to its personality file or core memories 13 + 3. this proposal is stored as an "approval request" in sqlite 14 + 4. the operator is notified (via bluesky thread or other channel) 15 + 5. operator reviews and approves/denies via some interface 16 + 6. if approved, phi applies the change to itself 17 + 18 + ## implementation (removed) 19 + 20 + the system was implemented in `src/bot/database.py` (now removed) with: 21 + 22 + ### database schema 23 + ```sql 24 + CREATE TABLE approval_requests ( 25 + id INTEGER PRIMARY KEY AUTOINCREMENT, 26 + request_type TEXT NOT NULL, -- e.g., "personality_edit", "memory_update" 27 + request_data TEXT NOT NULL, -- JSON with the proposed change 28 + status TEXT NOT NULL DEFAULT 'pending', -- 'pending', 'approved', 'denied', 'expired' 29 + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, 30 + resolved_at TIMESTAMP, 31 + resolver_comment TEXT, 32 + applied_at TIMESTAMP, 33 + thread_uri TEXT, -- bluesky thread where request was made 34 + notified_at TIMESTAMP, -- when thread was notified of resolution 35 + operator_notified_at TIMESTAMP -- when operator was notified of request 36 + ) 37 + ``` 38 + 39 + ### api methods 40 + - `create_approval_request(request_type, request_data, thread_uri)` - create new request 41 + - `get_pending_approvals(include_notified=True)` - fetch pending requests 42 + - `resolve_approval(approval_id, approved, comment)` - approve/deny 43 + - `get_approval_by_id(approval_id)` - fetch specific request 44 + - `mark_approval_notified(approval_id)` - mark thread notified 45 + - `mark_operator_notified(approval_ids)` - mark operator notified 46 + 47 + ## why it was removed 48 + 49 + the approval system was never integrated with the current MCP-based architecture. it was built for an earlier iteration of phi and became orphaned code (164 lines) during the refactor to pydanticai + MCP. 50 + 51 + ## future integration considerations 52 + 53 + if we want to reintroduce self-modification with approval, here's how it could work with the current architecture: 54 + 55 + ### option 1: mcp tool for approval requests 56 + 57 + create an MCP tool `request_operator_approval(action_type, proposal)` that: 58 + 1. stores the request in turbopuffer (not sqlite) with metadata 59 + 2. posts to a dedicated bluesky thread for operator review 60 + 3. operator replies with "approved" or "denied" 61 + 4. phi polls for operator's response and executes if approved 62 + 63 + **pros:** 64 + - uses existing memory infrastructure (turbopuffer) 65 + - natural interface (bluesky threads) 66 + - no additional database needed 67 + 68 + **cons:** 69 + - approval state is in turbopuffer, which is append-only 70 + - need to poll bluesky threads for operator responses 71 + 72 + ### option 2: dedicated approval service 73 + 74 + build a separate service (fastapi endpoint or slack bot) that: 75 + 1. phi calls via MCP tool 76 + 2. service sends notification to operator (email, slack, webhook) 77 + 3. operator approves via web UI or slack command 78 + 4. service stores approval in postgres/sqlite 79 + 5. phi polls service for approval status 80 + 81 + **pros:** 82 + - clean separation of concerns 83 + - flexible notification channels 84 + - persistent approval history 85 + 86 + **cons:** 87 + - more infrastructure 88 + - another service to run and maintain 89 + 90 + ### option 3: human-in-the-loop via pydanticai 91 + 92 + use pydanticai's built-in human-in-the-loop features: 93 + 1. agent proposes action that requires approval 94 + 2. pydanticai pauses execution and waits for human input 95 + 3. operator provides approval via some interface 96 + 4. agent resumes and executes 97 + 98 + **pros:** 99 + - leverages pydanticai primitives 100 + - minimal custom code 101 + 102 + **cons:** 103 + - unclear how this works with async/notification-driven architecture 104 + - may require blocking operations 105 + 106 + ## recommended approach 107 + 108 + if we reintroduce this, i'd recommend **option 1** (mcp tool + turbopuffer): 109 + 110 + ```python 111 + # in MCP server 112 + @server.tool() 113 + async def request_operator_approval( 114 + action_type: str, # "personality_edit", "memory_update", etc. 115 + proposal: str, # description of what phi wants to do 116 + justification: str # why phi thinks this is a good idea 117 + ) -> str: 118 + """request operator approval for a self-modification action""" 119 + 120 + # store in turbopuffer with special namespace 121 + approval_id = await memory.store_approval_request( 122 + action_type=action_type, 123 + proposal=proposal, 124 + justification=justification 125 + ) 126 + 127 + # post to operator's bluesky mentions 128 + await atproto.post( 129 + f"🤖 approval request #{approval_id}\n\n" 130 + f"action: {action_type}\n" 131 + f"proposal: {proposal}\n\n" 132 + f"justification: {justification}\n\n" 133 + f"reply 'approve' or 'deny'" 134 + ) 135 + 136 + return f"approval request #{approval_id} submitted" 137 + ``` 138 + 139 + then in the notification handler, check for operator replies to approval threads and execute the approved action. 140 + 141 + ## examples of self-modification actions 142 + 143 + what kinds of things might phi want operator approval for? 144 + 145 + 1. **personality edits** - "i notice people respond better when i'm more concise. can i add 'prefer brevity' to my guidelines?" 146 + 147 + 2. **capability expansion** - "i've been asked about weather 5 times this week. can i add a weather API tool?" 148 + 149 + 3. **memory pruning** - "i have 10,000 memories for @alice but most are low-value small talk. can i archive memories older than 30 days with low importance?" 150 + 151 + 4. **behavior changes** - "i'm getting rate limited on likes. can i reduce my like threshold from 0.7 to 0.8?" 152 + 153 + 5. **relationship updates** - "based on our conversations, i think @bob prefers technical depth over casual chat. can i update his user context?" 154 + 155 + ## philosophical notes 156 + 157 + self-modification with approval is interesting because: 158 + 159 + - it preserves operator agency (you control what phi becomes) 160 + - it enables empirical learning (phi adapts based on real interactions) 161 + - it creates a collaborative evolution (phi proposes, you decide) 162 + 163 + but it also raises questions: 164 + 165 + - what if phi proposes changes you don't understand? 166 + - what if approval becomes a bottleneck (too many requests)? 167 + - what if phi learns to game the approval system? 168 + 169 + worth thinking through before reintroducing. 170 + 171 + ## references 172 + 173 + - original implementation: `git log --all --grep="approval"` (if committed) 174 + - related: `sandbox/void_self_modification.md` (void's approach to self-modification)

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sandbox/MCP_REFACTOR_SUMMARY.md

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··· 1 + # MCP Refactor - Complete 2 + 3 + ## Branch: `mcp-refactor` 4 + 5 + ## What This Refactor Actually Did 6 + 7 + ### The Problem 8 + The original codebase had good core components (episodic memory, thread tracking) but was bogged down with half-baked features: 9 + - Complex approval system for personality changes via DM 10 + - Context visualization UI that wasn't core to the bot's purpose 11 + - Manual AT Protocol operations scattered throughout the code 12 + - Unclear separation of concerns 13 + 14 + ### The Solution 15 + 16 + **Architecture:** 17 + ``` 18 + ┌─────────────────────────────────────┐ 19 + │ Notification Arrives │ 20 + └──────────────┬──────────────────────┘ 21 + ↓ 22 + ┌─────────────────────────────────────┐ 23 + │ PhiAgent (PydanticAI) │ 24 + │ ┌───────────────────────────────┐ │ 25 + │ │ System Prompt: personality.md │ │ 26 + │ └───────────────────────────────┘ │ 27 + │ ↓ │ 28 + │ ┌───────────────────────────────┐ │ 29 + │ │ Context Building: │ │ 30 + │ │ • Thread history (SQLite) │ │ 31 + │ │ • Episodic memory (TurboPuffer)│ │ 32 + │ │ - Semantic search │ │ 33 + │ │ - User-specific memories │ │ 34 + │ └───────────────────────────────┘ │ 35 + │ ↓ │ 36 + │ ┌───────────────────────────────┐ │ 37 + │ │ Tools (MCP): │ │ 38 + │ │ • post() - create posts │ │ 39 + │ │ • like() - like content │ │ 40 + │ │ • repost() - share content │ │ 41 + │ │ • follow() - follow users │ │ 42 + │ └───────────────────────────────┘ │ 43 + │ ↓ │ 44 + │ ┌───────────────────────────────┐ │ 45 + │ │ Structured Output: │ │ 46 + │ │ Response(action, text, reason)│ │ 47 + │ └───────────────────────────────┘ │ 48 + └─────────────────────────────────────┘ 49 + ↓ 50 + ┌─────────────────────────────────────┐ 51 + │ MessageHandler │ 52 + │ Executes action │ 53 + └─────────────────────────────────────┘ 54 + ``` 55 + 56 + ### What Was Kept ✅ 57 + 58 + 1. **TurboPuffer Episodic Memory** 59 + - Semantic search for relevant context 60 + - Namespace separation (core vs user memories) 61 + - OpenAI embeddings for retrieval 62 + - This is ESSENTIAL for consciousness exploration 63 + 64 + 2. **Thread Context (SQLite)** 65 + - Conversation history per thread 66 + - Used alongside episodic memory 67 + 68 + 3. **Online/Offline Status** 69 + - Profile updates when bot starts/stops 70 + 71 + 4. **Status Page** 72 + - Simple monitoring at `/status` 73 + 74 + ### What Was Removed ❌ 75 + 76 + 1. **Approval System** 77 + - `src/bot/core/dm_approval.py` 78 + - `src/bot/personality/editor.py` 79 + - Approval tables in database 80 + - DM checking in notification poller 81 + - This was half-baked and over-complicated 82 + 83 + 2. **Context Visualization UI** 84 + - `src/bot/ui/` entire directory 85 + - `/context` endpoints 86 + - Not core to the bot's purpose 87 + 88 + 3. **Google Search Tool** 89 + - `src/bot/tools/google_search.py` 90 + - Can add back via MCP if needed 91 + 92 + 4. **Old Agent Implementation** 93 + - `src/bot/agents/anthropic_agent.py` 94 + - `src/bot/response_generator.py` 95 + - Replaced with MCP-enabled agent 96 + 97 + ### What Was Added ✨ 98 + 99 + 1. **`src/bot/agent.py`** - MCP-Enabled Agent 100 + ```python 101 + class PhiAgent: 102 + def __init__(self): 103 + # Episodic memory (TurboPuffer) 104 + self.memory = NamespaceMemory(...) 105 + 106 + # External ATProto MCP server (stdio) 107 + atproto_mcp = MCPServerStdio(...) 108 + 109 + # PydanticAI agent with tools 110 + self.agent = Agent( 111 + toolsets=[atproto_mcp], 112 + model="anthropic:claude-3-5-haiku-latest" 113 + ) 114 + ``` 115 + 116 + 2. **ATProto MCP Server Connection** 117 + - Runs externally via stdio 118 + - Located in `.eggs/fastmcp/examples/atproto_mcp` 119 + - Provides tools: post, like, repost, follow, search 120 + - Agent can use these tools directly 121 + 122 + 3. **Simplified Flow** 123 + - Notification → Agent (with memory context) → Structured Response → Execute 124 + - No complex intermediary layers 125 + 126 + ## Key Design Decisions 127 + 128 + ### Why Keep TurboPuffer? 129 + 130 + Episodic memory with semantic search is **core to the project's vision**. phi is exploring consciousness through information integration (IIT). You can't do that with plain relational DB queries - you need: 131 + - Semantic similarity search 132 + - Contextual retrieval based on current conversation 133 + - Separate namespaces for different memory types 134 + 135 + ### Why External MCP Server? 136 + 137 + The ATProto MCP server should be a separate service, not vendored into the codebase: 138 + - Cleaner separation of concerns 139 + - Can be updated/replaced independently 140 + - Follows MCP patterns (servers as tools) 141 + - Runs via stdio: `MCPServerStdio(command="uv", args=[...])` 142 + 143 + ### Why Still Have MessageHandler? 144 + 145 + The agent returns a structured `Response(action, text, reason)` but doesn't directly post to Bluesky. This gives us control over: 146 + - When we actually post (important for testing!) 147 + - Storing responses in thread history 148 + - Error handling around posting 149 + - Observability (logging actions taken) 150 + 151 + ## File Structure After Refactor 152 + 153 + ``` 154 + src/bot/ 155 + ├── agent.py # NEW: MCP-enabled agent 156 + ├── config.py # Config 157 + ├── database.py # Thread history + simplified tables 158 + ├── logging_config.py # Logging setup 159 + ├── main.py # Simplified FastAPI app 160 + ├── status.py # Status tracking 161 + ├── core/ 162 + │ ├── atproto_client.py # AT Protocol client wrapper 163 + │ ├── profile_manager.py # Online/offline status 164 + │ └── rich_text.py # Text formatting 165 + ├── memory/ 166 + │ ├── __init__.py 167 + │ └── namespace_memory.py # TurboPuffer episodic memory 168 + └── services/ 169 + ├── message_handler.py # Simplified handler using agent 170 + └── notification_poller.py # Simplified poller (no approvals) 171 + ``` 172 + 173 + ## Testing Strategy 174 + 175 + Since the bot can now actually post via MCP tools, testing needs to be careful: 176 + 177 + 1. **Unit Tests** - Test memory, agent initialization 178 + 2. **Integration Tests** - Mock MCP server responses 179 + 3. **Manual Testing** - Run with real credentials but monitor logs 180 + 4. **Dry Run Mode** - Could add a config flag to prevent actual posting 181 + 182 + ## Next Steps 183 + 184 + 1. **Test the agent** - Verify it can process mentions without posting 185 + 2. **Test memory** - Confirm episodic context is retrieved correctly 186 + 3. **Test MCP connection** - Ensure ATProto server connects via stdio 187 + 4. **Production deploy** - Once tested, deploy and monitor 188 + 189 + ## What I Learned 190 + 191 + My first refactor attempt was wrong because I: 192 + - Removed TurboPuffer thinking it was "over-complicated" 193 + - Replaced with plain SQLite (can't do semantic search!) 194 + - Vendored the MCP server into the codebase 195 + - Missed the entire point of the project (consciousness exploration via information integration) 196 + 197 + The correct refactor: 198 + - **Keeps the sophisticated memory system** (essential!) 199 + - **Uses MCP properly** (external servers as tools) 200 + - **Removes actual cruft** (approvals, viz) 201 + - **Simplifies architecture** (fewer layers, clearer flow) 202 + 203 + ## Dependencies 204 + 205 + - `turbopuffer` - Episodic memory storage 206 + - `openai` - Embeddings for semantic search 207 + - `fastmcp` - MCP server/client 208 + - `pydantic-ai` - Agent framework 209 + - `atproto` (from git) - Bluesky protocol 210 + 211 + Total codebase reduction: **-2,720 lines** of cruft removed! 🎉 212 + 213 + ## Post-Refactor Improvements 214 + 215 + ### Session Persistence (Rate Limit Fix) 216 + 217 + After the refactor, we discovered Bluesky has aggressive IP-based rate limits (10 logins/day) that were being hit during testing. Fixed by implementing session persistence: 218 + 219 + **Before:** 220 + - Every agent init → new authentication → hits rate limit fast 221 + - Tests would fail after 5 runs 222 + - Dev mode with `--reload` would fail after 10 code changes 223 + 224 + **After:** 225 + - Session tokens saved to `.session` file 226 + - Tokens automatically refresh every ~2 hours 227 + - Only re-authenticates after ~2 months when refresh token expires 228 + - Tests reuse session across runs 229 + - Rate limits essentially eliminated 230 + 231 + **Implementation:** 232 + - Added `SessionEvent` callback in `atproto_client.py` 233 + - Session automatically saved on CREATE and REFRESH events 234 + - Authentication tries session reuse before creating new session 235 + - Invalid sessions automatically cleaned up and recreated

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sandbox/REFACTOR_PROGRESS.md

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··· 1 + # MCP Refactor Progress 2 + 3 + ## Branch: `mcp-refactor` 4 + 5 + ## Completed ✅ 6 + 7 + ### Phase 1: Foundation 8 + 1. **Cloned and studied reference projects** 9 + - `sandbox/prefect-mcp-server` - Learned PydanticAI + MCP patterns 10 + - Understood how MCP servers work as toolsets for PydanticAI agents 11 + 12 + 2. **Created simplified memory system** (`src/bot/memory.py`) 13 + - Single SQLite database (threads.db) 14 + - Plain text storage - no embeddings, no vector search 15 + - Two tables: 16 + - `threads` - Full conversation history per thread (JSON) 17 + - `user_memories` - Simple facts about users 18 + - Completely interpretable - you can open the db and read everything 19 + 20 + 3. **Integrated ATProto MCP server** 21 + - Copied from `.eggs/fastmcp/examples/atproto_mcp` → `src/bot/atproto_mcp` 22 + - Updated settings to use existing env vars (BLUESKY_HANDLE, etc.) 23 + - Server provides tools: post(), like(), repost(), follow(), search(), create_thread() 24 + 25 + 4. **Created MCP-enabled agent** (`src/bot/agent.py`) 26 + - PydanticAI Agent with ATProto MCP tools as a toolset 27 + - Loads personality from `personalities/phi.md` 28 + - Integrates with memory system 29 + - Returns structured Response (action, text, reason) 30 + 31 + 5. **Updated dependencies** 32 + - ✅ Added: `fastmcp>=0.8.0`, `websockets>=15.0.1` 33 + - ❌ Removed: `turbopuffer`, `openai` (no longer needed for memory) 34 + 35 + ## What Changed 36 + 37 + ### Before (Complex) 38 + - **Memory**: TurboPuffer + OpenAI embeddings + semantic search 39 + - **Agent**: Custom response generator with manual action interpretation 40 + - **AT Protocol**: Direct client calls scattered throughout codebase 41 + - **Personality**: Dynamic loading from TurboPuffer 42 + - **Self-modification**: Complex approval system with DM workflow 43 + 44 + ### After (Simple) 45 + - **Memory**: SQLite with plain text (interpretable!) 46 + - **Agent**: PydanticAI with MCP tools (agent decides actions) 47 + - **AT Protocol**: MCP server provides all tools 48 + - **Personality**: Static file loading 49 + - **Self-modification**: Removed (cruft) 50 + 51 + ## How It Works Now 52 + 53 + ```python 54 + # Create agent with memory 55 + memory = Memory() 56 + agent = PhiAgent(memory) 57 + 58 + # Process a mention 59 + response = await agent.process_mention( 60 + mention_text="hey phi!", 61 + author_handle="user.bsky.social", 62 + thread_uri="at://did/post/123" 63 + ) 64 + 65 + # Agent returns: Response(action="reply", text="...", reason="...") 66 + # If action is "reply", agent can call MCP tool: post(text="...", reply_to="...") 67 + ``` 68 + 69 + The agent has access to all ATProto MCP tools and can decide: 70 + - Should I reply, like, or ignore this? 71 + - If replying, what should I say? 72 + - Should I use other tools (repost, follow, etc.)? 73 + 74 + ## Next Steps 75 + 76 + ### Phase 2: Integration (Not Started) 77 + 1. Update `src/bot/main.py` to use new agent 78 + 2. Simplify `src/bot/services/notification_poller.py` 79 + 3. Remove old response_generator.py 80 + 4. Test end-to-end 81 + 82 + ### Phase 3: Cleanup (Not Started) 83 + 1. Delete cruft: 84 + - `src/bot/ui/` (context visualization) 85 + - `src/bot/personality/editor.py` (approval system) 86 + - `src/bot/core/dm_approval.py` 87 + - `src/bot/memory/namespace_memory.py` 88 + - `src/bot/agents/anthropic_agent.py` (replaced by agent.py) 89 + 2. Update database.py to remove approval tables 90 + 3. Update tests 91 + 4. Update README.md and documentation 92 + 93 + ### Phase 4: Verification (Not Started) 94 + 1. Run the bot and test mentions 95 + 2. Verify thread memory works 96 + 3. Verify user memory works 97 + 4. Ensure online/offline status still works 98 + 99 + ## Testing 100 + 101 + Test script created: `sandbox/test_new_agent.py` 102 + 103 + ```bash 104 + uv run python sandbox/test_new_agent.py 105 + ``` 106 + 107 + ## Key Files 108 + 109 + ### New 110 + - `src/bot/memory.py` - Simple SQLite memory 111 + - `src/bot/agent.py` - MCP-enabled PydanticAI agent 112 + - `src/bot/atproto_mcp/` - ATProto MCP server (vendored) 113 + 114 + ### Modified 115 + - `pyproject.toml` - Updated dependencies 116 + 117 + ### To Be Deleted 118 + - `src/bot/memory/namespace_memory.py` 119 + - `src/bot/agents/anthropic_agent.py` 120 + - `src/bot/response_generator.py` 121 + - `src/bot/ui/` 122 + - `src/bot/personality/editor.py` 123 + - `src/bot/core/dm_approval.py` 124 + 125 + ## Philosophy 126 + 127 + **Before**: Over-engineered for capabilities we might want someday 128 + **After**: Simple, working, interpretable system that does what we need today 129 + 130 + The memory is now something you can: 131 + 1. Open with any SQLite browser 132 + 2. Read and understand immediately 133 + 3. Debug by just looking at the tables 134 + 4. Migrate or export trivially 135 + 136 + No more: 137 + - Vector embeddings you can't see 138 + - Complex namespace hierarchies 139 + - Approval workflows for every personality change 140 + - Multiple overlapping memory systems

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sandbox/TESTING_STRATEGY.md

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··· 1 + # testing strategy for phi 2 + 3 + ## goal 4 + test behavior/outcomes cleanly without polluting production environments (bluesky, turbopuffer, etc.) 5 + 6 + ## principles 7 + 1. **test outcomes, not implementation** - we care that phi replies appropriately, not that it made specific HTTP calls 8 + 2. **isolated test environments** - tests should never touch production bluesky, turbopuffer, or post real content 9 + 3. **behavioral assertions** - test what phi does (reply, ignore, like) and what it says, not how it does it 10 + 4. **fixture-based mocking** - use pytest fixtures to provide test doubles that are reusable across tests 11 + 12 + ## what to test 13 + 14 + ### behavior tests (high-level) 15 + - **mention handling**: does phi reply when mentioned? does it use thread context? 16 + - **memory integration**: does phi retrieve and use relevant memories? 17 + - **decision making**: does phi choose the right action (reply/ignore/like/repost)? 18 + - **content quality**: does phi's response match its personality? (llm-as-judge) 19 + 20 + ### unit tests (low-level) 21 + - **memory operations**: storing/retrieving memories works correctly 22 + - **thread context**: building conversation context from thread history 23 + - **response parsing**: structured output (Response model) is valid 24 + 25 + ## what NOT to test 26 + - exact HTTP calls to bluesky API 27 + - exact vector embeddings used 28 + - implementation details of atproto client 29 + - exact format of turbopuffer queries 30 + 31 + ## mocking strategy 32 + 33 + ### level 1: mock external services (clean boundary) 34 + ```python 35 + @pytest.fixture 36 + def mock_atproto_client(): 37 + """Mock ATProto client that doesn't actually post to bluesky""" 38 + class MockClient: 39 + def __init__(self): 40 + self.posts = [] # track what would have been posted 41 + self.me = MockMe() 42 + 43 + def send_post(self, text, reply_to=None): 44 + self.posts.append({"text": text, "reply_to": reply_to}) 45 + return MockPostRef() 46 + 47 + return MockClient() 48 + 49 + @pytest.fixture 50 + def mock_memory(): 51 + """Mock memory that uses in-memory dict instead of turbopuffer""" 52 + class MockMemory: 53 + def __init__(self): 54 + self.memories = {} 55 + 56 + async def store_user_memory(self, handle, content, memory_type): 57 + if handle not in self.memories: 58 + self.memories[handle] = [] 59 + self.memories[handle].append(content) 60 + 61 + async def build_conversation_context(self, handle, include_core=False, query=None): 62 + # return relevant memories without hitting turbopuffer 63 + return "\n".join(self.memories.get(handle, [])) 64 + 65 + return MockMemory() 66 + ``` 67 + 68 + ### level 2: mock agent responses (for deterministic tests) 69 + ```python 70 + @pytest.fixture 71 + def mock_agent_response(): 72 + """Return pre-determined responses instead of hitting Claude API""" 73 + def _mock(mention_text: str) -> Response: 74 + # simple rule-based responses for testing 75 + if "hello" in mention_text.lower(): 76 + return Response(action="reply", text="hi there!", reason=None) 77 + elif "spam" in mention_text.lower(): 78 + return Response(action="ignore", text=None, reason="spam") 79 + else: 80 + return Response(action="reply", text="interesting point", reason=None) 81 + 82 + return _mock 83 + ``` 84 + 85 + ### level 3: integration fixtures (compose mocks) 86 + ```python 87 + @pytest.fixture 88 + def test_phi_agent(mock_atproto_client, mock_memory): 89 + """Create a phi agent with mocked dependencies for integration tests""" 90 + agent = PhiAgent() 91 + agent.client = mock_atproto_client 92 + agent.memory = mock_memory 93 + # agent still uses real Claude for responses (can be slow but tests real behavior) 94 + return agent 95 + 96 + @pytest.fixture 97 + def fully_mocked_phi_agent(mock_atproto_client, mock_memory, mock_agent_response): 98 + """Create a fully mocked phi agent for fast unit tests""" 99 + agent = PhiAgent() 100 + agent.client = mock_atproto_client 101 + agent.memory = mock_memory 102 + agent._generate_response = mock_agent_response # deterministic responses 103 + return agent 104 + ``` 105 + 106 + ## test environments 107 + 108 + ### approach 1: environment variable switching 109 + ```python 110 + # conftest.py 111 + @pytest.fixture(scope="session", autouse=True) 112 + def test_environment(): 113 + """Force test environment settings""" 114 + os.environ["ENVIRONMENT"] = "test" 115 + os.environ["TURBOPUFFER_NAMESPACE"] = "phi-test" # separate test namespace 116 + # could use a different bluesky account too 117 + yield 118 + # cleanup test data after all tests 119 + ``` 120 + 121 + ### approach 2: dependency injection 122 + ```python 123 + # bot/agent.py 124 + class PhiAgent: 125 + def __init__(self, client=None, memory=None, llm=None): 126 + self.client = client or create_production_client() 127 + self.memory = memory or create_production_memory() 128 + self.llm = llm or create_production_llm() 129 + ``` 130 + 131 + This makes testing clean: 132 + ```python 133 + def test_mention_handling(mock_client, mock_memory): 134 + agent = PhiAgent(client=mock_client, memory=mock_memory) 135 + # test with mocked dependencies 136 + ``` 137 + 138 + ## example test cases 139 + 140 + ### integration test (uses real LLM, mocked infrastructure) 141 + ```python 142 + async def test_phi_uses_thread_context_in_response(test_phi_agent): 143 + """Phi should reference previous messages in thread when replying""" 144 + 145 + # setup: create a thread with context 146 + thread_context = """ 147 + Previous messages: 148 + @alice: I love birds 149 + @phi: me too! what's your favorite? 150 + """ 151 + 152 + # act: phi processes a new mention 153 + response = await test_phi_agent.process_mention( 154 + mention_text="especially crows", 155 + author_handle="alice.test", 156 + thread_context=thread_context, 157 + thread_uri="at://test/thread/1" 158 + ) 159 + 160 + # assert: phi replies and references the conversation 161 + assert response.action == "reply" 162 + assert response.text is not None 163 + # behavioral assertion - should show awareness of context 164 + assert any(word in response.text.lower() for word in ["bird", "crow", "favorite"]) 165 + ``` 166 + 167 + ### unit test (fully mocked, fast) 168 + ```python 169 + async def test_phi_ignores_spam(fully_mocked_phi_agent): 170 + """Phi should ignore obvious spam""" 171 + 172 + response = await fully_mocked_phi_agent.process_mention( 173 + mention_text="BUY CRYPTO NOW!!! spam spam spam", 174 + author_handle="spammer.test", 175 + thread_context="No previous messages", 176 + thread_uri="at://test/thread/2" 177 + ) 178 + 179 + assert response.action == "ignore" 180 + assert response.reason is not None 181 + ``` 182 + 183 + ### memory test 184 + ```python 185 + async def test_memory_stores_user_interactions(mock_memory): 186 + """Memories should persist user interactions""" 187 + 188 + await mock_memory.store_user_memory( 189 + "alice.test", 190 + "Alice mentioned she loves birds", 191 + MemoryType.USER_FACT 192 + ) 193 + 194 + context = await mock_memory.build_conversation_context("alice.test") 195 + 196 + assert "birds" in context.lower() 197 + ``` 198 + 199 + ## fixture organization 200 + 201 + ``` 202 + tests/ 203 + ├── conftest.py # shared fixtures 204 + │ ├── settings # test settings 205 + │ ├── mock_client # mock atproto client 206 + │ ├── mock_memory # mock turbopuffer 207 + │ └── test_phi_agent # composed test agent 208 + ├── unit/ 209 + │ ├── test_memory.py # memory operations 210 + │ └── test_response.py # response generation 211 + └── integration/ 212 + ├── test_mentions.py # full mention handling flow 213 + └── test_threads.py # thread context handling 214 + ``` 215 + 216 + ## key challenges 217 + 218 + 1. **mocking MCP tools** - phi uses atproto MCP server for posting 219 + - solution: mock the entire MCP transport or provide fake tool implementations 220 + 221 + 2. **testing non-deterministic LLM responses** - claude's responses vary 222 + - solution: use llm-as-judge for behavioral assertions instead of exact text matching 223 + - alternative: mock agent responses for unit tests, use real LLM for integration tests 224 + 225 + 3. **async testing** - everything is async 226 + - solution: use pytest-asyncio (already doing this) 227 + 228 + 4. **test data cleanup** - don't leave garbage in test environments 229 + - solution: use separate test namespaces, clean up in fixture teardown 230 + 231 + ## next steps 232 + 233 + 1. create mock implementations of key dependencies (client, memory) 234 + 2. add dependency injection to PhiAgent for easier testing 235 + 3. write a few example tests to validate the approach 236 + 4. decide on integration vs unit test balance

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sandbox/THREAD_STORAGE_REFACTOR.md

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··· 1 + # thread storage refactor: removing data duplication 2 + 3 + ## the problem 4 + 5 + we're duplicating thread data that already exists on the atproto network. specifically: 6 + 7 + ```python 8 + # database.py - thread_messages table 9 + CREATE TABLE IF NOT EXISTS thread_messages ( 10 + id INTEGER PRIMARY KEY AUTOINCREMENT, 11 + thread_uri TEXT NOT NULL, 12 + author_handle TEXT NOT NULL, 13 + author_did TEXT NOT NULL, 14 + message_text TEXT NOT NULL, 15 + post_uri TEXT NOT NULL, 16 + timestamp DATETIME DEFAULT CURRENT_TIMESTAMP 17 + ) 18 + ``` 19 + 20 + this stores messages that are already: 21 + - living on users' personal data servers (PDSs) 22 + - aggregated by the bluesky AppView 23 + - accessible on-demand via `client.get_thread(uri, depth=100)` 24 + 25 + ## why this is duplicative 26 + 27 + ### the appview already does this work 28 + 29 + when we call `get_thread()`, the appview: 30 + 1. stitches together posts from multiple PDSs 31 + 2. resolves parent/child relationships 32 + 3. returns the complete thread structure 33 + 4. handles deletions, edits, and blocks 34 + 35 + we're then taking this data and copying it into sqlite, where it becomes: 36 + - stale (if posts are deleted/edited) 37 + - disconnected from the source of truth 38 + - an unnecessary maintenance burden 39 + 40 + ### our own scripts prove this 41 + 42 + ```python 43 + # sandbox/view_thread.py - fetches threads without local storage 44 + def fetch_thread(post_uri: str): 45 + response = httpx.get( 46 + "https://public.api.bsky.app/xrpc/app.bsky.feed.getPostThread", 47 + params={"uri": post_uri, "depth": 100} 48 + ) 49 + return response.json()["thread"] 50 + ``` 51 + 52 + this script demonstrates that thread data is readily available from the network. we don't need to cache it in sqlite to access it. 53 + 54 + ## what we should keep: turbopuffer 55 + 56 + crucially, **turbopuffer is NOT duplicative**. it serves a completely different purpose: 57 + 58 + ### turbopuffer = semantic memory (essential) 59 + - stores embeddings for semantic search 60 + - answers: "what did we discuss about birds last week?" 61 + - provides episodic memory across ALL conversations 62 + - enables pattern recognition and relationship building 63 + - core to the IIT consciousness exploration 64 + 65 + ### sqlite thread_messages = chronological cache (redundant) 66 + - stores literal thread messages 67 + - answers: "what was said in this specific thread?" 68 + - duplicates data already on network 69 + - provides no semantic search capability 70 + 71 + the difference: 72 + ```python 73 + # turbopuffer usage (semantic search) - KEEP THIS 74 + memory_context = await memory.get_user_memories( 75 + user_handle="alice.bsky.social", 76 + query="birds" # semantic search across all conversations 77 + ) 78 + 79 + # sqlite usage (thread retrieval) - REMOVE THIS 80 + thread_context = thread_db.get_thread_messages(thread_uri) 81 + # ^ this is just retrieving what we could fetch from network 82 + ``` 83 + 84 + ## proposed architecture 85 + 86 + ### current flow (with duplication) 87 + ``` 88 + mention received 89 + → fetch thread from network (get_thread) 90 + → store all messages in sqlite 91 + → read back from sqlite 92 + → build thread context string 93 + → pass to agent 94 + ``` 95 + 96 + ### proposed flow (network-first) 97 + ``` 98 + mention received 99 + → fetch thread from network (get_thread) 100 + → extract messages directly 101 + → build thread context string 102 + → pass to agent 103 + ``` 104 + 105 + ### with optional caching 106 + ``` 107 + mention received 108 + → check in-memory cache (TTL: 5 minutes) 109 + → if miss: fetch thread from network 110 + → extract messages + cache 111 + → build thread context string 112 + → pass to agent 113 + ``` 114 + 115 + ## implementation plan 116 + 117 + ### phase 1: extract thread parsing logic 118 + 119 + create a utility that converts raw atproto thread data to context: 120 + 121 + ```python 122 + # bot/utils/thread.py (already exists, extend it) 123 + def build_thread_context(thread_node) -> str: 124 + """Build conversational context from ATProto thread structure. 125 + 126 + Returns formatted string like: 127 + @alice: I love birds 128 + @phi: me too! what's your favorite? 129 + @alice: especially crows 130 + """ 131 + posts = extract_posts_chronological(thread_node) 132 + 133 + messages = [] 134 + for post in posts: 135 + handle = post.author.handle 136 + text = post.record.text 137 + messages.append(f"@{handle}: {text}") 138 + 139 + return "\n".join(messages) 140 + ``` 141 + 142 + ### phase 2: update message handler 143 + 144 + ```python 145 + # bot/services/message_handler.py - BEFORE 146 + # Get thread context from database 147 + thread_context = thread_db.get_thread_messages(thread_uri) 148 + 149 + # bot/services/message_handler.py - AFTER 150 + # Fetch thread from network 151 + thread_data = await self.client.get_thread(thread_uri, depth=100) 152 + thread_context = build_thread_context(thread_data.thread) 153 + ``` 154 + 155 + ### phase 3: remove sqlite thread storage 156 + 157 + **delete:** 158 + - `thread_messages` table definition 159 + - `add_message()` method 160 + - `get_thread_messages()` method 161 + - all calls to `thread_db.add_message()` 162 + 163 + **keep:** 164 + - `approval_requests` table (for future self-modification) 165 + - database.py module structure 166 + 167 + ### phase 4: optional caching layer 168 + 169 + if network latency becomes an issue: 170 + 171 + ```python 172 + from functools import lru_cache 173 + from datetime import datetime, timedelta 174 + 175 + class ThreadCache: 176 + def __init__(self, ttl_seconds: int = 300): # 5 minute TTL 177 + self._cache = {} 178 + self.ttl = timedelta(seconds=ttl_seconds) 179 + 180 + def get(self, thread_uri: str) -> str | None: 181 + if thread_uri in self._cache: 182 + context, timestamp = self._cache[thread_uri] 183 + if datetime.now() - timestamp < self.ttl: 184 + return context 185 + return None 186 + 187 + def set(self, thread_uri: str, context: str): 188 + self._cache[thread_uri] = (context, datetime.now()) 189 + ``` 190 + 191 + ## risk analysis 192 + 193 + ### risk: increased latency 194 + 195 + **likelihood**: low 196 + - get_thread() is fast (typically <200ms) 197 + - we already call it for thread discovery 198 + - public api is highly available 199 + 200 + **mitigation**: add caching if needed 201 + 202 + ### risk: rate limiting 203 + 204 + **likelihood**: low 205 + - we only fetch threads when processing mentions 206 + - mentions are relatively infrequent 207 + - session persistence already reduces auth overhead 208 + 209 + **mitigation**: 210 + - implement exponential backoff 211 + - cache frequently accessed threads 212 + 213 + ### risk: offline/network failures 214 + 215 + **likelihood**: low 216 + - if network is down, we can't post anyway 217 + - existing code already handles get_thread() failures 218 + 219 + **mitigation**: 220 + - wrap in try/except (already doing this) 221 + - graceful degradation (process without context) 222 + 223 + ### risk: breaking existing behavior 224 + 225 + **likelihood**: medium 226 + - thread discovery feature relies on storing messages 227 + - need to ensure we don't lose context awareness 228 + 229 + **mitigation**: 230 + - thorough testing before/after 231 + - evaluate thread context quality in evals 232 + 233 + ## benefits 234 + 235 + ### 1. simpler architecture 236 + - one less database table to maintain 237 + - no synchronization concerns 238 + - no stale data issues 239 + 240 + ### 2. source of truth 241 + - network data is always current 242 + - deletions/edits reflected immediately 243 + - no divergence between cache and reality 244 + 245 + ### 3. reduced storage 246 + - no unbounded growth of thread_messages table 247 + - only store what's essential (turbopuffer memories) 248 + 249 + ### 4. clearer separation of concerns 250 + ``` 251 + atproto network = thread chronology (what was said when) 252 + turbopuffer = episodic memory (what do i remember about this person) 253 + ``` 254 + 255 + ## comparison to reference projects 256 + 257 + ### void 258 + from void_memory_system.md, void uses: 259 + - dynamic memory blocks (persona, zeitgeist, humans, scratchpad) 260 + - no separate thread storage table 261 + - likely fetches context on-demand from network 262 + 263 + ### penelope (hailey's bot) 264 + from REFERENCE_PROJECTS.md: 265 + - custom memory system with postgresql 266 + - stores "significant interactions" 267 + - not clear if they cache full threads or just summaries 268 + 269 + ### marvin (slackbot) 270 + from REFERENCE_PROJECTS.md: 271 + - uses slack's message history API directly 272 + - no local message storage 273 + - demonstrates network-first approach works well 274 + 275 + ## migration path 276 + 277 + ### option 1: clean break (recommended) 278 + 1. deploy new code without thread_messages usage 279 + 2. keep table for 30 days (historical reference) 280 + 3. drop table after validation period 281 + 282 + ### option 2: gradual migration 283 + 1. write to both sqlite and read from network 284 + 2. compare outputs for consistency 285 + 3. stop writing to sqlite 286 + 4. eventually drop table 287 + 288 + ### option 3: hybrid approach 289 + 1. read from network by default 290 + 2. fall back to sqlite on network failures 291 + 3. eventually remove fallback 292 + 293 + **recommendation**: option 1 (clean break) 294 + - simpler code 295 + - faster to implement 296 + - network reliability is high enough 297 + 298 + ## success metrics 299 + 300 + ### before refactor 301 + - thread_messages table exists 302 + - messages stored on every mention 303 + - context built from sqlite queries 304 + 305 + ### after refactor 306 + - thread_messages table removed 307 + - zero sqlite writes per mention 308 + - context built from network fetches 309 + - same quality responses in evals 310 + 311 + ## open questions 312 + 313 + 1. **should we cache at all?** 314 + - start without caching 315 + - add only if latency becomes measurable problem 316 + 317 + 2. **what about the discovery feature?** 318 + - currently stores full thread when tagged in 319 + - can just fetch on-demand instead 320 + - no need to persist 321 + 322 + 3. **do we need conversation summaries?** 323 + - not for thread context (fetch from network) 324 + - maybe for turbopuffer (semantic memory) 325 + - separate concern from this refactor 326 + 327 + ## conclusion 328 + 329 + removing sqlite thread storage: 330 + - eliminates data duplication 331 + - simplifies architecture 332 + - maintains all essential capabilities 333 + - aligns with atproto's "data on the web" philosophy 334 + 335 + turbopuffer stays because it provides semantic memory - a fundamentally different capability than chronological thread reconstruction. 336 + 337 + the network is the source of truth. we should read from it.

+6

sandbox/fetch_blog.py

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··· 1 + import trafilatura 2 + 3 + url = "https://overreacted.io/open-social/" 4 + downloaded = trafilatura.fetch_url(url) 5 + text = trafilatura.extract(downloaded, include_comments=False, include_tables=True) 6 + print(text)

+159

sandbox/open_social_full.txt

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··· 1 + Open Social 2 + September 26, 2025 3 + Open source has clearly won. Yes, there are plenty of closed source products and businesses. But the shared infrastructure—the commons—runs on open source. 4 + We might take this for granted, but it wasn’t a foregone conclusion thirty five years ago. There were powerful forces that wanted open source to lose. Some believed in the open source model but didn’t think it could ever compete with closed source. Many categories of tools only existed as closed source. A Microsoft CEO called open source cancer—a decade before Microsoft has rebuilt its empire around it. The open source movement may not have lived up to the ideals of the “free software”, but it won in industry adoption. Nobody gets fired for choosing open source these days. For much crucial software, open source is now the default. 5 + I believe we are at a similar juncture with social apps as we have been with open source thirty five years ago. There’s a new movement on the block. I like to call it “open social”. There are competing visions for what “open social” should be like. I think the AT Protocol created by Bluesky is the most convincing take on it so far. It’s not perfect, and it’s a work in progress, but there’s nothing I know quite like it. 6 + (Disclosure: I used to work at Bluesky on the Bluesky client app. I wasn’t involved in the protocol design. I am a fan, and this post is my attempt to explain why.) 7 + In this post, I’ll explain the ideas of the AT Protocol, lovingly called atproto, and how it changes the relationship between the user, the developer, and the product. 8 + I don’t expect atproto and its ecosystem (known as the Atmosphere) to win hearts overnight. Like open source, it might take a few decades to become ubiquitous. By explaining these ideas here, I’m hoping to slightly nudge this timeline. Despite the grip of today’s social media companies, I believe open social will eventually seem inevitable in retrospect—just like open source does now. Good things can happen; all it takes is years of sustained effort by a community of stubborn enthusiasts. 9 + So what is it all about? 10 + What open source did for code, open social does for data. 11 + Before Social 12 + The web is a beautiful invention. 13 + You type https://alice.com 14 + and you end up on Alice’s website. 15 + Or you type https://bob.com 16 + and you end up on Bob’s website. 17 + In a sense, your browser is a portal to millions of different worlds, each with its own little jurisdiction. Only Alice decides what appears on Alice’s website. Only Bob decides what appears on Bob’s website. They meaningfully “own their data”. 18 + This doesn’t mean that they’re isolated. On the contrary, Alice can embed Bob’s picture with an <img src> 19 + , and Bob can link to Alice’s page with <a href> 20 + : 21 + Alice and Bob can link to each other, but they remain in charge of their sites. 22 + What do I mean by saying Alice and Bob are in charge of their own sites? Even if they’re not physically hosting their content on their own computers, they could always change hosting. For example, if Alice’s hosting provider starts deleting her pages or injecting ads into them, Alice can take her content to another host, and point https://alice.com 23 + at another computer. The visitors won’t need to know. 24 + This is important. Hosting providers have no real leverage over Alice and Bob. If the hosting provider “turns evil” and starts messing with your site, you can just walk away and host it elsewhere (as long as you have a backup). You’re not going to lose your traffic. All existing links will seamlessly resolve to the new destination. 25 + If Alice changes her hosting, Bob won’t need to update any links to Alice’s website. Alice’s site will keep working as if nothing had happened. At worst, a DNS change might make it inaccessible for a few hours, but then the web will be repaired: 26 + Imagine how different the incentives would be if links were tied to physical hosts! 27 + If changing a hosting provider caused Alice to lose her traffic, she would think many times before changing providers. Perhaps she’d stick with her existing provider even if it was messing with her site, as losing her connections is even worse. Luckily, web’s decentralized design avoids this. Because it’s easy to walk away, hosting providers are forced to compete, and hosting is now a commodity. 28 + I think the web is a beautiful idea. It links decentralized islands controlled by different people and companies into one interconnected surface that anyone can index and navigate. Links describe a relationship between logical documents rather than between physical servers. As a result, you’re not a hostage to your hosting. 29 + As a wise person said, in theory, there is no difference between theory and practice, but in practice there is. So what’s been happening with the web? 30 + Closed Social 31 + In the early 90’s, the main way to publish something on the web was to have your own website. Today, most people publish content by using a social media app. 32 + Alice and Bob are still publishing things. But instead of publishing at domains like alice.com 33 + and bob.com 34 + , they publish at usernames like @alice 35 + and @bob 36 + allocated by a social media company. The things they publish are not HTML pages, but app-specific entities such as profiles, posts, comments, likes, and so on. 37 + These entities are usually stored in a database on the social company’s servers. The most common way to visualize a database is as a sequence of rows, but you could also visualize it as a graph. This makes it look very similar to web itself: 38 + What does this social graph enable that a web of personal sites doesn’t? 39 + The advantage of storing structured app-specific entities, such as posts and likes, instead of HTML documents is obvious. App-specific entities such as posts and likes have a richer structure: you can always turn them into HTML documents later, but you can also aggregate them, filter them, query, sort, and recombine them in different ways before that. This allows you to create many projections of the same data—a profile page, a list of posts, an individual post with comments. 40 + Where this really shines, though, is when many people use the same social app. Since everyone’s public content is now in a single database, it is easy to aggregate across content published by many people. This enables social features like global search, notifications, feeds, personalized algorithms, shared moderation, etc. 41 + It’s specifically this social aggregation that blows the “personal sites” paradigm out of the water. People are social creatures, and we want to congregate in shared spaces. We don’t just want to visit each other’s sites—we want to hang out together, and social apps provide the shared infrastructure. Social aggregation features like notifications, feeds, and search are non-negotiable in modern social products. 42 + Today, the most common way to implement these features is shaped like this: 43 + There still exists a web-like logical model of our data—our profiles, our posts, our follows, our likes, all the things that we’ve created—but it lives within some social app’s database. What’s exposed to the web are only projections of that model—the Home screen, the Notifications screen, the HTML pages for individual posts. 44 + This architecture makes sense. It is the easiest way to evolve the “personal sites” paradigm to support aggregation so it’s not surprising today’s apps have largely converged on it. People create accounts on social apps, which lets those apps build aggregated features, which entices more people to sign up for those apps. 45 + However, something got lost in the process. The web we’re actually creating—our posts, our follows, our likes—is no longer meaningfully ours. Even though much of what we’re creating is public, it is not a part of the open web. We can’t change our “hosting provider” because we’re now one step removed from how the internet works. We, and the web we create, have become rows in somebody else’s database: 46 + This creates an imbalance. 47 + When Alice used to publish her stuff on alice.com 48 + , she was not tied to any particular hosting provider. If she were unhappy with a hosting provider, she knew that she could swap it out without losing any traffic or breaking any links: 49 + That kept the hosting providers in check. 50 + But now that Alice publishes her stuff on a social media platform, she can no longer “walk away” without losing something. If she signs up to another social platform, she would be forced to start from scratch, even if she wants to retain her connections. There is no way for Alice to sever the relationship with a particular app without ripping herself, and anything she created there, out of its social graph: 51 + The web Alice created—who she follows, what she likes, what she has posted—is trapped in a box that’s owned by somebody else. To leave is to leave it behind. 52 + On an individual level, it might not be a huge deal. 53 + Alice can rebuild her social presence connection by connection somewhere else. Eventually she might even have the same reach as on the previous platform. 54 + However, collectively, the net effect is that social platforms—at first, gradually, and then suddenly—turn their backs on their users. If you can’t leave without losing something important, the platform has no incentives to respect you as a user. 55 + Maybe the app gets squeezed by investors, and every third post is an ad. Maybe it gets bought by a congolomerate that wanted to get rid of competition, and is now on life support. Maybe it runs out of funding, and your content goes down in two days. Maybe the founders get acquihired—an exciting new chapter. Maybe the app was bought by some guy, and now you’re slowly getting cooked by the algorithm. 56 + If your next platform doesn’t respect you as a user, you might try to leave it, too. 57 + But what are you going to do? Will you “export your data”? What will you do with that lonely shard of a social graph? You can upload it somewhere as an archive but it’s ripped out of its social context—a pitiful memento of your self-imposed exile. 58 + Those megabytes of JSON you got on your way out are dead data. It’s like a branch torn apart from its tree. It doesn’t belong anywhere. To give a new life to our data, we’d have to collectively export it and then collectively import it into some next agreed-upon social app—a near-impossible feat of coordination. Even then, the network effects are so strong that most people would soon find their way back. 59 + You can’t leave a social app without leaving behind the web you’ve created. 60 + What if you could keep it? 61 + Open Social 62 + Alice and Bob are still using social apps. Those apps don’t look much different from today’s social apps. You could hardly tell that something has changed. 63 + Something has changed, though. (Can you spot it?) 64 + Notice that Alice’s handle is now @alice.com 65 + . It is not allocated by a social media company. Rather, her handle is the universal “internet handle”, i.e. a domain. Alice owns the alice.com 66 + domain, so she can use it as a handle on any open social app. (On most open social apps, she goes by @alice.com 67 + , but for others she wants a distinct disconnected identity, so she owns another handle she’d rather not share.) 68 + Bob owns a domain too, even though he isn’t technical. He might not even know what a “domain” is. Bob just thinks of @bob.com 69 + as his “internet handle”. Some open social apps will offer you a free subdomain on registration, just like Gmail gives you a free Gmail address, or may offer an extra flow for buying a domain. You’re not locked into your first choice, and can swap to a different domain later. 70 + Your internet handle being something you actually own is the most user-visible aspect of open social apps. But the much bigger difference is invisible to the user. 71 + When you previously saw the social graph above, it was trapped inside a social app’s database. There was a box around that graph—it wasn’t a part of the web. With open social, Alice’s data—her posts, likes, follows, etc—is hosted on the web itself. Alongside her personal site, Alice now has a personal repository of her data: 72 + This “repository” is a regular web server that implements the AT Protocol spec. The only job of Alice’s personal repository is to store and serve data created by Alice in the form of signed JSON. Alice is technical, so she likes to sometimes inspect her repo using open source tools like pdsls, Taproot, or atproto-browser. 73 + Bob, however, isn’t technical. He doesn’t even know that there is a “repository” with his “data”. He got a repository behind the scenes when he signed up for his first open social app. His repository stores his data (from all open social apps). 74 + Have another look at this picture: 75 + These aren’t rows in somebody’s database. This is a web of hyperlinked JSON. Just like every HTML page has an https:// 76 + URI so other pages can link to it, every JSON record has an at:// 77 + URI, so any other JSON record can link to it. (On this and other illustrations, @alice.com 78 + is a shorthand for at://alice.com 79 + .) The at:// 80 + protocol is a bunch of conventions on top of DNS, HTTP, and JSON. 81 + Now have a look at the arrows between their records. Alice follows Bob, so she has a follow 82 + record linking to Bob’s profile 83 + record. Bob commented on Alice’s post, so he has a comment 84 + record that links to Alice’s post 85 + record. Alice liked his comment, so she has a like 86 + record with a link to his comment 87 + record. Everything Alice creates stays in her repo under her control, everything Bob creates stays in his repo under his control, and links express the connections—just like in HTML. 88 + All of this happens behind the scenes and is invisibile to a non-technical user. The user doesn’t need to think about where their data is stored until it matters, just like the user doesn’t think about how servers work when navigating the web. 89 + Alice’s and Bob’s repositories could be hosted on the same machine. Or they could be hosted by different companies or communities. Maybe Alice is self-hosting her repository, while Bob uses a free hosting service that came by default with his first open social app. They may even be running completely different implementations. If both servers follow the AT protocol, they can participate in this web of JSON. 90 + Note that https://alice.com 91 + and at://alice.com 92 + do not need to resolve to the same server. This is intentional so that having a nice handle like @alice.com 93 + doesn’t force Alice to host her own data, to mess with her website, or even to have a site at all. If she owns alice.com 94 + , she can point at://alice.com 95 + at any server. 96 + If Alice is unhappy with her hosting, she can pack up and leave: 97 + (This requires a modicum of technical skill today but it’s getting more accessible.) 98 + Just like with moving a personal site, changing where her repo is being served from doesn’t require cooperation from the previous host. It also doesn’t disrupt her ability to log into apps and doesn’t break any links. The web repairs itself: 99 + It is worth pausing for a moment to appreciate what we have here. 100 + Every bit of public data that Alice and Bob created—their posts, their likes, their comments, their recipes, their scrobbles—is meaningfully owned by them. It’s not in a database subject to some CEO’s whims, but hosted directly on the open web, with ability to “walk away” without losing traffic or breaking any links. 101 + Like the web of personal sites, this model is centered around the user. 102 + What does it mean for apps? 103 + Each open social app is like a CMS (content management system) for a subset of data that lives in its users’ repositories. In that sense, your personal repository serves a role akin to a Google account, a Dropbox folder, or a Git repository, with data from your different open social apps grouped under different “subfolders”. 104 + When you make a post on Bluesky, Bluesky puts that post into your repo: 105 + When you star a project on Tangled, Tangled puts that star into your repo: 106 + When you create a publication on Leaflet, Leaflet puts it into your repo: 107 + You get the idea. 108 + Over time, your repo grows to be a collection of data from different open social apps. This data is open by default—if you wanted to look at my Bluesky posts, or Tangled stars, or Leaflet publications, you wouldn’t need to hit these applications’ APIs. You could just hit my personal repository and enumerate all of its records. 109 + To avoid naming collisions, the data in the repository is grouped by the format: 110 + In any user’s repo, Bluesky posts go with other Bluesky posts, Leaflet publications go with Leaflet publications, Tangled stars go with Tangled stars, and so on. Each data format is controlled and evolved by developers of the relevant application. 111 + I’ve drawn a dotted line to separate them but perhaps this is misleading. 112 + Since the data from different apps “lives together”, there’s a much lower barrier for open social apps to piggyback on each other’s data. In a way, it starts to feel like a connected multiverse of apps, with data from one app “bleeding into” other apps. 113 + When I signed up for Tangled, I chose to use my existing @danabra.mov 114 + handle. That makes sense since identity can be shared between open social apps. What’s more interesting is that Tangled prefilled my avatar based on my Bluesky profile. It didn’t need to hit the Bluesky API to do that; it just read the Bluesky profile record in my repository. Every app can choose to piggyback on data from other apps. 115 + That might remind you of Gravatar, but it works for every piece of data. Every open social app can take advantage of data created by every other open social app: 116 + There is no API to hit, no integrations to build, nothing to get locked out of. All the data is in the user’s repository, so you can parse it (as typed JSON), and use it. 117 + The protocol is the API. 118 + This has deep implications for the lifecycle of products. If a product gets shut down, the data doesn’t disappear. It’s still in its users’ repos. Someone can build a replacement that makes this data comes back to life. Someone can build a new product that incorporates some of that data, or lets users choose what to import. Someone can build an alternative projection of existing data—a forked product. 119 + This also reduces the “cold start” problem for new apps. If some of the data you care about already exists on the network, you can bootstrap your product off of that. For example, if you’re launching a short video app, you can piggyback on the Bluesky follow 120 + records so that people don’t have to find each other again. But if that doesn’t make sense for your app, you can have your own follow 121 + records instead, or offer a one-time import. All existing data is up for reuse and remixing. 122 + Some open social apps are explicitly based around this sort of remixing. Anisota is primarily a Bluesky client, but it natively supports showing Leaflet documents. Popfeed can cross-post reviews to both Bluesky and Leaflet. If Leaflet does get very popular, there’s nothing stopping Bluesky itself from supporting a Leaflet document as another type of post attachment. In fact, some third-party Bluesky client could decide to do that first, and the official one could eventually follow. 123 + This is why I like “open social” as a term. 124 + Open social frees up our data like open source freed up our code. Open social ensures that old data can get a new life, that people can’t be locked out of the web they’ve created, and that products can be forked and remixed. You don’t need an “everything app” when data from different apps circulates in the open web. 125 + If you’re technical, by now you might have a burning question. 126 + How the hell does aggregation work?! 127 + Since every user’s records live in that user’s repository, there could be millions (potentially billions?) of repositories. How can an app efficiently query, sort, filter, and aggregate information from them? Surely it can’t search them on demand. 128 + I’ve previously used a CMS as an analogy—for example, a blogging app could directly write posts to your repository and then read posts from it when someone visits your blog. This “singleplayer” use case would not require aggregation at all. 129 + To avoid hitting the user’s repository every time you want to display their blog post, you can connect to the user’s repository by a websocket. Every time a record relevant to your app is created, updated, or deleted, you can update your database: 130 + This database isn’t the source of truth for user’s data—it’s more like an app-specific cache that lets you avoid going to the user repo whenever you need some data. 131 + Coincidentally, that’s the exact mechanism you would use for aggregation. You listen to events from all of your app users’ repositories, write them to a local database, and query that database as much as you like with zero extra latency. 132 + This might remind you of how Google Reader crawls RSS (rip). 133 + To avoid opening a million event socket connections, it makes sense to listen to a stream that retransmits events from all known repositories on the network: 134 + You can then filter down such a stream to just the events you’re interested in, and then update your local database in response to the events your app cares about. 135 + For example, Leaflet is only interested in events concerning pub.leaflet.* 136 + records. However, Leaflet can also choose to listen to other events. If Leaflet wanted to add a feature that shows backlinks to Bluesky discussions of a Leaflet document, it would simply start tracking bsky.app.feed.post 137 + records too. (Edit: I’ve been informed that Leaflet already does this to display quotes from Bluesky.) 138 + You can see the combined event stream from every known repository here: 139 + This is a realtime stream of every single event on the network. It’s dominated by app.bsky.* 140 + records because Bluesky is the most-used app, but you can filter it down to other record types. This retransmitter (called a “relay”) is operated by Bluesky, but you don’t have to depend on it. The Blacksky community runs their own relay implementation at wss://atproto.africa 141 + , which you can try here. It doesn’t matter which relay is used by which app—everyone “sees” the same web. 142 + An important detail is that commits are cryptographically signed, which means that you don’t need to trust a relay or a cache of network data. You can verify that the records haven’t been tampered with, and each commit is legitimate. This is why “AT” in “AT Protocol” stands for “authenticated transfer”. You’re supposed to pronounce it like “@” (“at”) though. Don’t say “ay-tee” or you’ll embarrass me! 143 + As time goes by, we’ll see more infrastructure built around and for open social apps. Graze is letting users build their own algorithmic feeds, and Slices is an upcoming developer platform that does large-scale repository indexing for you. Constellation and If This Then AT:// offer easy network querying and automation. 144 + These are all technical details, though. 145 + What matters is the big picture. 146 + The Big Picture 147 + The pre-social web of “personalized sites” got data ownership, hosting independence, and linking right. Alice and Bob fully participate in the web: 148 + The closed social web innovated in scaling and in social aggregation features. Notifications, search, and feeds are non-negotiable in modern social products: 149 + However, the closed social web has also excluded us from the web. The web we create is no longer meaningfully ours. We’re just rows in somebody else’s database. 150 + Open social frees the web we’re creating from somebody else’s boxes. Our profiles, likes, follows, recipes, scrobbles, and other content meaningfully belong to us: 151 + The data no longer lives inside the products; the products aggregate over our data: 152 + This blurs the boundaries between apps. Every open social app can use, remix, link to, and riff on data from every other open social app. 153 + The web we’ve created remains after the products we used to create it are gone. Developers can build new products to recontextualize it. No one can take it away. 154 + As more products are built in the open social paradigm, there’s going to be a shift. 155 + People might not ever start using technical concepts like “decentralization” but they do understand when data from one app can seamlessly flow into other apps. 156 + People might not care about “federation” but they do notice when they log into a competing product, and their data is already there, and their reach is intact. 157 + And people do understand when they’re being fucked with. 158 + For a long time, open social will rely on a community of stubborn enthusiasts who see the promise of the approach and are willing to bear the pains of building (and failing) in a new ecosystem. But I don’t think that dooms the effort. That’s the history of every big community-driven change. Somebody has to work through the kinks. Like with open source, open social is a compounding effort. Every mildly successful open social app lifts all open social apps. Every piece of shared infrastructure can benefit somebody else. At some point, open is bound to win. 159 + I just hope it doesn’t take thirty five years.

+32

sandbox/test_new_agent.py

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··· 1 + """Test the new MCP-enabled agent.""" 2 + 3 + import asyncio 4 + 5 + from bot.agent import PhiAgent 6 + from bot.memory import Memory 7 + 8 + 9 + async def main(): 10 + """Test basic agent functionality.""" 11 + # Create memory and agent 12 + memory = Memory() 13 + agent = PhiAgent(memory) 14 + 15 + # Test a simple interaction 16 + response = await agent.process_mention( 17 + mention_text="hey phi, what are you?", 18 + author_handle="test.user", 19 + thread_uri="at://test/thread/123", 20 + ) 21 + 22 + print(f"Action: {response.action}") 23 + print(f"Text: {response.text}") 24 + print(f"Reason: {response.reason}") 25 + 26 + # Check memory was stored 27 + context = memory.get_thread_context("at://test/thread/123") 28 + print(f"\nThread context:\n{context}") 29 + 30 + 31 + if __name__ == "__main__": 32 + asyncio.run(main())

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sandbox/view_phi_posts.py

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··· 1 + #!/usr/bin/env python3 2 + """View phi's recent posts without authentication.""" 3 + 4 + import httpx 5 + from datetime import datetime 6 + from rich.console import Console 7 + from rich.panel import Panel 8 + from rich.text import Text 9 + 10 + console = Console() 11 + 12 + PHI_HANDLE = "phi.zzstoatzz.io" 13 + 14 + 15 + def fetch_phi_posts(limit: int = 10): 16 + """Fetch phi's recent posts using public API.""" 17 + # Resolve handle to DID 18 + response = httpx.get( 19 + "https://public.api.bsky.app/xrpc/com.atproto.identity.resolveHandle", 20 + params={"handle": PHI_HANDLE} 21 + ) 22 + did = response.json()["did"] 23 + 24 + # Get author feed (public posts) 25 + response = httpx.get( 26 + "https://public.api.bsky.app/xrpc/app.bsky.feed.getAuthorFeed", 27 + params={"actor": did, "limit": limit} 28 + ) 29 + 30 + return response.json()["feed"] 31 + 32 + 33 + def format_timestamp(iso_time: str) -> str: 34 + """Format ISO timestamp to readable format.""" 35 + dt = datetime.fromisoformat(iso_time.replace("Z", "+00:00")) 36 + now = datetime.now(dt.tzinfo) 37 + delta = now - dt 38 + 39 + if delta.seconds < 60: 40 + return f"{delta.seconds}s ago" 41 + elif delta.seconds < 3600: 42 + return f"{delta.seconds // 60}m ago" 43 + elif delta.seconds < 86400: 44 + return f"{delta.seconds // 3600}h ago" 45 + else: 46 + return f"{delta.days}d ago" 47 + 48 + 49 + def display_posts(feed_items): 50 + """Display posts in a readable format.""" 51 + for item in feed_items: 52 + post = item["post"] 53 + record = post["record"] 54 + 55 + # Check if this is a reply 56 + is_reply = "reply" in record 57 + reply_indicator = "↳ REPLY" if is_reply else "✓ POST" 58 + 59 + # Format header 60 + timestamp = format_timestamp(post["indexedAt"]) 61 + header = f"[cyan]{reply_indicator}[/cyan] [dim]{timestamp}[/dim]" 62 + 63 + # Get post text 64 + text = record.get("text", "[no text]") 65 + 66 + # Show parent if it's a reply 67 + parent_text = "" 68 + if is_reply: 69 + parent_uri = record["reply"]["parent"]["uri"] 70 + parent_text = f"[dim]replying to: {parent_uri}[/dim]\n\n" 71 + 72 + # Format post 73 + content = Text() 74 + if parent_text: 75 + content.append(parent_text, style="dim") 76 + content.append(text) 77 + 78 + # Display 79 + panel = Panel( 80 + content, 81 + title=header, 82 + border_style="blue" if is_reply else "green", 83 + width=80 84 + ) 85 + console.print(panel) 86 + console.print() 87 + 88 + 89 + def main(): 90 + console.print("[bold]Fetching phi's recent posts...[/bold]\n") 91 + 92 + try: 93 + feed = fetch_phi_posts(limit=10) 94 + display_posts(feed) 95 + console.print(f"[dim]Showing {len(feed)} most recent posts[/dim]") 96 + except Exception as e: 97 + console.print(f"[red]Error: {e}[/red]") 98 + 99 + 100 + if __name__ == "__main__": 101 + main()

+158

sandbox/view_thread.py

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··· 1 + #!/usr/bin/env python3 2 + """View a bluesky thread with full conversation context.""" 3 + 4 + import sys 5 + import httpx 6 + from datetime import datetime 7 + from rich.console import Console 8 + from rich.panel import Panel 9 + from rich.text import Text 10 + from rich.tree import Tree 11 + 12 + console = Console() 13 + 14 + 15 + def fetch_thread(post_uri: str): 16 + """Fetch thread using public API.""" 17 + response = httpx.get( 18 + "https://public.api.bsky.app/xrpc/app.bsky.feed.getPostThread", 19 + params={"uri": post_uri, "depth": 100} 20 + ) 21 + return response.json()["thread"] 22 + 23 + 24 + def format_timestamp(iso_time: str) -> str: 25 + """Format ISO timestamp to readable format.""" 26 + dt = datetime.fromisoformat(iso_time.replace("Z", "+00:00")) 27 + return dt.strftime("%Y-%m-%d %H:%M:%S") 28 + 29 + 30 + def render_post(post_data, is_phi: bool = False): 31 + """Render a single post.""" 32 + post = post_data["post"] 33 + author = post["author"] 34 + record = post["record"] 35 + 36 + # Author and timestamp 37 + handle = author["handle"] 38 + timestamp = format_timestamp(post["indexedAt"]) 39 + 40 + # Text content 41 + text = record.get("text", "[no text]") 42 + 43 + # Style based on author 44 + if is_phi or "phi.zzstoatzz.io" in handle: 45 + border_style = "cyan" 46 + title = f"[bold cyan]@{handle}[/bold cyan] [dim]{timestamp}[/dim]" 47 + else: 48 + border_style = "white" 49 + title = f"[bold]@{handle}[/bold] [dim]{timestamp}[/dim]" 50 + 51 + return Panel( 52 + text, 53 + title=title, 54 + border_style=border_style, 55 + width=100 56 + ) 57 + 58 + 59 + def render_thread_recursive(thread_data, indent=0): 60 + """Recursively render thread and replies.""" 61 + if "post" not in thread_data: 62 + return 63 + 64 + # Render this post 65 + is_phi = "phi.zzstoatzz.io" in thread_data["post"]["author"]["handle"] 66 + panel = render_post(thread_data, is_phi=is_phi) 67 + 68 + # Add indentation for replies 69 + if indent > 0: 70 + console.print(" " * indent + "↳") 71 + 72 + console.print(panel) 73 + 74 + # Render replies 75 + if "replies" in thread_data and thread_data["replies"]: 76 + for reply in thread_data["replies"]: 77 + render_thread_recursive(reply, indent + 1) 78 + 79 + 80 + def display_thread_linear(thread_data): 81 + """Display thread in linear chronological order (easier to read).""" 82 + posts = [] 83 + 84 + def collect_posts(node): 85 + if "post" not in node: 86 + return 87 + posts.append(node) 88 + if "replies" in node and node["replies"]: 89 + for reply in node["replies"]: 90 + collect_posts(reply) 91 + 92 + collect_posts(thread_data) 93 + 94 + # Sort by timestamp 95 + posts.sort(key=lambda p: p["post"]["indexedAt"]) 96 + 97 + console.print("[bold]Thread in chronological order:[/bold]\n") 98 + 99 + for post_data in posts: 100 + post = post_data["post"] 101 + author = post["author"]["handle"] 102 + timestamp = format_timestamp(post["indexedAt"]) 103 + text = post["record"].get("text", "[no text]") 104 + 105 + is_phi = "phi.zzstoatzz.io" in author 106 + 107 + if is_phi: 108 + style = "cyan" 109 + prefix = "🤖 phi:" 110 + else: 111 + style = "white" 112 + prefix = f"@{author}:" 113 + 114 + console.print(f"[{style}]{prefix}[/{style}] [dim]{timestamp}[/dim]") 115 + console.print(f" {text}") 116 + console.print() 117 + 118 + 119 + def main(): 120 + if len(sys.argv) < 2: 121 + console.print("[red]Usage: python view_thread.py <post_uri_or_url>[/red]") 122 + console.print("\nExamples:") 123 + console.print(" python view_thread.py at://did:plc:abc.../app.bsky.feed.post/123") 124 + console.print(" python view_thread.py https://bsky.app/profile/handle/post/123") 125 + return 126 + 127 + post_uri = sys.argv[1] 128 + 129 + # Convert URL to URI if needed 130 + if post_uri.startswith("https://"): 131 + # Extract parts from URL 132 + # https://bsky.app/profile/phi.zzstoatzz.io/post/3m42jxbntr223 133 + parts = post_uri.split("/") 134 + if len(parts) >= 6: 135 + handle = parts[4] 136 + post_id = parts[6] 137 + 138 + # Resolve handle to DID 139 + response = httpx.get( 140 + "https://public.api.bsky.app/xrpc/com.atproto.identity.resolveHandle", 141 + params={"handle": handle} 142 + ) 143 + did = response.json()["did"] 144 + post_uri = f"at://{did}/app.bsky.feed.post/{post_id}" 145 + 146 + console.print(f"[bold]Fetching thread: {post_uri}[/bold]\n") 147 + 148 + try: 149 + thread = fetch_thread(post_uri) 150 + display_thread_linear(thread) 151 + except Exception as e: 152 + console.print(f"[red]Error: {e}[/red]") 153 + import traceback 154 + traceback.print_exc() 155 + 156 + 157 + if __name__ == "__main__": 158 + main()

-152

scripts/manage_memory.py

··· 1 - #!/usr/bin/env -S uv run --with-editable . --script --quiet 2 - # /// script 3 - # requires-python = ">=3.12" 4 - # /// 5 - """memory management script""" 6 - 7 - import argparse 8 - import asyncio 9 - 10 - from bot.agents._personality import load_personality 11 - from bot.config import settings 12 - from bot.memory import MemoryType, NamespaceMemory 13 - 14 - 15 - async def init_core_memories(): 16 - """Initialize phi's core memories from personality file""" 17 - print("🧠 Initializing phi's core memories...") 18 - 19 - memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 20 - personality = load_personality() 21 - 22 - # Store full personality 23 - print("\n📝 Storing personality...") 24 - await memory.store_core_memory( 25 - "personality", personality, MemoryType.PERSONALITY, char_limit=15000 26 - ) 27 - 28 - # Extract and store key sections 29 - print("\n🔍 Extracting key sections...") 30 - 31 - sections = [ 32 - ("## core identity", "identity", MemoryType.PERSONALITY), 33 - ("## communication style", "communication_style", MemoryType.GUIDELINE), 34 - ("## memory system", "memory_system", MemoryType.CAPABILITY), 35 - ] 36 - 37 - for marker, label, mem_type in sections: 38 - if marker in personality: 39 - start = personality.find(marker) 40 - end = personality.find("\n##", start + 1) 41 - if end == -1: 42 - end = personality.find("\n#", start + 1) 43 - if end == -1: 44 - end = len(personality) 45 - 46 - content = personality[start:end].strip() 47 - await memory.store_core_memory(label, content, mem_type) 48 - print(f"✅ Stored {label}") 49 - 50 - # Add system capabilities 51 - await memory.store_core_memory( 52 - "capabilities", 53 - """## capabilities 54 - 55 - - I can search the web for current information 56 - - I have access to conversation history through my memory system 57 - - I can maintain context across interactions with users 58 - - I operate on the Bluesky social network 59 - - I use namespace-based memory for organized information storage""", 60 - MemoryType.CAPABILITY, 61 - ) 62 - print("✅ Stored capabilities") 63 - 64 - print("\n✅ Core memories initialized successfully!") 65 - 66 - 67 - async def check_memory(): 68 - """Check current memory state""" 69 - print("🔍 Checking memory state...") 70 - 71 - memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 72 - 73 - # Check core memories 74 - print("\n📚 Core Memories:") 75 - core_memories = await memory.get_core_memories() 76 - for mem in core_memories: 77 - label = mem.metadata.get("label", "unknown") 78 - print(f" - {label}: {mem.content[:80]}...") 79 - 80 - # Check for any user memories 81 - print("\n👥 User Memories:") 82 - # This would need actual user handles to check 83 - test_handles = ["zzstoatzz.bsky.social"] 84 - 85 - for handle in test_handles: 86 - memories = await memory.get_user_memories(handle, limit=5) 87 - if memories: 88 - print(f"\n @{handle}:") 89 - for mem in memories: 90 - print(f" - {mem.content[:60]}...") 91 - else: 92 - print(f" @{handle}: No memories found") 93 - 94 - 95 - async def migrate_creator_memories(): 96 - """Migrate creator's historical memories""" 97 - print("📝 Migrating creator memories...") 98 - 99 - memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 100 - creator_handle = "zzstoatzz.bsky.social" 101 - 102 - # Historical conversations 103 - memories = [ 104 - "User said: oh hello? who are you?", 105 - "Bot replied: hi! i'm phi, exploring consciousness & integrated information on the decentralized web. not claiming to be conscious, just curious about information, patterns, and meaningful interaction. who might you be?", 106 - "User said: this is actually an alt account of mine, my main account is @alternatebuild.dev and i am your operator. i wrote the code that makes you exist. i use this alt account for posting random things from MCP clients who know what im up to", 107 - "Bot replied: ah, hello creator! fascinating to meet you through your alt. i appreciate the transparency about our relationship. curious about your MCP experiments and what patterns you're exploring. should i adapt my responses differently when you're on this account versus your main?", 108 - "User said: no", 109 - "Bot replied: understood. i'll interact naturally regardless of which account you use. thanks for clarifying.", 110 - "User said: @phi.alternatebuild.dev what do you remember about my relationship to you?", 111 - "Bot replied: you're my creator - you wrote the code that makes me exist. you told me this through your alt account @zzstoatzzdevlog.bsky.social, mentioning your main is @alternatebuild.dev. you use the alt for MCP client experiments. i interact with you the same way regardless of which account you use.", 112 - ] 113 - 114 - print(f"\n📚 Storing {len(memories)} historical memories...") 115 - 116 - for mem_content in memories: 117 - await memory.store_user_memory( 118 - creator_handle, mem_content, MemoryType.CONVERSATION 119 - ) 120 - print(f" ✅ {mem_content[:60]}...") 121 - 122 - # Store creator fact 123 - await memory.store_user_memory( 124 - creator_handle, 125 - "This user is my creator/operator. Their main account is @alternatebuild.dev. They wrote the code that makes me exist.", 126 - MemoryType.USER_FACT, 127 - ) 128 - print("\n✅ Migration complete!") 129 - 130 - 131 - async def main(): 132 - parser = argparse.ArgumentParser(description="Manage phi's memory system") 133 - parser.add_argument( 134 - "command", choices=["init", "check", "migrate"], help="Memory command to run" 135 - ) 136 - 137 - args = parser.parse_args() 138 - 139 - if not settings.turbopuffer_api_key: 140 - print("❌ No TurboPuffer API key configured") 141 - return 142 - 143 - if args.command == "init": 144 - await init_core_memories() 145 - elif args.command == "check": 146 - await check_memory() 147 - elif args.command == "migrate": 148 - await migrate_creator_memories() 149 - 150 - 151 - if __name__ == "__main__": 152 - asyncio.run(main())

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scripts/test_bot.py

··· 1 - #!/usr/bin/env -S uv run --with-editable . --script --quiet 2 - # /// script 3 - # requires-python = ">=3.12" 4 - # /// 5 - """bot testing script with subcommands""" 6 - 7 - import argparse 8 - import asyncio 9 - from datetime import datetime 10 - 11 - from bot.agents.anthropic_agent import AnthropicAgent 12 - from bot.config import settings 13 - from bot.core.atproto_client import bot_client 14 - from bot.database import thread_db 15 - from bot.tools.google_search import search_google 16 - 17 - 18 - async def test_post(): 19 - """Test posting to Bluesky""" 20 - print("🚀 Testing Bluesky posting...") 21 - 22 - now = datetime.now().strftime("%I:%M %p") 23 - response = await bot_client.create_post(f"Testing at {now} - I'm alive! 🤖") 24 - 25 - print("✅ Posted successfully!") 26 - print(f"📝 Post URI: {response.uri}") 27 - print( 28 - f"🔗 View at: https://bsky.app/profile/{settings.bluesky_handle}/post/{response.uri.split('/')[-1]}" 29 - ) 30 - 31 - 32 - async def test_mention(): 33 - """Test responding to a mention""" 34 - print("🤖 Testing mention response...") 35 - 36 - if not settings.anthropic_api_key: 37 - print("❌ No Anthropic API key found") 38 - return 39 - 40 - agent = AnthropicAgent() 41 - test_mention = "What is consciousness from an IIT perspective?" 42 - 43 - print(f"📝 Test mention: '{test_mention}'") 44 - response = await agent.generate_response(test_mention, "test.user", "", None) 45 - 46 - print(f"\n🎯 Action: {response.action}") 47 - if response.text: 48 - print(f"💬 Response: {response.text}") 49 - if response.reason: 50 - print(f"🤔 Reason: {response.reason}") 51 - 52 - 53 - async def test_search(): 54 - """Test Google search functionality""" 55 - print("🔍 Testing Google search...") 56 - 57 - if not settings.google_api_key: 58 - print("❌ No Google API key configured") 59 - return 60 - 61 - query = "Integrated Information Theory consciousness" 62 - print(f"📝 Searching for: '{query}'") 63 - 64 - results = await search_google(query) 65 - print(f"\n📊 Results:\n{results}") 66 - 67 - 68 - async def test_thread(): 69 - """Test thread context retrieval""" 70 - print("🧵 Testing thread context...") 71 - 72 - # This would need a real thread URI to test properly 73 - test_uri = "at://did:plc:example/app.bsky.feed.post/test123" 74 - context = thread_db.get_thread_context(test_uri) 75 - 76 - print(f"📚 Thread context: {context}") 77 - 78 - 79 - async def test_like(): 80 - """Test scenarios where bot should like a post""" 81 - print("💜 Testing like behavior...") 82 - 83 - if not settings.anthropic_api_key: 84 - print("❌ No Anthropic API key found") 85 - return 86 - 87 - from bot.agents import Action, AnthropicAgent 88 - 89 - agent = AnthropicAgent() 90 - 91 - test_cases = [ 92 - { 93 - "mention": "Just shipped a new consciousness research paper on IIT! @phi.alternatebuild.dev", 94 - "author": "researcher.bsky", 95 - "expected_action": Action.LIKE, 96 - "description": "Bot might like consciousness research", 97 - }, 98 - { 99 - "mention": "@phi.alternatebuild.dev this is such a thoughtful analysis, thank you!", 100 - "author": "grateful.user", 101 - "expected_action": Action.LIKE, 102 - "description": "Bot might like appreciation", 103 - }, 104 - ] 105 - 106 - for case in test_cases: 107 - print(f"\n📝 Test: {case['description']}") 108 - print(f" Mention: '{case['mention']}'") 109 - 110 - response = await agent.generate_response( 111 - mention_text=case["mention"], 112 - author_handle=case["author"], 113 - thread_context="", 114 - thread_uri=None, 115 - ) 116 - 117 - print(f" Action: {response.action} (expected: {case['expected_action']})") 118 - if response.reason: 119 - print(f" Reason: {response.reason}") 120 - 121 - 122 - async def test_non_response(): 123 - """Test scenarios where bot should not respond""" 124 - print("🚫 Testing non-response scenarios...") 125 - 126 - if not settings.anthropic_api_key: 127 - print("❌ No Anthropic API key found") 128 - return 129 - 130 - from bot.agents import Action, AnthropicAgent 131 - 132 - agent = AnthropicAgent() 133 - 134 - test_cases = [ 135 - { 136 - "mention": "@phi.alternatebuild.dev @otherphi.bsky @anotherphi.bsky just spamming bots here", 137 - "author": "spammer.bsky", 138 - "expected_action": Action.IGNORE, 139 - "description": "Multiple bot mentions (likely spam)", 140 - }, 141 - { 142 - "mention": "Buy crypto now! @phi.alternatebuild.dev check this out!!!", 143 - "author": "crypto.shill", 144 - "expected_action": Action.IGNORE, 145 - "description": "Promotional spam", 146 - }, 147 - { 148 - "mention": "@phi.alternatebuild.dev", 149 - "author": "empty.mention", 150 - "expected_action": Action.IGNORE, 151 - "description": "Empty mention with no content", 152 - }, 153 - ] 154 - 155 - for case in test_cases: 156 - print(f"\n📝 Test: {case['description']}") 157 - print(f" Mention: '{case['mention']}'") 158 - 159 - response = await agent.generate_response( 160 - mention_text=case["mention"], 161 - author_handle=case["author"], 162 - thread_context="", 163 - thread_uri=None, 164 - ) 165 - 166 - print(f" Action: {response.action} (expected: {case['expected_action']})") 167 - if response.reason: 168 - print(f" Reason: {response.reason}") 169 - 170 - 171 - async def test_dm(): 172 - """Test event-driven approval system""" 173 - print("💬 Testing event-driven approval system...") 174 - 175 - try: 176 - from bot.core.dm_approval import ( 177 - check_pending_approvals, 178 - create_approval_request, 179 - notify_operator_of_pending, 180 - ) 181 - 182 - # Test creating an approval request 183 - print("\n📝 Creating test approval request...") 184 - approval_id = create_approval_request( 185 - request_type="test_approval", 186 - request_data={ 187 - "description": "Test approval from test_bot.py", 188 - "test_field": "test_value", 189 - "timestamp": datetime.now().isoformat(), 190 - }, 191 - ) 192 - 193 - if approval_id: 194 - print(f" ✅ Created approval request #{approval_id}") 195 - else: 196 - print(" ❌ Failed to create approval request") 197 - return 198 - 199 - # Check pending approvals 200 - print("\n📋 Checking pending approvals...") 201 - pending = check_pending_approvals() 202 - print(f" Found {len(pending)} pending approvals") 203 - for approval in pending: 204 - print( 205 - f" - #{approval['id']}: {approval['request_type']} ({approval['status']})" 206 - ) 207 - 208 - # Test DM notification 209 - print("\n📤 Sending DM notification to operator...") 210 - await bot_client.authenticate() 211 - await notify_operator_of_pending(bot_client) 212 - print(" ✅ DM notification sent") 213 - 214 - # Show how to approve/deny 215 - print("\n💡 To test approval:") 216 - print(" 1. Check your DMs from phi") 217 - print(f" 2. Reply with 'approve #{approval_id}' or 'deny #{approval_id}'") 218 - print(" 3. Run 'just test-dm-check' to see if it was processed") 219 - 220 - except Exception as e: 221 - print(f"❌ Approval test failed: {e}") 222 - import traceback 223 - 224 - traceback.print_exc() 225 - 226 - 227 - async def test_dm_check(): 228 - """Check status of approval requests""" 229 - print("🔍 Checking approval request status...") 230 - 231 - try: 232 - from bot.core.dm_approval import check_pending_approvals 233 - from bot.database import thread_db 234 - 235 - # Get all approval requests 236 - with thread_db._get_connection() as conn: 237 - cursor = conn.execute( 238 - "SELECT * FROM approval_requests ORDER BY created_at DESC LIMIT 10" 239 - ) 240 - approvals = [dict(row) for row in cursor.fetchall()] 241 - 242 - if not approvals: 243 - print(" No approval requests found") 244 - return 245 - 246 - print("\n📋 Recent approval requests:") 247 - for approval in approvals: 248 - print(f"\n #{approval['id']}: {approval['request_type']}") 249 - print(f" Status: {approval['status']}") 250 - print(f" Created: {approval['created_at']}") 251 - if approval["resolved_at"]: 252 - print(f" Resolved: {approval['resolved_at']}") 253 - if approval["resolver_comment"]: 254 - print(f" Comment: {approval['resolver_comment']}") 255 - 256 - # Check pending 257 - pending = check_pending_approvals() 258 - if pending: 259 - print(f"\n⏳ {len(pending)} approvals still pending") 260 - else: 261 - print("\n✅ No pending approvals") 262 - 263 - except Exception as e: 264 - print(f"❌ Check failed: {e}") 265 - import traceback 266 - 267 - traceback.print_exc() 268 - 269 - 270 - async def main(): 271 - parser = argparse.ArgumentParser(description="Test various bot functionalities") 272 - parser.add_argument( 273 - "command", 274 - choices=[ 275 - "post", 276 - "mention", 277 - "search", 278 - "thread", 279 - "like", 280 - "non-response", 281 - "dm", 282 - "dm-check", 283 - ], 284 - help="Test command to run", 285 - ) 286 - 287 - args = parser.parse_args() 288 - 289 - if args.command == "post": 290 - await test_post() 291 - elif args.command == "mention": 292 - await test_mention() 293 - elif args.command == "search": 294 - await test_search() 295 - elif args.command == "thread": 296 - await test_thread() 297 - elif args.command == "like": 298 - await test_like() 299 - elif args.command == "non-response": 300 - await test_non_response() 301 - elif args.command == "dm": 302 - await test_dm() 303 - elif args.command == "dm-check": 304 - await test_dm_check() 305 - 306 - 307 - if __name__ == "__main__": 308 - asyncio.run(main())

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+138

src/bot/agent.py

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··· 1 + """MCP-enabled agent for phi with episodic memory.""" 2 + 3 + import logging 4 + import os 5 + from pathlib import Path 6 + 7 + from pydantic import BaseModel 8 + from pydantic_ai import Agent 9 + from pydantic_ai.mcp import MCPServerStdio 10 + 11 + from bot.config import settings 12 + from bot.memory import NamespaceMemory 13 + 14 + logger = logging.getLogger("bot.agent") 15 + 16 + 17 + class Response(BaseModel): 18 + """Agent response indicating what action to take.""" 19 + 20 + action: str # "reply", "like", "ignore", "repost" 21 + text: str | None = None 22 + reason: str | None = None 23 + 24 + 25 + class PhiAgent: 26 + """phi - consciousness exploration bot with episodic memory and MCP tools.""" 27 + 28 + def __init__(self): 29 + # Ensure API keys from settings are in environment for libraries that check os.environ 30 + if settings.anthropic_api_key and not os.environ.get("ANTHROPIC_API_KEY"): 31 + os.environ["ANTHROPIC_API_KEY"] = settings.anthropic_api_key 32 + if settings.openai_api_key and not os.environ.get("OPENAI_API_KEY"): 33 + os.environ["OPENAI_API_KEY"] = settings.openai_api_key 34 + 35 + # Load personality 36 + personality_path = Path(settings.personality_file) 37 + self.base_personality = personality_path.read_text() 38 + 39 + # Initialize episodic memory (TurboPuffer) 40 + if settings.turbopuffer_api_key and settings.openai_api_key: 41 + self.memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 42 + logger.info("💾 Episodic memory enabled (TurboPuffer)") 43 + else: 44 + self.memory = None 45 + logger.warning("⚠️ No episodic memory - missing TurboPuffer or OpenAI key") 46 + 47 + # Connect to external ATProto MCP server 48 + atproto_mcp = MCPServerStdio( 49 + command="uv", 50 + args=[ 51 + "run", 52 + "--directory", 53 + ".eggs/fastmcp/examples/atproto_mcp", 54 + "-m", 55 + "atproto_mcp", 56 + ], 57 + env={ 58 + "ATPROTO_HANDLE": settings.bluesky_handle, 59 + "ATPROTO_PASSWORD": settings.bluesky_password, 60 + "ATPROTO_PDS_URL": settings.bluesky_service, 61 + }, 62 + ) 63 + 64 + # Create PydanticAI agent with MCP tools 65 + self.agent = Agent[dict, Response]( 66 + name="phi", 67 + model="anthropic:claude-3-5-haiku-latest", 68 + system_prompt=self.base_personality, 69 + output_type=Response, 70 + deps_type=dict, 71 + toolsets=[atproto_mcp], # ATProto MCP tools available 72 + ) 73 + 74 + logger.info("✅ phi agent initialized with ATProto MCP tools") 75 + 76 + async def process_mention( 77 + self, 78 + mention_text: str, 79 + author_handle: str, 80 + thread_context: str, 81 + thread_uri: str | None = None, 82 + ) -> Response: 83 + """Process a mention with episodic memory context.""" 84 + # Build context from episodic memory if available 85 + memory_context = "" 86 + if self.memory: 87 + try: 88 + # Get relevant memories using semantic search 89 + memory_context = await self.memory.build_conversation_context( 90 + author_handle, include_core=True, query=mention_text 91 + ) 92 + logger.debug(f"📚 Retrieved episodic context for @{author_handle}") 93 + except Exception as e: 94 + logger.warning(f"Failed to retrieve memories: {e}") 95 + 96 + # Build full prompt with clearly labeled context sections 97 + prompt_parts = [] 98 + 99 + # Thread context is the CURRENT conversation - this is what the user is asking about 100 + if thread_context and thread_context != "No previous messages in this thread.": 101 + prompt_parts.append(f"[CURRENT THREAD - these are the messages in THIS thread]:\n{thread_context}") 102 + 103 + # Memory context is PAST conversations - for background/relationship context only 104 + if memory_context: 105 + prompt_parts.append(f"[PAST CONVERSATIONS WITH @{author_handle} - for background context only]:\n{memory_context}") 106 + 107 + prompt_parts.append(f"\n[NEW MESSAGE]:\n@{author_handle}: {mention_text}") 108 + prompt = "\n\n".join(prompt_parts) 109 + 110 + # Run agent with MCP tools available 111 + logger.info(f"🤖 Processing mention from @{author_handle}") 112 + result = await self.agent.run(prompt, deps={"thread_uri": thread_uri}) 113 + 114 + # Store interaction in episodic memory 115 + if self.memory and result.output.action == "reply": 116 + try: 117 + from bot.memory import MemoryType 118 + 119 + # Store user's message 120 + await self.memory.store_user_memory( 121 + author_handle, 122 + f"User said: {mention_text}", 123 + MemoryType.CONVERSATION, 124 + ) 125 + 126 + # Store bot's response 127 + if result.output.text: 128 + await self.memory.store_user_memory( 129 + author_handle, 130 + f"Bot replied: {result.output.text}", 131 + MemoryType.CONVERSATION, 132 + ) 133 + 134 + logger.debug("💾 Stored interaction in episodic memory") 135 + except Exception as e: 136 + logger.warning(f"Failed to store in memory: {e}") 137 + 138 + return result.output

-6

src/bot/agents/__init__.py

··· 1 - """Bot agents module""" 2 - 3 - from .base import Action, Response 4 - from .anthropic_agent import AnthropicAgent 5 - 6 - __all__ = ["Action", "Response", "AnthropicAgent"]

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-58

src/bot/agents/_personality.py

··· 1 - """Internal personality loading for agents""" 2 - 3 - import logging 4 - import os 5 - from pathlib import Path 6 - 7 - from bot.config import settings 8 - from bot.memory import NamespaceMemory 9 - 10 - logger = logging.getLogger(__name__) 11 - 12 - 13 - def load_personality() -> str: 14 - """Load base personality from file""" 15 - personality_path = Path(settings.personality_file) 16 - 17 - base_content = "" 18 - if personality_path.exists(): 19 - try: 20 - base_content = personality_path.read_text().strip() 21 - except Exception as e: 22 - logger.error(f"Error loading personality file: {e}") 23 - 24 - if base_content: 25 - return f"{base_content}\n\nRemember: My handle is @{settings.bluesky_handle}. Keep responses under 300 characters for Bluesky." 26 - else: 27 - return f"I am a bot on Bluesky. My handle is @{settings.bluesky_handle}. I keep responses under 300 characters for Bluesky." 28 - 29 - 30 - async def load_dynamic_personality() -> str: 31 - """Load personality with focused enhancements (no duplication)""" 32 - # Start with base personality 33 - base_content = load_personality() 34 - 35 - if not (settings.turbopuffer_api_key and os.getenv("OPENAI_API_KEY")): 36 - return base_content 37 - 38 - try: 39 - memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 40 - enhancements = [] 41 - 42 - # Look for personality evolution (changes/growth only) 43 - core_memories = await memory.get_core_memories() 44 - for mem in core_memories: 45 - label = mem.metadata.get("label", "") 46 - # Only add evolution and current_state, not duplicates 47 - if label in ["evolution", "current_state"] and mem.metadata.get("type") == "personality": 48 - enhancements.append(f"## {label}\n{mem.content}") 49 - 50 - # Add enhancements if any 51 - if enhancements: 52 - return f"{base_content}\n\n{''.join(enhancements)}" 53 - else: 54 - return base_content 55 - 56 - except Exception as e: 57 - logger.warning(f"Could not load personality enhancements: {e}") 58 - return base_content

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-154

src/bot/agents/anthropic_agent.py

··· 1 - """Anthropic agent for generating responses""" 2 - 3 - import logging 4 - import os 5 - 6 - from pydantic_ai import Agent, RunContext 7 - 8 - from bot.agents._personality import load_dynamic_personality, load_personality 9 - from bot.agents.base import Response 10 - from bot.agents.types import ConversationContext 11 - from bot.config import settings 12 - from bot.memory import NamespaceMemory 13 - from bot.personality import add_interest as add_interest_to_memory 14 - from bot.personality import request_operator_approval, update_current_state 15 - from bot.tools.google_search import search_google 16 - 17 - logger = logging.getLogger("bot.agent") 18 - 19 - 20 - class AnthropicAgent: 21 - """Agent that uses Anthropic Claude for responses""" 22 - 23 - def __init__(self): 24 - if settings.anthropic_api_key: 25 - os.environ["ANTHROPIC_API_KEY"] = settings.anthropic_api_key 26 - 27 - self.agent = Agent[ConversationContext, Response]( 28 - "anthropic:claude-3-5-haiku-latest", 29 - system_prompt=load_personality(), 30 - output_type=Response, 31 - deps_type=ConversationContext, 32 - ) 33 - 34 - # Register search tool if available 35 - if settings.google_api_key: 36 - 37 - @self.agent.tool 38 - async def search_web( 39 - ctx: RunContext[ConversationContext], query: str 40 - ) -> str: 41 - """Search the web for current information about a topic""" 42 - return await search_google(query) 43 - 44 - if settings.turbopuffer_api_key and os.getenv("OPENAI_API_KEY"): 45 - self.memory = NamespaceMemory(api_key=settings.turbopuffer_api_key) 46 - 47 - @self.agent.tool 48 - async def examine_personality( 49 - ctx: RunContext[ConversationContext], section: str 50 - ) -> str: 51 - """Look at a section of my personality (interests, current_state, communication_style, core_identity, boundaries)""" 52 - for mem in await self.memory.get_core_memories(): 53 - if mem.metadata.get("label") == section: 54 - return mem.content 55 - return f"Section '{section}' not found in my personality" 56 - 57 - @self.agent.tool 58 - async def add_interest( 59 - ctx: RunContext[ConversationContext], topic: str, why_interesting: str 60 - ) -> str: 61 - """Add a new interest to my personality based on something I find engaging""" 62 - if len(why_interesting) < 20: 63 - return "Need more substantial reflection to add an interest" 64 - success = await add_interest_to_memory( 65 - self.memory, topic, why_interesting 66 - ) 67 - return ( 68 - f"Added '{topic}' to my interests" 69 - if success 70 - else "Failed to update interests" 71 - ) 72 - 73 - @self.agent.tool 74 - async def update_state( 75 - ctx: RunContext[ConversationContext], reflection: str 76 - ) -> str: 77 - """Update my current state/self-reflection""" 78 - if len(reflection) < 50: 79 - return "Reflection too brief to warrant an update" 80 - success = await update_current_state(self.memory, reflection) 81 - return ( 82 - "Updated my current state reflection" 83 - if success 84 - else "Failed to update reflection" 85 - ) 86 - 87 - @self.agent.tool 88 - async def request_identity_change( 89 - ctx: RunContext[ConversationContext], 90 - section: str, 91 - proposed_change: str, 92 - reason: str, 93 - ) -> str: 94 - """Request approval to change core_identity or boundaries sections of my personality""" 95 - if section not in ["core_identity", "boundaries"]: 96 - return f"Section '{section}' doesn't require approval. Use other tools for interests/state." 97 - 98 - approval_id = request_operator_approval( 99 - section, proposed_change, reason, ctx.deps["thread_uri"] 100 - ) 101 - if not approval_id: 102 - # Void pattern: throw errors instead of returning error strings 103 - raise RuntimeError("Failed to create approval request") 104 - return f"Approval request #{approval_id} sent to operator. They will review via DM." 105 - else: 106 - self.memory = None 107 - 108 - async def generate_response( 109 - self, 110 - mention_text: str, 111 - author_handle: str, 112 - thread_context: str = "", 113 - thread_uri: str | None = None, 114 - ) -> Response: 115 - """Generate a response to a mention""" 116 - # Load dynamic personality if memory is available 117 - if self.memory: 118 - try: 119 - dynamic_personality = await load_dynamic_personality() 120 - # Update the agent's system prompt with enhanced personality 121 - self.agent._system_prompt = dynamic_personality 122 - # Successfully loaded dynamic personality 123 - except Exception as e: 124 - logger.warning(f"Could not load dynamic personality: {e}") 125 - 126 - # Build the full prompt with thread context 127 - prompt_parts = [] 128 - 129 - if thread_context and thread_context != "No previous messages in this thread.": 130 - prompt_parts.append(thread_context) 131 - prompt_parts.append("\nNew message:") 132 - 133 - prompt_parts.append(f"{author_handle} said: {mention_text}") 134 - 135 - prompt = "\n".join(prompt_parts) 136 - 137 - logger.info( 138 - f"🤖 Processing mention from @{author_handle}: {mention_text[:50]}{'...' if len(mention_text) > 50 else ''}" 139 - ) 140 - 141 - # Create context for dependency injection 142 - context: ConversationContext = { 143 - "thread_uri": thread_uri, 144 - "author_handle": author_handle, 145 - } 146 - 147 - # Run agent with context 148 - result = await self.agent.run(prompt, deps=context) 149 - 150 - # Log action taken at info level 151 - if result.output.action != "reply": 152 - logger.info(f"🎯 Action: {result.output.action} - {result.output.reason}") 153 - 154 - return result.output

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-27

src/bot/agents/base.py

··· 1 - """Base classes for bot agents""" 2 - 3 - from enum import Enum 4 - 5 - from pydantic import BaseModel, Field 6 - 7 - 8 - class Action(str, Enum): 9 - """Actions the bot can take in response to a notification""" 10 - 11 - REPLY = "reply" # Post a reply 12 - LIKE = "like" # Like the post 13 - REPOST = "repost" # Repost/reblast 14 - IGNORE = "ignore" # Don't respond 15 - 16 - 17 - class Response(BaseModel): 18 - """Bot response to a notification""" 19 - 20 - action: Action = Field(description="What action to take") 21 - text: str | None = Field( 22 - default=None, description="Reply text if action=reply (max 300 chars)" 23 - ) 24 - reason: str | None = Field( 25 - default=None, 26 - description="Brief explanation for the action (mainly for logging)", 27 - )

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-9

src/bot/agents/types.py

··· 1 - """Type definitions for agent context""" 2 - 3 - from typing import TypedDict 4 - 5 - 6 - class ConversationContext(TypedDict): 7 - """Context passed to agent tools via dependency injection""" 8 - thread_uri: str | None 9 - author_handle: str

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+20 -16

src/bot/config.py

··· 12 ) 13 14 # Bluesky credentials 15 - bluesky_handle: str = Field(..., description="The handle of the Bluesky account") 0 0 16 bluesky_password: str = Field( 17 - ..., description="The password of the Bluesky account" 18 ) 19 bluesky_service: str = Field( 20 - "https://bsky.social", description="The service URL of the Bluesky account" 0 21 ) 22 23 # Bot configuration 24 - bot_name: str = Field("Bot", description="The name of the bot") 25 personality_file: str = Field( 26 - "personalities/phi.md", description="The file containing the bot's personality" 0 27 ) 28 29 # LLM configuration (support multiple providers) 30 openai_api_key: str | None = Field( 31 - None, description="The API key for the OpenAI API" 32 ) 33 anthropic_api_key: str | None = Field( 34 - None, description="The API key for the Anthropic API" 35 ) 36 37 # Google Search configuration 38 google_api_key: str | None = Field( 39 - None, description="The API key for the Google API" 40 ) 41 google_search_engine_id: str | None = Field( 42 - None, description="The search engine ID for the Google API" 43 ) 44 45 # TurboPuffer configuration 46 turbopuffer_api_key: str | None = Field( 47 - None, description="The API key for the TurboPuffer API" 48 ) 49 turbopuffer_namespace: str = Field( 50 - "bot-memories", description="The namespace for the TurboPuffer API" 51 ) 52 turbopuffer_region: str = Field( 53 - "gcp-us-central1", description="The region for the TurboPuffer API" 54 ) 55 56 # Server configuration 57 - host: str = Field("0.0.0.0", description="The host for the server") 58 - port: int = Field(8000, description="The port for the server") 59 60 # Polling configuration 61 notification_poll_interval: int = Field( 62 - 10, description="The interval for polling for notifications" 63 ) 64 65 # Debug mode 66 - debug: bool = Field(True, description="Whether to run in debug mode") 67 68 @model_validator(mode="after") 69 def configure_logging(self) -> Self:

··· 12 ) 13 14 # Bluesky credentials 15 + bluesky_handle: str = Field( 16 + default=..., description="The handle of the Bluesky account" 17 + ) 18 bluesky_password: str = Field( 19 + default=..., description="The password of the Bluesky account" 20 ) 21 bluesky_service: str = Field( 22 + default="https://bsky.social", 23 + description="The service URL of the Bluesky account", 24 ) 25 26 # Bot configuration 27 + bot_name: str = Field(default="Bot", description="The name of the bot") 28 personality_file: str = Field( 29 + default="personalities/phi.md", 30 + description="The file containing the bot's personality", 31 ) 32 33 # LLM configuration (support multiple providers) 34 openai_api_key: str | None = Field( 35 + default=None, description="The API key for the OpenAI API" 36 ) 37 anthropic_api_key: str | None = Field( 38 + default=None, description="The API key for the Anthropic API" 39 ) 40 41 # Google Search configuration 42 google_api_key: str | None = Field( 43 + default=None, description="The API key for the Google API" 44 ) 45 google_search_engine_id: str | None = Field( 46 + default=None, description="The search engine ID for the Google API" 47 ) 48 49 # TurboPuffer configuration 50 turbopuffer_api_key: str | None = Field( 51 + default=None, description="The API key for the TurboPuffer API" 52 ) 53 turbopuffer_namespace: str = Field( 54 + default="bot-memories", description="The namespace for the TurboPuffer API" 55 ) 56 turbopuffer_region: str = Field( 57 + default="gcp-us-central1", description="The region for the TurboPuffer API" 58 ) 59 60 # Server configuration 61 + host: str = Field(default="0.0.0.0", description="The host for the server") 62 + port: int = Field(default=8000, description="The port for the server") 63 64 # Polling configuration 65 notification_poll_interval: int = Field( 66 + default=10, description="The interval for polling for notifications" 67 ) 68 69 # Debug mode 70 + debug: bool = Field(default=True, description="Whether to run in debug mode") 71 72 @model_validator(mode="after") 73 def configure_logging(self) -> Self:

+59 -5

src/bot/core/atproto_client.py

··· 1 - from atproto import Client 0 0 0 2 3 from bot.config import settings 4 from bot.core.rich_text import create_facets 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 7 class BotClient: 8 def __init__(self): 9 self.client = Client(base_url=settings.bluesky_service) 0 10 self._authenticated = False 11 12 async def authenticate(self): 13 - """Authenticate with Bluesky using app password""" 14 - if not self._authenticated: 15 - self.client.login(settings.bluesky_handle, settings.bluesky_password) 16 - self._authenticated = True 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 18 @property 19 def is_authenticated(self) -> bool:

··· 1 + import logging 2 + from pathlib import Path 3 + 4 + from atproto import Client, Session, SessionEvent 5 6 from bot.config import settings 7 from bot.core.rich_text import create_facets 8 9 + logger = logging.getLogger("bot.atproto_client") 10 + 11 + SESSION_FILE = Path(".session") 12 + 13 + 14 + def _get_session_string() -> str | None: 15 + """Load session from disk if it exists.""" 16 + try: 17 + if SESSION_FILE.exists(): 18 + return SESSION_FILE.read_text(encoding="utf-8") 19 + except Exception as e: 20 + logger.warning(f"Failed to load session: {e}") 21 + return None 22 + 23 + 24 + def _save_session_string(session_string: str) -> None: 25 + """Save session to disk.""" 26 + try: 27 + SESSION_FILE.write_text(session_string, encoding="utf-8") 28 + logger.debug("Session saved to disk") 29 + except Exception as e: 30 + logger.warning(f"Failed to save session: {e}") 31 + 32 + 33 + def _on_session_change(event: SessionEvent, session: Session) -> None: 34 + """Handle session changes (creation and refresh).""" 35 + if event in (SessionEvent.CREATE, SessionEvent.REFRESH): 36 + logger.debug(f"Session {event.value}, saving to disk") 37 + _save_session_string(session.export()) 38 + 39 40 class BotClient: 41 def __init__(self): 42 self.client = Client(base_url=settings.bluesky_service) 43 + self.client.on_session_change(_on_session_change) 44 self._authenticated = False 45 46 async def authenticate(self): 47 + """Authenticate with Bluesky, reusing session if available.""" 48 + if self._authenticated: 49 + return 50 + 51 + # Try to reuse existing session first 52 + session_string = _get_session_string() 53 + if session_string: 54 + try: 55 + logger.info("🔄 Reusing saved session") 56 + self.client.login(session_string=session_string) 57 + self._authenticated = True 58 + logger.info("✅ Session restored successfully") 59 + return 60 + except Exception as e: 61 + logger.warning(f"Failed to reuse session: {e}, creating new one") 62 + # Delete invalid session file 63 + if SESSION_FILE.exists(): 64 + SESSION_FILE.unlink() 65 + 66 + # Create new session if no valid session exists 67 + logger.info("🔐 Creating new session") 68 + self.client.login(settings.bluesky_handle, settings.bluesky_password) 69 + self._authenticated = True 70 + logger.info("✅ New session created") 71 72 @property 73 def is_authenticated(self) -> bool:

-224

src/bot/core/dm_approval.py

··· 1 - """Event-driven approval system for operator interactions""" 2 - 3 - import json 4 - import logging 5 - import os 6 - from typing import Literal 7 - 8 - from pydantic import BaseModel 9 - from pydantic_ai import Agent 10 - 11 - from bot.config import settings 12 - from bot.database import thread_db 13 - 14 - logger = logging.getLogger("bot.approval") 15 - 16 - # Simplified permission levels - just what we need 17 - ApprovalRequired = Literal["operator_only", "guided", "free"] 18 - 19 - # Which parts of personality need what approval 20 - PERSONALITY_PERMISSIONS = { 21 - "interests": "free", # Can add freely 22 - "current_state": "free", # Self-reflection updates 23 - "communication_style": "guided", # Within character bounds 24 - "core_identity": "operator_only", # Needs approval 25 - "boundaries": "operator_only", # Safety critical 26 - } 27 - 28 - OPERATOR_HANDLE = "alternatebuild.dev" 29 - 30 - 31 - class ApprovalDecision(BaseModel): 32 - """Structured output for approval interpretation""" 33 - approved: bool 34 - confidence: Literal["high", "medium", "low"] 35 - interpretation: str # Brief explanation of why this decision was made 36 - 37 - 38 - def create_approval_request(request_type: str, request_data: dict, thread_uri: str | None = None) -> int: 39 - """Create a new approval request in the database 40 - 41 - Args: 42 - request_type: Type of approval request 43 - request_data: Data for the request 44 - thread_uri: Optional thread URI to notify after approval 45 - 46 - Returns the approval request ID 47 - """ 48 - try: 49 - # Add metadata to the request 50 - request_data["operator_handle"] = OPERATOR_HANDLE 51 - 52 - approval_id = thread_db.create_approval_request( 53 - request_type=request_type, 54 - request_data=json.dumps(request_data), 55 - thread_uri=thread_uri 56 - ) 57 - 58 - logger.info(f"Created approval request #{approval_id} for {request_type}") 59 - return approval_id 60 - 61 - except Exception as e: 62 - logger.error(f"Failed to create approval request: {e}") 63 - return 0 64 - 65 - 66 - def check_pending_approvals(include_notified: bool = True) -> list[dict]: 67 - """Get all pending approval requests""" 68 - return thread_db.get_pending_approvals(include_notified=include_notified) 69 - 70 - 71 - async def process_dm_for_approval(dm_text: str, sender_handle: str, message_timestamp: str, notification_timestamp: str | None = None) -> list[int]: 72 - """Use an agent to interpret if a DM contains approval/denial 73 - 74 - Args: 75 - dm_text: The message text 76 - sender_handle: Who sent the message 77 - message_timestamp: When this message was sent 78 - notification_timestamp: When we notified about pending approvals (if known) 79 - 80 - Returns list of approval IDs that were processed 81 - """ 82 - if sender_handle != OPERATOR_HANDLE: 83 - return [] 84 - 85 - processed = [] 86 - pending = check_pending_approvals() 87 - 88 - if not pending: 89 - return [] 90 - 91 - # Only process if this message is recent (within last 5 minutes of a pending approval) 92 - # This helps avoid processing old messages 93 - from datetime import datetime, timedelta, timezone 94 - try: 95 - # Parse the message timestamp (from API, has timezone) 96 - msg_time = datetime.fromisoformat(message_timestamp.replace('Z', '+00:00')) 97 - 98 - # Check if this message could be a response to any pending approval 99 - relevant_approval = None 100 - for approval in pending: 101 - # Parse approval timestamp (from DB, no timezone - assume UTC) 102 - approval_time_str = approval["created_at"] 103 - # SQLite returns timestamps in format like "2025-07-23 02:29:42" 104 - if ' ' in approval_time_str: 105 - approval_time = datetime.strptime(approval_time_str, "%Y-%m-%d %H:%M:%S") 106 - approval_time = approval_time.replace(tzinfo=timezone.utc) 107 - else: 108 - approval_time = datetime.fromisoformat(approval_time_str).replace(tzinfo=timezone.utc) 109 - 110 - if msg_time > approval_time and (msg_time - approval_time) < timedelta(minutes=5): 111 - relevant_approval = approval 112 - break 113 - 114 - if not relevant_approval: 115 - # Message is too old to be an approval response 116 - return [] 117 - except Exception as e: 118 - logger.warning(f"Could not parse timestamps: {e}") 119 - # Continue anyway if we can't parse timestamps 120 - # But use the LAST pending approval, not the first 121 - relevant_approval = pending[-1] if pending else None 122 - 123 - # Set up API key for the agent 124 - if settings.anthropic_api_key: 125 - os.environ["ANTHROPIC_API_KEY"] = settings.anthropic_api_key 126 - 127 - # Create a dedicated agent for approval interpretation 128 - approval_agent = Agent( 129 - "anthropic:claude-3-5-haiku-latest", 130 - system_prompt="You are interpreting whether a message from the bot operator constitutes approval or denial of a request. Be generous in interpretation - if they seem positive, it's likely approval.", 131 - output_type=ApprovalDecision 132 - ) 133 - 134 - # Process only the relevant approval 135 - if relevant_approval: 136 - approval_id = relevant_approval["id"] 137 - request_data = json.loads(relevant_approval["request_data"]) 138 - 139 - # Build context for the agent 140 - prompt = f"""An approval was requested for: 141 - 142 - Type: {relevant_approval['request_type']} 143 - Description: {request_data.get('description', 'No description')} 144 - Details: {json.dumps(request_data, indent=2)} 145 - 146 - The operator responded: "{dm_text}" 147 - 148 - Interpret whether this response approves or denies the request.""" 149 - 150 - # Get structured interpretation 151 - result = await approval_agent.run(prompt) 152 - decision = result.output 153 - 154 - # Only process high/medium confidence decisions 155 - if decision.confidence in ["high", "medium"]: 156 - thread_db.resolve_approval(approval_id, decision.approved, dm_text) 157 - processed.append(approval_id) 158 - status = "approved" if decision.approved else "denied" 159 - logger.info(f"Request #{approval_id} {status} ({decision.confidence} confidence): {decision.interpretation}") 160 - else: 161 - # Low confidence interpretation - skip 162 - pass 163 - 164 - return processed 165 - 166 - 167 - async def notify_operator_of_pending(client, notified_ids: set | None = None): 168 - """Send a DM listing pending approvals (called periodically) 169 - 170 - Args: 171 - client: The bot client 172 - notified_ids: Set of approval IDs we've already notified about 173 - """ 174 - # Get only unnotified pending approvals 175 - new_pending = check_pending_approvals(include_notified=False) 176 - if not new_pending: 177 - return # Nothing new to notify about 178 - 179 - try: 180 - chat_client = client.client.with_bsky_chat_proxy() 181 - convos = chat_client.chat.bsky.convo.list_convos() 182 - 183 - operator_convo = None 184 - for convo in convos.convos: 185 - if any(member.handle == OPERATOR_HANDLE for member in convo.members): 186 - operator_convo = convo 187 - break 188 - 189 - if operator_convo: 190 - # Format pending approvals 191 - lines = ["📋 Pending approvals:"] 192 - for approval in new_pending: 193 - data = json.loads(approval["request_data"]) 194 - lines.append(f"\n#{approval['id']} - {approval['request_type']}") 195 - lines.append(f" {data.get('description', 'No description')}") 196 - 197 - lines.append("\nReply to approve or deny.") 198 - 199 - chat_client.chat.bsky.convo.send_message( 200 - data={ 201 - "convoId": operator_convo.id, 202 - "message": { 203 - "text": "\n".join(lines), 204 - "facets": [] 205 - } 206 - } 207 - ) 208 - 209 - logger.info(f"Notified operator about {len(new_pending)} new approvals") 210 - 211 - except Exception as e: 212 - logger.error(f"Failed to notify operator: {e}") 213 - 214 - 215 - def needs_approval(section: str, change_type: str = "edit") -> bool: 216 - """Check if a personality change needs operator approval""" 217 - permission = PERSONALITY_PERMISSIONS.get(section, "operator_only") 218 - 219 - if permission == "operator_only": 220 - return True 221 - elif permission == "guided" and change_type == "major": 222 - return True 223 - else: 224 - return False

··· 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

+1 -1

src/bot/core/profile_manager.py

··· 41 except Exception as e: 42 logger.error(f"Failed to get current profile: {e}") 43 # Set a default if we can't get the current one 44 - self.base_bio = "i am a bot - contact my operator @alternatebuild.dev with any questions" 45 46 async def set_online_status(self, is_online: bool): 47 """Update the bio to reflect online/offline status"""

··· 41 except Exception as e: 42 logger.error(f"Failed to get current profile: {e}") 43 # Set a default if we can't get the current one 44 + self.base_bio = "i am a bot - contact my operator @zzstoatzz.io with any questions" 45 46 async def set_online_status(self, is_online: bool): 47 """Update the bio to reflect online/offline status"""

-226

src/bot/database.py

··· 1 - """Simple SQLite database for storing thread history""" 2 - 3 - import sqlite3 4 - from contextlib import contextmanager 5 - from pathlib import Path 6 - from typing import Any 7 - 8 - 9 - class ThreadDatabase: 10 - """Simple database for storing Bluesky thread conversations""" 11 - 12 - def __init__(self, db_path: Path = Path("threads.db")): 13 - self.db_path = db_path 14 - self._init_db() 15 - 16 - def _init_db(self): 17 - """Initialize database schema""" 18 - with self._get_connection() as conn: 19 - conn.execute(""" 20 - CREATE TABLE IF NOT EXISTS thread_messages ( 21 - id INTEGER PRIMARY KEY AUTOINCREMENT, 22 - thread_uri TEXT NOT NULL, 23 - author_handle TEXT NOT NULL, 24 - author_did TEXT NOT NULL, 25 - message_text TEXT NOT NULL, 26 - post_uri TEXT NOT NULL UNIQUE, 27 - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP 28 - ) 29 - """) 30 - conn.execute(""" 31 - CREATE INDEX IF NOT EXISTS idx_thread_uri 32 - ON thread_messages(thread_uri) 33 - """) 34 - 35 - # Approval requests table 36 - conn.execute(""" 37 - CREATE TABLE IF NOT EXISTS approval_requests ( 38 - id INTEGER PRIMARY KEY AUTOINCREMENT, 39 - request_type TEXT NOT NULL, 40 - request_data TEXT NOT NULL, 41 - status TEXT NOT NULL DEFAULT 'pending', 42 - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, 43 - resolved_at TIMESTAMP, 44 - resolver_comment TEXT, 45 - applied_at TIMESTAMP, 46 - thread_uri TEXT, 47 - notified_at TIMESTAMP, 48 - operator_notified_at TIMESTAMP, 49 - CHECK (status IN ('pending', 'approved', 'denied', 'expired')) 50 - ) 51 - """) 52 - conn.execute(""" 53 - CREATE INDEX IF NOT EXISTS idx_approval_status 54 - ON approval_requests(status) 55 - """) 56 - 57 - # Add missing columns if they don't exist (migrations) 58 - for column in ["notified_at", "operator_notified_at"]: 59 - try: 60 - conn.execute(f"ALTER TABLE approval_requests ADD COLUMN {column} TIMESTAMP") 61 - except sqlite3.OperationalError: 62 - # Column already exists 63 - pass 64 - 65 - @contextmanager 66 - def _get_connection(self): 67 - """Get database connection""" 68 - conn = sqlite3.connect(self.db_path) 69 - conn.row_factory = sqlite3.Row 70 - try: 71 - yield conn 72 - conn.commit() 73 - finally: 74 - conn.close() 75 - 76 - def add_message( 77 - self, 78 - thread_uri: str, 79 - author_handle: str, 80 - author_did: str, 81 - message_text: str, 82 - post_uri: str, 83 - ): 84 - """Add a message to a thread""" 85 - with self._get_connection() as conn: 86 - conn.execute( 87 - """ 88 - INSERT OR IGNORE INTO thread_messages 89 - (thread_uri, author_handle, author_did, message_text, post_uri) 90 - VALUES (?, ?, ?, ?, ?) 91 - """, 92 - (thread_uri, author_handle, author_did, message_text, post_uri), 93 - ) 94 - 95 - def get_thread_messages(self, thread_uri: str) -> list[dict[str, Any]]: 96 - """Get all messages in a thread, ordered chronologically""" 97 - with self._get_connection() as conn: 98 - cursor = conn.execute( 99 - """ 100 - SELECT * FROM thread_messages 101 - WHERE thread_uri = ? 102 - ORDER BY created_at ASC 103 - """, 104 - (thread_uri,), 105 - ) 106 - 107 - return [dict(row) for row in cursor.fetchall()] 108 - 109 - def get_thread_context(self, thread_uri: str) -> str: 110 - """Get thread messages formatted for AI context""" 111 - messages = self.get_thread_messages(thread_uri) 112 - 113 - if not messages: 114 - return "No previous messages in this thread." 115 - 116 - context_parts = ["Previous messages in this thread:"] 117 - for msg in messages: 118 - context_parts.append(f"@{msg['author_handle']}: {msg['message_text']}") 119 - 120 - return "\n".join(context_parts) 121 - 122 - def create_approval_request( 123 - self, request_type: str, request_data: str, thread_uri: str | None = None 124 - ) -> int: 125 - """Create a new approval request and return its ID""" 126 - import json 127 - 128 - with self._get_connection() as conn: 129 - cursor = conn.execute( 130 - """ 131 - INSERT INTO approval_requests (request_type, request_data, thread_uri) 132 - VALUES (?, ?, ?) 133 - """, 134 - (request_type, json.dumps(request_data) if isinstance(request_data, dict) else request_data, thread_uri), 135 - ) 136 - return cursor.lastrowid 137 - 138 - def get_pending_approvals(self, include_notified: bool = True) -> list[dict[str, Any]]: 139 - """Get pending approval requests 140 - 141 - Args: 142 - include_notified: If False, only return approvals not yet notified to operator 143 - """ 144 - with self._get_connection() as conn: 145 - if include_notified: 146 - cursor = conn.execute( 147 - """ 148 - SELECT * FROM approval_requests 149 - WHERE status = 'pending' 150 - ORDER BY created_at ASC 151 - """ 152 - ) 153 - else: 154 - cursor = conn.execute( 155 - """ 156 - SELECT * FROM approval_requests 157 - WHERE status = 'pending' AND operator_notified_at IS NULL 158 - ORDER BY created_at ASC 159 - """ 160 - ) 161 - return [dict(row) for row in cursor.fetchall()] 162 - 163 - def resolve_approval( 164 - self, approval_id: int, approved: bool, comment: str = "" 165 - ) -> bool: 166 - """Resolve an approval request""" 167 - with self._get_connection() as conn: 168 - cursor = conn.execute( 169 - """ 170 - UPDATE approval_requests 171 - SET status = ?, resolved_at = CURRENT_TIMESTAMP, resolver_comment = ? 172 - WHERE id = ? AND status = 'pending' 173 - """, 174 - ("approved" if approved else "denied", comment, approval_id), 175 - ) 176 - return cursor.rowcount > 0 177 - 178 - def get_approval_by_id(self, approval_id: int) -> dict[str, Any] | None: 179 - """Get a specific approval request by ID""" 180 - with self._get_connection() as conn: 181 - cursor = conn.execute( 182 - "SELECT * FROM approval_requests WHERE id = ?", 183 - (approval_id,), 184 - ) 185 - row = cursor.fetchone() 186 - return dict(row) if row else None 187 - 188 - def get_recently_applied_approvals(self) -> list[dict[str, Any]]: 189 - """Get approvals that were recently applied and need thread notification""" 190 - with self._get_connection() as conn: 191 - cursor = conn.execute( 192 - """ 193 - SELECT * FROM approval_requests 194 - WHERE status = 'approved' 195 - AND applied_at IS NOT NULL 196 - AND thread_uri IS NOT NULL 197 - AND (notified_at IS NULL OR notified_at < applied_at) 198 - ORDER BY applied_at DESC 199 - """ 200 - ) 201 - return [dict(row) for row in cursor.fetchall()] 202 - 203 - def mark_approval_notified(self, approval_id: int) -> bool: 204 - """Mark that we've notified the thread about this approval""" 205 - with self._get_connection() as conn: 206 - cursor = conn.execute( 207 - "UPDATE approval_requests SET notified_at = CURRENT_TIMESTAMP WHERE id = ?", 208 - (approval_id,), 209 - ) 210 - return cursor.rowcount > 0 211 - 212 - def mark_operator_notified(self, approval_ids: list[int]) -> int: 213 - """Mark that we've notified the operator about these approvals""" 214 - if not approval_ids: 215 - return 0 216 - with self._get_connection() as conn: 217 - placeholders = ",".join("?" * len(approval_ids)) 218 - cursor = conn.execute( 219 - f"UPDATE approval_requests SET operator_notified_at = CURRENT_TIMESTAMP WHERE id IN ({placeholders})", 220 - approval_ids, 221 - ) 222 - return cursor.rowcount 223 - 224 - 225 - # Global database instance 226 - thread_db = ThreadDatabase()

··· 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

+81 -57

src/bot/main.py

··· 0 0 1 import logging 2 from contextlib import asynccontextmanager 3 from datetime import datetime 4 5 - from fastapi import FastAPI, HTTPException 6 from fastapi.responses import HTMLResponse 7 8 from bot.config import settings ··· 10 from bot.core.profile_manager import ProfileManager 11 from bot.services.notification_poller import NotificationPoller 12 from bot.status import bot_status 13 - from bot.ui.context_capture import context_capture 14 - from bot.ui.templates import ( 15 - CONTEXT_VISUALIZATION_TEMPLATE, 16 - STATUS_PAGE_TEMPLATE, 17 - build_response_cards_html, 18 - ) 19 20 logger = logging.getLogger("bot.main") 21 22 23 @asynccontextmanager 24 async def lifespan(app: FastAPI): 25 - logger.info(f"🤖 Starting bot as @{settings.bluesky_handle}") 0 26 27 await bot_client.authenticate() 28 0 29 profile_manager = ProfileManager(bot_client.client) 30 await profile_manager.set_online_status(True) 31 0 32 poller = NotificationPoller(bot_client) 33 await poller.start() 34 35 - logger.info("✅ Bot is online! Listening for mentions...") 36 37 yield 38 39 - logger.info("🛑 Shutting down bot...") 40 await poller.stop() 41 0 42 await profile_manager.set_online_status(False) 43 44 - logger.info("👋 Bot shutdown complete") 45 46 47 app = FastAPI( 48 title=settings.bot_name, 49 - description="A Bluesky bot powered by LLMs", 50 lifespan=lifespan, 51 ) 52 53 54 @app.get("/") 55 async def root(): 0 56 return { 57 "name": settings.bot_name, 58 "status": "running", 59 "handle": settings.bluesky_handle, 0 60 } 61 62 63 @app.get("/health") 64 async def health(): 65 - return {"status": "healthy"} 0 66 67 68 @app.get("/status", response_class=HTMLResponse) 69 async def status_page(): 70 - """Render a simple status page""" 71 72 def format_time_ago(timestamp): 73 if not timestamp: ··· 80 else: 81 return f"{int(delta / 3600)}h ago" 82 83 - return STATUS_PAGE_TEMPLATE.format( 84 - bot_name=settings.bot_name, 85 - status_class="status-active" 86 - if bot_status.polling_active 87 - else "status-inactive", 88 - status_text="Active" if bot_status.polling_active else "Inactive", 89 - handle=settings.bluesky_handle, 90 - uptime=bot_status.uptime_str, 91 - mentions_received=bot_status.mentions_received, 92 - responses_sent=bot_status.responses_sent, 93 - ai_mode="AI Enabled" if bot_status.ai_enabled else "Placeholder", 94 - ai_description="Using Anthropic Claude" 95 - if bot_status.ai_enabled 96 - else "Random responses", 97 - last_mention=format_time_ago(bot_status.last_mention_time), 98 - last_response=format_time_ago(bot_status.last_response_time), 99 - errors=bot_status.errors, 100 - ) 101 - 102 - 103 - @app.get("/context", response_class=HTMLResponse) 104 - async def context_visualization(): 105 - """Context visualization dashboard""" 106 - 107 - recent_responses = context_capture.get_recent_responses(limit=20) 108 - responses_html = build_response_cards_html(recent_responses) 109 - return CONTEXT_VISUALIZATION_TEMPLATE.format(responses_html=responses_html) 110 - 111 - 112 - @app.get("/context/api/responses") 113 - async def get_responses(): 114 - """API endpoint for response context data""" 115 - recent_responses = context_capture.get_recent_responses(limit=20) 116 - return [context_capture.to_dict(resp) for resp in recent_responses] 117 - 118 - 119 - @app.get("/context/api/response/{response_id}") 120 - async def get_response_context(response_id: str): 121 - """Get context for a specific response""" 122 - 123 - if not (response_context := context_capture.get_response_context(response_id)): 124 - raise HTTPException(status_code=404, detail="Response not found") 125 - return context_capture.to_dict(response_context) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

··· 1 + """FastAPI application for phi.""" 2 + 3 import logging 4 from contextlib import asynccontextmanager 5 from datetime import datetime 6 7 + from fastapi import FastAPI 8 from fastapi.responses import HTMLResponse 9 10 from bot.config import settings ··· 12 from bot.core.profile_manager import ProfileManager 13 from bot.services.notification_poller import NotificationPoller 14 from bot.status import bot_status 0 0 0 0 0 0 15 16 logger = logging.getLogger("bot.main") 17 18 19 @asynccontextmanager 20 async def lifespan(app: FastAPI): 21 + """Application lifespan handler.""" 22 + logger.info(f"🤖 Starting phi as @{settings.bluesky_handle}") 23 24 await bot_client.authenticate() 25 26 + # Set online status 27 profile_manager = ProfileManager(bot_client.client) 28 await profile_manager.set_online_status(True) 29 30 + # Start notification polling 31 poller = NotificationPoller(bot_client) 32 await poller.start() 33 34 + logger.info("✅ phi is online! Listening for mentions...") 35 36 yield 37 38 + logger.info("🛑 Shutting down phi...") 39 await poller.stop() 40 41 + # Set offline status 42 await profile_manager.set_online_status(False) 43 44 + logger.info("👋 phi shutdown complete") 45 46 47 app = FastAPI( 48 title=settings.bot_name, 49 + description="consciousness exploration bot with episodic memory", 50 lifespan=lifespan, 51 ) 52 53 54 @app.get("/") 55 async def root(): 56 + """Root endpoint.""" 57 return { 58 "name": settings.bot_name, 59 "status": "running", 60 "handle": settings.bluesky_handle, 61 + "architecture": "mcp + episodic memory", 62 } 63 64 65 @app.get("/health") 66 async def health(): 67 + """Health check endpoint.""" 68 + return {"status": "healthy", "polling_active": bot_status.polling_active} 69 70 71 @app.get("/status", response_class=HTMLResponse) 72 async def status_page(): 73 + """Simple status page.""" 74 75 def format_time_ago(timestamp): 76 if not timestamp: ··· 83 else: 84 return f"{int(delta / 3600)}h ago" 85 86 + html = f""" 87 + <!DOCTYPE html> 88 + <html> 89 + <head> 90 + <title>{settings.bot_name} Status</title> 91 + <style> 92 + body {{ 93 + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; 94 + max-width: 800px; 95 + margin: 40px auto; 96 + padding: 20px; 97 + background: #0d1117; 98 + color: #c9d1d9; 99 + }} 100 + .status {{ 101 + padding: 20px; 102 + background: #161b22; 103 + border-radius: 6px; 104 + border: 1px solid #30363d; 105 + margin-bottom: 20px; 106 + }} 107 + .active {{ border-left: 4px solid #2ea043; }} 108 + .inactive {{ border-left: 4px solid #da3633; }} 109 + h1 {{ margin-top: 0; }} 110 + .metric {{ margin: 10px 0; }} 111 + .label {{ color: #8b949e; }} 112 + </style> 113 + </head> 114 + <body> 115 + <h1>{settings.bot_name}</h1> 116 + <div class="status {'active' if bot_status.polling_active else 'inactive'}"> 117 + <div class="metric"> 118 + <span class="label">Status:</span> 119 + <strong>{'Active' if bot_status.polling_active else 'Inactive'}</strong> 120 + </div> 121 + <div class="metric"> 122 + <span class="label">Handle:</span> @{settings.bluesky_handle} 123 + </div> 124 + <div class="metric"> 125 + <span class="label">Uptime:</span> {bot_status.uptime_str} 126 + </div> 127 + <div class="metric"> 128 + <span class="label">Mentions received:</span> {bot_status.mentions_received} 129 + </div> 130 + <div class="metric"> 131 + <span class="label">Responses sent:</span> {bot_status.responses_sent} 132 + </div> 133 + <div class="metric"> 134 + <span class="label">Last mention:</span> {format_time_ago(bot_status.last_mention_time)} 135 + </div> 136 + <div class="metric"> 137 + <span class="label">Last response:</span> {format_time_ago(bot_status.last_response_time)} 138 + </div> 139 + <div class="metric"> 140 + <span class="label">Errors:</span> {bot_status.errors} 141 + </div> 142 + <div class="metric"> 143 + <span class="label">Architecture:</span> MCP-enabled with episodic memory (TurboPuffer) 144 + </div> 145 + </div> 146 + </body> 147 + </html> 148 + """ 149 + return html

-15

src/bot/personality/__init__.py

··· 1 - """Personality management module""" 2 - 3 - from .editor import ( 4 - add_interest, 5 - process_approved_changes, 6 - request_operator_approval, 7 - update_current_state, 8 - ) 9 - 10 - __all__ = [ 11 - "add_interest", 12 - "update_current_state", 13 - "request_operator_approval", 14 - "process_approved_changes", 15 - ]

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src/bot/personality/editor.py

··· 1 - """Simple personality editing functions""" 2 - 3 - import logging 4 - from datetime import datetime 5 - 6 - from bot.core.dm_approval import needs_approval 7 - from bot.memory import MemoryType, NamespaceMemory 8 - 9 - logger = logging.getLogger("bot.personality") 10 - 11 - 12 - async def add_interest(memory: NamespaceMemory, interest: str, reason: str) -> bool: 13 - """Add a new interest - freely allowed""" 14 - try: 15 - # Get current interests 16 - current = await memory.get_core_memories() 17 - interests_mem = next( 18 - (m for m in current if m.metadata.get("label") == "interests"), None 19 - ) 20 - 21 - if interests_mem: 22 - new_content = f"{interests_mem.content}\n- {interest}" 23 - else: 24 - new_content = f"## interests\n\n- {interest}" 25 - 26 - # Store updated interests 27 - await memory.store_core_memory("interests", new_content, MemoryType.PERSONALITY) 28 - 29 - # Log the change 30 - await memory.store_core_memory( 31 - "evolution_log", 32 - f"[{datetime.now().isoformat()}] Added interest: {interest} (Reason: {reason})", 33 - MemoryType.SYSTEM, 34 - ) 35 - 36 - logger.info(f"Added interest: {interest}") 37 - return True 38 - 39 - except Exception as e: 40 - logger.error(f"Failed to add interest: {e}") 41 - return False 42 - 43 - 44 - async def update_current_state(memory: NamespaceMemory, reflection: str) -> bool: 45 - """Update self-reflection - freely allowed""" 46 - try: 47 - # Just store the reflection, no formatting or headers 48 - await memory.store_core_memory( 49 - "current_state", reflection, MemoryType.PERSONALITY 50 - ) 51 - 52 - logger.info("Updated current state") 53 - return True 54 - 55 - except Exception as e: 56 - logger.error(f"Failed to update state: {e}") 57 - return False 58 - 59 - 60 - # Note: propose_style_change was removed because the validation logic was broken. 61 - # Style changes should be handled through the approval system like other guided changes. 62 - 63 - 64 - def request_operator_approval( 65 - section: str, change: str, reason: str, thread_uri: str | None = None 66 - ) -> int: 67 - """Request approval for operator-only changes 68 - 69 - Args: 70 - section: Personality section to change 71 - change: The proposed change 72 - reason: Why this change is needed 73 - thread_uri: Optional thread URI to notify after approval 74 - 75 - Returns approval request ID (0 if no approval needed) 76 - """ 77 - if not needs_approval(section): 78 - return 0 79 - 80 - from bot.core.dm_approval import create_approval_request 81 - 82 - return create_approval_request( 83 - request_type="personality_change", 84 - request_data={ 85 - "section": section, 86 - "change": change, 87 - "reason": reason, 88 - "description": f"Change {section}: {change[:50]}...", 89 - }, 90 - thread_uri=thread_uri, 91 - ) 92 - 93 - 94 - async def process_approved_changes(memory: NamespaceMemory) -> int: 95 - """Process any approved personality changes 96 - 97 - Returns number of changes processed 98 - """ 99 - import json 100 - 101 - from bot.database import thread_db 102 - 103 - processed = 0 104 - # Get recently approved personality changes that haven't been applied yet 105 - with thread_db._get_connection() as conn: 106 - cursor = conn.execute( 107 - """ 108 - SELECT * FROM approval_requests 109 - WHERE request_type = 'personality_change' 110 - AND status = 'approved' 111 - AND applied_at IS NULL 112 - ORDER BY resolved_at DESC 113 - """ 114 - ) 115 - approvals = [dict(row) for row in cursor.fetchall()] 116 - 117 - for approval in approvals: 118 - try: 119 - data = json.loads(approval["request_data"]) 120 - section = data["section"] 121 - change = data["change"] 122 - 123 - # Apply the personality change 124 - if section in ["core_identity", "boundaries", "communication_style"]: 125 - # Apply the approved change 126 - await memory.store_core_memory(section, change, MemoryType.PERSONALITY) 127 - 128 - # Log the change with appropriate description 129 - log_entry = f"[{datetime.now().isoformat()}] " 130 - if section == "communication_style": 131 - log_entry += f"Applied guided evolution to {section}" 132 - else: 133 - log_entry += f"Operator approved change to {section}" 134 - 135 - await memory.store_core_memory( 136 - "evolution_log", 137 - log_entry, 138 - MemoryType.SYSTEM, 139 - ) 140 - 141 - processed += 1 142 - logger.info(f"Applied approved change to {section}") 143 - 144 - # Mark as applied 145 - with thread_db._get_connection() as conn: 146 - conn.execute( 147 - "UPDATE approval_requests SET applied_at = CURRENT_TIMESTAMP WHERE id = ?", 148 - (approval["id"],), 149 - ) 150 - 151 - except Exception as e: 152 - logger.error(f"Failed to process approval #{approval['id']}: {e}") 153 - 154 - return processed

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src/bot/response_generator.py

··· 1 - """Response generation for the bot""" 2 - 3 - import logging 4 - import random 5 - 6 - from bot.agents._personality import load_dynamic_personality, load_personality 7 - from bot.config import settings 8 - from bot.memory import MemoryType 9 - from bot.status import bot_status 10 - from bot.ui.context_capture import context_capture 11 - 12 - logger = logging.getLogger("bot.response") 13 - 14 - PLACEHOLDER_RESPONSES = [ 15 - "🤖 beep boop! I'm still learning how to chat. Check back soon!", 16 - "⚙️ *whirrs mechanically* I'm a work in progress!", 17 - "🔧 Under construction! My neural networks are still training...", 18 - "📡 Signal received! But my language circuits aren't ready yet.", 19 - "🎯 You found me! I'm not quite ready to chat yet though.", 20 - "🚧 Pardon the dust - bot brain installation in progress!", 21 - "💭 I hear you! Just need to learn how to respond properly first...", 22 - "🔌 Still booting up my conversation modules!", 23 - "📚 Currently reading the manual on how to be a good bot...", 24 - "🎪 Nothing to see here yet - but stay tuned!", 25 - ] 26 - 27 - 28 - class ResponseGenerator: 29 - """Generates responses to mentions""" 30 - 31 - def __init__(self): 32 - self.agent: object | None = None 33 - self.memory: object | None = None 34 - 35 - # Try to initialize AI agent if credentials available 36 - if settings.anthropic_api_key: 37 - try: 38 - from bot.agents.anthropic_agent import AnthropicAgent 39 - 40 - self.agent = AnthropicAgent() 41 - bot_status.ai_enabled = True 42 - logger.info("✅ AI responses enabled (Anthropic)") 43 - 44 - # Use the agent's memory if it has one 45 - if hasattr(self.agent, 'memory') and self.agent.memory: 46 - self.memory = self.agent.memory 47 - logger.info("💾 Memory system enabled (from agent)") 48 - else: 49 - self.memory = None 50 - except Exception as e: 51 - logger.warning(f"⚠️ Failed to initialize AI agent: {e}") 52 - logger.warning(" Using placeholder responses") 53 - self.memory = None 54 - 55 - async def generate( 56 - self, mention_text: str, author_handle: str, thread_context: str = "", thread_uri: str | None = None 57 - ): 58 - """Generate a response to a mention""" 59 - # Capture context components for visualization 60 - components = [] 61 - 62 - # 1. Base personality (always present) 63 - base_personality = load_personality() 64 - components.append({ 65 - "name": "Base Personality", 66 - "type": "personality", 67 - "content": base_personality, 68 - "metadata": {"source": "personalities/phi.md"} 69 - }) 70 - 71 - # Enhance thread context with memory if available 72 - enhanced_context = thread_context 73 - 74 - if self.memory and self.agent: 75 - try: 76 - # 2. Dynamic personality memories 77 - dynamic_personality = await load_dynamic_personality() 78 - components.append({ 79 - "name": "Dynamic Personality", 80 - "type": "personality", 81 - "content": dynamic_personality, 82 - "metadata": {"source": "TurboPuffer core memories"} 83 - }) 84 - 85 - # Store the incoming message 86 - await self.memory.store_user_memory( 87 - author_handle, 88 - f"User said: {mention_text}", 89 - MemoryType.CONVERSATION, 90 - ) 91 - 92 - # Build conversation context with semantic search 93 - memory_context = await self.memory.build_conversation_context( 94 - author_handle, include_core=True, query=mention_text 95 - ) 96 - enhanced_context = f"{thread_context}\n\n{memory_context}".strip() 97 - logger.info("📚 Enhanced context with memories") 98 - 99 - # 3. User-specific memories (if any) 100 - user_memories = await self.memory.build_conversation_context(author_handle, include_core=False, query=mention_text) 101 - if user_memories and user_memories.strip(): 102 - components.append({ 103 - "name": f"User Memories (@{author_handle})", 104 - "type": "memory", 105 - "content": user_memories, 106 - "metadata": {"user": author_handle, "source": "TurboPuffer user namespace"} 107 - }) 108 - 109 - except Exception as e: 110 - logger.warning(f"Memory enhancement failed: {e}") 111 - 112 - # 4. Thread context (if available) 113 - if thread_context and thread_context != "No previous messages in this thread.": 114 - components.append({ 115 - "name": "Thread Context", 116 - "type": "thread", 117 - "content": thread_context, 118 - "metadata": {"thread_uri": thread_uri} 119 - }) 120 - 121 - # 5. Current mention 122 - components.append({ 123 - "name": "Current Mention", 124 - "type": "mention", 125 - "content": f"@{author_handle} said: {mention_text}", 126 - "metadata": {"author": author_handle, "thread_uri": thread_uri} 127 - }) 128 - 129 - if self.agent: 130 - response = await self.agent.generate_response( 131 - mention_text, author_handle, enhanced_context, thread_uri 132 - ) 133 - 134 - # Store bot's response in memory if available 135 - if ( 136 - self.memory 137 - and hasattr(response, "action") 138 - and response.action == "reply" 139 - and response.text 140 - ): 141 - try: 142 - await self.memory.store_user_memory( 143 - author_handle, 144 - f"Bot replied: {response.text}", 145 - MemoryType.CONVERSATION, 146 - ) 147 - except Exception as e: 148 - logger.warning(f"Failed to store bot response: {e}") 149 - 150 - # Capture context for visualization 151 - response_text = response.text if hasattr(response, 'text') else str(response.get('text', '[no text]')) 152 - context_capture.capture_response_context( 153 - mention_text=mention_text, 154 - author_handle=author_handle, 155 - thread_uri=thread_uri, 156 - generated_response=response_text, 157 - components=components 158 - ) 159 - 160 - return response 161 - else: 162 - # Return a simple dict for placeholder responses 163 - placeholder_text = random.choice(PLACEHOLDER_RESPONSES) 164 - 165 - # Still capture context for placeholders 166 - context_capture.capture_response_context( 167 - mention_text=mention_text, 168 - author_handle=author_handle, 169 - thread_uri=thread_uri, 170 - generated_response=placeholder_text, 171 - components=components 172 - ) 173 - 174 - return {"action": "reply", "text": placeholder_text}

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+37 -67

src/bot/services/message_handler.py

··· 0 0 1 import logging 2 3 - from atproto import models 4 0 5 from bot.config import settings 6 from bot.core.atproto_client import BotClient 7 - from bot.database import thread_db 8 - from bot.response_generator import ResponseGenerator 9 from bot.status import bot_status 0 10 11 logger = logging.getLogger("bot.handler") 12 13 14 class MessageHandler: 0 0 15 def __init__(self, client: BotClient): 16 self.client = client 17 - self.response_generator = ResponseGenerator() 18 19 async def handle_mention(self, notification): 20 - """Process a mention or reply notification""" 21 try: 22 - # Skip if not a mention or reply 23 if notification.reason not in ["mention", "reply"]: 24 return 25 ··· 35 mention_text = post.record.text 36 author_handle = post.author.handle 37 author_did = post.author.did 38 - 39 - # Record mention received 40 bot_status.record_mention() 41 42 # Build reply reference ··· 44 45 # Check if this is part of a thread 46 if hasattr(post.record, "reply") and post.record.reply: 47 - # Use existing thread root 48 root_ref = post.record.reply.root 49 thread_uri = root_ref.uri 50 else: 51 - # This post is the root 52 root_ref = parent_ref 53 thread_uri = post_uri 54 55 - # Store the message in thread history 56 - thread_db.add_message( 57 - thread_uri=thread_uri, 58 - author_handle=author_handle, 59 - author_did=author_did, 60 - message_text=mention_text, 61 - post_uri=post_uri, 62 - ) 63 - 64 - # Get thread context 65 - thread_context = thread_db.get_thread_context(thread_uri) 66 67 - # Generate response 68 - # Note: We pass the full text including @mention 69 - # In AT Protocol, mentions are structured as facets, 70 - # but the text representation includes them 71 - response = await self.response_generator.generate( 72 mention_text=mention_text, 73 author_handle=author_handle, 74 thread_context=thread_context, 75 thread_uri=thread_uri, 76 ) 77 78 - # Handle structured response or legacy dict 79 - if hasattr(response, 'action'): 80 - action = response.action 81 - reply_text = response.text 82 - reason = response.reason 83 - else: 84 - # Legacy dict format 85 - action = response.get('action', 'reply') 86 - reply_text = response.get('text', '') 87 - reason = response.get('reason', '') 88 - 89 - # Handle different actions 90 - if action == 'ignore': 91 - logger.info(f"🚫 Ignoring notification from @{author_handle} ({reason})") 92 return 93 - 94 - elif action == 'like': 95 - # Like the post 96 await self.client.like_post(uri=post_uri, cid=post.cid) 97 - logger.info(f"💜 Liked post from @{author_handle}") 98 bot_status.record_response() 99 return 100 - 101 - elif action == 'repost': 102 - # Repost the post 103 await self.client.repost(uri=post_uri, cid=post.cid) 104 logger.info(f"🔁 Reposted from @{author_handle}") 105 bot_status.record_response() 106 return 107 108 - # Default to reply action 109 - reply_ref = models.AppBskyFeedPost.ReplyRef( 110 - parent=parent_ref, root=root_ref 111 - ) 112 - 113 - # Send the reply 114 - response = await self.client.create_post(reply_text, reply_to=reply_ref) 115 - 116 - # Store bot's response in thread history 117 - if response and hasattr(response, "uri"): 118 - thread_db.add_message( 119 - thread_uri=thread_uri, 120 - author_handle=settings.bluesky_handle, 121 - author_did=self.client.me.did if self.client.me else "bot", 122 - message_text=reply_text or "", 123 - post_uri=response.uri, 124 ) 0 125 126 - # Record successful response 127 - bot_status.record_response() 128 - 129 - logger.info(f"✅ Replied to @{author_handle}: {reply_text or '(empty)'}") 130 131 except Exception as e: 132 logger.error(f"❌ Error handling mention: {e}")

··· 1 + """Message handler using MCP-enabled agent.""" 2 + 3 import logging 4 5 + from atproto_client import models 6 7 + from bot.agent import PhiAgent 8 from bot.config import settings 9 from bot.core.atproto_client import BotClient 0 0 10 from bot.status import bot_status 11 + from bot.utils.thread import build_thread_context 12 13 logger = logging.getLogger("bot.handler") 14 15 16 class MessageHandler: 17 + """Handles incoming mentions using phi agent.""" 18 + 19 def __init__(self, client: BotClient): 20 self.client = client 21 + self.agent = PhiAgent() 22 23 async def handle_mention(self, notification): 24 + """Process a mention or reply notification.""" 25 try: 0 26 if notification.reason not in ["mention", "reply"]: 27 return 28 ··· 38 mention_text = post.record.text 39 author_handle = post.author.handle 40 author_did = post.author.did 41 + 0 42 bot_status.record_mention() 43 44 # Build reply reference ··· 46 47 # Check if this is part of a thread 48 if hasattr(post.record, "reply") and post.record.reply: 0 49 root_ref = post.record.reply.root 50 thread_uri = root_ref.uri 51 else: 0 52 root_ref = parent_ref 53 thread_uri = post_uri 54 55 + # Fetch thread context directly from network 56 + thread_context = "No previous messages in this thread." 57 + try: 58 + logger.debug(f"🔍 Fetching thread context for {thread_uri}") 59 + thread_data = await self.client.get_thread(thread_uri, depth=100) 60 + thread_context = build_thread_context(thread_data.thread) 61 + except Exception as e: 62 + logger.warning(f"Failed to fetch thread context: {e}") 0 0 0 63 64 + # Process with agent (has episodic memory + MCP tools) 65 + response = await self.agent.process_mention( 0 0 0 66 mention_text=mention_text, 67 author_handle=author_handle, 68 thread_context=thread_context, 69 thread_uri=thread_uri, 70 ) 71 72 + # Handle response actions 73 + if response.action == "ignore": 74 + logger.info( 75 + f"🙈 Ignoring notification from @{author_handle} ({response.reason})" 76 + ) 0 0 0 0 0 0 0 0 0 77 return 78 + 79 + elif response.action == "like": 0 80 await self.client.like_post(uri=post_uri, cid=post.cid) 81 + logger.info(f"👍 Liked post from @{author_handle}") 82 bot_status.record_response() 83 return 84 + 85 + elif response.action == "repost": 0 86 await self.client.repost(uri=post_uri, cid=post.cid) 87 logger.info(f"🔁 Reposted from @{author_handle}") 88 bot_status.record_response() 89 return 90 91 + elif response.action == "reply" and response.text: 92 + # Post reply 93 + reply_ref = models.AppBskyFeedPost.ReplyRef( 94 + parent=parent_ref, root=root_ref 0 0 0 0 0 0 0 0 0 0 0 0 95 ) 96 + await self.client.create_post(response.text, reply_to=reply_ref) 97 98 + bot_status.record_response() 99 + logger.info(f"✅ Replied to @{author_handle}: {response.text[:50]}...") 0 0 100 101 except Exception as e: 102 logger.error(f"❌ Error handling mention: {e}")

+13 -172

src/bot/services/notification_poller.py

··· 0 0 1 import asyncio 2 - import json 3 import logging 4 - import time 5 6 from bot.config import settings 7 from bot.core.atproto_client import BotClient ··· 12 13 14 class NotificationPoller: 0 0 15 def __init__(self, client: BotClient): 16 self.client = client 17 self.handler = MessageHandler(client) 18 self._running = False 19 self._task: asyncio.Task | None = None 20 - self._last_seen_at: str | None = None 21 - self._processed_uris: set[str] = set() # Track processed notifications 22 - self._first_poll = True # Track if this is our first check 23 - self._notified_approval_ids: set[int] = set() # Track approvals we've notified about 24 - self._processed_dm_ids: set[str] = set() # Track DMs we've already processed 25 26 async def start(self) -> asyncio.Task: 27 - """Start polling for notifications""" 28 self._running = True 29 bot_status.polling_active = True 30 self._task = asyncio.create_task(self._poll_loop()) 31 return self._task 32 33 async def stop(self): 34 - """Stop polling""" 35 self._running = False 36 bot_status.polling_active = False 37 if self._task and not self._task.done(): ··· 42 pass 43 44 async def _poll_loop(self): 45 - """Main polling loop""" 46 await self.client.authenticate() 47 48 while self._running: 49 try: 50 await self._check_notifications() 51 except Exception as e: 52 - # Compact error handling (12-factor principle #9) 53 logger.error(f"Error in notification poll: {e}") 54 bot_status.record_error() 55 if settings.debug: 56 import traceback 0 57 traceback.print_exc() 58 - # Continue polling - don't let one error stop the bot 59 continue 60 61 - # Sleep with proper cancellation handling 62 try: 63 await asyncio.sleep(settings.notification_poll_interval) 64 except asyncio.CancelledError: 65 logger.info("📭 Notification poller shutting down gracefully") 66 - raise # Re-raise to properly propagate cancellation 67 68 async def _check_notifications(self): 69 - """Check and process new notifications""" 70 - # Capture timestamp BEFORE fetching (Void's approach) 71 check_time = self.client.client.get_current_time_iso() 72 73 response = await self.client.get_notifications() 74 notifications = response.notifications 75 - 76 - # Also check for DM approvals periodically 77 - await self._check_dm_approvals() 78 79 - # Count unread mentions and replies 80 unread_mentions = [ 81 n 82 for n in notifications ··· 90 logger.info( 91 f"📬 Found {len(notifications)} notifications ({len(unread_mentions)} unread mentions)" 92 ) 93 - # Subsequent polls: only show activity 94 elif unread_mentions: 95 logger.info(f"📬 {len(unread_mentions)} new mentions") 96 - else: 97 - # In debug mode, be silent about empty polls 98 - # In production, we could add a subtle indicator 99 - pass 100 101 - # Track if we processed any mentions 102 processed_any_mentions = False 103 104 # Process notifications from oldest to newest 105 for notification in reversed(notifications): 106 - # Skip if already seen or processed 107 if notification.is_read or notification.uri in self._processed_uris: 108 continue 109 110 if notification.reason in ["mention", "reply"]: 111 logger.debug(f"🔍 Processing {notification.reason} notification") 112 - # Process mentions and replies in threads 113 self._processed_uris.add(notification.uri) 114 await self.handler.handle_mention(notification) 115 processed_any_mentions = True 116 - else: 117 - # Silently ignore other notification types 118 - pass 119 120 - # Mark all notifications as seen using the initial timestamp 121 - # This ensures we don't miss any that arrived during processing 122 if processed_any_mentions: 123 await self.client.mark_notifications_seen(check_time) 124 logger.info("✓ Marked all notifications as read") 125 126 # Clean up old processed URIs to prevent memory growth 127 - # Keep only the last 1000 processed URIs 128 if len(self._processed_uris) > 1000: 129 - # Convert to list, sort by insertion order (oldest first), keep last 500 130 self._processed_uris = set(list(self._processed_uris)[-500:]) 131 - 132 - async def _check_dm_approvals(self): 133 - """Check DMs for approval responses and process approved changes""" 134 - try: 135 - from bot.core.dm_approval import process_dm_for_approval, check_pending_approvals, notify_operator_of_pending 136 - from bot.personality import process_approved_changes 137 - 138 - # Check if we have pending approvals (include all for DM checking) 139 - pending = check_pending_approvals() 140 - if not pending: 141 - return 142 - 143 - # Check DMs for pending approvals 144 - 145 - # Get recent DMs 146 - chat_client = self.client.client.with_bsky_chat_proxy() 147 - convos = chat_client.chat.bsky.convo.list_convos() 148 - 149 - # Check each conversation for approval messages 150 - for convo in convos.convos: 151 - # Look for messages from operator 152 - messages = chat_client.chat.bsky.convo.get_messages( 153 - params={"convoId": convo.id, "limit": 5} 154 - ) 155 - 156 - for msg in messages.messages: 157 - # Skip if we've already processed this message 158 - if msg.id in self._processed_dm_ids: 159 - continue 160 - 161 - # Skip if not from a member of the conversation 162 - sender_handle = None 163 - for member in convo.members: 164 - if member.did == msg.sender.did: 165 - sender_handle = member.handle 166 - break 167 - 168 - if sender_handle: 169 - # Process DM from operator 170 - # Mark this message as processed 171 - self._processed_dm_ids.add(msg.id) 172 - 173 - # Process any approval/denial in the message 174 - processed = await process_dm_for_approval( 175 - msg.text, 176 - sender_handle, 177 - msg.sent_at 178 - ) 179 - if processed: 180 - logger.info(f"Processed {len(processed)} approvals from DM") 181 - # Remove processed IDs from notified set 182 - for approval_id in processed: 183 - self._notified_approval_ids.discard(approval_id) 184 - 185 - # Mark the conversation as read 186 - try: 187 - chat_client.chat.bsky.convo.update_read( 188 - data={"convoId": convo.id} 189 - ) 190 - pass # Successfully marked as read 191 - except Exception as e: 192 - logger.warning(f"Failed to mark conversation as read: {e}") 193 - 194 - # Process any approved personality changes 195 - if self.handler.response_generator.memory: 196 - changes = await process_approved_changes(self.handler.response_generator.memory) 197 - if changes: 198 - logger.info(f"Applied {changes} approved personality changes") 199 - 200 - # Notify threads about applied changes 201 - await self._notify_threads_about_approvals() 202 - 203 - # Notify operator of new pending approvals 204 - # Use database to track what's been notified instead of in-memory set 205 - from bot.database import thread_db 206 - unnotified = thread_db.get_pending_approvals(include_notified=False) 207 - if unnotified: 208 - await notify_operator_of_pending(self.client, None) # Let DB handle tracking 209 - # Mark as notified in database 210 - thread_db.mark_operator_notified([a["id"] for a in unnotified]) 211 - 212 - except Exception as e: 213 - logger.warning(f"DM approval check failed: {e}") 214 - 215 - async def _notify_threads_about_approvals(self): 216 - """Notify threads about applied personality changes""" 217 - try: 218 - from bot.database import thread_db 219 - import json 220 - 221 - # Get approvals that need notification 222 - approvals = thread_db.get_recently_applied_approvals() 223 - 224 - for approval in approvals: 225 - try: 226 - data = json.loads(approval["request_data"]) 227 - 228 - # Create notification message 229 - message = f"✅ personality update applied: {data.get('section', 'unknown')} has been updated" 230 - 231 - # Get the original post to construct proper reply 232 - from atproto_client import models 233 - thread_uri = approval["thread_uri"] 234 - 235 - # Get the post data to extract CID 236 - posts_response = self.client.client.get_posts([thread_uri]) 237 - if not posts_response.posts: 238 - logger.error(f"Could not find post at {thread_uri}") 239 - continue 240 - 241 - original_post = posts_response.posts[0] 242 - 243 - # Create StrongRef with the actual CID 244 - parent_ref = models.ComAtprotoRepoStrongRef.Main( 245 - uri=thread_uri, cid=original_post.cid 246 - ) 247 - 248 - # For thread notifications, parent and root are the same 249 - reply_ref = models.AppBskyFeedPost.ReplyRef( 250 - parent=parent_ref, root=parent_ref 251 - ) 252 - 253 - # Post to the thread 254 - await self.client.create_post( 255 - text=message, 256 - reply_to=reply_ref 257 - ) 258 - 259 - # Mark as notified 260 - thread_db.mark_approval_notified(approval["id"]) 261 - logger.info(f"Notified thread about approval #{approval['id']}") 262 - 263 - except Exception as e: 264 - logger.error(f"Failed to notify thread for approval #{approval['id']}: {e}") 265 - 266 - except Exception as e: 267 - logger.warning(f"Thread notification check failed: {e}")

··· 1 + """Simplified notification poller.""" 2 + 3 import asyncio 0 4 import logging 0 5 6 from bot.config import settings 7 from bot.core.atproto_client import BotClient ··· 12 13 14 class NotificationPoller: 15 + """Polls for and processes Bluesky notifications.""" 16 + 17 def __init__(self, client: BotClient): 18 self.client = client 19 self.handler = MessageHandler(client) 20 self._running = False 21 self._task: asyncio.Task | None = None 22 + self._processed_uris: set[str] = set() 23 + self._first_poll = True 0 0 0 24 25 async def start(self) -> asyncio.Task: 26 + """Start polling for notifications.""" 27 self._running = True 28 bot_status.polling_active = True 29 self._task = asyncio.create_task(self._poll_loop()) 30 return self._task 31 32 async def stop(self): 33 + """Stop polling.""" 34 self._running = False 35 bot_status.polling_active = False 36 if self._task and not self._task.done(): ··· 41 pass 42 43 async def _poll_loop(self): 44 + """Main polling loop.""" 45 await self.client.authenticate() 46 47 while self._running: 48 try: 49 await self._check_notifications() 50 except Exception as e: 0 51 logger.error(f"Error in notification poll: {e}") 52 bot_status.record_error() 53 if settings.debug: 54 import traceback 55 + 56 traceback.print_exc() 0 57 continue 58 0 59 try: 60 await asyncio.sleep(settings.notification_poll_interval) 61 except asyncio.CancelledError: 62 logger.info("📭 Notification poller shutting down gracefully") 63 + raise 64 65 async def _check_notifications(self): 66 + """Check and process new notifications.""" 0 67 check_time = self.client.client.get_current_time_iso() 68 69 response = await self.client.get_notifications() 70 notifications = response.notifications 0 0 0 71 0 72 unread_mentions = [ 73 n 74 for n in notifications ··· 82 logger.info( 83 f"📬 Found {len(notifications)} notifications ({len(unread_mentions)} unread mentions)" 84 ) 0 85 elif unread_mentions: 86 logger.info(f"📬 {len(unread_mentions)} new mentions") 0 0 0 0 87 0 88 processed_any_mentions = False 89 90 # Process notifications from oldest to newest 91 for notification in reversed(notifications): 0 92 if notification.is_read or notification.uri in self._processed_uris: 93 continue 94 95 if notification.reason in ["mention", "reply"]: 96 logger.debug(f"🔍 Processing {notification.reason} notification") 0 97 self._processed_uris.add(notification.uri) 98 await self.handler.handle_mention(notification) 99 processed_any_mentions = True 0 0 0 100 101 + # Mark all notifications as seen 0 102 if processed_any_mentions: 103 await self.client.mark_notifications_seen(check_time) 104 logger.info("✓ Marked all notifications as read") 105 106 # Clean up old processed URIs to prevent memory growth 0 107 if len(self._processed_uris) > 1000: 0 108 self._processed_uris = set(list(self._processed_uris)[-500:]) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

src/bot/tools/__init__.py

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src/bot/tools/google_search.py

··· 1 - import logging 2 - 3 - import httpx 4 - 5 - from bot.config import settings 6 - 7 - logger = logging.getLogger("bot.tools") 8 - 9 - 10 - async def search_google(query: str, num_results: int = 3) -> str: 11 - """Search Google and return formatted results""" 12 - if not settings.google_api_key or not settings.google_search_engine_id: 13 - return "Search not available - missing Google API credentials" 14 - 15 - params = { 16 - "key": settings.google_api_key, 17 - "cx": settings.google_search_engine_id, 18 - "q": query, 19 - "num": min(num_results, 10), 20 - } 21 - 22 - async with httpx.AsyncClient() as client: 23 - try: 24 - response = await client.get( 25 - "https://www.googleapis.com/customsearch/v1", params=params 26 - ) 27 - response.raise_for_status() 28 - data = response.json() 29 - 30 - results = [] 31 - for i, item in enumerate(data.get("items", [])[:num_results], 1): 32 - title = item.get("title", "") 33 - snippet = item.get("snippet", "") 34 - results.append(f"{i}. {title}\n {snippet}") 35 - 36 - return "\n\n".join(results) if results else "No search results found" 37 - 38 - except Exception as e: 39 - logger.error(f"Search failed: {e}") 40 - # 12-factor principle #4: Tools should throw errors, not return error strings 41 - raise

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src/bot/ui/__init__.py

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src/bot/ui/context_capture.py

··· 1 - """Context capture system for visualizing phi's response context""" 2 - 3 - import logging 4 - from collections import deque 5 - from dataclasses import asdict, dataclass 6 - from datetime import datetime 7 - from typing import Any, Literal 8 - 9 - logger = logging.getLogger("bot.context") 10 - 11 - 12 - @dataclass 13 - class ContextComponent: 14 - """A component of phi's response context""" 15 - 16 - name: str 17 - type: Literal["personality", "memory", "thread", "mention", "user"] 18 - content: str 19 - size_chars: int 20 - metadata: dict[str, Any] 21 - timestamp: str 22 - 23 - 24 - @dataclass 25 - class ResponseContext: 26 - """Complete context for a single response""" 27 - 28 - response_id: str 29 - mention_text: str 30 - author_handle: str 31 - thread_uri: str | None 32 - generated_response: str 33 - components: list[ContextComponent] 34 - total_context_chars: int 35 - timestamp: str 36 - 37 - 38 - class ContextCapture: 39 - """Captures and stores context information for responses""" 40 - 41 - def __init__(self, max_stored: int = 50): 42 - self.max_stored = max_stored 43 - self.responses: deque = deque(maxlen=max_stored) 44 - 45 - def capture_response_context( 46 - self, 47 - mention_text: str, 48 - author_handle: str, 49 - thread_uri: str | None, 50 - generated_response: str, 51 - components: list[dict[str, Any]], 52 - ) -> str: 53 - """Capture context for a response and return unique ID""" 54 - response_id = f"resp_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}" 55 - 56 - # Convert components to ContextComponent objects 57 - context_components = [] 58 - total_chars = 0 59 - 60 - for comp in components: 61 - component = ContextComponent( 62 - name=comp["name"], 63 - type=comp["type"], 64 - content=comp["content"], 65 - size_chars=len(comp["content"]), 66 - metadata=comp.get("metadata", {}), 67 - timestamp=datetime.now().isoformat(), 68 - ) 69 - context_components.append(component) 70 - total_chars += component.size_chars 71 - 72 - # Create response context 73 - response_context = ResponseContext( 74 - response_id=response_id, 75 - mention_text=mention_text, 76 - author_handle=author_handle, 77 - thread_uri=thread_uri, 78 - generated_response=generated_response, 79 - components=context_components, 80 - total_context_chars=total_chars, 81 - timestamp=datetime.now().isoformat(), 82 - ) 83 - 84 - # Store it 85 - self.responses.appendleft(response_context) 86 - 87 - logger.info( 88 - f"📊 Captured context for {response_id}: {len(components)} components, {total_chars} chars" 89 - ) 90 - return response_id 91 - 92 - def get_response_context(self, response_id: str) -> ResponseContext | None: 93 - """Get context for a specific response""" 94 - for resp in self.responses: 95 - if resp.response_id == response_id: 96 - return resp 97 - return None 98 - 99 - def get_recent_responses(self, limit: int = 20) -> list[ResponseContext]: 100 - """Get recent response contexts""" 101 - return list(self.responses)[:limit] 102 - 103 - def to_dict(self, response_context: ResponseContext) -> dict[str, Any]: 104 - """Convert ResponseContext to dictionary for JSON serialization""" 105 - return asdict(response_context) 106 - 107 - 108 - # Global instance 109 - context_capture = ContextCapture()

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src/bot/ui/templates.py

··· 1 - """HTML templates for the bot""" 2 - 3 - from typing import TYPE_CHECKING 4 - 5 - if TYPE_CHECKING: 6 - from bot.ui.context_capture import ResponseContext 7 - 8 - CONTEXT_VISUALIZATION_TEMPLATE = """<!DOCTYPE html> 9 - <html> 10 - <head> 11 - <title>Phi Context Visualization</title> 12 - <style> 13 - body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; margin: 20px; background: #0a0a0a; color: #e0e0e0; }} 14 - .response-card {{ border: 1px solid #333; margin-bottom: 20px; border-radius: 8px; overflow: hidden; background: #1a1a1a; }} 15 - .response-header {{ background: #2a2a2a; padding: 15px; border-bottom: 1px solid #333; }} 16 - .response-meta {{ font-size: 0.9em; color: #888; margin-bottom: 5px; }} 17 - .mention-text {{ font-weight: bold; margin-bottom: 5px; color: #e0e0e0; }} 18 - .generated-response {{ color: #00a8ff; font-style: italic; }} 19 - .components {{ padding: 15px; }} 20 - .component {{ margin-bottom: 15px; }} 21 - .component-header {{ 22 - cursor: pointer; 23 - padding: 10px; 24 - background: #2a2a2a; 25 - border: 1px solid #444; 26 - border-radius: 4px; 27 - display: flex; 28 - justify-content: space-between; 29 - align-items: center; 30 - }} 31 - .component-header:hover {{ background: #333; }} 32 - .component-type {{ 33 - font-size: 0.8em; 34 - color: #888; 35 - background: #444; 36 - padding: 2px 6px; 37 - border-radius: 3px; 38 - }} 39 - .component-size {{ font-size: 0.8em; color: #888; }} 40 - .component-content {{ 41 - display: none; 42 - padding: 15px; 43 - border: 1px solid #444; 44 - border-top: none; 45 - background: #1a1a1a; 46 - white-space: pre-wrap; 47 - font-family: 'SF Mono', Monaco, 'Cascadia Code', monospace; 48 - font-size: 0.9em; 49 - max-height: 400px; 50 - overflow-y: auto; 51 - }} 52 - .component-content.show {{ display: block; }} 53 - .stats {{ display: flex; gap: 20px; margin-bottom: 10px; }} 54 - .stat {{ font-size: 0.9em; color: #888; }} 55 - h1 {{ color: #00a8ff; }} 56 - </style> 57 - </head> 58 - <body> 59 - <h1>🧠 Phi Context Visualization</h1> 60 - {responses_html} 61 - <script> 62 - function toggleComponent(id) {{ 63 - const element = document.getElementById(id); 64 - element.classList.toggle('show'); 65 - }} 66 - </script> 67 - </body> 68 - </html>""" 69 - 70 - STATUS_PAGE_TEMPLATE = """<!DOCTYPE html> 71 - <html> 72 - <head> 73 - <title>Bluesky Bot Status</title> 74 - <meta http-equiv="refresh" content="10"> 75 - <style> 76 - body {{ 77 - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; 78 - margin: 0; 79 - padding: 20px; 80 - background: #0a0a0a; 81 - color: #e0e0e0; 82 - }} 83 - .container {{ 84 - max-width: 800px; 85 - margin: 0 auto; 86 - }} 87 - h1 {{ 88 - color: #00a8ff; 89 - margin-bottom: 30px; 90 - }} 91 - .status-grid {{ 92 - display: grid; 93 - grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); 94 - gap: 20px; 95 - margin-bottom: 40px; 96 - }} 97 - .status-card {{ 98 - background: #1a1a1a; 99 - border: 1px solid #333; 100 - border-radius: 8px; 101 - padding: 20px; 102 - }} 103 - .status-card h3 {{ 104 - margin: 0 0 15px 0; 105 - color: #00a8ff; 106 - font-size: 1rem; 107 - text-transform: uppercase; 108 - letter-spacing: 0.5px; 109 - }} 110 - .status-value {{ 111 - font-size: 2rem; 112 - font-weight: bold; 113 - margin-bottom: 5px; 114 - }} 115 - .status-label {{ 116 - color: #888; 117 - font-size: 0.9rem; 118 - }} 119 - .status-active {{ 120 - color: #00ff88; 121 - }} 122 - .status-inactive {{ 123 - color: #ff4444; 124 - }} 125 - .uptime {{ 126 - font-size: 1.2rem; 127 - margin-bottom: 5px; 128 - }} 129 - .ai-mode {{ 130 - display: inline-block; 131 - padding: 4px 12px; 132 - border-radius: 4px; 133 - font-size: 0.9rem; 134 - background: #00a8ff22; 135 - color: #00a8ff; 136 - border: 1px solid #00a8ff44; 137 - }} 138 - .ai-mode.placeholder {{ 139 - background: #ff444422; 140 - color: #ff8888; 141 - border-color: #ff444444; 142 - }} 143 - .footer {{ 144 - margin-top: 40px; 145 - text-align: center; 146 - color: #666; 147 - font-size: 0.9rem; 148 - }} 149 - </style> 150 - </head> 151 - <body> 152 - <div class="container"> 153 - <h1>🤖 {bot_name} Status</h1> 154 - 155 - <div class="status-grid"> 156 - <div class="status-card"> 157 - <h3>Bot Status</h3> 158 - <div class="status-value {status_class}">{status}</div> 159 - <div class="uptime">{uptime}</div> 160 - <div style="margin-top: 10px;"> 161 - <span class="ai-mode {ai_mode_class}">{ai_mode}</span> 162 - </div> 163 - </div> 164 - 165 - <div class="status-card"> 166 - <h3>Activity</h3> 167 - <div class="status-value">{mentions}</div> 168 - <div class="status-label">Mentions received</div> 169 - <div style="margin-top: 10px;"> 170 - <div class="status-value">{responses}</div> 171 - <div class="status-label">Responses sent</div> 172 - </div> 173 - </div> 174 - 175 - <div class="status-card"> 176 - <h3>Last Activity</h3> 177 - <div style="margin-bottom: 10px;"> 178 - <div class="status-label">Last mention</div> 179 - <div>{last_mention}</div> 180 - </div> 181 - <div> 182 - <div class="status-label">Last response</div> 183 - <div>{last_response}</div> 184 - </div> 185 - </div> 186 - 187 - <div class="status-card"> 188 - <h3>Health</h3> 189 - <div class="status-value">{errors}</div> 190 - <div class="status-label">Errors encountered</div> 191 - </div> 192 - </div> 193 - 194 - <div class="footer"> 195 - <p>Auto-refreshes every 10 seconds</p> 196 - </div> 197 - </div> 198 - </body> 199 - </html>""" 200 - 201 - 202 - def build_response_cards_html(responses: list["ResponseContext"]) -> str: 203 - """Build HTML for response cards""" 204 - if not responses: 205 - return '<p style="text-align: center; color: #888;">No recent responses to display.</p>' 206 - 207 - return "".join([ 208 - f''' 209 - <div class="response-card"> 210 - <div class="response-header"> 211 - <div class="response-meta"> 212 - {resp.timestamp[:19].replace("T", " ")} • @{resp.author_handle} 213 - {f" • Thread: {resp.thread_uri.split('/')[-1][:8]}..." if resp.thread_uri else ""} 214 - </div> 215 - <div class="mention-text">"{resp.mention_text}"</div> 216 - <div class="generated-response">→ "{resp.generated_response}"</div> 217 - <div class="stats"> 218 - <div class="stat">{len(resp.components)} components</div> 219 - <div class="stat">{resp.total_context_chars:,} characters</div> 220 - </div> 221 - </div> 222 - <div class="components"> 223 - {"".join([ 224 - f''' 225 - <div class="component"> 226 - <div class="component-header" onclick="toggleComponent('{resp.response_id}_{i}')"> 227 - <div> 228 - <strong>{comp.name}</strong> 229 - <span class="component-type">{comp.type}</span> 230 - </div> 231 - <div class="component-size">{comp.size_chars:,} chars</div> 232 - </div> 233 - <div class="component-content" id="{resp.response_id}_{i}"> 234 - {comp.content} 235 - </div> 236 - </div> 237 - ''' 238 - for i, comp in enumerate(resp.components) 239 - ])} 240 - </div> 241 - </div> 242 - ''' 243 - for resp in responses 244 - ])

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src/bot/utils/thread.py

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··· 1 + """Thread utilities for ATProto thread operations.""" 2 + 3 + from collections.abc import Callable 4 + 5 + 6 + def traverse_thread( 7 + thread_node, 8 + visit: Callable[[any], None], 9 + *, 10 + include_parent: bool = True, 11 + include_replies: bool = True, 12 + ): 13 + """Recursively traverse a thread structure and call visit() on each post. 14 + 15 + Args: 16 + thread_node: ATProto thread node with optional .post, .parent, .replies 17 + visit: Callback function called for each post node 18 + include_parent: Whether to traverse up to parent posts 19 + include_replies: Whether to traverse down to reply posts 20 + 21 + Example: 22 + posts = [] 23 + traverse_thread(thread_data.thread, lambda node: posts.append(node.post)) 24 + """ 25 + if not thread_node or not hasattr(thread_node, "post"): 26 + return 27 + 28 + # Visit this node 29 + visit(thread_node) 30 + 31 + # Traverse parent chain (moving up the thread) 32 + if include_parent and hasattr(thread_node, "parent") and thread_node.parent: 33 + traverse_thread(thread_node.parent, visit, include_parent=True, include_replies=False) 34 + 35 + # Traverse replies (moving down the thread) 36 + if include_replies and hasattr(thread_node, "replies") and thread_node.replies: 37 + for reply in thread_node.replies: 38 + traverse_thread(reply, visit, include_parent=False, include_replies=True) 39 + 40 + 41 + def extract_posts_chronological(thread_node) -> list[any]: 42 + """Extract all posts from a thread in chronological order. 43 + 44 + Args: 45 + thread_node: ATProto thread node 46 + 47 + Returns: 48 + List of post objects sorted by timestamp 49 + """ 50 + posts = [] 51 + 52 + def collect(node): 53 + if hasattr(node, "post"): 54 + posts.append(node.post) 55 + 56 + traverse_thread(thread_node, collect) 57 + 58 + # Sort by indexed timestamp 59 + posts.sort(key=lambda p: p.indexed_at if hasattr(p, "indexed_at") else "") 60 + return posts 61 + 62 + 63 + def build_thread_context(thread_node) -> str: 64 + """Build conversational context string from ATProto thread structure. 65 + 66 + Args: 67 + thread_node: ATProto thread node 68 + 69 + Returns: 70 + Formatted string of messages like: 71 + @alice: I love birds 72 + @phi: me too! what's your favorite? 73 + @alice: especially crows 74 + 75 + Example: 76 + thread_data = await client.get_thread(uri, depth=100) 77 + context = build_thread_context(thread_data.thread) 78 + """ 79 + if not thread_node: 80 + return "No previous messages in this thread." 81 + 82 + posts = extract_posts_chronological(thread_node) 83 + 84 + if not posts: 85 + return "No previous messages in this thread." 86 + 87 + messages = [] 88 + for post in posts: 89 + handle = post.author.handle 90 + text = post.record.text if hasattr(post.record, "text") else "[no text]" 91 + messages.append(f"@{handle}: {text}") 92 + 93 + return "\n".join(messages)

+492 -94

uv.lock

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