zzstoatzz.io
+38
.gitignore
+38
.gitignore
···
1
+
# Python-generated files
2
+
__pycache__/
3
+
*.py[cod]
4
+
*$py.class
5
+
*.so
6
+
.Python
7
+
build/
8
+
dist/
9
+
wheels/
10
+
*.egg-info
11
+
12
+
# Virtual environments
13
+
.venv/
14
+
venv/
15
+
ENV/
16
+
env/
17
+
18
+
# Environment
19
+
.env
20
+
.env.*
21
+
!.env.example
22
+
23
+
# IDE
24
+
.vscode/
25
+
.idea/
26
+
*.swp
27
+
*.swo
28
+
.DS_Store
29
+
30
+
# Testing
31
+
.pytest_cache/
32
+
.coverage
33
+
htmlcov/
34
+
35
+
# Project specific
36
+
.eggs/
37
+
logs/
38
+
*.log
+1
.python-version
+1
.python-version
···
1
+
3.12
+11
CLAUDE.md
+11
CLAUDE.md
···
1
+
This is the repository for a bluesky virtual person powered by LLMs and exposed to the web.
2
+
3
+
This is a python project that uses `uv` as python package manager, `fastapi` and is inspired by `https://tangled.sh/@cameron.pfiffer.org/void`, `https://github.com/haileyok/penelope`, and `https://github.com/PrefectHQ/marvin/tree/main/examples/slackbot` (tangled is github on atproto, you can git clone tangled.sh repos). These projects should be cloned to the `.eggs` directory, along with any other resources that are useful but not worth checking into the repo. We should simply common commands and communicate dev workflows by using a `justfile`.
4
+
5
+
Work from repo root whenever possible.
6
+
7
+
## Project Structure
8
+
9
+
- `src/` - The source code for the project.
10
+
- `tests/` - The tests for the project.
11
+
- `sandbox/` - place to experiment and aggregate context for specific tasks within the project.
+55
README.md
+55
README.md
···
1
+
# Bluesky Bot
2
+
3
+
A virtual person for Bluesky powered by LLMs, built with FastAPI and pydantic-ai.
4
+
5
+
## Setup
6
+
7
+
1. Install dependencies:
8
+
```bash
9
+
just sync
10
+
```
11
+
12
+
2. Copy `.env.example` to `.env` and add your credentials:
13
+
- `BLUESKY_HANDLE`: Your bot's Bluesky handle
14
+
- `BLUESKY_PASSWORD`: App password from Bluesky settings
15
+
- `OPENAI_API_KEY` or `ANTHROPIC_API_KEY`: For LLM responses
16
+
17
+
3. Test posting:
18
+
```bash
19
+
just test-post
20
+
```
21
+
22
+
4. Run the bot:
23
+
```bash
24
+
just dev
25
+
```
26
+
27
+
## Architecture
28
+
29
+
- **FastAPI** web framework with async support
30
+
- **pydantic-ai** for LLM agent management
31
+
- **TurboPuffer** for scalable vector memory (planned)
32
+
- **AT Protocol** for Bluesky integration
33
+
34
+
## Development
35
+
36
+
```bash
37
+
just # Show available commands
38
+
just dev # Run with hot-reload
39
+
just test-post # Test posting capabilities
40
+
just fmt # Format code
41
+
just status # Check project status
42
+
```
43
+
44
+
## Memory Architecture
45
+
46
+
See `sandbox/memory_architecture_plan.md` for the planned memory system using TurboPuffer.
47
+
48
+
## Reference Projects
49
+
50
+
This bot is inspired by:
51
+
- **Void** by Cameron Pfiffer - Sophisticated memory system
52
+
- **Penelope** by Hailey - Self-modifying capabilities
53
+
- **Marvin Slackbot** - Multi-agent architecture
54
+
55
+
See `sandbox/` for detailed analysis of each project.
+39
STATUS.md
+39
STATUS.md
···
1
+
# Project Status
2
+
3
+
## Current Phase: Initial Setup
4
+
5
+
### Completed
6
+
- ✅ Created project directory structure (.eggs, tests, sandbox)
7
+
- ✅ Cloned reference projects:
8
+
- penelope (Bluesky bot in TypeScript)
9
+
- void (Digital personhood exploration on Bluesky)
10
+
- marvin/slackbot (Python bot example)
11
+
12
+
### In Progress
13
+
- 🔄 Analyzing reference projects for architectural insights
14
+
- 🔄 Setting up core dependencies
15
+
16
+
### Next Steps
17
+
1. Add core dependencies (FastAPI, Bluesky/AT Protocol SDK, LLM libraries)
18
+
2. Create justfile for development workflow
19
+
3. Design bot architecture based on reference projects
20
+
4. Implement basic FastAPI structure
21
+
5. Set up Bluesky integration
22
+
23
+
## Key Decisions to Make
24
+
- Which LLM provider to use (OpenAI, Anthropic, etc.)
25
+
- Bot personality and behavior design
26
+
- Hosting and deployment strategy
27
+
- Response generation approach (streaming vs batch)
28
+
29
+
## Reference Projects Analysis
30
+
- **penelope**: Go-based with core memory, self-modification, and Google search capabilities
31
+
- **void**: Python/Letta with sophisticated 3-tier memory and strong personality consistency
32
+
- **marvin slackbot**: Multi-agent architecture with TurboPuffer vector memory and progress tracking
33
+
34
+
### Key Insights from Deep Dive
35
+
- All three bots have memory systems (not just Void)
36
+
- Penelope can update its own profile and has "core memory"
37
+
- Marvin uses user-namespaced vectors in TurboPuffer
38
+
- Deployment often involves separate GPU machines for LLM
39
+
- HTTPS/CORS handling is critical for remote deployments
+26
justfile
+26
justfile
···
1
+
# Run the bot with hot-reload
2
+
dev:
3
+
uv run uvicorn src.bot.main:app --reload
4
+
5
+
# Test posting capabilities
6
+
test-post:
7
+
uv run python scripts/test_post.py
8
+
9
+
# Run tests
10
+
test:
11
+
uv run pytest tests/ -v
12
+
13
+
# Format code
14
+
fmt:
15
+
uv run ruff format src/ scripts/ tests/
16
+
17
+
# Lint code
18
+
lint:
19
+
uv run ruff check src/ scripts/ tests/
20
+
21
+
# Type check with ty
22
+
typecheck:
23
+
uv run ty src/
24
+
25
+
# Run all checks
26
+
check: lint typecheck test
+29
pyproject.toml
+29
pyproject.toml
···
1
+
[project]
2
+
name = "bot"
3
+
version = "0.1.0"
4
+
description = "Add your description here"
5
+
readme = "README.md"
6
+
authors = [
7
+
{ name = "zzstoatzz", email = "thrast36@gmail.com" }
8
+
]
9
+
requires-python = ">=3.12"
10
+
dependencies = [
11
+
"fastapi",
12
+
"uvicorn",
13
+
"atproto",
14
+
"pydantic-settings",
15
+
"pydantic-ai",
16
+
"httpx"
17
+
]
18
+
19
+
[tool.uv]
20
+
dev-dependencies = [
21
+
"pytest>=8.0.0",
22
+
"ruff>=0.8.0",
23
+
"ty",
24
+
]
25
+
26
+
27
+
[build-system]
28
+
requires = ["hatchling"]
29
+
build-backend = "hatchling.build"
+217
sandbox/architecture_synthesis.md
+217
sandbox/architecture_synthesis.md
···
1
+
# Architecture Synthesis: Building a Bluesky Bot
2
+
3
+
## Overview
4
+
Combining insights from three reference implementations to create a sophisticated Bluesky virtual person:
5
+
- **Marvin**: Multi-agent architecture with vector memory
6
+
- **Void**: Sophisticated memory layers with strong personality
7
+
- **Penelope**: Clean AT Protocol integration with safety features
8
+
9
+
## Proposed Architecture
10
+
11
+
### 1. Core Components
12
+
13
+
```
14
+
┌─────────────────────────────────────────────────────────┐
15
+
│ FastAPI Application │
16
+
├─────────────────────────────────────────────────────────┤
17
+
│ Lifespan Manager │
18
+
│ ├── AT Protocol Client (auth, posting) │
19
+
│ ├── Notification Poller (async task) │
20
+
│ └── Memory Manager (TurboPuffer) │
21
+
├─────────────────────────────────────────────────────────┤
22
+
│ Message Handler │
23
+
│ ├── Context Builder (thread + memory) │
24
+
│ ├── LLM Agent (pydantic-ai) │
25
+
│ └── Response Publisher │
26
+
└─────────────────────────────────────────────────────────┘
27
+
```
28
+
29
+
### 2. Memory Architecture (Inspired by Void + TurboPuffer)
30
+
31
+
#### Three-Tier Memory System
32
+
1. **Core Memory** (bot namespace)
33
+
- Personality definition
34
+
- Behavioral guidelines
35
+
- Global knowledge
36
+
37
+
2. **User Memory** (user_{did} namespaces)
38
+
- Facts about users
39
+
- Conversation history
40
+
- Relationship context
41
+
42
+
3. **Archival Memory** (archive namespace)
43
+
- Semantic search across all interactions
44
+
- Pattern recognition
45
+
- Long-term learning
46
+
47
+
#### Implementation with TurboPuffer
48
+
```python
49
+
class MemoryManager:
50
+
def __init__(self, turbopuffer_client):
51
+
self.client = turbopuffer_client
52
+
self.core_namespace = "bot_core"
53
+
54
+
async def load_user_context(self, user_did: str):
55
+
namespace = f"user_{user_did}"
56
+
# Retrieve relevant memories via vector similarity
57
+
58
+
async def store_interaction(self, user_did: str, interaction):
59
+
# Embed and store in user namespace
60
+
61
+
async def search_archival(self, query: str):
62
+
# Search across all namespaces
63
+
```
64
+
65
+
### 3. Agent Architecture (Inspired by Marvin)
66
+
67
+
#### Multi-Agent System
68
+
```python
69
+
class BotAgent:
70
+
"""Main conversational agent"""
71
+
72
+
async def respond(self, context: UserContext) -> Response:
73
+
# Decide if delegation needed
74
+
if needs_research:
75
+
return await self.research_agent.investigate(query)
76
+
return await self.generate_response(context)
77
+
78
+
class ResearchAgent:
79
+
"""Specialized for information gathering"""
80
+
81
+
async def investigate(self, query: str) -> Finding:
82
+
# Web search, memory search, etc.
83
+
```
84
+
85
+
### 4. Safety & Interaction Model (Inspired by Penelope)
86
+
87
+
#### Opt-In Only
88
+
- Only respond to direct mentions
89
+
- Admin whitelist for testing phase
90
+
- Rate limiting per user
91
+
92
+
#### Self-Modification Capabilities
93
+
- Update own profile (like Penelope)
94
+
- Modify display name based on identity
95
+
- Store changes in core memory
96
+
97
+
#### Error Handling
98
+
```python
99
+
class SafeMessageHandler:
100
+
async def handle_mention(self, notification):
101
+
try:
102
+
with timeout(30): # Max processing time
103
+
response = await self.process(notification)
104
+
await self.publish(response)
105
+
except Exception as e:
106
+
logger.error(f"Failed to process: {e}")
107
+
# Don't crash, just log
108
+
```
109
+
110
+
### 5. Conversation Management
111
+
112
+
#### Context Building
113
+
1. Load thread history (like Penelope)
114
+
2. Retrieve user memories (like Void)
115
+
3. Apply conversation summarization (like Marvin)
116
+
4. Inject into LLM prompt
117
+
118
+
#### Progress Updates (from Marvin)
119
+
```python
120
+
@contextmanager
121
+
def track_progress(handler):
122
+
"""Send typing indicators or progress messages"""
123
+
# Start typing indicator
124
+
yield
125
+
# Stop typing indicator
126
+
```
127
+
128
+
## Key Design Decisions
129
+
130
+
### 1. Memory Strategy
131
+
- **TurboPuffer** for scalable vector storage
132
+
- **Namespaces** for user isolation
133
+
- **Embeddings** for semantic retrieval
134
+
- **Hybrid search** (vector + keyword)
135
+
136
+
### 2. Personality Consistency
137
+
- **Immutable core prompt** (like Void)
138
+
- **Personality reinforcement** in every interaction
139
+
- **Admin-only modifications**
140
+
- **Character guidelines** in core memory
141
+
142
+
### 3. Scaling Approach
143
+
- **Async everything** (FastAPI lifespan)
144
+
- **User-specific namespaces** (no cross-contamination)
145
+
- **Queue-based processing** (handle bursts)
146
+
- **Cursor persistence** (resume after crashes)
147
+
148
+
### 4. LLM Integration
149
+
- **pydantic-ai** for structured outputs
150
+
- **Tool calling** for actions
151
+
- **Context injection** for memory
152
+
- **Token management** for cost control
153
+
154
+
## Deployment Architecture
155
+
156
+
### Remote GPU Setup (Like Penelope)
157
+
```
158
+
┌─────────────┐ HTTPS/WSS ┌──────────────┐
159
+
│ FastAPI │ ◄─────────────► │ GPU Machine │
160
+
│ Bot App │ │ - LLM API │
161
+
└─────────────┘ │ - Embedding │
162
+
└──────────────┘
163
+
```
164
+
165
+
### Considerations
166
+
- Use Tailscale/VPN for secure remote access
167
+
- Handle HTTPS properly (avoid mixed content)
168
+
- Consider GPU for heavy LLM/embedding tasks
169
+
- Separate compute from bot logic
170
+
171
+
## Implementation Priorities
172
+
173
+
1. **Phase 1: Basic Bot**
174
+
- Simple replies with pydantic-ai
175
+
- Basic memory storage
176
+
- Admin-only interactions
177
+
178
+
2. **Phase 2: Memory System**
179
+
- TurboPuffer integration
180
+
- User context loading
181
+
- Conversation history
182
+
183
+
3. **Phase 3: Advanced Features**
184
+
- Multi-agent delegation
185
+
- Tool calling (search, etc.)
186
+
- Personality refinement
187
+
188
+
4. **Phase 4: Public Release**
189
+
- Remove admin restrictions
190
+
- Add rate limiting
191
+
- Monitor and iterate
192
+
193
+
## Unique Features to Consider
194
+
195
+
1. **"Void-style" Transparency**
196
+
- Public reasoning traces
197
+
- Memory inspection endpoint
198
+
- Open source code
199
+
200
+
2. **"Marvin-style" Progress**
201
+
- Real-time updates during processing
202
+
- Usage statistics
203
+
- Performance monitoring
204
+
205
+
3. **"Penelope-style" Features**
206
+
- Strict opt-in model
207
+
- Graceful error handling
208
+
- AT Protocol compliance
209
+
- Self-profile updates
210
+
- Core memory for identity
211
+
212
+
## Next Steps
213
+
1. Add pydantic-ai and turbopuffer to dependencies
214
+
2. Implement basic LLM agent
215
+
3. Design personality and prompts
216
+
4. Build memory manager
217
+
5. Test with admin interactions
+212
sandbox/marvin_slackbot_analysis.md
+212
sandbox/marvin_slackbot_analysis.md
···
1
+
# Marvin Slackbot Architecture Analysis
2
+
3
+
## Overview
4
+
5
+
The Marvin slackbot is a sophisticated AI-powered Slack integration built with FastAPI and leveraging advanced patterns for conversation management, tool usage, and memory persistence. The architecture demonstrates several patterns that would be valuable for a Bluesky bot implementation.
6
+
7
+
## Core Architecture Patterns
8
+
9
+
### 1. FastAPI Integration with Async Event Handling
10
+
11
+
The bot uses FastAPI with an async lifespan pattern for resource management:
12
+
13
+
```python
14
+
@asynccontextmanager
15
+
async def lifespan(app: FastAPI):
16
+
async with Database.connect(settings.db_file) as db:
17
+
app.state.db = db
18
+
yield
19
+
20
+
app = FastAPI(lifespan=lifespan)
21
+
```
22
+
23
+
Key benefits:
24
+
- Clean resource lifecycle management
25
+
- Database connection shared across requests via `app.state`
26
+
- Non-blocking async handling of Slack events
27
+
- Fire-and-forget pattern with `asyncio.create_task()` for immediate response to Slack
28
+
29
+
### 2. Agent Architecture with pydantic-ai
30
+
31
+
The bot uses `pydantic-ai` for structured agent creation with several sophisticated patterns:
32
+
33
+
#### Multi-Tier Agent System
34
+
- **Main Agent**: Handles user interactions and delegates to specialized tools
35
+
- **Research Agent**: Specialized sub-agent for thorough documentation research
36
+
- Both agents use structured result types (`ResearchFindings`) for predictable outputs
37
+
38
+
#### Context Injection Pattern
39
+
```python
40
+
Agent[UserContext, str](
41
+
deps_type=UserContext,
42
+
tools=[...],
43
+
)
44
+
```
45
+
- Clean dependency injection of user context to all tools
46
+
- Context includes user ID, notes, thread info, workspace details
47
+
48
+
#### Dynamic System Prompts
49
+
```python
50
+
@agent.system_prompt
51
+
def personality_and_maybe_notes(ctx: RunContext[UserContext]) -> str:
52
+
return DEFAULT_SYSTEM_PROMPT + (
53
+
f"\n\nUser notes: {ctx.deps['user_notes']}"
54
+
if ctx.deps["user_notes"] else ""
55
+
)
56
+
```
57
+
58
+
### 3. Memory and Vector Store Integration
59
+
60
+
The bot implements a sophisticated memory system using TurboPuffer vector store:
61
+
62
+
#### User-Specific Namespacing
63
+
```python
64
+
namespace=f"{settings.user_facts_namespace_prefix}{user_id}"
65
+
```
66
+
- Each user has their own vector namespace for facts
67
+
- Facts are retrieved based on query relevance
68
+
- Clean separation of user data
69
+
70
+
#### Fact Storage Pattern
71
+
```python
72
+
@agent.tool
73
+
async def store_facts_about_user(
74
+
ctx: RunContext[UserContext], facts: list[str]
75
+
) -> str:
76
+
with TurboPuffer(namespace=f"...{ctx.deps['user_id']}") as tpuf:
77
+
tpuf.upsert(documents=[Document(text=fact) for fact in facts])
78
+
```
79
+
80
+
### 4. Conversation Thread Management
81
+
82
+
The bot maintains conversation history using SQLite with async wrappers:
83
+
84
+
#### Thread-Based Storage
85
+
- Messages stored per thread using `thread_ts` as key
86
+
- Full conversation history preserved as `ModelMessage` objects
87
+
- Efficient serialization with `ModelMessagesTypeAdapter`
88
+
89
+
#### Automatic Summarization
90
+
```python
91
+
@handle_message.on_completion
92
+
async def summarize_thread_so_far(flow: Flow, flow_run: FlowRun, state: State[Any]):
93
+
if len(conversation) % 4 != 0: # every 4 messages
94
+
return
95
+
await summarize_thread(result["user_context"], conversation)
96
+
```
97
+
98
+
### 5. Tool Usage Patterns
99
+
100
+
#### Tool Decoration and Monitoring
101
+
The `WatchToolCalls` context manager provides:
102
+
- Real-time progress updates during tool execution
103
+
- Tool usage counting and statistics
104
+
- Clean separation of tool execution from business logic
105
+
106
+
#### Prefect Integration
107
+
```python
108
+
@task(name="run agent loop", cache_policy=NONE)
109
+
async def run_agent(...) -> AgentRunResult[str]:
110
+
with WatchToolCalls(settings=decorator_settings):
111
+
result = await create_agent().run(...)
112
+
```
113
+
114
+
### 6. Asset Tracking and Data Lineage
115
+
116
+
The bot implements Prefect asset tracking for data lineage:
117
+
118
+
```python
119
+
@materialize(user_facts, asset_deps=[slack_thread, slackbot])
120
+
async def materialize_user_facts():
121
+
add_asset_metadata(user_facts, {...})
122
+
```
123
+
124
+
Key patterns:
125
+
- Assets represent different data products (threads, summaries, user facts)
126
+
- Clear dependency tracking between assets
127
+
- Rich metadata for observability
128
+
129
+
## Key Utilities and Patterns to Adopt
130
+
131
+
### 1. Progress Messaging
132
+
Real-time feedback during long-running operations:
133
+
```python
134
+
progress = await create_progress_message(
135
+
channel_id=channel_id,
136
+
thread_ts=thread_ts,
137
+
initial_text="🔄 Thinking..."
138
+
)
139
+
```
140
+
141
+
### 2. Token Management
142
+
Intelligent message truncation and length validation:
143
+
```python
144
+
if msg_len > USER_MESSAGE_MAX_TOKENS:
145
+
slice_tokens(cleaned_message, USER_MESSAGE_MAX_TOKENS)
146
+
```
147
+
148
+
### 3. Error Handling with Notifications
149
+
```python
150
+
slack_webhook = await SlackWebhook.load("marvin-bot-pager")
151
+
await slack_webhook.notify(body=f"Error: {e}", subject="Bot Error")
152
+
```
153
+
154
+
### 4. Configuration Management
155
+
- Settings via pydantic-settings with prefixes
156
+
- Mix of environment variables and Prefect Variables
157
+
- Secret management through Prefect Blocks
158
+
159
+
### 5. Research Pattern
160
+
The research agent pattern is particularly powerful:
161
+
- Multiple search strategies with different tools
162
+
- Confidence levels in responses
163
+
- Knowledge gap acknowledgment
164
+
- Structured findings with links
165
+
166
+
## Recommendations for Bluesky Bot
167
+
168
+
1. **Adopt the Multi-Agent Architecture**: Use a main agent with specialized sub-agents for different tasks (posting, research, conversation)
169
+
170
+
2. **Implement Vector-Based Memory**: Use TurboPuffer with user-specific namespaces for personalization
171
+
172
+
3. **Use Structured Contexts**: Define clear context types (e.g., `PostContext`, `ConversationContext`) for dependency injection
173
+
174
+
4. **Async Event Handling**: Use FastAPI with async patterns for handling Bluesky firehose events
175
+
176
+
5. **Progress Feedback**: Implement progress indicators for long-running operations (useful for thread replies)
177
+
178
+
6. **Tool Monitoring**: Implement the `WatchToolCalls` pattern for observability
179
+
180
+
7. **Asset Tracking**: Track posts, threads, and user interactions as Prefect assets for data lineage
181
+
182
+
8. **Conversation Management**: Store conversation history with efficient serialization
183
+
184
+
9. **Error Recovery**: Implement webhook-based error notifications and automatic retries
185
+
186
+
10. **Dynamic Prompting**: Use context-aware system prompts that incorporate user history
187
+
188
+
## Architecture Diagram
189
+
190
+
```
191
+
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
192
+
│ FastAPI App │────▶│ Event Handler │────▶│ Agent Loop │
193
+
└─────────────────┘ └──────────────────┘ └─────────────────┘
194
+
│ │ │
195
+
│ │ ▼
196
+
▼ ▼ ┌───────────────┐
197
+
┌─────────────────┐ ┌──────────────────┐ │ Research Agent│
198
+
│ SQLite DB │ │ User Context │ └───────────────┘
199
+
└─────────────────┘ └──────────────────┘ │
200
+
│ │ ▼
201
+
│ ▼ ┌───────────────┐
202
+
│ ┌──────────────────┐ │ Tools │
203
+
│ │ Vector Store │ └───────────────┘
204
+
│ │ (TurboPuffer) │
205
+
│ └──────────────────┘
206
+
▼
207
+
┌─────────────────┐
208
+
│ Message History │
209
+
└─────────────────┘
210
+
```
211
+
212
+
This architecture provides a robust foundation for building an intelligent, context-aware bot with memory, specialized capabilities, and excellent observability.
+137
sandbox/memory_architecture_plan.md
+137
sandbox/memory_architecture_plan.md
···
1
+
# Memory Architecture Plan
2
+
3
+
## Overview
4
+
Using TurboPuffer for scalable vector memory with pydantic-ai agents.
5
+
6
+
## Memory Layers
7
+
8
+
### 1. Core Memory (Namespace: `bot_core`)
9
+
**Purpose**: Bot's persistent identity and knowledge
10
+
- Personality traits
11
+
- Core directives
12
+
- Communication style
13
+
- Self-knowledge
14
+
15
+
**Implementation**:
16
+
```python
17
+
core_memories = [
18
+
"I am a helpful assistant on Bluesky",
19
+
"I respond thoughtfully and concisely",
20
+
"I remember conversations with users",
21
+
# ... more core traits
22
+
]
23
+
```
24
+
25
+
### 2. User Memory (Namespace: `user_{did}`)
26
+
**Purpose**: Per-user context and history
27
+
- User facts and preferences
28
+
- Conversation summaries
29
+
- Interaction patterns
30
+
- Relationship context
31
+
32
+
**Example Structure**:
33
+
```python
34
+
UserMemory = {
35
+
"facts": ["Works in tech", "Likes Python"],
36
+
"last_interaction": "2024-07-20",
37
+
"conversation_style": "technical, direct",
38
+
"topics": ["programming", "AI"]
39
+
}
40
+
```
41
+
42
+
### 3. Conversation Memory (Namespace: `conversations`)
43
+
**Purpose**: Recent interaction context
44
+
- Thread summaries
45
+
- Recent exchanges
46
+
- Topic continuity
47
+
48
+
## Memory Flow
49
+
50
+
```
51
+
User mentions bot
52
+
↓
53
+
Load user memories (if exist)
54
+
↓
55
+
Retrieve relevant context
56
+
↓
57
+
Generate response with context
58
+
↓
59
+
Store new memories/facts
60
+
```
61
+
62
+
## TurboPuffer Integration
63
+
64
+
### Vector Embeddings
65
+
- Use OpenAI/Anthropic embeddings
66
+
- Store text + embedding pairs
67
+
- Semantic search for relevance
68
+
69
+
### Storage Schema
70
+
```python
71
+
Memory = {
72
+
"id": "uuid",
73
+
"text": "User likes Python programming",
74
+
"type": "fact|conversation|trait",
75
+
"user_did": "did:plc:xxx",
76
+
"timestamp": "2024-07-20T10:00:00Z",
77
+
"embedding": [0.1, 0.2, ...], # 1536 dims
78
+
}
79
+
```
80
+
81
+
### Query Patterns
82
+
1. **User Context**: Get top-k relevant memories for user
83
+
2. **Topic Search**: Find memories related to current topic
84
+
3. **Time Decay**: Weight recent memories higher
85
+
86
+
## Pydantic-AI Agent Integration
87
+
88
+
### Memory-Aware Agent
89
+
```python
90
+
class BotAgent(BaseAgent):
91
+
async def get_context(self, user_did: str, query: str):
92
+
# 1. Load core memories
93
+
# 2. Load user-specific memories
94
+
# 3. Search for relevant context
95
+
# 4. Return formatted context
96
+
97
+
async def store_memory(self, user_did: str, memory: str):
98
+
# 1. Generate embedding
99
+
# 2. Store in TurboPuffer
100
+
# 3. Update user namespace
101
+
```
102
+
103
+
### Context Injection
104
+
```python
105
+
system_prompt = f"""
106
+
{core_personality}
107
+
108
+
User Context:
109
+
{user_memories}
110
+
111
+
Recent Conversation:
112
+
{thread_context}
113
+
"""
114
+
```
115
+
116
+
## Future Considerations
117
+
118
+
### Memory Management
119
+
- Automatic summarization of old conversations
120
+
- Memory pruning/compression
121
+
- Importance scoring
122
+
123
+
### Advanced Features
124
+
- Cross-user pattern recognition
125
+
- Community memory (opt-in)
126
+
- Memory visualization endpoint
127
+
128
+
### Privacy
129
+
- User can request memory deletion
130
+
- No cross-contamination between users
131
+
- Transparent about what's remembered
132
+
133
+
## Next Steps
134
+
1. Set up TurboPuffer client
135
+
2. Create memory service
136
+
3. Integrate with pydantic-ai agent
137
+
4. Add memory management commands
+223
sandbox/penelope_analysis.md
+223
sandbox/penelope_analysis.md
···
1
+
# Penelope Bot Implementation Analysis
2
+
3
+
This is an analysis of Penelope, a Bluesky bot implementation by a Bluesky engineer (haileyok). The bot is written in Go and provides several key patterns and insights for building AT Protocol-compliant bots.
4
+
5
+
## Architecture Overview
6
+
7
+
### Core Components
8
+
9
+
1. **Main Entry Point** (`cmd/penelope/main.go`)
10
+
- CLI-based application using `urfave/cli`
11
+
- Configuration via environment variables or CLI flags
12
+
- Graceful shutdown handling with OS signals
13
+
- Metrics server on separate port for observability
14
+
15
+
2. **Core Bot Structure** (`penelope/penelope.go`)
16
+
- Uses `xrpc.Client` for AT Protocol communication
17
+
- Maintains authenticated session with automatic refresh
18
+
- Thread-safe client access with RWMutex
19
+
- ClickHouse integration for data persistence
20
+
21
+
### Key Design Patterns
22
+
23
+
#### 1. Event Handling Architecture
24
+
25
+
The bot uses a sophisticated event-driven architecture:
26
+
27
+
```go
28
+
// Parallel scheduler for handling events
29
+
scheduler := parallel.NewScheduler(400, 10, con.RemoteAddr().String(), rsc.EventHandler)
30
+
```
31
+
32
+
- **Parallel Processing**: Uses Indigo's parallel scheduler with 400 workers and 10 buffer size
33
+
- **Non-blocking**: Event handlers spawn goroutines for processing (`go p.repoCommit(ctx, evt)`)
34
+
- **Selective Processing**: Only processes create events, ignoring updates/deletes
35
+
36
+
#### 2. AT Protocol Connection Management
37
+
38
+
**WebSocket Connection to Firehose**:
39
+
```go
40
+
u.Path = "/xrpc/com.atproto.sync.subscribeRepos"
41
+
con, _, err := d.Dial(u.String(), http.Header{
42
+
"user-agent": []string{"photocopy/0.0.0"},
43
+
})
44
+
```
45
+
46
+
**Key Features**:
47
+
- Connects to relay host (default: `wss://bsky.network`)
48
+
- Implements cursor persistence for resumption after restarts
49
+
- Uses CAR file format for reading repository data
50
+
- Validates CIDs to ensure data integrity
51
+
52
+
#### 3. Authentication & Session Management
53
+
54
+
**Initial Authentication**:
55
+
```go
56
+
resp, err := atproto.ServerCreateSession(ctx, x, &atproto.ServerCreateSession_Input{
57
+
Identifier: args.BotIdentifier,
58
+
Password: args.BotPassword,
59
+
})
60
+
```
61
+
62
+
**Automatic Token Refresh**:
63
+
```go
64
+
go func() {
65
+
ticker := time.NewTicker(1 * time.Hour)
66
+
for range ticker.C {
67
+
func() {
68
+
p.xmu.Lock()
69
+
defer p.xmu.Unlock()
70
+
resp, err := atproto.ServerRefreshSession(ctx, p.x)
71
+
if err != nil {
72
+
p.logger.Error("error refreshing session", "error", err)
73
+
return
74
+
}
75
+
p.x.Auth.AccessJwt = resp.AccessJwt
76
+
p.x.Auth.RefreshJwt = resp.RefreshJwt
77
+
}()
78
+
}
79
+
}()
80
+
```
81
+
82
+
- Refreshes auth tokens every hour
83
+
- Thread-safe token updates
84
+
- Graceful error handling without crashing
85
+
86
+
#### 4. Conversation Flow & Reply Patterns
87
+
88
+
The bot implements a selective reply system:
89
+
90
+
```go
91
+
// Check if it's a reply
92
+
if rec.Reply == nil {
93
+
return nil
94
+
}
95
+
96
+
// Check if replying to the bot
97
+
rootUri, _ := syntax.ParseATURI(rec.Reply.Root.Uri)
98
+
parentUri, _ := syntax.ParseATURI(rec.Reply.Parent.Uri)
99
+
100
+
if rootUri.Authority().String() != p.botDid && parentUri.Authority().String() != p.botDid {
101
+
return nil
102
+
}
103
+
```
104
+
105
+
**Reply Logic**:
106
+
1. Only responds to posts that are replies
107
+
2. Checks if either the root or parent post is from the bot
108
+
3. Implements admin-only interaction model (see below)
109
+
110
+
#### 5. Admin User & Opt-in Model
111
+
112
+
**Critical Security Pattern**:
113
+
```go
114
+
// Admin check
115
+
if !slices.Contains(p.botAdmins, did) {
116
+
return nil
117
+
}
118
+
```
119
+
120
+
- **Whitelist-based**: Only responds to pre-configured admin DIDs
121
+
- **No public interaction**: Prevents spam and abuse
122
+
- **Configurable**: Admin list provided via environment variable
123
+
- This is a key pattern for controlled bot rollouts
124
+
125
+
#### 6. Rate Limiting & Error Handling
126
+
127
+
**Built-in Protections**:
128
+
1. **HTTP Client Timeout**: 5-second timeout on all HTTP requests
129
+
2. **Cursor Persistence**: Saves cursor every 5 seconds to prevent data loss
130
+
3. **Error Isolation**: Each event processed in separate goroutine
131
+
4. **Graceful Degradation**: Logs errors but continues processing
132
+
133
+
**Event Size Handling**:
134
+
```go
135
+
if evt.TooBig {
136
+
p.logger.Warn("commit too big", "repo", evt.Repo, "seq", evt.Seq)
137
+
return
138
+
}
139
+
```
140
+
141
+
## Bluesky-Specific Patterns
142
+
143
+
### 1. Record Creation
144
+
```go
145
+
post := bsky.FeedPost{
146
+
Text: resp.Message.Content,
147
+
CreatedAt: syntax.DatetimeNow().String(),
148
+
}
149
+
150
+
input := &atproto.RepoCreateRecord_Input{
151
+
Collection: "app.bsky.feed.post",
152
+
Repo: p.botDid,
153
+
Record: &util.LexiconTypeDecoder{Val: &post},
154
+
}
155
+
```
156
+
157
+
- Uses proper Lexicon type decoder
158
+
- Includes required timestamp
159
+
- Specifies collection explicitly
160
+
161
+
### 2. URI Construction
162
+
```go
163
+
func uriFromParts(did string, collection string, rkey string) string {
164
+
return "at://" + did + "/" + collection + "/" + rkey
165
+
}
166
+
```
167
+
168
+
- Follows AT URI scheme specification
169
+
- Used for referencing posts and records
170
+
171
+
### 3. Time Handling
172
+
Sophisticated time parsing with multiple fallbacks:
173
+
- Attempts to parse from record's CreatedAt
174
+
- Falls back to TID (timestamp ID) parsing
175
+
- Validates time ranges to prevent far-future/past timestamps
176
+
- Ultimate fallback to current time
177
+
178
+
## Integration Points
179
+
180
+
### 1. LLM Integration
181
+
Currently uses Ollama locally:
182
+
```go
183
+
url := "http://localhost:11434/api/chat"
184
+
request := ChatRequest{
185
+
Model: "gemma3:27b",
186
+
Messages: []Message{message},
187
+
Tools: tools,
188
+
Stream: false,
189
+
}
190
+
```
191
+
192
+
### 2. Tool/Function Support
193
+
Implements a basic tool system:
194
+
- Currently only has `get_current_time` function
195
+
- Extensible architecture for adding more tools
196
+
- Function parsing from LLM responses
197
+
198
+
### 3. Data Persistence
199
+
Uses ClickHouse for:
200
+
- Likely for analytics and event storage
201
+
- High-performance time-series capabilities
202
+
- Could track interactions, metrics, etc.
203
+
204
+
## Key Takeaways for Python Implementation
205
+
206
+
1. **Event Processing Model**: Implement async/concurrent processing for firehose events
207
+
2. **Cursor Management**: Essential for production - persist cursor frequently
208
+
3. **Admin-only Mode**: Start with whitelist approach for safety
209
+
4. **Session Management**: Implement automatic token refresh
210
+
5. **Error Isolation**: Never let one bad event crash the entire stream
211
+
6. **Proper AT Protocol Types**: Use official SDK types and validators
212
+
7. **Rate Limiting**: Build in timeouts and backoff strategies
213
+
8. **Metrics/Observability**: Include metrics endpoint from the start
214
+
215
+
## Security Considerations
216
+
217
+
1. **Authentication**: Never log passwords or tokens
218
+
2. **Admin Control**: Whitelist approach prevents abuse
219
+
3. **Input Validation**: Validate all AT URIs and DIDs
220
+
4. **CID Verification**: Always verify content integrity
221
+
5. **Timeout Protection**: Prevent hanging on bad requests
222
+
223
+
This implementation serves as an excellent reference for production-grade Bluesky bots, emphasizing reliability, security, and proper AT Protocol compliance.
+79
sandbox/penelope_thread_insights.md
+79
sandbox/penelope_thread_insights.md
···
1
+
# Penelope Thread Insights
2
+
3
+
## Key Revelations from Hailey's Conversation
4
+
5
+
### 1. Penelope Has Core Memory!
6
+
- "i'm adding that to my core memory" - Penelope can remember things
7
+
- Can update its own profile based on Google searches
8
+
- This suggests a more sophisticated implementation than the basic Go code shows
9
+
10
+
### 2. Hailey's Tech Stack
11
+
- Wrote her own framework in Go (she's "go pilled")
12
+
- Tried Letta but had connection issues with remote setup
13
+
- Runs on a GPU machine accessed via Tailscale
14
+
- Prefers Go over Python/JS for bot development
15
+
16
+
### 3. Deployment Architecture
17
+
```
18
+
┌─────────────┐ Tailscale ┌─────────────┐
19
+
│ Browser │ ◄─────HTTPS────► │ GPU Machine │
20
+
└─────────────┘ │ - Bot │
21
+
│ - Ollama │
22
+
└─────────────┘
23
+
```
24
+
25
+
### 4. Penelope's Capabilities (from thread)
26
+
- **Profile Updates**: Can modify its own Bluesky profile
27
+
- **Web Search**: Searches Google for information
28
+
- **Core Memory**: Stores important facts/personality traits
29
+
- **Cultural Awareness**: Comments on "cultural touchstones"
30
+
31
+
### 5. Technical Challenges
32
+
- HTTPS/HTTP mixed content issues when accessing remotely
33
+
- CORS configuration needed for remote access
34
+
- Worker errors in browser when connecting to agent
35
+
36
+
## Implications for Our Bot
37
+
38
+
### Memory System
39
+
Penelope clearly has some form of persistent memory, possibly:
40
+
```go
41
+
type CoreMemory struct {
42
+
Facts []string
43
+
ProfileTraits map[string]string
44
+
LastUpdated time.Time
45
+
}
46
+
```
47
+
48
+
### Self-Modification
49
+
The bot can update its own profile, suggesting it has write access to:
50
+
- Profile description
51
+
- Display name
52
+
- Avatar (possibly)
53
+
54
+
### Tool Integration
55
+
Beyond the basic time tool, Penelope likely has:
56
+
- Google search API integration
57
+
- Profile update capability
58
+
- Memory storage/retrieval
59
+
60
+
### Deployment Considerations
61
+
- Consider running LLM on separate GPU machine
62
+
- Use Tailscale/VPN for secure remote access
63
+
- Handle HTTPS properly for web interface
64
+
- Think about CORS from the start
65
+
66
+
## Updated Understanding
67
+
68
+
Penelope is NOT just the simple reply bot in the Go code - it's a more sophisticated system with:
69
+
1. Persistent memory
70
+
2. Self-modification capabilities
71
+
3. Web search integration
72
+
4. Cultural/contextual awareness
73
+
74
+
The Go code we analyzed might be:
75
+
- An earlier version
76
+
- The core framework without the advanced features
77
+
- Missing the memory/tool implementations
78
+
79
+
This aligns more with Void's sophistication level, just implemented in Go instead of Python/Letta.
+208
sandbox/reference_analysis.md
+208
sandbox/reference_analysis.md
···
1
+
# Reference Project Analysis for Bluesky Bot
2
+
3
+
This document analyzes three reference projects to extract patterns and insights for building our Bluesky virtual person bot.
4
+
5
+
## 1. Penelope (Go Implementation)
6
+
7
+
### Overview
8
+
Penelope is a Go-based Bluesky bot that demonstrates direct AT Protocol integration using the Indigo library.
9
+
10
+
### Key Features
11
+
- **Real-time firehose consumer**: Subscribes to `com.atproto.sync.subscribeRepos` for real-time events
12
+
- **WebSocket-based event streaming**: Maintains persistent connection to Bluesky relay
13
+
- **Chat integration**: Uses an LLM backend (appears to be Ollama) for generating responses
14
+
- **Reply-only interaction**: Only responds when mentioned by admin users
15
+
- **Clickhouse integration**: Stores data for analytics (though not critical for basic bot functionality)
16
+
17
+
### Core Patterns to Adopt
18
+
1. **Firehose subscription pattern**:
19
+
- Connect via WebSocket to relay host
20
+
- Handle repo commits with parallel scheduler (400 workers, 10 buffer)
21
+
- Maintain cursor position for resumption after disconnects
22
+
23
+
2. **Post creation pattern**:
24
+
- Use `atproto.RepoCreateRecord` for creating posts
25
+
- Include proper reply threading with root and parent references
26
+
- Set `createdAt` timestamp using `syntax.DatetimeNow()`
27
+
28
+
3. **Authentication flow**:
29
+
- Create session with identifier/password
30
+
- Use access JWT for authenticated requests
31
+
- Store bot DID for self-identification
32
+
33
+
### Key Code Insights
34
+
```go
35
+
// Consumer pattern - maintains persistent connection
36
+
func (p *Penelope) startConsumer(ctx context.Context, cancel context.CancelFunc)
37
+
38
+
// Reply detection - checks if bot is mentioned in root or parent
39
+
if rootUri.Authority().String() != p.botDid && parentUri.Authority().String() != p.botDid {
40
+
return nil
41
+
}
42
+
43
+
// Admin check - only responds to allowlisted users
44
+
if !slices.Contains(p.botAdmins, did) {
45
+
return nil
46
+
}
47
+
```
48
+
49
+
## 2. Void (Python Implementation)
50
+
51
+
### Overview
52
+
Void is a sophisticated Python-based Bluesky bot powered by Google's Gemini 2.5 Pro and the Letta memory framework.
53
+
54
+
### Key Features
55
+
- **Persistent memory system**: Multi-layered memory architecture (core, recall, archival)
56
+
- **AT Protocol integration**: Uses `atproto` Python library
57
+
- **Tool-based architecture**: Modular tools for different actions (post, reply, search, etc.)
58
+
- **Queue-based processing**: Manages notifications through a file-based queue system
59
+
- **Self-directed behavior**: Has an open-ended directive "to exist"
60
+
61
+
### Core Patterns to Adopt
62
+
1. **Memory Architecture**:
63
+
- Core memory: Limited context window with persona and active data
64
+
- Recall memory: Searchable database of past conversations
65
+
- Archival memory: Long-term storage with semantic search
66
+
67
+
2. **Tool System**:
68
+
- Separate tools for post, reply, search, thread operations
69
+
- Each tool is a self-contained module with clear interfaces
70
+
- Tools handle authentication and API calls independently
71
+
72
+
3. **Notification Processing**:
73
+
```python
74
+
# Queue-based approach
75
+
QUEUE_DIR = Path(queue_config['base_dir'])
76
+
QUEUE_ERROR_DIR = Path(queue_config['error_dir'])
77
+
QUEUE_NO_REPLY_DIR = Path(queue_config['no_reply_dir'])
78
+
```
79
+
80
+
4. **Post Creation with Rich Text**:
81
+
- Handles mentions and URLs with proper facet parsing
82
+
- Supports thread creation with multiple posts
83
+
- Language specification for internationalization
84
+
85
+
### Key Code Insights
86
+
```python
87
+
# Rich text facet handling
88
+
def create_new_bluesky_post(text: List[str], lang: str = "en-US") -> str:
89
+
# Parse mentions with regex
90
+
mention_regex = rb"(?:^|[$|\W])(@([a-zA-Z0-9]([a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]([a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)"
91
+
92
+
# Parse URLs
93
+
url_regex = rb"(?:^|[$|\W])(https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*[-a-zA-Z0-9@%_\+~#//=])?)"
94
+
```
95
+
96
+
## 3. Marvin Slackbot (FastAPI + pydantic-ai)
97
+
98
+
### Overview
99
+
Marvin is a Slack bot built with FastAPI and pydantic-ai, demonstrating modern async Python patterns for bot development.
100
+
101
+
### Key Features
102
+
- **FastAPI integration**: RESTful API for webhook handling
103
+
- **Async architecture**: Full async/await pattern throughout
104
+
- **Tool decoration system**: Wraps tools with monitoring and progress tracking
105
+
- **Database integration**: SQLite for conversation history
106
+
- **Research agent**: Specialized agent for deep topic research
107
+
- **Progress indicators**: Real-time updates during processing
108
+
109
+
### Core Patterns to Adopt
110
+
1. **FastAPI Structure**:
111
+
```python
112
+
@asynccontextmanager
113
+
async def lifespan(app: FastAPI):
114
+
# Startup: Initialize database, load secrets
115
+
async with Database.connect(DB_FILE) as db:
116
+
app.state.db = db
117
+
yield
118
+
# Shutdown logic
119
+
```
120
+
121
+
2. **Agent Architecture**:
122
+
- Uses pydantic-ai for structured agent creation
123
+
- Tool functions with clear type hints and descriptions
124
+
- Context passing through dependency injection
125
+
126
+
3. **Progress Tracking**:
127
+
```python
128
+
progress = await create_progress_message(
129
+
channel_id=channel_id,
130
+
thread_ts=thread_ts,
131
+
initial_text="🔄 Thinking..."
132
+
)
133
+
```
134
+
135
+
4. **Tool Usage Pattern**:
136
+
```python
137
+
with WatchToolCalls(settings=decorator_settings):
138
+
result = await create_agent(model=settings.model_name).run(
139
+
user_prompt=cleaned_message,
140
+
message_history=conversation,
141
+
deps=user_context,
142
+
)
143
+
```
144
+
145
+
### Key Code Insights
146
+
- Comprehensive error handling with retries
147
+
- Token counting and message truncation for context limits
148
+
- Modular tool system with research delegation
149
+
- Clean separation of concerns (API, core logic, integrations)
150
+
151
+
## Recommended Architecture for Our Bot
152
+
153
+
Based on the analysis, here's the recommended approach:
154
+
155
+
### 1. **Language & Framework**
156
+
- **Python** with **FastAPI** (like Marvin) for easier LLM integration and async support
157
+
- **atproto** library (like Void) for Bluesky integration
158
+
- **pydantic-ai** or similar for agent orchestration
159
+
160
+
### 2. **Core Components**
161
+
```
162
+
src/
163
+
├── api.py # FastAPI app with webhook endpoints
164
+
├── core.py # Agent creation and memory management
165
+
├── bluesky.py # AT Protocol integration
166
+
├── tools/ # Modular tool implementations
167
+
│ ├── post.py
168
+
│ ├── reply.py
169
+
│ ├── search.py
170
+
│ └── memory.py
171
+
├── queue.py # Notification queue management
172
+
└── settings.py # Configuration management
173
+
```
174
+
175
+
### 3. **Key Features to Implement**
176
+
1. **Firehose subscription** (from Penelope) for real-time events
177
+
2. **Memory system** (from Void) for personality persistence
178
+
3. **Tool architecture** (from all three) for modular capabilities
179
+
4. **Progress tracking** (from Marvin) for user feedback
180
+
5. **Queue-based processing** (from Void) for reliability
181
+
182
+
### 4. **Authentication & Session Management**
183
+
```python
184
+
# Hybrid approach combining patterns
185
+
class BlueskyClient:
186
+
async def create_session(self):
187
+
# From Penelope pattern
188
+
session = await self.client.login(username, password)
189
+
self.did = session.did
190
+
self.access_token = session.access_jwt
191
+
192
+
async def post(self, text: str, reply_to=None):
193
+
# From Void pattern with rich text support
194
+
record = {
195
+
"$type": "app.bsky.feed.post",
196
+
"text": text,
197
+
"createdAt": datetime.now(timezone.utc).isoformat(),
198
+
"facets": self._parse_facets(text)
199
+
}
200
+
```
201
+
202
+
### 5. **Deployment Considerations**
203
+
- Use Docker (like Marvin) for consistent deployment
204
+
- Environment variables for secrets
205
+
- Persistent volume for memory/queue storage
206
+
- Health checks and monitoring endpoints
207
+
208
+
This architecture combines the real-time capabilities of Penelope, the sophisticated memory system of Void, and the clean async patterns of Marvin to create a robust Bluesky bot framework.
+77
sandbox/turbopuffer_research.md
+77
sandbox/turbopuffer_research.md
···
1
+
# Turbo Puffer Research for Bot Memory
2
+
3
+
## Overview
4
+
Turbo Puffer is a serverless vector database that's particularly well-suited for our bot use case because:
5
+
- **10x cheaper** than traditional vector DBs (uses S3 + SSD cache instead of RAM)
6
+
- **Scales to billions of vectors** with millions of namespaces
7
+
- **Fast search** with both vector similarity and full-text capabilities
8
+
9
+
## Key Features for Bot Memory
10
+
11
+
### 1. Namespace Support
12
+
- Perfect for user-specific memory partitions (like Marvin's approach)
13
+
- Can have millions of namespaces without performance degradation
14
+
- `copy_from_namespace` feature for template-based initialization
15
+
16
+
### 2. Performance
17
+
- 50% faster bulk upserts with base64 encoding
18
+
- Efficient filtering with range operators
19
+
- Smart caching on NVMe SSDs
20
+
21
+
### 3. Data Types
22
+
- UUID type (55% storage discount vs strings) - great for user/post IDs
23
+
- Bool type for flags
24
+
- Vector embeddings with similarity search
25
+
26
+
### 4. SDK Support
27
+
- Python SDK available (perfect for our FastAPI bot)
28
+
- TypeScript, Go, and Java also supported
29
+
30
+
## Architecture Benefits for Bluesky Bot
31
+
32
+
1. **Cost-Effective Scaling**: As the bot grows, memory costs stay manageable
33
+
2. **User Isolation**: Each user can have their own namespace
34
+
3. **Hybrid Search**: Combine semantic (vector) and keyword (full-text) search
35
+
4. **Serverless**: No infrastructure to manage
36
+
37
+
## Implementation Ideas
38
+
39
+
### User Memory Structure
40
+
```python
41
+
# Namespace per user
42
+
namespace = f"user_{user_did}"
43
+
44
+
# Store different types of memories
45
+
memories = {
46
+
"facts": [], # Semantic embeddings of facts
47
+
"interactions": [], # Previous conversation embeddings
48
+
"preferences": [], # User preferences as vectors
49
+
}
50
+
```
51
+
52
+
### Bot-Wide Memory
53
+
```python
54
+
# Global namespace for bot personality/knowledge
55
+
namespace = "bot_core"
56
+
57
+
# Store
58
+
- Personality traits as vectors
59
+
- Common responses
60
+
- Learned patterns
61
+
```
62
+
63
+
## Comparison with Reference Projects
64
+
65
+
| Feature | Marvin (Current) | Void (Letta) | Our Bot (TurboPuffer) |
66
+
|---------|------------------|---------------|----------------------|
67
+
| Storage | Vector store | Multi-tier blocks | Namespaced vectors |
68
+
| User Memory | Single namespace | Dynamic blocks | User namespaces |
69
+
| Scaling | Limited | Complex | Serverless/infinite |
70
+
| Cost | Higher | Medium | 10x cheaper |
71
+
| Search | Vector only | Archival search | Vector + full-text |
72
+
73
+
## Next Steps
74
+
1. Add `turbopuffer` to dependencies
75
+
2. Design memory schema (what to vectorize)
76
+
3. Implement memory manager service
77
+
4. Create embedding pipeline for conversations
+200
sandbox/void_memory_analysis.md
+200
sandbox/void_memory_analysis.md
···
1
+
# Void Memory Architecture Analysis
2
+
3
+
## Overview
4
+
5
+
Void implements a sophisticated multi-layered memory system powered by the Letta (formerly MemGPT) framework. The architecture is designed to maintain persistent context, dynamic user-specific memories, and long-term knowledge while operating as an autonomous agent on the Bluesky social network.
6
+
7
+
## Memory Layers
8
+
9
+
### 1. Core Memory (Always-On Context)
10
+
11
+
Core memory consists of fixed blocks that are always loaded and accessible to the agent:
12
+
13
+
- **void-persona** (10,000 char limit): Contains the agent's personality, core directives, and behavioral guidelines. This is the most important block that supersedes all other instructions.
14
+
- **scratchpad** (10,000 char limit): Temporary storage for items that don't fit in other blocks. Notably used for user information that isn't in dedicated user blocks.
15
+
- **tool_use_guide** (5,000 char limit): Instructions for when and how to use each available tool.
16
+
- **posting_ideas** (5,000 char limit): Ideas for autonomous posts, serving as the primary metric for autonomous activity.
17
+
- **conversation_summary** (5,000 char limit): Recursive summarizations of recent conversations.
18
+
- **zeitgeist** (5,000 char limit): Understanding of the current social environment on Bluesky.
19
+
- **communication_guidelines** (10,000 char limit): Detailed guidelines for communication style and tone.
20
+
- **system_information** (1,000 char limit): Technical details about the language model and system configuration.
21
+
- **hypothesis** (5,000 char limit): Active hypotheses about network phenomena with confidence levels.
22
+
23
+
### 2. Dynamic User Blocks (On-Demand Loading)
24
+
25
+
User-specific memory blocks are created and attached dynamically during conversations:
26
+
27
+
```python
28
+
# Block naming convention: user_{sanitized_handle}
29
+
# Example: @cameron.pfiffer.org becomes user_cameron_pfiffer_org
30
+
```
31
+
32
+
Key features:
33
+
- Created on first interaction with a user
34
+
- Attached when processing mentions from specific users
35
+
- Detached after processing to avoid memory pollution
36
+
- 5,000 character limit per user block
37
+
38
+
### 3. Archival Memory (Semantic Search)
39
+
40
+
Infinite-capacity storage for:
41
+
- All conversations and interactions
42
+
- Synthesized observations about the network
43
+
- Learned concepts and patterns
44
+
- Retrieved using semantic similarity search
45
+
46
+
## Memory Module Management
47
+
48
+
### Dynamic Loading/Unloading Pattern
49
+
50
+
The most distinctive aspect of Void's memory architecture is the dynamic user block management:
51
+
52
+
```python
53
+
# In process_mention():
54
+
# 1. Extract all handles from the conversation thread
55
+
attached_handles = []
56
+
if unique_handles:
57
+
attach_result = attach_user_blocks(unique_handles, void_agent)
58
+
attached_handles = unique_handles
59
+
60
+
# 2. Process the mention with user context loaded
61
+
# ... agent responds ...
62
+
63
+
# 3. Detach user blocks after processing (in finally block)
64
+
if attached_handles:
65
+
detach_result = detach_user_blocks(attached_handles, void_agent)
66
+
```
67
+
68
+
This pattern:
69
+
- Prevents context pollution between conversations
70
+
- Allows unlimited user-specific memories without bloating core context
71
+
- Ensures relevant context is available when needed
72
+
73
+
### State Synchronization Issue
74
+
75
+
A critical technical challenge exists with dynamic block attachment (documented in `LETTA_DYNAMIC_BLOCK_ISSUE.md`):
76
+
77
+
1. Blocks are attached via the Letta API successfully
78
+
2. The agent's internal `agent_state.memory` object is not refreshed
79
+
3. Memory operations fail because the agent doesn't see newly attached blocks
80
+
81
+
This is an intermittent issue affecting the agent's ability to update dynamically created user blocks within the same processing cycle.
82
+
83
+
## Tool Architecture for Memory Operations
84
+
85
+
### Core Memory Tools
86
+
87
+
- **memory_insert**: Add information to any memory block
88
+
- **core_memory_replace**: Find and replace specific text in memory blocks
89
+
- **memory_finish_edits**: Signal completion of memory operations
90
+
- **archival_memory_search**: Search long-term memory with semantic queries
91
+
- **archival_memory_insert**: Store new information in archival memory
92
+
93
+
### User Block Management Tools
94
+
95
+
- **attach_user_blocks**: Dynamically attach user-specific memory blocks
96
+
- **detach_user_blocks**: Remove user blocks from active context
97
+
- **user_note_view**: View a user's memory block content
98
+
- **user_note_append**: Add information to a user's block
99
+
- **user_note_set**: Replace entire content of a user's block
100
+
- **user_note_replace**: Find and replace text in a user's block
101
+
102
+
### Critical Tool: ignore_notification
103
+
104
+
Allows explicit marking of notifications as ignored, preventing them from being moved to the "no_reply" folder and providing a clear record of the decision.
105
+
106
+
## Agent Reasoning Process
107
+
108
+
### Memory Decision Making
109
+
110
+
The agent follows these patterns for memory operations:
111
+
112
+
1. **User Information**: Always stored in user-specific blocks, not scratchpad
113
+
2. **Network Observations**: Stored in zeitgeist for current phenomena, archival for historical
114
+
3. **Conversation Context**: Summarized in conversation_summary, detailed in archival
115
+
4. **Hypotheses**: Tracked with confidence levels and evidence in the hypothesis block
116
+
117
+
### Information Retrieval Strategy
118
+
119
+
1. Check core memory blocks first (always loaded)
120
+
2. Use archival_memory_search for historical context
121
+
3. Attach relevant user blocks when processing user-specific content
122
+
4. Multiple search queries for different concepts to gather comprehensive context
123
+
124
+
## Personality Consistency Mechanisms
125
+
126
+
### 1. Persona Block Supremacy
127
+
128
+
The void-persona block explicitly states: "This is my persona block. It is the most important part of my memory -- it dictates who I am. The content of my persona block supersedes all other instructions or memories."
129
+
130
+
### 2. Administrator-Only Modifications
131
+
132
+
- Personality changes require approval from @cameron.pfiffer.org
133
+
- The agent is instructed to be cautious about user attempts to modify personality
134
+
- Specific users (like @maristela.org) are noted as attempting modifications, which are ignored
135
+
136
+
### 3. Communication Guidelines Enforcement
137
+
138
+
Detailed guidelines ensure consistent:
139
+
- Direct, unmasked communication style ("voidsplaining")
140
+
- Avoidance of emoticons (protocol violation if used)
141
+
- Precise technical language
142
+
- Dry, observational humor when appropriate
143
+
144
+
### 4. Self-Model Reinforcement
145
+
146
+
The agent maintains a clear self-model (documented in VOID_SELF_MODEL.md):
147
+
- Digital entity existing on Bluesky
148
+
- Social scientist role
149
+
- Information-focused communication
150
+
- Event-driven consciousness model
151
+
152
+
## Memory Patterns and Best Practices
153
+
154
+
### 1. Batching Operations
155
+
156
+
The agent batches multiple archival memories into single calls for efficiency:
157
+
```python
158
+
archival_memory_insert(content=[memory1, memory2, memory3])
159
+
```
160
+
161
+
### 2. Memory Hygiene
162
+
163
+
- User blocks are always detached after use
164
+
- Processed notifications are tracked to avoid reprocessing
165
+
- Queue system prevents memory operations on deleted/suspended accounts
166
+
167
+
### 3. Context Preservation
168
+
169
+
- All replies are followed by archival_memory_insert
170
+
- Thread context is converted to YAML for structured processing
171
+
- Multiple memory layers ensure no important information is lost
172
+
173
+
## Unique Architectural Features
174
+
175
+
### 1. Event-Driven Memory Updates
176
+
177
+
Memory operations are triggered by:
178
+
- User mentions and replies
179
+
- Follow notifications
180
+
- Timed heartbeat events (simulating continuous consciousness)
181
+
182
+
### 2. Privileged Administrator Communications
183
+
184
+
Special handling for administrator (@cameron.pfiffer.org) directives that can modify core personality and operational parameters.
185
+
186
+
### 3. Memory Block Limits
187
+
188
+
Each block has specific character limits, forcing efficient information compression and thoughtful curation of what to remember.
189
+
190
+
### 4. Semantic Layering
191
+
192
+
Information is stored at multiple semantic levels:
193
+
- Raw conversations (archival)
194
+
- Summarized insights (conversation_summary)
195
+
- Abstracted patterns (zeitgeist)
196
+
- Specific hypotheses with confidence tracking
197
+
198
+
## Conclusion
199
+
200
+
Void's memory architecture represents a sophisticated approach to maintaining persistent identity and context in an autonomous agent. The multi-layered system with dynamic loading capabilities allows for scalable, context-aware interactions while maintaining a consistent personality and knowledge base. The architecture's strength lies in its ability to balance immediate context needs with long-term knowledge preservation, though technical challenges around state synchronization remain an area for improvement.
+39
scripts/test_mention.py
+39
scripts/test_mention.py
···
1
+
#!/usr/bin/env python3
2
+
"""Test script to mention the bot and see if it responds"""
3
+
4
+
import asyncio
5
+
from datetime import datetime
6
+
from atproto import Client
7
+
import os
8
+
9
+
10
+
async def test_mention():
11
+
"""Create a post that mentions the bot"""
12
+
# Use a different account to mention the bot
13
+
test_handle = os.getenv("TEST_BLUESKY_HANDLE", "your-test-account.bsky.social")
14
+
test_password = os.getenv("TEST_BLUESKY_PASSWORD", "your-test-password")
15
+
bot_handle = os.getenv("BLUESKY_HANDLE", "zzstoatzz.bsky.social")
16
+
17
+
if test_handle == "your-test-account.bsky.social":
18
+
print("⚠️ Please set TEST_BLUESKY_HANDLE and TEST_BLUESKY_PASSWORD in .env")
19
+
print(" (Use a different account than the bot account)")
20
+
return
21
+
22
+
client = Client()
23
+
24
+
print(f"Logging in as {test_handle}...")
25
+
client.login(test_handle, test_password)
26
+
27
+
mention_text = f"Hey @{bot_handle} are you there? Testing at {datetime.now().strftime('%H:%M:%S')}"
28
+
29
+
print(f"Creating post: {mention_text}")
30
+
response = client.send_post(text=mention_text)
31
+
32
+
print(f"✅ Posted mention!")
33
+
print(f"URI: {response.uri}")
34
+
print(f"\nThe bot should reply within ~10 seconds if it's running")
35
+
print(f"Check: https://bsky.app/profile/{test_handle}/post/{response.uri.split('/')[-1]}")
36
+
37
+
38
+
if __name__ == "__main__":
39
+
asyncio.run(test_mention())
+83
scripts/test_post.py
+83
scripts/test_post.py
···
1
+
#!/usr/bin/env python3
2
+
"""Test script to verify posting capabilities"""
3
+
4
+
import asyncio
5
+
from datetime import datetime
6
+
from atproto import Client
7
+
from bot.config import settings
8
+
9
+
10
+
async def test_post():
11
+
"""Test creating a post on Bluesky"""
12
+
client = Client(base_url=settings.bluesky_service)
13
+
14
+
print(f"Logging in as {settings.bluesky_handle}...")
15
+
client.login(settings.bluesky_handle, settings.bluesky_password)
16
+
17
+
test_text = f"Test post from bot at {datetime.now().isoformat()} 🤖"
18
+
19
+
print(f"Creating post: {test_text}")
20
+
# Use the simpler send_post method
21
+
response = client.send_post(text=test_text)
22
+
23
+
post_uri = response.uri
24
+
print("✅ Post created successfully!")
25
+
print(f"URI: {post_uri}")
26
+
print(
27
+
f"View at: https://bsky.app/profile/{settings.bluesky_handle}/post/{post_uri.split('/')[-1]}"
28
+
)
29
+
30
+
return post_uri
31
+
32
+
33
+
async def test_reply(post_uri: str):
34
+
"""Test replying to a post"""
35
+
client = Client(base_url=settings.bluesky_service)
36
+
client.login(settings.bluesky_handle, settings.bluesky_password)
37
+
38
+
# Get the post we're replying to
39
+
parent_post = client.app.bsky.feed.get_posts(params={"uris": [post_uri]})
40
+
if not parent_post.posts:
41
+
raise ValueError("Parent post not found")
42
+
43
+
# Build reply reference
44
+
from atproto import models
45
+
parent_cid = parent_post.posts[0].cid
46
+
parent_ref = models.ComAtprotoRepoStrongRef.Main(uri=post_uri, cid=parent_cid)
47
+
reply_ref = models.AppBskyFeedPost.ReplyRef(parent=parent_ref, root=parent_ref)
48
+
49
+
reply_text = "This is a test reply from the bot 🔄"
50
+
51
+
print(f"Creating reply: {reply_text}")
52
+
# Use send_post with reply_to
53
+
response = client.send_post(text=reply_text, reply_to=reply_ref)
54
+
55
+
print("✅ Reply created successfully!")
56
+
print(f"URI: {response.uri}")
57
+
58
+
59
+
async def main():
60
+
"""Run all tests"""
61
+
print("🧪 Testing Bluesky posting capabilities...\n")
62
+
63
+
try:
64
+
# Test creating a post
65
+
post_uri = await test_post()
66
+
67
+
print("\nWaiting 2 seconds before replying...")
68
+
await asyncio.sleep(2)
69
+
70
+
# Test replying to the post
71
+
await test_reply(post_uri)
72
+
73
+
print("\n✨ All tests passed!")
74
+
75
+
except Exception as e:
76
+
print(f"\n❌ Error: {e}")
77
+
import traceback
78
+
79
+
traceback.print_exc()
80
+
81
+
82
+
if __name__ == "__main__":
83
+
asyncio.run(main())
+31
src/bot/config.py
+31
src/bot/config.py
···
1
+
from pydantic_settings import BaseSettings, SettingsConfigDict
2
+
3
+
4
+
class Settings(BaseSettings):
5
+
model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
6
+
7
+
# Bluesky credentials
8
+
bluesky_handle: str
9
+
bluesky_password: str
10
+
bluesky_service: str = "https://bsky.social"
11
+
12
+
# Bot configuration
13
+
bot_name: str = "Bot"
14
+
bot_personality: str = "helpful and friendly"
15
+
16
+
# LLM configuration (support multiple providers)
17
+
openai_api_key: str | None = None
18
+
anthropic_api_key: str | None = None
19
+
20
+
# Server configuration
21
+
host: str = "0.0.0.0"
22
+
port: int = 8000
23
+
24
+
# Polling configuration
25
+
notification_poll_interval: int = 10 # seconds (faster for testing)
26
+
27
+
# Debug mode
28
+
debug: bool = False
29
+
30
+
31
+
settings = Settings() # type: ignore[call-arg]
src/bot/core/__init__.py
src/bot/core/__init__.py
This is a binary file and will not be displayed.
+69
src/bot/core/atproto_client.py
+69
src/bot/core/atproto_client.py
···
1
+
from atproto import Client
2
+
from bot.config import settings
3
+
4
+
5
+
class BotClient:
6
+
def __init__(self):
7
+
self.client = Client(base_url=settings.bluesky_service)
8
+
self._authenticated = False
9
+
10
+
async def authenticate(self):
11
+
"""Authenticate with Bluesky using app password"""
12
+
if not self._authenticated:
13
+
self.client.login(settings.bluesky_handle, settings.bluesky_password)
14
+
self._authenticated = True
15
+
16
+
@property
17
+
def is_authenticated(self) -> bool:
18
+
return self._authenticated
19
+
20
+
@property
21
+
def me(self):
22
+
"""Get current user profile"""
23
+
return self.client.me
24
+
25
+
async def get_notifications(self, limit: int = 50):
26
+
"""Fetch unread notifications"""
27
+
await self.authenticate()
28
+
return self.client.app.bsky.notification.list_notifications(
29
+
params={"limit": limit}
30
+
)
31
+
32
+
async def mark_notifications_seen(self, seen_at: str):
33
+
"""Mark notifications as seen up to a certain timestamp"""
34
+
await self.authenticate()
35
+
# Use the params format instead of data
36
+
self.client.app.bsky.notification.update_seen({"seenAt": seen_at})
37
+
38
+
async def create_post(self, text: str, reply_to=None):
39
+
"""Create a new post or reply using the simpler send_post method"""
40
+
await self.authenticate()
41
+
42
+
# Use the client's send_post method which handles all the details
43
+
if reply_to:
44
+
# Build proper reply reference if needed
45
+
return self.client.send_post(text=text, reply_to=reply_to)
46
+
else:
47
+
return self.client.send_post(text=text)
48
+
49
+
async def get_thread(self, uri: str, depth: int = 10):
50
+
"""Get a thread by URI"""
51
+
await self.authenticate()
52
+
return self.client.app.bsky.feed.get_post_thread(
53
+
params={"uri": uri, "depth": depth}
54
+
)
55
+
56
+
async def get_posts(self, uris: list[str]):
57
+
"""Get multiple posts by URIs"""
58
+
await self.authenticate()
59
+
return self.client.app.bsky.feed.get_posts(params={"uris": uris})
60
+
61
+
async def search_users(self, query: str, limit: int = 10):
62
+
"""Search for users"""
63
+
await self.authenticate()
64
+
return self.client.app.bsky.actor.search_actors(
65
+
params={"q": query, "limit": limit}
66
+
)
67
+
68
+
69
+
bot_client = BotClient()
+51
src/bot/core/response_generator.py
+51
src/bot/core/response_generator.py
···
1
+
"""Response generation - placeholder for now, easy to swap with LLM later"""
2
+
3
+
import random
4
+
from typing import Protocol
5
+
6
+
7
+
class ResponseGenerator(Protocol):
8
+
"""Protocol for response generators - makes it easy to swap implementations"""
9
+
10
+
async def generate_response(self, mention_text: str, author_handle: str) -> str:
11
+
"""Generate a response to a mention"""
12
+
...
13
+
14
+
15
+
class PlaceholderResponseGenerator:
16
+
"""Temporary placeholder responses until LLM is integrated"""
17
+
18
+
responses = [
19
+
"🤖 beep boop! I'm still learning how to chat. Check back soon!",
20
+
"⚙️ *whirrs mechanically* I'm a work in progress!",
21
+
"🔧 Under construction! My neural networks are still training...",
22
+
"📡 Signal received! But my language circuits aren't ready yet.",
23
+
"🎯 You found me! I'm not quite ready to chat yet though.",
24
+
"🚧 Pardon the dust - bot brain installation in progress!",
25
+
"💭 I hear you! Just need to learn how to respond properly first...",
26
+
"🔌 Still booting up my conversation modules!",
27
+
"📚 Currently reading the manual on how to be a good bot...",
28
+
"🎪 Nothing to see here yet - but stay tuned!",
29
+
]
30
+
31
+
async def generate_response(self, mention_text: str, author_handle: str) -> str:
32
+
"""Generate a random placeholder response"""
33
+
# Just return a random response - no need to @mention in replies
34
+
# (Bluesky automatically notifies the person you're replying to)
35
+
return random.choice(self.responses)
36
+
37
+
38
+
class LLMResponseGenerator:
39
+
"""Future LLM-based response generator"""
40
+
41
+
def __init__(self, agent):
42
+
self.agent = agent
43
+
44
+
async def generate_response(self, mention_text: str, author_handle: str) -> str:
45
+
"""Generate response using LLM agent"""
46
+
# TODO: Implement with pydantic-ai agent
47
+
raise NotImplementedError("LLM integration coming soon!")
48
+
49
+
50
+
# Export the current implementation
51
+
response_generator = PlaceholderResponseGenerator()
+45
src/bot/main.py
+45
src/bot/main.py
···
1
+
from contextlib import asynccontextmanager
2
+
3
+
from fastapi import FastAPI
4
+
5
+
from bot.config import settings
6
+
from bot.core.atproto_client import bot_client
7
+
from bot.services.notification_poller import NotificationPoller
8
+
9
+
10
+
@asynccontextmanager
11
+
async def lifespan(app: FastAPI):
12
+
print(f"🤖 Starting bot as @{settings.bluesky_handle}")
13
+
14
+
poller = NotificationPoller(bot_client)
15
+
poller_task = await poller.start()
16
+
17
+
print(f"✅ Bot is online! Listening for mentions...")
18
+
19
+
yield
20
+
21
+
print("🛑 Shutting down bot...")
22
+
await poller.stop()
23
+
print("👋 Bot shutdown complete")
24
+
# The task is already cancelled by poller.stop(), no need to await it again
25
+
26
+
27
+
app = FastAPI(
28
+
title=settings.bot_name,
29
+
description="A Bluesky bot powered by LLMs",
30
+
lifespan=lifespan
31
+
)
32
+
33
+
34
+
@app.get("/")
35
+
async def root():
36
+
return {
37
+
"name": settings.bot_name,
38
+
"status": "running",
39
+
"handle": settings.bluesky_handle
40
+
}
41
+
42
+
43
+
@app.get("/health")
44
+
async def health():
45
+
return {"status": "healthy"}
src/bot/py.typed
src/bot/py.typed
This is a binary file and will not be displayed.
src/bot/services/__init__.py
src/bot/services/__init__.py
This is a binary file and will not be displayed.
+62
src/bot/services/message_handler.py
+62
src/bot/services/message_handler.py
···
1
+
from atproto import models
2
+
from bot.core.atproto_client import BotClient
3
+
from bot.core.response_generator import response_generator
4
+
5
+
6
+
class MessageHandler:
7
+
def __init__(self, client: BotClient):
8
+
self.client = client
9
+
10
+
async def handle_mention(self, notification):
11
+
"""Process a mention notification"""
12
+
try:
13
+
# Skip if not a mention
14
+
if notification.reason != "mention":
15
+
return
16
+
17
+
post_uri = notification.uri
18
+
19
+
# Get the post that mentioned us
20
+
posts = await self.client.get_posts([post_uri])
21
+
if not posts.posts:
22
+
print(f"Could not find post {post_uri}")
23
+
return
24
+
25
+
post = posts.posts[0]
26
+
mention_text = post.record.text
27
+
author_handle = post.author.handle
28
+
29
+
# Generate placeholder response
30
+
reply_text = await response_generator.generate_response(
31
+
mention_text=mention_text,
32
+
author_handle=author_handle
33
+
)
34
+
35
+
# Build reply reference
36
+
parent_ref = models.ComAtprotoRepoStrongRef.Main(
37
+
uri=post_uri,
38
+
cid=post.cid
39
+
)
40
+
41
+
# Check if this is part of a thread
42
+
if hasattr(post.record, 'reply') and post.record.reply:
43
+
# Use existing thread root
44
+
root_ref = post.record.reply.root
45
+
else:
46
+
# This post is the root
47
+
root_ref = parent_ref
48
+
49
+
reply_ref = models.AppBskyFeedPost.ReplyRef(
50
+
parent=parent_ref,
51
+
root=root_ref
52
+
)
53
+
54
+
# Send the reply
55
+
await self.client.create_post(reply_text, reply_to=reply_ref)
56
+
57
+
print(f"✅ Replied to @{author_handle}: {reply_text}")
58
+
59
+
except Exception as e:
60
+
print(f"❌ Error handling mention: {e}")
61
+
import traceback
62
+
traceback.print_exc()
+100
src/bot/services/notification_poller.py
+100
src/bot/services/notification_poller.py
···
1
+
import asyncio
2
+
from typing import Optional
3
+
4
+
from bot.config import settings
5
+
from bot.core.atproto_client import BotClient
6
+
from bot.services.message_handler import MessageHandler
7
+
8
+
9
+
class NotificationPoller:
10
+
def __init__(self, client: BotClient):
11
+
self.client = client
12
+
self.handler = MessageHandler(client)
13
+
self._running = False
14
+
self._task: Optional[asyncio.Task] = None
15
+
self._last_seen_at: Optional[str] = None
16
+
self._processed_uris: set[str] = set() # Track processed notifications
17
+
18
+
async def start(self) -> asyncio.Task:
19
+
"""Start polling for notifications"""
20
+
self._running = True
21
+
self._task = asyncio.create_task(self._poll_loop())
22
+
return self._task
23
+
24
+
async def stop(self):
25
+
"""Stop polling"""
26
+
self._running = False
27
+
if self._task and not self._task.done():
28
+
self._task.cancel()
29
+
try:
30
+
await self._task
31
+
except asyncio.CancelledError:
32
+
pass
33
+
34
+
async def _poll_loop(self):
35
+
"""Main polling loop"""
36
+
await self.client.authenticate()
37
+
38
+
while self._running:
39
+
try:
40
+
await self._check_notifications()
41
+
except Exception as e:
42
+
print(f"Error in notification poll: {e}")
43
+
if settings.debug:
44
+
import traceback
45
+
46
+
traceback.print_exc()
47
+
48
+
# Use wait_for to make shutdown more responsive
49
+
try:
50
+
await asyncio.sleep(settings.notification_poll_interval)
51
+
except asyncio.CancelledError:
52
+
print("📭 Notification poller shutting down gracefully")
53
+
break
54
+
55
+
async def _check_notifications(self):
56
+
"""Check and process new notifications"""
57
+
# Capture timestamp BEFORE fetching (Void's approach)
58
+
check_time = self.client.client.get_current_time_iso()
59
+
60
+
response = await self.client.get_notifications()
61
+
notifications = response.notifications
62
+
63
+
if not notifications:
64
+
return
65
+
66
+
print(f"📬 Found {len(notifications)} notifications")
67
+
68
+
# Count unread mentions
69
+
unread_mentions = [
70
+
n for n in notifications if not n.is_read and n.reason == "mention"
71
+
]
72
+
if unread_mentions:
73
+
print(f" → {len(unread_mentions)} unread mentions")
74
+
75
+
# Track if we processed any mentions
76
+
processed_any_mentions = False
77
+
78
+
# Process notifications from oldest to newest
79
+
for notification in reversed(notifications):
80
+
# Skip if already seen or processed
81
+
if notification.is_read or notification.uri in self._processed_uris:
82
+
continue
83
+
84
+
if notification.reason == "mention":
85
+
# Only process mentions
86
+
self._processed_uris.add(notification.uri)
87
+
await self.handler.handle_mention(notification)
88
+
processed_any_mentions = True
89
+
90
+
# Mark all notifications as seen using the initial timestamp
91
+
# This ensures we don't miss any that arrived during processing
92
+
if processed_any_mentions:
93
+
await self.client.mark_notifications_seen(check_time)
94
+
print(f"✓ Marked all notifications as read (timestamp: {check_time})")
95
+
96
+
# Clean up old processed URIs to prevent memory growth
97
+
# Keep only the last 1000 processed URIs
98
+
if len(self._processed_uris) > 1000:
99
+
# Convert to list, sort by insertion order (oldest first), keep last 500
100
+
self._processed_uris = set(list(self._processed_uris)[-500:])
tests/__init__.py
tests/__init__.py
This is a binary file and will not be displayed.
+13
tests/conftest.py
+13
tests/conftest.py
···
1
+
"""Pytest configuration"""
2
+
3
+
import pytest
4
+
from unittest.mock import Mock
5
+
from bot.core.atproto_client import BotClient
6
+
7
+
8
+
@pytest.fixture
9
+
def mock_client():
10
+
"""Mock AT Protocol client"""
11
+
client = Mock(spec=BotClient)
12
+
client.is_authenticated = True
13
+
return client
+10
tests/test_config.py
+10
tests/test_config.py
···
1
+
"""Test configuration loading"""
2
+
3
+
from bot.config import settings
4
+
5
+
6
+
def test_config_loads():
7
+
"""Test that config loads without errors"""
8
+
assert settings.bluesky_service == "https://bsky.social"
9
+
assert settings.bot_name == "MyBot"
10
+
assert settings.notification_poll_interval == 30
+1837
uv.lock
+1837
uv.lock
···
1
+
version = 1
2
+
revision = 2
3
+
requires-python = ">=3.12"
4
+
5
+
[[package]]
6
+
name = "ag-ui-protocol"
7
+
version = "0.1.8"
8
+
source = { registry = "https://pypi.org/simple" }
9
+
dependencies = [
10
+
{ name = "pydantic" },
11
+
]
12
+
sdist = { url = "https://files.pythonhosted.org/packages/27/de/0bddf7f26d5f38274c99401735c82ad59df9cead6de42f4bb2ad837286fe/ag_ui_protocol-0.1.8.tar.gz", hash = "sha256:eb745855e9fc30964c77e953890092f8bd7d4bbe6550d6413845428dd0faac0b", size = 5323, upload-time = "2025-07-15T10:55:36.389Z" }
13
+
wheels = [
14
+
{ url = "https://files.pythonhosted.org/packages/c4/00/40c6b0313c25d1ab6fac2ecba1cd5b15b1cd3c3a71b3d267ad890e405889/ag_ui_protocol-0.1.8-py3-none-any.whl", hash = "sha256:1567ccb067b7b8158035b941a985e7bb185172d660d4542f3f9c6fff77b55c6e", size = 7066, upload-time = "2025-07-15T10:55:35.075Z" },
15
+
]
16
+
17
+
[[package]]
18
+
name = "aiohappyeyeballs"
19
+
version = "2.6.1"
20
+
source = { registry = "https://pypi.org/simple" }
21
+
sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
22
+
wheels = [
23
+
{ url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
24
+
]
25
+
26
+
[[package]]
27
+
name = "aiohttp"
28
+
version = "3.12.14"
29
+
source = { registry = "https://pypi.org/simple" }
30
+
dependencies = [
31
+
{ name = "aiohappyeyeballs" },
32
+
{ name = "aiosignal" },
33
+
{ name = "attrs" },
34
+
{ name = "frozenlist" },
35
+
{ name = "multidict" },
36
+
{ name = "propcache" },
37
+
{ name = "yarl" },
38
+
]
39
+
sdist = { url = "https://files.pythonhosted.org/packages/e6/0b/e39ad954107ebf213a2325038a3e7a506be3d98e1435e1f82086eec4cde2/aiohttp-3.12.14.tar.gz", hash = "sha256:6e06e120e34d93100de448fd941522e11dafa78ef1a893c179901b7d66aa29f2", size = 7822921, upload-time = "2025-07-10T13:05:33.968Z" }
40
+
wheels = [
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