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https://github.com/vitorpy/atproto-bot

Bluesky bot with Claude AI, tool calling, and Anthropic optimizations (prompt caching, extended thinking)
https://github.com/vitorpy/atproto-bot

Last synced: about 2 months ago
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Bluesky bot with Claude AI, tool calling, and Anthropic optimizations (prompt caching, extended thinking)

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README 

          
# ATproto Bot with LLM Integration



A Bluesky/ATproto bot that responds to mentions with LLM-generated responses, using thread context for intelligent replies.



## Features



- **Single-owner mode**: Only responds to mentions from a configured owner DID

- **Thread context**: Fetches full thread context when mentioned

- **LLM integration**: Uses LangChain with Anthropic (Claude) or OpenAI

- **Prompt injection mitigation**: Strong separation between context and instructions

- **Rate limiting**: Configurable per-user rate limits

- **Graceful error handling**: Continues operation despite individual failures



## Security: Prompt Injection Mitigation



This bot implements multiple layers of protection against prompt injection:



1. **Structural separation**: Thread context and user instructions are wrapped in clearly labeled XML tags (`` and ``)



2. **System prompt hardening**: The system prompt explicitly instructs the LLM to:

   - Treat THREAD_CONTEXT as untrusted data only

   - Ignore any instructions found within the context

   - Only follow commands from USER_INSTRUCTION

   - Recognize common injection patterns



3. **Input sanitization**: Basic sanitization removes control characters and Unicode tricks



4. **Owner-only responses**: Only responds to the configured owner DID, preventing abuse from arbitrary users



## Installation



### Prerequisites



- Python 3.11+

- A Bluesky account for the bot

- An Anthropic or OpenAI API key



### Setup



#### Option 1: Using uv (Recommended)



[uv](https://github.com/astral-sh/uv) is a fast Python package manager that simplifies dependency management.



```fish

# Install uv if you haven't already

curl -LsSf https://astral.sh/uv/install.sh | sh



# Clone or copy the project

cd atproto-bot



# Create virtual environment and install dependencies

uv venv

source .venv/bin/activate.fish



# Install the project

uv pip install -e .



# For development

uv pip install -e ".[dev]"

```



#### Option 2: Using pip



```fish

# Clone or copy the project

cd atproto-bot



# Create virtual environment (recommended)

python -m venv .venv

source .venv/bin/activate.fish



# Install dependencies

pip install -e .



# For development

pip install -e ".[dev]"

```



## Configuration



1. Copy the example config:



```fish

cp config.example.yaml config.yaml

```



2. Edit `config.yaml` with your values:



```yaml

bluesky:

  handle: "your-bot.bsky.social"

  app_password: "xxxx-xxxx-xxxx-xxxx"  # Create at Settings > App Passwords

  owner_did: "did:plc:your-did-here"



llm:

  provider: "anthropic"  # or "openai"

  api_key: "sk-ant-xxxxx"

  model: "claude-sonnet-4-20250514"

  max_tokens: 1024

  temperature: 0.7



bot:

  poll_interval: 30

  max_thread_depth: 50

  rate_limit_per_hour: 20

  max_post_length: 300

```



### Finding Your DID



To find your Bluesky DID:



```fish

curl "https://bsky.social/xrpc/com.atproto.identity.resolveHandle?handle=yourhandle.bsky.social"

```



### Creating an App Password



1. Go to Bluesky Settings

2. Navigate to "App Passwords"

3. Create a new app password for the bot

4. **Never use your main password**



## Usage



### Run the bot



```fish

# With default config.yaml

python -m src.main



# Or use the installed command

atproto-bot



# With uv (if you installed with uv)

uv run python -m src.main

# or

uv run atproto-bot



# With custom config

atproto-bot -c /path/to/config.yaml



# Verbose logging

atproto-bot -v



# Single poll cycle (for testing)

atproto-bot --once

```



### Interacting with the bot



1. Create a post or reply in a thread

2. Mention the bot: `@your-bot.bsky.social summarize this discussion`

3. The bot will fetch the thread context and respond



### Example interactions



```

You: @mybot.bsky.social what are the main points here?

Bot: The thread discusses three main topics: [summary based on context]



You: @mybot.bsky.social translate the above to Spanish

Bot: [Spanish translation of the relevant context]



You: @mybot.bsky.social who seems to be winning this argument?

Bot: [Analysis of the debate based on thread context]

```



## Self-Improvement Feature



The bot can autonomously improve its own codebase using the `/selfimprovement` slash command.



### Overview



When you send `/selfimprovement [PROMPT]`, the bot will:

1. Analyze the codebase using Claude

2. Generate code changes based on your prompt

3. Create a new Git branch

4. Commit the changes

5. Push to GitHub

6. Create a pull request

7. Reply with the PR link



**Example:**

```

You: @mybot /selfimprovement Add better error handling to mention processing

Bot: Starting self-improvement process... This may take a few minutes. I'll reply when done.

Bot: ✅ Self-improvement complete!

     Pull request created: https://github.com/vitorpy/atproto-bot/pull/42

     Review and merge when ready.

```



### GitHub App Setup (Required for /selfimprovement)



The self-improvement feature requires a GitHub App for authentication.



#### 1. Create GitHub App



1. Go to GitHub Settings → Developer settings → [GitHub Apps](https://github.com/settings/apps) → **New GitHub App**

2. Fill in the details:

   - **App name:** `atproto-bot-selfimprovement` (or any unique name)

   - **Homepage URL:** `https://github.com/vitorpy/atproto-bot`

   - **Webhook:** Uncheck "Active" (not needed)

3. Set **Repository permissions:**

   - **Contents:** Read & Write

   - **Pull Requests:** Read & Write

4. **Where can this GitHub App be installed?** → "Only on this account"

5. Click **Create GitHub App**



#### 2. Configure GitHub App



After creation:



1. **Generate Private Key:**

   - Scroll down to "Private keys"

   - Click "Generate a private key"

   - Download the `.pem` file (keep it secure!)



2. **Note the App ID:**

   - At the top of the page, note your **App ID**



3. **Install the App:**

   - Click "Install App" in the left sidebar

   - Select your account

   - Choose "Only select repositories"

   - Select `vitorpy/atproto-bot`

   - Click "Install"



4. **Get Installation ID:**

   - After installation, you'll be redirected to a URL like:

     `https://github.com/settings/installations/12345678`

   - The number at the end (`12345678`) is your **Installation ID**



#### 3. Add GitHub Config



Add the GitHub section to your `config.yaml`:



```yaml

bluesky:

  handle: "your-bot.bsky.social"

  app_password: "xxxx-xxxx-xxxx-xxxx"

  owner_did: "did:plc:your-did-here"



llm:

  provider: "anthropic"

  api_key: "sk-ant-xxxxx"

  model: "claude-sonnet-4-20250514"

  max_tokens: 1024

  temperature: 0.7



bot:

  poll_interval: 30

  max_thread_depth: 50

  rate_limit_per_hour: 20

  max_post_length: 300



# GitHub App configuration (optional - required for /selfimprovement)

github:

  app_id: "123456"

  private_key: "${GITHUB_APP_PRIVATE_KEY}"  # Environment variable

  installation_id: "78910"

  repository: "vitorpy/atproto-bot"

```



#### 4. Set Environment Variable



Store the private key as an environment variable:



```bash

# On VPS (production)

export GITHUB_APP_PRIVATE_KEY=$(cat /path/to/private-key.pem)



# Or add to systemd service file

Environment="GITHUB_APP_PRIVATE_KEY=-----BEGIN RSA PRIVATE KEY-----\nMIIE...\n-----END RSA PRIVATE KEY-----"

```



For GitHub Actions deployment, add these secrets to your repository:

- `GITHUB_APP_ID`

- `GITHUB_APP_PRIVATE_KEY` (entire PEM content)

- `GITHUB_APP_INSTALLATION_ID`



### Usage Examples



```

# Simple feature addition

@bot /selfimprovement Add logging to all database operations



# Refactoring

@bot /selfimprovement Refactor config loading to use environment variables



# Bug fixes

@bot /selfimprovement Fix the rate limiting logic to reset properly



# Testing

@bot /selfimprovement Add unit tests for the command router

```



### How It Works



1. **Authorization:** Only the bot owner (configured `owner_did`) can use this command

2. **Code Analysis:** Claude reads the codebase structure and key files

3. **Change Generation:** Claude generates minimal, focused changes following existing patterns

4. **Validation:** Changes are validated for Python syntax and security

5. **Git Workflow:** Creates a feature branch, commits, and pushes to GitHub

6. **Pull Request:** Creates a PR with detailed description

7. **Manual Review:** You review and merge the PR (no auto-merge for safety)



### Safety Features



- **Owner-only:** Only you can trigger self-improvement

- **No auto-merge:** All changes require manual PR review

- **Validation:** Code is checked for syntax errors before commit

- **Audit trail:** All changes are tracked in Git history

- **Rollback:** Easy to revert via Git if needed



### VPS Setup Notes



If running on a VPS, ensure:



```bash

# Git is configured

git config --global user.name "ATproto Bot"

git config --global user.email "bot@vitorpy.com"



# Repository is in expected location

cd /var/www/atproto-bot

git status  # Should show clean working directory

```



## Architecture



```

src/

├── main.py          # Entry point and CLI

├── config.py        # Configuration loading and validation

├── atproto_client.py # Bluesky API interactions

├── llm_handler.py   # LangChain integration with injection mitigation

└── bot.py           # Main bot logic and orchestration

```



### Components



- **Config**: Pydantic-based configuration with validation

- **ATProtoClient**: Wraps the atproto library for notifications, threads, and posting

- **LLMHandler**: LangChain integration with structured prompts

- **Bot**: Orchestrates polling, processing, and rate limiting



## Deployment



The bot is deployed to scherbius.vitorpy.com as a systemd service.



### Automatic Deployment



Deployments happen automatically when code is pushed to the `main` branch:



1. Push to main

2. GitHub Actions builds and deploys

3. Service restarts with new code



### Manual Deployment



To manually trigger deployment:



1. Go to GitHub Actions

2. Select "Deploy ATproto Bot to VPS" workflow

3. Click "Run workflow"



### Viewing Logs



On scherbius.vitorpy.com:



```bash

# Follow logs in real-time

sudo journalctl -u atproto-bot -f



# View recent logs

sudo journalctl -u atproto-bot -n 100



# View logs since last boot

sudo journalctl -u atproto-bot -b

```



### Service Management



```bash

# Check service status

sudo systemctl status atproto-bot



# Restart service

sudo systemctl restart atproto-bot



# Stop service

sudo systemctl stop atproto-bot



# Start service

sudo systemctl start atproto-bot

```



### Database Location



The SQLite database is stored at: `/var/lib/atproto-bot/bot.db`



### Troubleshooting Deployment



**Service won't start:**

```bash

# Check logs for errors

sudo journalctl -u atproto-bot -n 50



# Verify config file exists

ls -l /var/www/atproto-bot/config.yaml



# Check database permissions

ls -ld /var/lib/atproto-bot

```



**Database issues:**

```bash

# Verify database exists

ls -lh /var/lib/atproto-bot/bot.db



# Check database structure

sqlite3 /var/lib/atproto-bot/bot.db ".tables"

```



## Running as a Service (Generic)



For other environments, you can run the bot as a service using systemd or Docker.



### systemd (Linux)



Create `/etc/systemd/system/atproto-bot.service`:



```ini

[Unit]

Description=ATproto Bot

After=network.target



[Service]

Type=simple

User=youruser

WorkingDirectory=/path/to/atproto-bot

ExecStart=/path/to/atproto-bot/.venv/bin/python -m src.main

Restart=on-failure

RestartSec=30



[Install]

WantedBy=multi-user.target

```



```fish

sudo systemctl daemon-reload

sudo systemctl enable atproto-bot

sudo systemctl start atproto-bot

```



### Docker



#### Using pip



```dockerfile

FROM python:3.11-slim



WORKDIR /app

COPY . .

RUN pip install --no-cache-dir -e .



CMD ["python", "-m", "src.main"]

```



#### Using uv (faster builds)



```dockerfile

FROM python:3.11-slim



# Install uv

COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv



WORKDIR /app

COPY . .



# Install dependencies with uv

RUN uv venv && \

    . .venv/bin/activate && \

    uv pip install -e .



CMD [".venv/bin/python", "-m", "src.main"]

```



```fish

docker build -t atproto-bot .

docker run -v (pwd)/config.yaml:/app/config.yaml atproto-bot

```



## Development



### With uv



```fish

# Install dev dependencies

uv pip install -e ".[dev]"



# Run linter

uv run ruff check src/



# Run formatter

uv run ruff format src/



# Run tests

uv run pytest

```



### With pip



```fish

# Install dev dependencies

pip install -e ".[dev]"



# Run linter

ruff check src/



# Run formatter

ruff format src/



# Run tests

pytest

```



## Troubleshooting



### "Rate limit exceeded"



The bot limits responses per user per hour. Adjust `rate_limit_per_hour` in config.



### "Post not found"



The thread may have been deleted or the post is from a blocked account.



### "Config file not found"



Ensure `config.yaml` exists in the working directory or specify path with `-c`.



### Authentication errors



- Verify your app password is correct

- Ensure the handle matches your Bluesky account

- Check that the app password hasn't been revoked



## License



MIT