37f9d3610d
- Add AGENT_MAIL_QUICKSTART.md: 5-minute setup guide - Add examples/python-agent/AGENT_MAIL_EXAMPLE.md: working code examples - Add examples/python-agent/agent_with_mail.py: runnable multi-agent demo - Update README.md: add Agent Mail to features and docs index - Update AGENTS.md: enhance with quickstart/example references - Update examples README: add Agent Mail example to index Amp-Thread-ID: https://ampcode.com/threads/T-5d5e711d-7b5f-42ca-b75a-5b6cd843ad98 Co-authored-by: Amp <amp@ampcode.com>
90 lines
2.4 KiB
Markdown
90 lines
2.4 KiB
Markdown
# Python Agent Example
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A simple Python script demonstrating how an AI agent can use bd to manage tasks.
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## Features
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- Finds ready work using `bd ready --json`
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- Claims tasks by updating status
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- Simulates discovering new issues during work
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- Links discovered issues with `discovered-from` dependency
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- Completes tasks and moves to the next one
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## Prerequisites
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- Python 3.7+
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- bd installed: `go install github.com/steveyegge/beads/cmd/bd@latest`
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- A beads database initialized: `bd init`
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## Usage
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```bash
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# Make the script executable
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chmod +x agent.py
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# Run the agent
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./agent.py
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```
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## What It Does
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1. Queries for ready work (no blocking dependencies)
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2. Claims the highest priority task
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3. "Works" on the task (simulated)
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4. If the task involves implementation, discovers a testing task
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5. Creates the new testing task and links it with `discovered-from`
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6. Completes the original task
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7. Repeats until no ready work remains
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## Example Output
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```
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🚀 Beads Agent starting...
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============================================================
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Iteration 1/10
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============================================================
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📋 Claiming task: bd-1
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🤖 Working on: Implement user authentication (bd-1)
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Priority: 1, Type: feature
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💡 Discovered: Missing test coverage for this feature
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✨ Creating issue: Add tests for Implement user authentication
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🔗 Linking bd-2 ← discovered-from ← bd-1
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✅ Completing task: bd-1 - Implemented successfully
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🔄 New work discovered and linked. Running another cycle...
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```
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## Integration with Real Agents
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To integrate with a real LLM-based agent:
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1. Replace `simulate_work()` with actual LLM calls
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2. Parse the LLM's response for discovered issues/bugs
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3. Use the issue ID to track context across conversations
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4. Export/import JSONL to share state across agent sessions
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## Advanced Usage
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```python
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# Create an agent with custom behavior
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agent = BeadsAgent()
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# Find specific types of work
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ready = agent.run_bd("ready", "--priority", "1", "--assignee", "bot")
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# Create issues with labels
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agent.run_bd("create", "New task", "-l", "urgent,backend")
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# Query dependency tree
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tree = agent.run_bd("dep", "tree", "bd-1")
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```
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## See Also
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- [AGENT_MAIL_EXAMPLE.md](AGENT_MAIL_EXAMPLE.md) - Multi-agent coordination with Agent Mail
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- [../bash-agent/](../bash-agent/) - Bash version of this example
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- [../claude-desktop-mcp/](../claude-desktop-mcp/) - MCP server for Claude Desktop
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