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Crow

An Agent Development Environment (ADE) for building, running, and improving autonomous coding agents.

Status: In active development. Phase 0 complete (ACP server, iterative refinement, task pipeline). See ROADMAP.md for what's next.

What is Crow?

Crow is NOT just an ACP server, NOT just an IDE. It's a complete environment where:

  • Humans plan in a journal (Logseq-inspired knowledge base)
  • Humans + agents prime the environment together (pair programming)
  • Autonomous agents work in the primed environment (read journal → write code → document decisions)
  • Humans review in the journal and provide feedback
  • Knowledge accumulates and agents get better over time

Quick Start

Installation

Install the Crow CLI globally:

cd /home/thomas/src/projects/orchestrator-project/crow
uv build
uv tool install dist/crow_ai-0.1.2-py3-none-any.whl --python 3.12

This installs the crow command globally, available from any directory.

Configuration:

  • API keys and secrets: ~/.crow/.env
  • User settings: ~/.crow/config.yaml
  • Sessions: ~/.crow/sessions/
  • Logs: ~/.crow/logs/

Usage

Start the ACP server:

crow acp

Using with ACP clients:

uv --project /path/to/acp-python-sdk run python acp-python-sdk/examples/client.py crow acp

Other commands:

crow              # Show help
crow --version    # Show version

Reinstalling After Changes

cd /home/thomas/src/projects/orchestrator-project/crow
uv build
uv tool install dist/crow_ai-0.1.2-py3-none-any.whl --python 3.12

Development Setup

For development work on Crow itself:

cd /home/thomas/src/projects/orchestrator-project/crow
uv --project crow sync

See AGENTS.md for detailed development workflow instructions.

Documentation

Features

Current (Phase 0 - Complete)

  • ✅ ACP Server - Streaming ACP server wrapping OpenHands SDK
  • ✅ Iterative Refinement - Planning → Implementation → Critique → Documentation loop
  • ✅ Task Pipeline - Split PLAN.md into tasks, run sequentially
  • ✅ MCP Integration - playwright, zai-vision, fetch, web_search
  • ✅ Session Management - Multiple concurrent sessions with persistence
  • ✅ Slash Commands - /help, /clear, /status

In Progress (Phase 1-3)

  • 🚧 Restructure - Moving files from root to src/crow/
  • 📋 Jinja2 Templates - Replace hardcoded prompts with templates
  • 📋 Environment Priming - Human + agent pair programming before autonomous phase

Planned (Phase 4-8)

  • 📋 Project Management - /projects/ directory, git repos, journals
  • 📋 Journal Page - Logseq-inspired knowledge base
  • 📋 Web UI - CodeBlitz/Monaco integration
  • 📋 Feedback Loops - Capture human feedback, feed to agents
  • 📋 Telemetry - Self-hosted Laminar/Langfuse

Architecture

Crow
├── ACP Server (src/crow/agent/)
│   └── Streaming ACP protocol implementation
├── Orchestration (src/crow/orchestration/)
│   ├── Environment priming
│   ├── Task splitting
│   ├── Iterative refinement
│   └── Task pipeline
├── Web UI (Future)
│   ├── CodeBlitz/Monaco editor
│   ├── Journal page
│   ├── Project browser
│   └── Terminal
└── Projects (/projects/)
    └── Each project = git repo + journal

The Problem We're Solving

Current AI coding tools:

  • ❌ Drop agents into empty workspaces (no context)
  • ❌ Lose agent decisions in markdown files ("lost like tears in rain")
  • ❌ No feedback loop (human review not captured)
  • ❌ No knowledge accumulation

Our solution:

  • ✅ Environment priming - Human + agent set up context first
  • ✅ Journal - All decisions documented and linked
  • ✅ Feedback loops - Human review captured and fed back
  • ✅ Knowledge accumulation - Agents get better over time

Contributing

This is a personal project, but feedback and ideas are welcome!

License

MIT

Acknowledgments


"The agent is the primary developer, humans are the critics/product managers."

Modified with Crow ADE

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