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.
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
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 3.12This 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/
Start the ACP server:
crow acpUsing with ACP clients:
uv --project /path/to/acp-python-sdk run python acp-python-sdk/examples/client.py crow acpOther commands:
crow # Show help
crow --version # Show versioncd /home/thomas/src/projects/orchestrator-project/crow
uv build
uv tool install dist/crow_ai-0.1.2-py3-none-any.whl --python 3.12For development work on Crow itself:
cd /home/thomas/src/projects/orchestrator-project/crow
uv --project crow syncSee AGENTS.md for detailed development workflow instructions.
- INSTALLATION.md - Installation and configuration guide
- DESIGN.md - Vision, architecture, and design decisions
- CURRENT_STATE.md - Analysis of current code and what needs fixing
- ROADMAP.md - Development phases and timeline
- AGENTS.md - Project-specific knowledge for agents
- REFACTOR_PLAN.md - Original refactor plan (superseded by ROADMAP.md)
- ✅ 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
- 🚧 Restructure - Moving files from root to
src/crow/ - 📋 Jinja2 Templates - Replace hardcoded prompts with templates
- 📋 Environment Priming - Human + agent pair programming before autonomous phase
- 📋 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
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
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
This is a personal project, but feedback and ideas are welcome!
MIT
- Agent Client Protocol
- OpenHands SDK
- Model Context Protocol
- Trae Solo - Autonomous development inspiration
- Google Antigravity - Agent-first IDE inspiration
- Logseq - Knowledge management inspiration
- CodeBlitz - Web IDE foundation
"The agent is the primary developer, humans are the critics/product managers."
Modified with Crow ADE
