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Message-driven workflows with observable flows for emergent AI

Project description

ClearFlow

Coverage Status PyPI PyPI Downloads Python License llms.txt

Message-driven orchestration of AI workflows. Type-safe, immutable, 100% coverage.

Why ClearFlow?

  • Message-driven architecture – Commands trigger actions, Events record facts
  • 100% test coverage – Every path proven to work
  • Type-safe flows – Full static typing with pyright strict mode
  • Deep immutability – All state transformations create new immutable data
  • Minimal dependencies – Only Pydantic for validation and immutability
  • Single completion – Exactly one end message type per flow
  • AI-Ready Documentation – llms.txt for optimal coding assistant integration

How It Works

ClearFlow uses Messages to orchestrate AI workflows:

Command → Node → Event → Node → Event → End
  • Commands request actions: "analyze this portfolio"
  • Events record what happened: "risk assessment completed"
  • Nodes process messages and emit new ones
  • Flows route messages between nodes based on type

Every message knows where it came from (causality tracking), making complex AI workflows debuggable and testable.

Quick Start

pip install clearflow

Note: ClearFlow is in alpha. Pin your version in production (clearflow==0.x.y) as breaking changes may occur in minor releases.

Real-World Examples

Example What It Shows
Chat Message routing between user and AI
Portfolio Analysis Multi-language model coordination with Events
RAG Document processing pipeline with causality tracking

AI Assistant Integration

ClearFlow provides comprehensive documentation in llms.txt format for optimal AI assistant support.

Claude Code Setup

Add ClearFlow documentation to Claude Code with one command:

claude mcp add-json clearflow-docs '{
    "type":"stdio",
    "command":"uvx",
    "args":["--from", "mcpdoc", "mcpdoc", "--urls", "ClearFlow:https://raw.githubusercontent.com/artificial-sapience/clearflow/main/llms.txt"]
}'

For IDEs (Cursor, Windsurf), see the mcpdoc documentation.

ClearFlow vs PocketFlow

Aspect ClearFlow PocketFlow
Architecture Message-driven (Commands/Events) State-based transformations
State Immutable messages with causality tracking Mutable, passed via shared param
Routing Message type-based explicit routes Action-based graph edges
Completion Single end message type Multiple exits allowed
Type safety Full static typing with pyright strict Dynamic (no annotations)

ClearFlow provides message-driven orchestration with immutable causality tracking and type safety. PocketFlow emphasizes brevity and flexibility with minimal overhead.

Development

Install uv

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

Clone and set up development environment

git clone https://github.com/artificial-sapience/clearflow.git
cd clearflow
uv sync --all-extras     # Creates venv and installs deps automatically
./quality-check.sh       # Run all checks

License

MIT

Acknowledgments

Inspired by PocketFlow's Node-Flow-State pattern.

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