Kaizen

Self-improving agents through iterations.

Kaizen is a system designed to help agents improve over time by learning from their trajectories. It uses a combination of an MCP server for tool integration, vector storage for memory, and LLM-based conflict resolution to refine its knowledge base.

Features

• MCP Server: Exposes tools to get guidelines and save trajectories.
• Conflict Resolution: Intelligently merges new insights with existing guidelines using LLMs.
• Trajectory Analysis: Automatically analyzes agent trajectories to generate guidelines and best practices.
• Milvus Integration: Uses Milvus (or Milvus Lite) for efficient vector storage and retrieval.

Architecture

Quick Start

Installation

Prerequisites:

• Python 3.12 or higher
• uv (recommended) or pip

git clone <repository_url>
cd kaizen
uv sync && source .venv/bin/activate

Configuration

For direct OpenAI usage:

export OPENAI_API_KEY=sk-...

For LiteLLM proxy usage and model selection (including global fallback via KAIZEN_MODEL_NAME), see CONFIGURATION.md.

Running the MCP Server

uv run fastmcp run kaizen/frontend/mcp/mcp_server.py --transport sse --port 8201

Verify it's running:

npx @modelcontextprotocol/inspector@latest http://127.0.0.1:8201/sse --cli --method tools/list

Available tools:

• get_entities(task: str, entity_type: str): Get relevant entities for a specific task, filtered by type (e.g., 'guideline', 'policy').
• get_guidelines(task: str): Get relevant guidelines for a specific task (backward compatibility alias).
• save_trajectory(trajectory_data: str, task_id: str | None): Save a conversation trajectory and generate new guidelines.
• create_entity(content: str, entity_type: str, metadata: str | None, enable_conflict_resolution: bool): Create a single entity in the namespace.
• delete_entity(entity_id: str): Delete a specific entity by its ID.

Tip Provenance

Kaizen automatically tracks the origin of every guideline it generates or stores. Every tip entity contains metadata identifying its source:

• creation_mode: Identifies how the tip was created (auto-phoenix via trace observability, auto-mcp via trajectory saving tools, or manual).
• source_task_id: The ID of the original trace or task that inspired the tip, providing full audibility.

See the Low-Code Tracing Guide for more details.

Documentation

• KAIZEN_LITE.md - Lightweight mode via Claude Code plugin (no infra required)
• CONFIGURATION.md - Detailed configuration options
• POLICIES.md - Policy support and schema
• CLI.md - Command-line interface documentation
• CLAUDE_CODE_DEMO.md - Claude Code demo walkthrough

Development

Running Tests

uv run pytest

Phoenix Sync Tests

Tests for the Phoenix trajectory sync functionality are skipped by default since they require familiarity with the Phoenix integration. To include them:

# Run all tests including Phoenix tests
uv run pytest --run-phoenix

# Run only Phoenix tests
uv run pytest -m phoenix

End-to-End (E2E) Low-Code Verification

To run the full end-to-end verification pipeline (Agent -> Trace -> Tip):

KAIZEN_E2E=true uv run pytest tests/e2e/test_e2e_pipeline.py -s

See docs/LOW_CODE_TRACING.md for more details.