Blog posts: Coding agents repeat the same mistakes because they start fresh every session. Evolve gives agents memory — they learn from what worked and what didn't, so each session is better than the last.
Evolve is a system designed to help agents improve over time by learning from their trajectories. The Lite version is designed to effortlessly slot into existing agent assistants like Claude Code and Codex. 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.
On the AppWorld benchmark, Evolve improved agent reliability by +8.9 points overall, with a 74% relative increase on hard multi-step tasks. Evolve 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.
Important
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Prerequisites:
- Python 3.12 or higher
uv(recommended) orpip
From Source
# Clone the repository and install dependencies
git clone https://github.com/agenttoolkit/altk-evolve.git
cd altk-evolve
uv venv --python=3.12 && source .venv/bin/activate
uv sync
# Build the UI
cd frontend/ui
npm ci && npm run build
cd ../..From PyPI
pip install altk-evolveOptional Backend Dependencies:
The default filesystem backend uses simple text matching and requires no additional dependencies. For semantic vector similarity search, install one of these backends:
For PostgreSQL with pgvector support (recommended for production):
uv sync --extra pgvectorFor Milvus support (optimized for large-scale vector search):
uv sync --extra milvusSee the Backend Configuration Guide for detailed comparison and setup instructions.
For direct OpenAI usage:
export OPENAI_API_KEY=sk-...For LiteLLM proxy usage and model selection (including global fallback via EVOLVE_MODEL_NAME), see the configuration guide.
Start the Web UI and MCP server
uv run evolve-mcpThe Web UI can be accessed from: http://127.0.0.1:8000/ui/
If you only want to access the Web UI and API (without the MCP server stdio blocking the terminal), you can run the FastAPI application directly using uvicorn:
uv run uvicorn altk_evolve.frontend.mcp.mcp_server:app --host 127.0.0.1 --port 8000Then navigate to http://127.0.0.1:8000/ui/.
If you're attaching Evolve to an MCP client that requires a direct command (like Claude Desktop):
uv run evolve-mcpOr for SSE transport:
uv run evolve-mcp --transport sse --port 8201Verify it's running:
npx @modelcontextprotocol/inspector@latest http://127.0.0.1:8201/sse --cli --method tools/listAvailable 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.
Entity search filters reserve bare keys for top-level schema columns only: id, type, content, and created_at.
If you need to filter on JSON metadata, use the metadata.<key> form. For example, use filters={"type": "trajectory", "metadata.task_id": "123"} instead of filters={"type": "trajectory", "task_id": "123"}.
Existing integrations that stored custom fields in entity metadata should update filter writers to add the metadata. prefix for those keys.
- Proactive: Learns how to recognize problems and their solutions, and generates guidelines that get automatically applied to new tasks.
- Conflict Resolution: Update existing guidelines when new information contradicts them.
- On Command: An array of tools to manage guidelines whether in the agent or through a CLI
Evolve is built on a modular architecture which forms a feedback loop, taking conversation traces (trajectories) from an agent, extracting key insights into a database, feeding it back into the agent.
Lite Mode omits the Interaction layer. All activity is performed in-agent
Evolve 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-phoenixvia trace observability,auto-mcpvia trajectory saving tools, ormanual).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.
Evolve is an active project, and real‑world usage helps guide its direction.
If you’re experimenting with Evolve or exploring on‑the‑job learning for agents, feel free to open an issue or discussion to share use cases, ideas, or feedback.
See the Contributing Guide to understand our development process, or how to submit changes, report bugs, or propose features.