altk-evolve

Evolve: On‑the‑job learning for AI agents

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] ⭐ Star the repo: it helps others discover it.

Latest from Evolve

Research, releases, and practical findings from the Evolve team.

• 2026-09-15 · Article — Your Agent Aced the Task. Will It Do It Again? Diagnosing where an agent's decisions wobble and stabilizing them so success repeats.
• 2026-09-08 · Paper — Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course Turning unstable trajectory steps into episodic memory to keep agents consistent across repeated runs.
• 2026-08-18 · Article — How Much Memory Does Your Agent Actually Need? Why agent memory should be calibrated to the model instead of simply increased.
• 2026-08-11 · Article — Thinking of ACE? We Can Do It with Fewer Tokens Comparing Evolve's targeted guideline retrieval with ACE's playbook approach and token usage.
• 2026-04-08 · Article — ALTK‑Evolve: On‑the‑Job Learning for AI Agents An introduction to learning reusable guidance from agent trajectories.
• 2026-04-07 · Announcement — IBM introduces ALTK Evolve Evolve is opened to builders creating agents that learn from experience.
• 2026-03-11 · Paper — Trajectory-Informed Memory Generation for Self-Improving Agent Systems The research framework and evaluation underlying Evolve.

Quick Start (Lite)

IBM Bob →

Claude Code →

Codex →

Quick Start (Evolve MCP Server)

Installation

Prerequisites:

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

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-evolve

Optional 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 pgvector

For Milvus support (optimized for large-scale vector search):

uv sync --extra milvus

See the Backend Configuration Guide for detailed comparison and setup instructions.

Configuration

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.

Running Services

Start the Web UI and MCP server

uv run evolve-mcp

The Web UI can be accessed from: http://127.0.0.1:8000/ui/

Starting the Web UI and MCP Server

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 8000

Then navigate to http://127.0.0.1:8000/ui/.

Starting only the MCP Server

If you're attaching Evolve to an MCP client that requires a direct command (like Claude Desktop):

uv run evolve-mcp

Or for SSE transport:

uv run evolve-mcp --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 = "guideline", include_public: bool = False): Get relevant entities for a specific task. Set include_public=True to merge in public entities from all other namespaces; those results are annotated with [public: {owner_id}].
• get_guidelines(task: str): Get relevant guidelines for a specific task (backward compatibility alias for get_entities).
• get_guidelines_with_attribution(task: str): Return formatted guidelines with the entity IDs used to build the prompt.
• get_relevant_guidelines(task: str, top_k: int | None, core_support: int | None): Retrieve the always-on guideline core plus a task-relevant dosage.
• list_entities(...): Return structured, filtered, cursor-paginated entity inventory for user and administrative UIs.
• get_entity(entity_id: str, user_id: str | None, record_access: bool = True): Return one structured entity, enforcing ownership when a caller ID is supplied.
• patch_entity_metadata(entity_id: str, metadata_patch: str, user_id: str | None): Merge JSON metadata through the memory hook seam.
• record_access(entity_ids: list[str], accessed_at: str | None): Explicitly stamp the retention engine's last_accessed signal.
• validate_retention_policy(policy: str): Validate and normalize a JSON retention policy without scanning data.
• put_retention_policy(policy_id: str, name: str, policy: str, ...): Create or replace an Evolve-owned retention policy.
• get_retention_policy(policy_id: str) / list_retention_policies(): Read the namespace's policy catalog for operators and management UIs.
• run_retention(policy_id: str, dry_run: bool = True, ...): Dry-run or apply a stored policy and persist a structured, entity-linked report.
• list_retention_runs(...): Read namespace-scoped retention run history, optionally filtered by agent or policy.
• get_compliance_status(): Report backend health, retention availability, hook coverage, and configured plugin health.
• save_trajectory(trajectory_data: str, task_id: str | None, owner_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, owner_id: str | None, visibility: str = "private"): Create a single entity. Pass visibility="public" and owner_id to make it immediately discoverable by other namespaces.
• publish_entity(entity_id: str, user_id: str | None): Make an entity publicly visible to all namespaces. Records the caller as owner and stamps published_at.
• unpublish_entity(entity_id: str, user_id: str | None = None): Revert an entity to private visibility. Ownership is enforced server-side: if the entity has an owner_id, user_id must match it.
• delete_entity(entity_id: str): Delete a specific entity by its ID.

Filter Migration Note

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.

Features

• 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
• Sharing: Publish individual entities so other agents can discover and retrieve them across namespaces.

Architecture

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

Entity Sharing

Evolve supports sharing entities across namespaces using a simple public/private visibility model.

Visibility is stored in each entity's metadata and is private by default. Existing entities without a visibility field are unaffected.

Metadata field	Description
owner_id	User ID who created or last published the entity
visibility	"private" (default) or "public"
published_at	ISO-8601 timestamp of the most recent publish

MCP Tools

Personal facts on a shared Evolve service:

store_user_facts(namespace_id="service-instance-1", user_id="alice", message="I prefer concise answers")
retrieve_user_facts(namespace_id="service-instance-1", user_id="alice", query="answer preferences")

Pass the service instance ID as namespace_id and the individual user's ID as user_id on both calls. Explicitly scoped retrieval filters by that exact pair, including query fallback, and never falls back to another user's facts. Empty explicit namespaces or user IDs are rejected. Calls omitting namespace_id retain the configured default namespace and legacy default-user fallback. Integrating clients must supply the scope; upgrading Evolve alone cannot infer which service instance an unscoped request belongs to.

Publishing an entity:

publish_entity(entity_id="42", user_id="alice")

Sets visibility=public and records the owner and publish timestamp.

Unpublishing:

unpublish_entity(entity_id="42", user_id="alice")

Reverts the entity to private. The entity stays in its namespace — only its visibility changes.

Retrieving public entities from all namespaces:

get_entities(task="write safer code", include_public=True)