The Training Platform for Specialized Model

Console | Site | Self-host

Overmind trains models you own, on data only you have. Point it at your repo and it turns production traces — or any dataset you bring — into a fine-tuned model, benchmarked against the model you run today and served on one API, with no ML infrastructure to build. The weights are yours to download, retrain or roll back. It starts by reading your code into a context graph of your agent, so it arrives already knowing what your agents do, and the evals, datasets and training are built for your agent rather than a generic recipe.

Every stage is available from the Console, the overmind CLI, the REST API, and an MCP server for Cursor, Claude Code, OpenCode and Codex. Use the hosted version at console.overmindlab.ai or run it yourself with Docker Compose.

Get started

Hosted

Sign up at console.overmindlab.ai, pick your coding agent on Get started — Cursor, Claude Code, OpenCode or Codex — and paste the onboarding prompt into it with your agent's repo open. It installs overmind, runs overmind init and overmind sync, and builds the context graph. From then on everything is a /overmind command in the same chat:

Run it yourself

Self-hosting keeps traces and training data inside your own network. The hosted and self-hosted stacks are the same code.

git clone https://github.com/overmind-core/overmind.git && cd overmind
cp .env.example .env                        # OpenRouter, S3, a training backend (Modal or Baseten), an LLM key for the Data Workshop agent
docker compose up -d                        # Postgres, Redis, API on :8000, Celery workers, beat, Grafana on :3001
cd frontend && bun install && bun run dev   # Console on :5173

On first boot the API runs migrations and seeds the built-in evaluators; Swagger is at /api/docs/. docker compose exec -T api python manage.py shell < seed.py loads a full demo workspace.

The API will not start without these. .env.example documents every other key.

Two keys gate features rather than boot: OPENROUTER_API_KEY for judges, evals and every routed model call, and one LLM key for the Data Workshop agent — it uses the first of CURSOR_API_KEY, OPENROUTER_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY or GEMINI_API_KEY it finds. Without STRIPE_SECRET_KEY, usage is metered and shown with no remaining-credit cap.

Send a first trace

pip install "overmind[tracing]"
export OVERMIND_API_KEY=ovr_…   # project key from Console → Settings; add OVERMIND_API_URL for self-host

import overmind

overmind.init(
    service_name="support-agent", capability_id="<capability-uuid>", providers="auto"
)


@overmind.tool()
def search(query: str) -> list[dict]: ...


def handle(request: dict, session_id: str) -> dict:
    with overmind.run(
        "support-run", intent=request["question"], conversation_id=session_id
    ) as run:
        answer = agent(request)
        run.deliver(answer)  # the final output that gets scored
        return answer

providers="auto" instruments the LLM SDKs you already use over OpenTelemetry; without a key, tracing is off and nothing breaks. Any OTel exporter can POST /api/v1/traces instead, and existing traces in Langfuse, LangSmith, Braintrust or Galileo can be synced through a connector. Open Observability → Task executions to see the trace and its score.

Connect your coding agent

Overmind ships an MCP server at /api/mcp/: 32 tools, 14 resources and 11 prompts covering everything the Console can do, scoped to one project by its API key. Every tool declares what it costs to run (free, compute, llm, gpu) and none can delete anything. overmind init --ide <cursor|claude|opencode|codex> writes the config for you, or by hand:

{
  "mcpServers": {
    "overmind": {
      "url": "https://api.overmindlab.ai/api/mcp/",
      "headers": { "X-Api-Key": "ovr_…" }
    }
  }
}

claude mcp add --transport http overmind https://api.overmindlab.ai/api/mcp/ --header "X-Api-Key: ovr_…"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "overmind": {
      "type": "remote",
      "url": "https://api.overmindlab.ai/api/mcp/",
      "enabled": true,
      "headers": { "X-Api-Key": "ovr_…" }
    }
  }
}

[mcp_servers.overmind]
url = "https://api.overmindlab.ai/api/mcp/"
http_headers = { "X-Api-Key" = "ovr_…" }

Prompts such as investigate-capability, finetune-capability and ship-model chain the tools into complete workflows.

For a self-hosted instance, replace the host with your API URL (http://localhost:8000 locally). Keys are written to git-ignored files only; overmind sync will not write a key into a tracked file.

Documentation

All documentation lives at docs.overmindlab.ai:

Repository

overbae/      Django 6 API — api/ (DRF, OTLP, OpenAI-compatible), models/, services/ (eval, datasets, mcp, sft_assets), tasks/ (Celery), modal/ (GPU workers)
frontend/     React 19 Console — src/openapi/ is generated by `make generate_api_client`, never hand-edited
overmind/     Python SDK + CLI, published to PyPI as `overmind`; skills/overmind/ is the /overmind skill
tests/        pytest — `make test`
AGENTS.md     how we work, for humans and coding agents; .claude/skills/ documents each subsystem

Backend is uv (make test, make lint-backend, make check-migrations); frontend is Bun (bun run typecheck, bun run lint, bun run test); SDK is make -C overmind test. Training and serving run on Modal or Baseten (FINETUNING_BACKEND); inference is vLLM behind /api/v1/chat/completions.

Contributing

Open an issue, or a PR from a feature branch using .github/PULL_REQUEST_TEMPLATE.md — main is protected and AGENTS.md describes how we work. Questions go to the Discord.

Telemetry

The SDK and CLI send anonymous usage analytics to PostHog — one cli.invoked event per CLI run and sdk_init on library use; never prompts, trace contents, keys or dataset contents. Opt out with OVERMIND_ANALYTICS_ENABLED=false or DO_NOT_TRACK=1; analytics is also off when CI is set. Your traces go only to your own project.

docs.overmindlab.ai · overmindlab.ai