Console | Site | Self-host
Overmind continuously trains & improves your agents, with data from your production traces
Point it at your agent's codebase and it turns production traces (or any dataset) into a fine-tuned model, benchmarked against the eval metrics you define and served via 1 unified API, with no ML infrastructure to build.
The weights are yours to download, retrain or roll back.
Available from the Console, the overmind CLI, the REST API, and an MCP server for Cursor, Claude Code, OpenCode and Codex. Hosted at console.overmindlab.ai or run it yourself.
| Agent & Capabilities | A graph of your agent — capabilities, prompts, tools, and tasks — scanned from the repo. |
| Observability | OpenTelemetry traces, scored as they arrive and matched to the capability that produced them. |
| Datasets | Production traces or uploaded files become versioned eval and training datasets. |
| Eval | What "good" means per capability, measured on live traces and in batch. |
| Optimisers | Prompt, tool, and control-flow experiments in your repo; the winner is a git diff. |
| Models | Fine-tunes you own, trained on your data and benchmarked against production. |
| Inference | Trained and frontier models on one OpenAI-compatible API. |
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:
| Command |
What it does |
/overmind ensure-tracing |
Inspect traces and instrument the agent |
/overmind dataset |
Build, clean, upload or export a dataset |
/overmind finetune |
Fine-tune, deploy and smoke-test a model |
/overmind optimise |
Run prompt and code optimisation |
/overmind backtest |
Compare models against the agent's own traces |
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 # set the required keys below
docker compose up -d # Postgres, Redis, API on :8000, Console on :5173, Celery workers, beat, Grafana on :3001
On first boot the API runs migrations and seeds the built-in evaluators; Swagger is at /api/docs/. Sign in at http://localhost:5173 with any email and password. docker compose exec api python manage.py seed_demo --owner <your email> loads a full demo workspace.
What the API needs to boot
The API refuses to start until every required key is set, and the error names each missing one. .env.example documents every key.
| Required |
Used for |
OPENROUTER_API_KEY |
Judges, evals, trace scoring, the Data Workshop, optimiser scoring, frontier inference |
MODAL_TOKEN_ID, MODAL_TOKEN_SECRET |
Modal training and serving workers (MODAL_ENVIRONMENT defaults to overmind-dev) |
INFERENCE_API_URL, INFERENCE_API_KEY |
Serving trained models — the endpoint printed by modal deploy and its shared secret |
AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_BUCKET_NAME |
The fine-tuning checkpoint archive |
HF_TOKEN |
Gated Hugging Face base models; also set it in the Modal secret |
| Optional |
Effect |
CURSOR_API_KEY |
Recommended for the Data Workshop: runs the dataset agent as a Cursor Composer session |
OPENAI_API_KEY |
The embedding_cosine evaluator |
ANTHROPIC_API_KEY, GEMINI_API_KEY |
Fallback engines for the Data Workshop; unused while OPENROUTER_API_KEY is set |
STRIPE_SECRET_KEY |
Paid plans and the credit cap; without it, usage is metered with no cap |
Fine-tuning and serving also need the Modal workers deployed (modal deploy overbae/modal/modal_vllm_worker.py, register_model.py and modal_sft_worker.py) and a Modal secret named overmind-inference with the AWS keys, INFERENCE_API_KEY and HF_TOKEN.
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/ with tools, resources and prompts for the platform's agent workflows. Account API keys and OAuth connections can access every active project their account is authorized to use: call list_projects, then pass the selected project_id on project tools and resource URIs. Membership is checked on every operation. Project API keys remain limited to their configured project. Every tool declares what it costs to run (free, compute, llm, gpu) and none can delete anything.
The optional plugin packages the MCP connection, Overmind branding and nine workflow skills. OAuth connections remain authorized until revoked; access tokens expire after one hour and refresh tokens rotate. Direct MCP connections can use an API key without OAuth or a plugin.
overmind init --ide <cursor|claude|opencode|codex> prepares the local configuration and overmind sync installs the repository's project API key. To configure an API key by hand:
Cursor — .cursor/mcp.json
{
"mcpServers": {
"overmind": {
"url": "https://api.overmindlab.ai/api/mcp/",
"headers": { "X-Api-Key": "ovr_…" }
}
}
}
Claude Code
claude mcp add --transport http overmind https://api.overmindlab.ai/api/mcp/ --header "X-Api-Key: ovr_…"
OpenCode — opencode.json
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"overmind": {
"type": "remote",
"url": "https://api.overmindlab.ai/api/mcp/",
"enabled": true,
"headers": { "X-Api-Key": "ovr_…" }
}
}
}
Codex — .codex/config.toml
[mcp_servers.overmind]
url = "https://api.overmindlab.ai/api/mcp/"
http_headers = { "X-Api-Key" = "ovr_…" }
What the tools cover
| Domain |
Tools |
| Projects |
list_projects |
| Observability |
inspect_capability_health, query_failures, query_traces, query_task_executions, get_job |
| Datasets |
list_datasets, inspect_dataset, query_dataset, create_dataset_from_traces, create_dataset_from_llm_calls, message_dataset_agent, run_dataset |
| Evaluations |
check_evaluation_readiness, upsert_evaluator, run_evaluation, compare_evaluations, annotate_evaluation_sample |
| Training |
check_finetune_readiness, estimate_finetune, start_finetune, retry_deployment, set_active_model, run_inference, get_model_swap_prompt |
| Optimiser |
check_optimizer_readiness, start_optimizer, inspect_optimizer_result |
| Connectors |
inspect_connectors, configure_connector, sync_connector |
| Instrumentation |
get_instrumentation_plan, verify_instrumentation |
| Catalog |
get_model_catalog |
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.
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.
Licence
The platform (overbae/, frontend/) is AGPL-3.0. The SDK and CLI (overmind/) stay MIT. A commercial licence is a paid alternative from Overmind Ltd if you need different terms — [email protected].
docs.overmindlab.ai · overmindlab.ai
Overmind 指向你的 AI 智能体的代码库和生产轨迹，把它们变成一个微调模型，按你定义的指标做基准评估，并通过一个统一 API 提供服务——不需要你另建一套 ML 基础设施。