Introduction

What is Overmind?

Automatically optimize your agents — better prompts, better tools, better models.

Overmind improves production agents by analyzing their code, capturing traces from runs, and driving autonomous optimization that keeps only changes that measurably help.

For a product-level overview, see the Platform Overview.

Today, Overmind includes:

• Overmind Optimizer — autonomous optimization loops for data-backed production agents
• Tracing SDKs — production telemetry that feeds the optimizer
• Fine-tuning (Beta) — private model customization on your traces

You register your agent, define (or let Overmind infer) a policy that describes correct behavior, and start optimization. Overmind then:

1. Runs your agent on a test dataset and records detailed traces of every LLM call and tool invocation.
2. Scores outputs against an evaluation spec derived from your policy.
3. Diagnoses failures with a strong reasoning model that sees your code, policy, traces, and scores.
4. Produces candidate code fixes (best-of-N) across prompts, tool descriptions, model selection, and agent logic.
5. Accepts or reverts each candidate using regression-aware criteria, keeping only changes that genuinely improve the agent.

After several iterations you get a measurably better agent, plus a readable report and diff.

How it works

           Your Python agent (registered entrypoint)
                        │
                        ▼
         overmind optimize <name>
                        │
   ┌────────────────────┴────────────────────┐
   │                                         │
   ▼                                         │
Run agent on dataset ──▶ Traces + outputs    │
                                │            │
                                ▼            │
                         Score vs. eval      │
                         spec (+ policy)     │
                                │            │
                                ▼            │
                         Diagnose failures   │
                                │            │
                                ▼            │
                   Generate N candidate fixes│
                                │            │
                                ▼            │
                   Validate + re-score       │
                                │            │
                                ▼            │
                   Accept best / revert rest ┘
                                │
                                ▼
                   optimized agent + report (console)

Quick start

Requirements: Python 3.10+, Cursor or Claude Code, and API keys for at least one LLM provider (OpenAI, Anthropic).

1. Install:

pip install overmind

2. Initialize (configures API keys and installs skills into your IDE):

cd your-agent-project/
overmind init

3. Open Cursor or Claude Code and type these in the chat panel, in order:

/overmind-register-agent path/to/your/agent.py
/overmind-generate-spec-and-dataset my-agent
/overmind-optimize-agent my-agent

Each skill reads your codebase, asks what it can’t infer, and handles the rest. Results are pushed to console.overmindlab.ai/agents.

See the Getting Started guide for requirements, what each step does, and next steps.

Tracing SDKs

If you want to trace LLM calls from a running application — independently of the optimizer — Overmind ships Python and JavaScript SDKs. Call init() once and every LLM call is captured with model, inputs/outputs, latency, token counts, and cost. See How To Use Tracing, the Python SDK reference, or the JS/TS SDK reference.

Guides

New to Overmind? Start with Getting Started, then How to Use Overmind.

Tracing only (no optimization)? Go to How To Use Tracing, then the Python SDK or JS/TS SDK.

Exploring fine-tuning? See Fine-tuning (Beta) — enable tracing first, then contact us.

For supported providers, frameworks, and the OTLP endpoint, see Integrations.