Snapshot
Continue to About ↓- What it does
- Overmind is a model training and agent observability platform for engineering teams running AI agents in production.
More detail
- It turns an agent's traces, prompts and repo context into versioned evaluation and training datasets, fine-tunes an open model on your data, benchmarks it against your current model, and serves it through an OpenAI-compatible API or as downloadable weights you can host yourself.
- Best for
- Engineering teams already running agents in production, with traces coming from OpenTelemetry, LangSmith, Langfuse, Braintrust or Galileo, who want a specialized model they own instead of paying a frontier model for every narrow task.
- Pricing
- Free is $0/month with a one-time $50 in credits and one seat per project.
More detail
- Pro is $120/month with $120 in credits each month, unlimited seats and priority support
- Enterprise is custom-priced with SSO, dedicated support and a compliance scope agreed per contract.
- Credits are $0.01 each and pay for tr
- Main limitation
- Teams still at chatbot or one-shot prompt prototyping — Overmind's own docs call its optimizer overkill for that — and teams with no instrumented production data, since the documented workflow starts from traces or uploaded files.
- Record
- Living and monitored
- Reviewed
- Evidence
- Public web research
- Machine access
- Structured profile ↗
About
- Overmind is a model training and agent observability platform for engineering teams running AI agents in production.
- It turns an agent's traces, prompts and repo context into versioned evaluation and training datasets, fine-tunes an open model on your data, benchmarks it against your current model, and serves it through an OpenAI-compatible API or as downloadable weights you can host yourself.
What it helps with
- Agent failures are hard to diagnose because the failing step, tool call and surrounding context are scattered across log
- Real production traffic isn't usable as training or evaluation data without cleaning, filtering and versioning
- Teams can't prove a new model or prompt is better than the one in production before shipping it
- Narrow tasks don't justify frontier-model costs, but teams lack an owned specialized model
- Trained models and their benchmarks get disconnected from the data and configuration that produced them
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| Agent observability | Yes | Repository scan builds a capability map; traces via Python SDK, OpenTelemetry spans, or imports from Langfuse, LangSmith, Braintrust and Galileo, with sessions, span detail and live eval scoring. |
| Data preparation | Yes | Data Workshop runs coding-agent-written Python in a sandbox, records versioned cells with row-level changes, and freezes the version each downstream run uses. |
| Evaluation and baseline comparison | Yes | Generated evaluators and eval sets score recorded or fresh outputs against a pinned baseline, with per-metric scores and judge reasoning. |
| Model fine-tuning | Yes | LoRA adapters or full fine-tuning where the base model supports it, with model recommendations, cost and duration estimates, and live run metrics. |
| Hosted inference | Yes | Fine-tuned models served through OpenAI-compatible chat completions and model endpoints, with capability aliases for controlled rollout. |
| Downloadable weights | Yes | Trained weights and checkpoints can be downloaded from the Console or CLI and served on your own infrastructure. |
| Repository-level optimization | Yes | /overmind optimize has a coding agent write candidate diffs, scores them against an eval set, and returns the winning patch for normal code review. |
| Open source / self-hosting | Yes | Platform is AGPL-3.0 and runnable with Docker Compose; Python SDK is MIT-licensed. |
| Per-model compute pricing published | Unknown | Pricing page says training and serving prices are per model but the actual rates are not disclosed on public pages. |
| Independent reviews | Unknown | No independent review coverage or customer discussion was found in the supplied evidence. |
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Evidence
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- Verification
- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- overmindlab.ai
Technical profile details
Record ID
26872752-efcf-4849-b8a0-566097248cddCanonical URLhttps://profiles.scribble.network/listing/overmind-26872752