Inspect agent behavior in context
Connect a code repository and production traces to inspect decision steps, tool calls, and the context associated with failures.
Overmind
Freemium
Overmind helps AI engineering teams turn production traces into specialized models they own, with tools to prepare data, train, evaluate, and deploy.
What is Overmind?
Overmind is a model-training platform for teams building AI agents. It connects repository context and production traces with a workflow for preparing datasets, fine-tuning open models, evaluating results, and deploying models. Teams can host trained models on Overmind or run them on their own infrastructure, and can download weights and checkpoints. The platform is designed to help teams develop specialized models for agent tasks and assess them against their current model before deployment.
What can Overmind do?
Prepare trace-based datasets
Build datasets from production traces, then clean, filter, and prepare examples for training and evaluation in the Data Workshop.
Evaluate models on real tasks
Test prompts and models against tasks drawn from production data, compare results with a current model, inspect failures, and check for regressions.
Fine-tune and deploy open models
Train an open model on prepared data; Overmind supports training and deployment, with a choice to host the model on Overmind or run it on your own infrastructure.
Export model artifacts
Download trained weights and checkpoints through the Console or Overmind CLI. Models served through Overmind endpoints are available through an OpenAI-compatible API.
Self-host the platform
Run the platform on your own infrastructure using Docker Compose. The source code is published under the AGPL-3.0 license.
Use Cases
“Diagnose agent failures”
Trace a failing agent step back to its tool call and surrounding context, using repository information and production traces to investigate what went wrong.
“Build business-specific training examples”
Convert operational traces into cleaned examples that capture organization-specific tasks and prepare them for model training or evaluation.
“Compare candidate models before release”
Evaluate prompts or models against production-derived tasks and compare their performance with the model currently in use before deploying changes.
“Run a specialized model on your infrastructure”
Fine-tune a model for an agent task, export its weights and checkpoints, and serve it outside Overmind when your deployment workflow calls for self-hosting.
Frequently Asked Questions
Can I train and deploy a model on the Free plan?
Yes. Training and deployment use credits. The Free plan includes a one-time $50 credit; it also includes one seat per project.
How are credits used?
Credits are charged for training runs, inference on Overmind endpoints, evaluation runs, and Agent Optimizer runs. Sending and browsing traces, agent discovery and sync, dataset preparation, and Console, CLI, and MCP access do not use credits. One credit is $0.01, and unused credits do not expire.
Can I take a trained model out of Overmind?
Yes. Download trained weights and checkpoints from the Console or Overmind CLI, then serve them on your own infrastructure.
Can teams use Overmind collaboratively?
The Free plan provides one seat per project. Pro includes unlimited seats and priority support; Enterprise offers dedicated technical support and SSO and custom authentication.
Can Overmind be self-hosted?
Yes. The platform can be run on your own infrastructure with Docker Compose. Its code is available under the AGPL-3.0 license.
Quick Facts
- Product type
- Model training and deployment platform for AI teams
- Primary workflow
- Production traces → dataset preparation → training and evaluation → deployment
- Model ownership
- Teams can download trained weights and checkpoints
- Deployment options
- Overmind-hosted endpoints or the team's own infrastructure
- Platform access
- Console, CLI, and MCP server
Overmind Traffic Analysis
Traffic data is for reference only.
- Domain Rating
- 44
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