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Overmind is a platform for building and improving AI applications and models. It brings together observability, data preparation, evaluation, model training and inference, so you can follow an improvement from the source data or observed failure through to a measured result. You can start with an existing application or a dataset and a task. Connect Overmind to your coding agent, describe what you want to achieve, and work through the results in the same conversation.

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What you can do with Overmind

Understand your application

Map what your agent does, trace its executions, and investigate failures, quality, latency and model usage.

Prepare data

Turn files, datasets or recorded traces into reproducible training and evaluation inputs.

Evaluate and improve

Measure outputs against explicit checks, compare models, and test prompt or code changes.

Train and serve models

Fine-tune language or decision models, benchmark their results, and call ready deployments from your application.
These workflows connect: a failure found in a trace can become an evaluation example, and a prepared dataset can support training and a held-out comparison. You can also use each workflow on its own.

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How you use it

Your coding agent is where you describe the task. Through MCP, it discovers your Overmind projects and uses the platform’s tools to prepare data, run evaluations, launch training and inspect results. It also writes code and uses the CLI for local files and repository work. The Overmind Console gives you a visual view of the same project: datasets, traces, evaluations, training progress and deployments. Use it to inspect what the agent has created and follow running jobs. A single prompt can cover a complete workflow. The agent works out the steps, carries the project context forward and brings back the results or decisions that need your input. Overmind retains the data, recipes and run records so you can inspect or continue the work later. Learn how the interfaces work together.

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Get started

  1. Connect your coding agent. Paste the setup prompt below into your coding environment and follow the account and client setup steps.
  2. Choose a project and starting point. Bring a dataset and describe the output you need, or open your application’s repository to work from its code and traces.
  3. Ask for an outcome. Request data preparation, an investigation, a model comparison or a training run. Your agent checks the proposed execution and expected cost before launching training.
The connection guide explains setup and verification. Once connected, choose a task:

Train a decision model

Learn the full preparation, training and benchmarking workflow for predictions with typed probabilities.

Fine-tune a language model

Train a model for text responses or tool calls using examples of the behaviour you want.

Connect an application

Map a repository’s capabilities, then add tracing and evaluate its behaviour.

Explore the tutorials

Follow a worked example of training, tracing or evaluation.
For a specific task, use the how-to guides. Use Reference for tools and formats, or Explanation for the concepts behind the workflows.