The open source training platform for continuously improving agents and models
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Joined January 2026
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Overmind turns anyone into an AI lab.
• Builds a context graph from code and traces
• Curates training and eval datasets
• Evaluates prompts and models
• Trains smaller, specialized models
github.com/overmind-core/ove…
Start here: docs.overmindlab.ai/tutorial…
The prompt connects Overmind to your coding agent.
What does it mean to break Big Tech's grip on enterprise AI?
• When a company rents an AI model, rivals can rent the same one.
• The edge comes from training AI on how it's used.
AI labs do this. Now a company can too.
Overmind turns any company into an AI lab.
ALT Headline: The model is the part anyone can rent. The loop is what keeps making it better. Below, a loop of four boxes joined by orange arrows: your agent at work, usage and feedback, train on your own data, a better agent, then back to the start. In the middle: Your AI lab.
Thanks to @iamVictorDey and @Forbes for the write-up.
Full piece here: forbes.com/sites/victordey/2…
Overmind retweeted
Overmind is live on Product Hunt! 🚀
Some big companies have trained their own models this year.
That took an ML team. Most companies don't have one.
They do have coding agents though. So Overmind lets those agents do the ML team's work.
Live: producthunt.com/posts/overmi…
ALT Black starfield with white pixel flowers along the bottom. Small orange label: Our mission. Headline: Turn your coding agent into an AI lab.
Overmind is live on Product Hunt! 🚀
Some big companies have trained their own models this year.
That took an ML team. Most companies don't have one.
They do have coding agents though. So Overmind lets those agents do the ML team's work.
Live: producthunt.com/posts/overmi…
ALT Black starfield with white pixel flowers along the bottom. Small orange label: Our mission. Headline: Turn your coding agent into an AI lab.
Start with a single command in Claude Code. End with a trained 0.8B model to sort support messages. Accuracy went from 37% to 84% on 300 messages.
nitter.cf/OvermindLab/status/211…
Paste into Claude Code:
Read console.overmindlab.ai/setup… and set up Overmind. Then fine-tune Qwen 3.5 0.8B on 1,000 of these support messages and benchmark it against the untouched model on messages it never saw: huggingface.co/datasets/lega…
We used this method to train a 0.8B model on 1,000 support traces, the agent's accuracy went from 36.7% to 84.0%. The training run took 23 minutes from launch to live.
console.overmindlab.ai/
Enterprises still go for 30-200B models because big feels safe. Small and trained often does the job better.
AI got a user interface. Using it is a prompt away.
Next comes a training interface, where improving your agent is as easy as using it.
@JensenHuang put our whole roadmap on one slide at #nvidiagtc berlin: "take control of your intelligence, build your own ai" (the @pollenrobotics microducks waddling around his feet still stole the show!!)
nvidia calls them ai factories, with gpus at one end and enterprise value at the other. almost every conversation i had was about some stretch of that chain
the best debates were about rl. everyone is racing to own the rl stack, yet every compute provider told me the same thing: enterprises still pick 30-200b general models because big feels safe. a focused 1-3b specialist would often do the job better. that is an education gap, not a tech one.
huge thanks to the @nvidia startup team. jessica held the stage with so much warmth, naomi ran everything so smoothly you forgot how hard it was, and howard went from the hardwood to nvidia without losing his court vision. bonus: howard and naomi share my altitude, so for once i got to talk to people eye to eye
the future we are building at @OvermindLab: every agent in production supervised by one open-source platform that trains on your own data, runs on infra you own, and ships a better version of itself every single day 🚀
"Traces are only useful if they feed a better agent."
In practice, on 4,000+ contract questions:
Qwen3.5-9B trained on the task:
• 91.6 F1
• $1.03 per 1k questions
GPT-5.6 Luna:
• 82.0 F1
• $20.94 per 1k questions
Bigger isn't better. Trained is.
Agent traces are useful only if they feed a better agent.
Overmind is an open-source training platform for builders who want to improve specialized AI agents using production traces or their own datasets.
It helps you turn those traces into versioned evaluation and training data, then test improvements against the metrics you define.
Key features:
• Agent context graph – scans a repo for an agent’s capabilities, prompts, tools, and tasks
• Trace-based datasets – turns production traces or uploaded files into versioned eval and training datasets
• Per-capability evals – measures what “good” means on live traces and in batch
• Optimisation experiments – tests prompt, tool, and control-flow changes in your repo; the winner is a git diff
• Model workflow – fine-tunes models on your data and serves trained or frontier models through one API
The platform is open-source (AGPL-3.0); the SDK and CLI are MIT licensed.
Link in the reply 👇
Full write-up, including biomedical and aviation results: overmindlab.ai/research/when…
Try it on your own traces: overmindlab.ai
Overmind's eye sees all at @NVIDIAGTC
The vision in Europe is clear. The future belongs to companies that build their own intelligence.
Overmind brings vision and future into alignment.
Stay tuned.
in berlin this week for #nvidiagtc, representing @OvermindLab. the last few years were about building the biggest engine, and the next few will be won by the pit crew. come say hi if you are building agents 🙌
Overmind retweeted
I used to wonder why there has been rising interest in "open weight models" across the realms... 🌎
Then I realised the @OvermindLab eye sees all.
holy shit who is building this?
great segment on @theallinpod @DavidSacks @chamath @Jason
@OvermindLab is by far the easiest way to customise small models on your own proprietary data. direct from your coding agent, no in-house research lab required!
If your traces live in Langfuse, leave them there.
Overmind imports them and trains a model on them.
Works from Claude Code, Cursor, Codex or OpenCode.
Get started: pip install overmind
Top 14 open-source tools for LLM monitoring in 2026
▪️ Langfuse
▪️ Opik
▪️ Overmind
▪️ MLflow
▪️ LangWatch
▪️ AgentOps
▪️ Helicone
▪️ OpenLIT
▪️ OpenLLMetry
▪️ Evidently
▪️ Weave
▪️ OpenTelemetry Collector
▪️ Grafana OSS stack
▪️ Laminar
Bookmark the list to keep it handy!
Here is also a practical guide explaining all these observability tools - what each one does, and where it best fits → turingpost.com/p/llm-observa…
How to connect Langfuse, LangSmith, Braintrust or Galileo: docs.overmindlab.ai/core/obs…
Select the runs worth learning from and each becomes a training row, so the decisions your agent already makes become the training set.
Turn your company into its own AI lab.
github.com/overmind-core/ove…
