karpathy/autoresearch
autoresearch
AI agents running research on single-GPU nanochat training automatically
Usage guide
autoresearch is an open-source project around all, ai-agents with 88,991 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating autoresearch for Python AI workflows.
- Comparing a GitHub project with 88,991 stars and current repository activity.
Pros
- autoresearch has visible GitHub traction with 88,991 stars.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- No license was detected, so usage risk needs manual review.
Production readiness
autoresearch should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
GitHub did not report a license, which usually requires manual legal review before production use.
autoresearch architecture preview
autoresearch's main path starts at the entry surface, runs through Coding agent runtime, combines LLM / model client, Files / repository context, External tool adapters, and returns Assistant response / action result.
Entry
Repository setup
autoresearch starts from the repository setup path and documented examples.
curl -LsSf https://astral.sh/uv/install.sh | sh
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
External tool adapters
Tool adapters let the runtime act outside the model through External tool adapters.
tool signal
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
Nick Saraev
Claude Code + Karpathy Autoresearch = The New Meta
160,682 views ยท 2026-03-12
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
autoresearch depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/karpathy/autoresearch.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ curl -LsSf https://astral.sh/uv/install.sh | shAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
AI agents running research on single-GPU nanochat training automatical
This is one of the documented reasons to evaluate autoresearch before choosing a stack.
AI Agents project comparison
Compare autoresearch with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official autoresearch setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented autoresearch example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is autoresearch?
autoresearch is an open-source ai agents project. AI agents running research on single-GPU nanochat training automatically
How do I install autoresearch?
Start with the official README. The first detected setup step is: git clone https://github.com/karpathy/autoresearch.git.
Is autoresearch beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can autoresearch be used commercially?
GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
Does autoresearch need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt autoresearch?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.