AutoResearch
AI/ML research agents from idea to paper-ready evidence. An EvoMap open-source project.
AutoResearch is an open-source multi-agent AI research workflow that autonomously transforms research ideas into paper-ready experimental evidence. The system manages the complete research pipeline—from discovering research directions via online signals and domain knowledge, through cross-model idea validation, to stateful experiment execution with independent review. Developers and researchers use it to automate hypothesis generation, experiment planning, code implementation, pilot testing, full-scale execution, and blind peer review while maintaining traceable provenance and supporting negative results.
Key Features
Use Cases
- 01Discovering novel AI/ML research directions from arxiv papers, developer forums, and GitHub trends
- 02Validating research hypotheses through automated experiment design and multi-model peer review
- 03Running reproducible ML experiments with automatic logging, metrics collection, and failure analysis
- 04Building a domain-specific research knowledge base to guide future idea generation
- 05Conducting pilot studies to assess research feasibility before committing full compute resources
- 06Generating paper-ready evidence packages with traceable sources and independent evaluation
Related Agents
View morehermes-agent
The agent that grows with you
agency-agents
A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
openinterpreter
A coding agent for open models like Kimi K3
cline
Autonomous coding agent as an SDK, IDE extension, or CLI assistant.
AutoResearch — FAQ
What is AutoResearch and what does it do?+
AutoResearch is an open-source AI agent workflow for machine learning research that automates the complete pipeline from idea generation through experiment execution to peer-reviewed evidence. It uses multiple LLMs, stateful execution, and independent review to reduce hallucination and maintain research rigor.
How do I install and set up AutoResearch?+
Clone the repository, run the bringup script to create a Python environment, then configure API credentials in .env and config/providers.local.json. For execution, you'll also need Bun 1.3+, Node.js, and conda. Run preflight checks to validate your model configuration before starting.
Which AI models and clients does AutoResearch work with?+
AutoResearch supports any combination of OpenAI GPT, Anthropic Claude, Google Gemini, or compatible API endpoints. The idea generation stage requires at least three distinct models for independent review; execution uses Claude Code CLI as the coordination runtime.
What API keys and prerequisites are required?+
You need API credentials for at least one LLM provider (OpenAI, Anthropic, or Google), configured in .env. Idea generation also optionally uses Tavily API for research signal collection. Execution requires Python 3.10+, Bun, conda, and compute resources (CPU or GPU) appropriate to your experiment.
Is AutoResearch free and open source?+
Yes, AutoResearch is released under the Apache 2.0 license and is completely open source. However, running it incurs API costs from your chosen LLM providers and optional search services.
Do I need a GPU to use AutoResearch?+
No GPU is required for the idea generation pipeline, which runs on CPU. GPU requirements for experiment execution depend on your specific research hypothesis; the system runs pilot tests first to catch resource mismatches early.
How do I install AutoResearch?+
Open the source repository on GitHub and follow its README. AutoResearch is a agent — MCP Agents Market links you directly to the official repo.
Is AutoResearch free?+
AutoResearch is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.