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Agent

AutoResearchClaw

by aiming-lab14.1kPythonUpdated 2026-08-19

Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞

Claude CodeOpenClawCodex CLICopilot CLIGemini CLIKimi CLI

AutoResearchClaw is a fully autonomous AI research agent that transforms a single research idea into a conference-ready academic paper through a 23-stage pipeline. It conducts real literature searches via OpenAlex, Semantic Scholar, and arXiv; designs and executes hardware-aware experiments in a sandboxed environment; performs multi-agent peer review; and generates LaTeX output with verified citations. The agent supports both fully autonomous operation and human-in-the-loop co-pilot mode for collaborative research, and integrates with OpenClaw, Claude Code, and other ACP-compatible AI assistants.

Key Features

23-stage autonomous research pipeline from topic decomposition to LaTeX paper export with NeurIPS/ICML/ICLR templates
Multi-source literature discovery with real papers from OpenAlex, Semantic Scholar, and arXiv APIs with 4-layer citation verification
Hardware-aware experiment execution that auto-detects GPU (CUDA/MPS/CPU) and generates runnable Python code with self-healing repair
Human-in-the-loop co-pilot mode with 6 intervention levels (full-auto, gate-only, checkpoint, step-by-step, co-pilot, custom)
OpenCode Beast Mode integration for complex multi-file experiments with custom architectures and ablation studies
MetaClaw cross-run learning that captures lessons from failures and converts them into reusable skills for future runs
Multi-agent debate systems for hypothesis generation, result analysis, and peer review with methodology-evidence consistency checks
Anti-fabrication safeguards with VerifiedRegistry, experiment diagnosis and repair loops, and unverified number sanitization

Use Cases

  • 01Generate a complete machine learning research paper from a single topic idea with experiments, results, and verified citations
  • 02Conduct autonomous literature reviews across multiple databases with automatic relevance screening and knowledge extraction
  • 03Design and run hardware-aware experiments that adapt to available GPU resources and self-heal code failures
  • 04Collaborate with AI on hypothesis refinement and baseline selection using Co-Pilot mode at critical decision points
  • 05Build cross-domain research papers spanning ML, NLP, biology, physics, and statistics with specialist execution agents
  • 06Create reproducible research workflows with SHA256 checksums, immutable manifests, and versioned artifact snapshots

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AutoResearchClaw — FAQ

What is AutoResearchClaw?+

AutoResearchClaw is an autonomous AI research agent that executes a 23-stage pipeline to generate complete academic papers from a research idea, including literature review, experiment design and execution, statistical analysis, peer review, and LaTeX formatting. It can run fully autonomously or in collaborative co-pilot mode.

How do I install AutoResearchClaw?+

Clone the repository, create a Python 3.11+ virtual environment, run 'pip install -e .', then execute 'researchclaw setup' for interactive configuration including OpenCode Beast Mode and Docker/LaTeX checks. Finally, run 'researchclaw init' to generate your config file with LLM provider settings.

Which AI clients work with AutoResearchClaw?+

AutoResearchClaw works standalone via CLI, integrates with OpenClaw for chat-based operation across Discord/Telegram/Slack, and supports any ACP-compatible agent including Claude Code, Codex CLI, Copilot CLI, Gemini CLI, and Kimi CLI. It can also be used as a Python API.

What API keys are required?+

At minimum, you need an LLM API key (OpenAI, Anthropic, or any OpenAI-compatible provider). Optional keys include Semantic Scholar API for higher rate limits, OpenAlex API for authenticated access, and Tavily API for web-augmented literature search. When using ACP agents, no API keys are needed as the agent handles authentication.

Is AutoResearchClaw free to use?+

The software is MIT licensed and free to use, but you pay for LLM API calls and any cloud compute resources. The pipeline includes cost guardrails with configurable budget thresholds to prevent runaway expenses.

What are the system prerequisites?+

Python 3.11+, a virtual environment, and optionally Docker for sandboxed experiments and LaTeX for PDF compilation. The agent auto-detects GPU hardware (NVIDIA CUDA, Apple MPS, or CPU-only) and adapts code generation accordingly.

How do I install AutoResearchClaw?+

Open the source repository on GitHub and follow its README. AutoResearchClaw is a agent — MCP Agents Market links you directly to the official repo.

Is AutoResearchClaw free?+

AutoResearchClaw is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.

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