Just chat with OpenClaw: "Research X" → done.

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📖 Integration Guide

🧪 We're looking for testers! Try the pipeline with your own research idea — from any field — and tell us what you think. Your feedback directly shapes the next version. → Testing Guide | → 中文测试指南

• [03/16/2026] v0.2.0 — Three multi-agent subsystems (CodeAgent, BenchmarkAgent, FigureAgent), hardened Docker sandbox with network-policy-aware execution, 4-round paper quality audit (AI-slop detection, 7-dim review scoring, NeurIPS checklist), and 15+ bug fixes from production runs.
• [03/15/2026] v0.1.0 — We release AutoResearchClaw: a fully autonomous 23-stage research pipeline that turns a single research idea into a conference-ready paper. No human intervention required.

pip install -e . && researchclaw run --topic "Your research idea here" --auto-approve

You think it. AutoResearchClaw writes it.

Drop a research topic — get back a full academic paper with real literature from arXiv & Semantic Scholar, hardware-aware sandbox experiments (GPU/MPS/CPU auto-detected), statistical analysis, multi-agent peer review, and conference-ready LaTeX targeting NeurIPS/ICML/ICLR. No babysitting. No copy-pasting. No hallucinated references.

The pipeline runs end-to-end without human intervention. When experiments fail, it self-heals. When hypotheses don't hold, it pivots. When citations are fake, it kills them.

# 1. Clone & install
git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
python3 -m venv .venv && source .venv/bin/activate
pip install -e .

# 2. Configure
cp config.researchclaw.example.yaml config.arc.yaml
# Edit config.arc.yaml — set your LLM API endpoint and key

# 3. Run
export OPENAI_API_KEY="sk-..."
researchclaw run --config config.arc.yaml --topic "Your research idea" --auto-approve

Output → artifacts/rc-YYYYMMDD-HHMMSS-<hash>/deliverables/ — compile-ready LaTeX, BibTeX, experiment code, charts.

project:
  name: "my-research"

research:
  topic: "Your research topic here"

llm:
  base_url: "https://api.openai.com/v1"
  api_key_env: "OPENAI_API_KEY"
  primary_model: "gpt-4o"
  fallback_models: ["gpt-4o-mini"]

experiment:
  mode: "sandbox"
  sandbox:
    python_path: ".venv/bin/python"

AutoResearchClaw is an OpenClaw-compatible service. Install it in OpenClaw and launch autonomous research with a single message — or use it standalone via CLI, Claude Code, or any AI coding assistant.

If you already use OpenClaw as your AI assistant:

1️⃣  Share the GitHub repo URL with OpenClaw
2️⃣  OpenClaw auto-reads RESEARCHCLAW_AGENTS.md → understands the pipeline
3️⃣  Say: "Research [your topic]"
4️⃣  Done — OpenClaw clones, installs, configures, runs, and returns results

That's it. OpenClaw handles git clone, pip install, config setup, and pipeline execution automatically. You just chat.

For deeper integration, AutoResearchClaw includes a bridge adapter system with 6 optional capabilities:

# config.arc.yaml
openclaw_bridge:
  use_cron: true              # ⏰ Scheduled research runs
  use_message: true           # 💬 Progress notifications (Discord/Slack/Telegram)
  use_memory: true            # 🧠 Cross-session knowledge persistence
  use_sessions_spawn: true    # 🔀 Spawn parallel sub-sessions for concurrent stages
  use_web_fetch: true         # 🌐 Live web search during literature review
  use_browser: false          # 🖥️ Browser-based paper collection

Each flag activates a typed adapter protocol. When OpenClaw provides these capabilities, the adapters consume them without code changes. See docs/integration-guide.md for full details.

AutoResearchClaw can use any ACP-compatible coding agent as its LLM backend — no API keys required. The agent communicates via acpx, maintaining a single persistent session across all 23 pipeline stages.

# config.yaml — ACP example
llm:
  provider: "acp"
  acp:
    agent: "claude"   # Any ACP-compatible agent CLI command
    cwd: "."          # Working directory for the agent
  # No base_url or api_key needed — the agent handles its own auth.

# Just run — the agent uses its own credentials
researchclaw run --config config.yaml --topic "Your research idea" --auto-approve

Phase A: Research Scoping          Phase E: Experiment Execution
  1. TOPIC_INIT                      12. EXPERIMENT_RUN
  2. PROBLEM_DECOMPOSE               13. ITERATIVE_REFINE  ← self-healing

Phase B: Literature Discovery      Phase F: Analysis & Decision
  3. SEARCH_STRATEGY                 14. RESULT_ANALYSIS    ← multi-agent
  4. LITERATURE_COLLECT  ← real API  15. RESEARCH_DECISION  ← PIVOT/REFINE
  5. LITERATURE_SCREEN   [gate]
  6. KNOWLEDGE_EXTRACT               Phase G: Paper Writing
                                     16. PAPER_OUTLINE
Phase C: Knowledge Synthesis         17. PAPER_DRAFT
  7. SYNTHESIS                       18. PEER_REVIEW        ← evidence check
  8. HYPOTHESIS_GEN    ← debate      19. PAPER_REVISION

Phase D: Experiment Design         Phase H: Finalization
  9. EXPERIMENT_DESIGN   [gate]      20. QUALITY_GATE      [gate]
 10. CODE_GENERATION                 21. KNOWLEDGE_ARCHIVE
 11. RESOURCE_PLANNING               22. EXPORT_PUBLISH     ← LaTeX
                                     23. CITATION_VERIFY    ← relevance check

Gate stages (5, 9, 20) pause for human approval or auto-approve with --auto-approve. On rejection, the pipeline rolls back.

Decision loops: Stage 15 can trigger REFINE (→ Stage 13) or PIVOT (→ Stage 8), with automatic artifact versioning.

# === Project ===
project:
  name: "my-research"              # Project identifier
  mode: "docs-first"               # docs-first | semi-auto | full-auto

# === Research ===
research:
  topic: "..."                     # Research topic (required)
  domains: ["ml", "nlp"]           # Research domains for literature search
  daily_paper_count: 8             # Target papers per search query
  quality_threshold: 4.0           # Minimum quality score for papers

# === Runtime ===
runtime:
  timezone: "America/New_York"     # For timestamps
  max_parallel_tasks: 3            # Concurrent experiment limit
  approval_timeout_hours: 12       # Gate stage timeout
  retry_limit: 2                   # Retry count on stage failure

# === LLM ===
llm:
  provider: "openai-compatible"    # "openai-compatible" (default) or "acp"
  base_url: "https://..."          # API endpoint (required for openai-compatible)
  api_key_env: "OPENAI_API_KEY"    # Env var for API key (required for openai-compatible)
  api_key: ""                      # Or hardcode key here
  primary_model: "gpt-4o"          # Primary model
  fallback_models: ["gpt-4o-mini"] # Fallback chain
  s2_api_key: ""                   # Semantic Scholar API key (optional, higher rate limits)
  acp:                             # Only used when provider: "acp"
    agent: "claude"                # ACP agent CLI command (claude, codex, gemini, etc.)
    cwd: "."                       # Working directory for the agent

# === Experiment ===
experiment:
  mode: "sandbox"                  # simulated | sandbox | docker | ssh_remote
  time_budget_sec: 300             # Max execution time per run (default: 300s)
  max_iterations: 10               # Max optimization iterations
  metric_key: "val_loss"           # Primary metric name
  metric_direction: "minimize"     # minimize | maximize
  sandbox:
    python_path: ".venv/bin/python"
    gpu_required: false
    allowed_imports: [math, random, json, csv, numpy, torch, sklearn]
    max_memory_mb: 4096
  docker:
    image: "researchclaw/experiment:latest"
    network_policy: "setup_only"   # none | setup_only | pip_only | full
    gpu_enabled: true
    memory_limit_mb: 8192
    auto_install_deps: true        # Auto-detect imports → requirements.txt
  ssh_remote:
    host: ""                       # GPU server hostname
    gpu_ids: []                    # Available GPU IDs
    remote_workdir: "/tmp/researchclaw_experiments"

# === Export ===
export:
  target_conference: "neurips_2025"  # neurips_2025 | iclr_2026 | icml_2026
  authors: "Anonymous"
  bib_file: "references"

# === Prompts ===
prompts:
  custom_file: ""                  # Path to custom prompts YAML (empty = defaults)

# === Security ===
security:
  hitl_required_stages: [5, 9, 20] # Stages requiring human approval
  allow_publish_without_approval: false
  redact_sensitive_logs: true

# === Knowledge Base ===
knowledge_base:
  backend: "markdown"              # markdown | obsidian
  root: "docs/kb"

# === Notifications ===
notifications:
  channel: "console"               # console | discord | slack
  target: ""

# === OpenClaw Bridge ===
openclaw_bridge:
  use_cron: false                  # Scheduled research runs
  use_message: false               # Progress notifications
  use_memory: false                # Cross-session knowledge persistence
  use_sessions_spawn: false        # Spawn parallel sub-sessions
  use_web_fetch: false             # Live web search
  use_browser: false               # Browser-based paper collection

Inspired by:

• 🔬 AI Scientist (Sakana AI) — Automated research pioneer
• 🧠 AutoResearch (Andrej Karpathy) — End-to-end research automation
• 🌐 FARS (Analemma) — Fully Automated Research System

MIT — see LICENSE for details.

If you find AutoResearchClaw useful, please cite:

@misc{liu2026autoresearchclaw,
  author       = {Liu, Jiaqi and Xia, Peng and Han, Siwei and Qiu, Shi and Zhang, Letian and Chen, Guiming  and Tu, Haoqin and Yang, Xinyu and and Zhou, Jiawei and Zhu, Hongtu and Li, Yun and Zheng, Zeyu and Xie, Cihang and Ding, Mingyu and Yao, Huaxiu},
  title        = {AutoResearchClaw: Fully Autonomous Research from Idea to Paper},
  year         = {2026},
  organization = {GitHub},
  url          = {https://github.com/aiming-lab/AutoResearchClaw},
}

_{Built with 🦞 by the AutoResearchClaw team}