# AutoResearchClaw

**Repository Path**: btpig/AutoResearchClaw

## Basic Information

- **Project Name**: AutoResearchClaw
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-16
- **Last Updated**: 2026-03-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
  <img src="image/logo.png" width="700" alt="AutoResearchClaw Logo">
</p>

<h2 align="center"><b>Chat an Idea. Get a Paper. Fully Autonomous.</b></h2>



<p align="center">
  <b><i><font size="5">Just chat with <a href="#openclaw-integration">OpenClaw</a>: "Research X" → done.</font></i></b>
</p>

<p align="center">
  <img src="image/framework.png" width="100%" alt="AutoResearchClaw Framework">
</p>


<p align="center">
  <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="MIT License"></a>
  <a href="https://python.org"><img src="https://img.shields.io/badge/Python-3.11%2B-3776AB?logo=python&logoColor=white" alt="Python 3.11+"></a>
  <a href="#testing"><img src="https://img.shields.io/badge/Tests-1128%20passed-brightgreen?logo=pytest&logoColor=white" alt="1128 Tests Passed"></a>
  <a href="https://github.com/Jiaaqiliu/AutoResearchClaw"><img src="https://img.shields.io/badge/GitHub-AutoResearchClaw-181717?logo=github" alt="GitHub"></a>
  <a href="#openclaw-integration"><img src="https://img.shields.io/badge/OpenClaw-Compatible-ff4444?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCI+PHBhdGggZD0iTTEyIDJDNi40OCAyIDIgNi40OCAyIDEyczQuNDggMTAgMTAgMTAgMTAtNC40OCAxMC0xMFMxNy41MiAyIDEyIDJ6IiBmaWxsPSJ3aGl0ZSIvPjwvc3ZnPg==" alt="OpenClaw Compatible"></a>
</p>

<p align="center">
  <a href="docs/README_CN.md">🇨🇳 中文</a> ·
  <a href="docs/README_JA.md">🇯🇵 日本語</a> ·
  <a href="docs/README_KO.md">🇰🇷 한국어</a> ·
  <a href="docs/README_FR.md">🇫🇷 Français</a> ·
  <a href="docs/README_DE.md">🇩🇪 Deutsch</a> ·
  <a href="docs/README_ES.md">🇪🇸 Español</a> ·
  <a href="docs/README_PT.md">🇧🇷 Português</a> ·
  <a href="docs/README_RU.md">🇷🇺 Русский</a> ·
  <a href="docs/README_AR.md">🇸🇦 العربية</a>
</p>

<p align="center">
  <a href="docs/integration-guide.md">📖 Integration Guide</a>
</p>

---

> **🧪 We're looking for testers!** Try the pipeline with your own research idea — from any field — and [tell us what you think](docs/TESTER_GUIDE.md). Your feedback directly shapes the next version. **[→ Testing Guide](docs/TESTER_GUIDE.md)** | **[→ 中文测试指南](docs/TESTER_GUIDE_CN.md)**

---

## ⚡ One Command. One Paper.

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


---

## 🤔 What Is This?

**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.

<table>
<tr><td>📄</td><td><code>paper_draft.md</code></td><td>Full academic paper (Introduction, Related Work, Method, Experiments, Results, Conclusion)</td></tr>
<tr><td>📐</td><td><code>paper.tex</code></td><td>Conference-ready LaTeX (NeurIPS / ICLR / ICML templates)</td></tr>
<tr><td>📚</td><td><code>references.bib</code></td><td>Real BibTeX references from Semantic Scholar and arXiv — auto-pruned to match inline citations</td></tr>
<tr><td>🔍</td><td><code>verification_report.json</code></td><td>4-layer citation integrity + relevance verification (arXiv, CrossRef, DataCite, LLM)</td></tr>
<tr><td>🧪</td><td><code>experiment runs/</code></td><td>Generated code + sandbox results + structured JSON metrics</td></tr>
<tr><td>📊</td><td><code>charts/</code></td><td>Auto-generated condition comparison charts with error bars and confidence intervals</td></tr>
<tr><td>📝</td><td><code>reviews.md</code></td><td>Multi-agent peer review with methodology-evidence consistency checks</td></tr>
<tr><td>🧬</td><td><code>evolution/</code></td><td>Self-learning lessons extracted from each run</td></tr>
<tr><td>📦</td><td><code>deliverables/</code></td><td>All final outputs in one folder — compile-ready for Overleaf</td></tr>
</table>

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.

---

## 🚀 Quick Start

```bash
# 1. Clone & install
git clone https://github.com/Jiaaqiliu/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.

<details>
<summary>📝 Minimum required config</summary>

```yaml
project:
  name: "my-research"

research:
  topic: "Your research topic here"

llm:
  provider: "openai"  # or "openrouter" for access to 200+ models
  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"
```

</details>

<details>
<summary>🌐 Using OpenRouter (200+ models via single API)</summary>

[OpenRouter](https://openrouter.ai) provides unified access to models from Anthropic, Google, Meta, Mistral, and more through a single API key.

```yaml
llm:
  provider: "openrouter"
  api_key_env: "OPENROUTER_API_KEY"
  primary_model: "anthropic/claude-3.5-sonnet"
  fallback_models:
    - "google/gemini-pro-1.5"
    - "meta-llama/llama-3.1-70b-instruct"
```

**Setup:**
1. Get your API key at https://openrouter.ai/keys
2. Export: `export OPENROUTER_API_KEY="sk-or-..."`
3. Browse models: https://openrouter.ai/models

**Popular models:**
- `anthropic/claude-3.5-sonnet` — Best for research reasoning
- `google/gemini-pro-1.5` — 1M context window
- `meta-llama/llama-3.1-70b-instruct` — Open-source, strong performance

</details>

---

## 🧠 What Makes It Different

| Capability | How It Works |
|-----------|-------------|
| **🔄 PIVOT / REFINE Loop** | Stage 15 autonomously decides: PROCEED, REFINE (tweak params), or PIVOT (new direction). Artifacts auto-versioned. |
| **🤖 Multi-Agent Debate** | Hypothesis generation, result analysis, and peer review each use structured multi-perspective debate. |
| **🧬 Self-Learning** | Lessons extracted per run (decision rationale, runtime warnings, metric anomalies) with 30-day time-decay. Future runs learn from past mistakes. |
| **📚 Knowledge Base** | Every run builds structured KB across 6 categories (decisions, experiments, findings, literature, questions, reviews). |
| **🛡️ Sentinel Watchdog** | Background quality monitor: NaN/Inf detection, paper-evidence consistency, citation relevance scoring, anti-fabrication guard. |

---

## 🦞 OpenClaw Integration

<table>
<tr>

**AutoResearchClaw is an [OpenClaw](https://github.com/openclaw/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.

</tr>
</table>

### 🚀 Use with OpenClaw (Recommended)

If you already use [OpenClaw](https://github.com/openclaw/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.

<details>
<summary>💡 What happens under the hood</summary>

1. OpenClaw reads `RESEARCHCLAW_AGENTS.md` → learns the research orchestrator role
2. OpenClaw reads `README.md` → understands installation and pipeline structure
3. OpenClaw copies `config.researchclaw.example.yaml` → `config.yaml`
4. Asks for your LLM API key (or uses your environment variable)
5. Runs `pip install -e .` + `researchclaw run --topic "..." --auto-approve`
6. Returns the paper, LaTeX, experiments, and citations

</details>

### 🔌 OpenClaw Bridge (Advanced)

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

```yaml
# 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`](docs/integration-guide.md) for full details.

### ACP (Agent Client Protocol)

AutoResearchClaw can use **any ACP-compatible coding agent** as its LLM backend — no API keys required. The agent communicates via [acpx](https://github.com/openclaw/acpx), maintaining a single persistent session across all 23 pipeline stages.

| Agent | Command | Notes |
|-------|---------|-------|
| Claude Code | `claude` | Anthropic |
| Codex CLI | `codex` | OpenAI |
| Gemini CLI | `gemini` | Google |
| OpenCode | `opencode` | SST |
| Kimi CLI | `kimi` | Moonshot |

```yaml
# 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.
```

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

### 🛠️ Other Ways to Run

| Method | How |
|--------|-----|
| **Standalone CLI** | `researchclaw run --topic "..." --auto-approve` |
| **Python API** | `from researchclaw.pipeline import Runner; Runner(config).run()` |
| **Claude Code** | Reads `RESEARCHCLAW_CLAUDE.md` — just say *"Run research on [topic]"* |
| **OpenCode** | Reads `.claude/skills/` — same natural language interface |
| **Any AI CLI** | Provide `RESEARCHCLAW_AGENTS.md` as context → agent auto-bootstraps |

---

## 🔬 Pipeline: 23 Stages, 8 Phases

```
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.

<details>
<summary>📋 What Each Phase Does</summary>

| Phase | What Happens |
|-------|-------------|
| **A: Scoping** | LLM decomposes the topic into a structured problem tree with research questions |
| **A+: Hardware** | Auto-detects GPU (NVIDIA CUDA / Apple MPS / CPU-only), warns if local hardware is limited, adapts code generation accordingly |
| **B: Literature** | Multi-source search (arXiv-first, then Semantic Scholar) for real papers, screens by relevance, extracts knowledge cards |
| **C: Synthesis** | Clusters findings, identifies research gaps, generates testable hypotheses via multi-agent debate |
| **D: Design** | Designs experiment plan, generates hardware-aware runnable Python (GPU tier → package selection), estimates resource needs |
| **E: Execution** | Runs experiments in sandbox, detects NaN/Inf and runtime bugs, self-heals code via targeted LLM repair |
| **F: Analysis** | Multi-agent analysis of results; autonomous PROCEED / REFINE / PIVOT decision with rationale |
| **G: Writing** | Outlines → section-by-section drafting (5,000-6,500 words) → peer reviews (with methodology-evidence consistency) → revises with length guard |
| **H: Finalization** | Quality gate, knowledge archival, LaTeX export with conference template, citation integrity + relevance verification |

</details>

---

## ✨ Key Features

| Feature | Description |
|---------|------------|
| **📚 Multi-Source Literature** | Real papers from arXiv (primary) + Semantic Scholar — query expansion, deduplication, circuit breaker with graceful degradation |
| **🔍 4-Layer Citation Verification** | arXiv ID check → CrossRef/DataCite DOI → Semantic Scholar title match → LLM relevance scoring. Hallucinated refs auto-removed. |
| **🖥️ Hardware-Aware Execution** | Auto-detects GPU (NVIDIA CUDA / Apple MPS / CPU-only) and adapts code generation, imports, and experiment scale accordingly |
| **🧪 Sandbox Experiments** | AST-validated code, immutable harness, NaN/Inf fast-fail, self-healing repair, iterative refinement (up to 10 rounds), partial result capture |
| **📝 Conference-Grade Writing** | NeurIPS/ICML/ICLR templates, section-by-section drafting (5,000-6,500 words), anti-fabrication guard, revision length guard, anti-disclaimer enforcement |
| **📐 Template Switching** | `neurips_2025`, `iclr_2026`, `icml_2026` — Markdown → LaTeX with math, tables, figures, cross-refs, `\cite{}` |
| **🚦 Quality Gates** | 3 human-in-the-loop gates (Stages 5, 9, 20) with rollback. Skip with `--auto-approve`. |

---

## ⚙️ Configuration Reference

<details>
<summary>Click to expand full configuration reference</summary>

```yaml
# === 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: 600             # Max execution time per run (default: 600s)
  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
```

</details>

---

## 🙏 Acknowledgments

Inspired by:

- 🔬 [AI Scientist](https://github.com/SakanaAI/AI-Scientist) (Sakana AI) — Automated research pioneer
- 🧠 [AutoResearch](https://github.com/karpathy/autoresearch) (Andrej Karpathy) — End-to-end research automation
- 🌐 [FARS](https://analemma.ai/blog/introducing-fars/) (Analemma) — Fully Automated Research System

---

## 📄 License

MIT — see [LICENSE](LICENSE) for details.

---

## 📌 Citation

If you find AutoResearchClaw useful, please cite:

```bibtex
@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},
}
```

<p align="center">
  <sub>Built with 🦞 by the AutoResearchClaw team</sub>
</p>