research-claw

Your self-hosted AI research assistant — manage papers, search literature, track deadlines, and collaborate across channels.

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https://github.com/user-attachments/assets/4bb9cdb6-6e4f-4325-9307-a8518d225761

Real user demo · Mobile · Powered by GLM-5 Papers produced: LLM-Based Autonomous Multi-Agent Systems Survey · Hierarchical Memory Sharing in MAS

Table of Contents

• What is Research Claw?
• Key Features
• Getting Started
• How It Works
• Configuration Reference
• Documentation
• Contributing
• License

What is Research Claw?

Research Claw is a personal AI research assistant you run on your own machine. It manages your LaTeX projects, syncs with Overleaf, searches literature, tracks deadlines — and answers you on the channels you already use (CLI, Web UI, Telegram, Feishu, QQ, DingTalk).

Instead of switching between your editor, Overleaf, terminal, and search engine, you talk to one assistant that handles it all:

You: Create a paper project "MoE-Survey" and link it to Overleaf.
Bot: ✅ Project created. Overleaf linked. Switched to MoE-Survey.

You: Research the latest MoE papers and draft an introduction.
Bot: 🔎 Searching arXiv... 📝 Writing introduction... ✅ Compiled successfully.

You: /sync push
Bot: ✅ Pushed 3 files to Overleaf.

Key Features

:writing_hand: Writing & Compilation

• Read, write, and refactor .tex / .bib files through chat
• One-command LaTeX compilation with auto error diagnosis
• Built-in venue skills — NeurIPS, ICML, ICLR, AAAI, ACL, CVPR…

:arrows_counterclockwise: Overleaf & Git

• Bidirectional Overleaf sync — pull edits, push changes
• Every AI edit auto-committed to Git — roll back in seconds
• Interactive /git mode for history, diff, and rollback

:busts_in_silhouette: Multi-Agent Collaboration

• Delegate research, writing, and review to specialized sub-agents
• Sub-agents work in isolated sandboxes — no accidental overwrites
• /task mode decomposes goals into a DAG and executes in parallel

:mag: Literature Search

• arXiv, PubMed, OpenAlex integration
• Full-text PDF reading for in-depth analysis

:satellite: Research Radar & Automation

• Scheduled tasks track your field — new papers, trends, deadlines
• Daily scans, weekly digests, direction-drift detection
• Push to Telegram, Feishu, DingTalk, Email, or any Apprise channel

:brain: Memory & Context

• Project-level memory across sessions
• Automated context summarization within token limits
• Memory-powered automation for continuity

:globe_with_meridians: Access Anywhere

Web UI  •  CLI  •  Feishu (Lark)  •  Telegram  •  QQ  •  DingTalk — no public IP required

https://github.com/user-attachments/assets/fccb837c-cfc5-4063-b803-2ae900fb4a20

Getting Started

1. Install

Linux / macOS:

git clone https://github.com/nanoAgentTeam/research-claw.git
cd research-claw

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Research Claw relies on POSIX features (signal handling, process management, etc.) and does not run natively on Windows. The recommended approach is WSL2 (Windows Subsystem for Linux) — it runs a real Linux kernel, so networking, filesystem, and process management all behave natively. No code changes needed.

Step 1: Install WSL2

Run in PowerShell (Administrator):

get download list: wsl --list --online

NAME            FRIENDLY NAME
Ubuntu          Ubuntu
Ubuntu-18.04    Ubuntu 18.04 LTS
Ubuntu-20.04    Ubuntu 20.04 LTS

Choose a version to install.

wsl --install -d Ubuntu-20.04

Reboot after installation. On first launch you'll be asked to create a username and password.

Step 2: Install Python 3.11

sudo apt update && sudo apt install -y python3.11 python3.11-venv python3-pip git

Step 3: Clone and install

# Recommended: keep code on the Linux filesystem for better performance
git clone https://github.com/nanoAgentTeam/research-claw.git ~/research-claw
cd ~/research-claw

python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Performance tip: Do not run the project under /mnt/c/. Cross-filesystem IO between WSL and Windows is ~3-5x slower. Keep code under ~/ for near-native Linux performance.

Networking & ports: WSL2 networking (API calls, web search, academic search) works out of the box. When running in Gateway mode, Windows browsers can access the Web UI at http://localhost:18790 — ports are forwarded automatically.

Browser automation (optional): If you need the browser_use tool, install Chromium:

# Option A: install directly
sudo apt install -y chromium-browser

# Option B: via playwright
pip install playwright && playwright install --with-deps chromium

Windows 11 includes WSLg for GUI support; on Windows 10 headless mode works fine.

2. Configure

# Start the gateway — this launches the Web UI
python cli/main.py gateway --port 18790

Open http://localhost:18790/ui in your browser:

1. Provider Management — Add your LLM provider (API Key, model name, base URL). Any OpenAI-compatible API works (GPT, DeepSeek, Qwen, Claude, etc.)
2. Channel Accounts — (optional) Add IM bot credentials (Feishu / Telegram / QQ / DingTalk)
3. Push Subscriptions — (optional) Configure where automation results get delivered

All settings are stored in settings.json. Advanced users can edit this file directly — see Configuration Reference.

3. Overleaf Authorization (optional)

Overleaf sync enables bidirectional sync between your local LaTeX project and Overleaf — every AI edit can be pushed, and every collaborator's edit can be pulled.

python cli/main.py login

This will prompt you to choose an Overleaf instance:

The login command will call the corresponding login tool, generate .olauth, and save the instance config to settings.json.

Once .olauth is created, the system auto-detects it. Use /sync pull and /sync push inside any project.

4. Run

Option A — CLI — interact directly in your terminal:

python cli/main.py agent

Option B — Gateway — Web UI + IM channels, chat from anywhere:

python cli/main.py gateway --port 18790

How It Works

Architecture

graph TB
    subgraph Channels["Access Channels"]
        direction LR
        CLI["CLI"]
        WebUI["Web UI"]
        Feishu["Feishu"]
        TG["Telegram"]
        QQ["QQ"]
        DT["DingTalk"]
    end

    Channels --> MB["MessageBus"]
    MB --> AL["AgentLoop — Main Agent"]
    AL --> CR["CommandRouter"]
    AL --> CM["ContextManager"]
    AL --> TR["ToolRegistry (40+ tools)"]
    AL --> SA["Sub-Agents (Workers)"]

    TR --> Proj["Project\nGit · LaTeX · Overleaf"]
    TR --> LLM["LLM Provider\nOpenAI-compatible · Hot-swap"]
    TR --> Auto["Automation\nScheduled cron jobs"]

Workspaces

The system has two spaces:

workspace/
├── Default/                    # Lobby — project management & chat
└── MyPaper/
    ├── project.yaml            # Project config
    ├── MyPaper/                # Core directory (LaTeX files + Git repo)
    │   ├── main.tex
    │   └── references.bib
    └── 0314_01/