Github · GitHub Repository Radar
apurvsinghgautam robin
AI-Powered Dark Web OSINT Tool
Stars
6,995
Forks
1,311
Watchers: 6,995
Language
License: MIT License
Repository Radar Score
58 / 100
Growth
- 7d
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- 30d
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- 0.0%
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Score breakdown
- popularity 71
- growth 0
- activity 90
- freshness 100
- community 87
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Robin is an AI-powered tool for conducting dark web OSINT investigations. It leverages LLMs to refine queries, filter search results from dark web search engines, and provide an investigation summary.
Installation • Contributing • Acknowledgements
- ⚙️ Modular Architecture – Clean separation between search, scrape, and LLM workflows.
- 🤖 Multi-Model Support – Easily switch between OpenAI, Claude, Gemini, Ollama, or any OpenAI-compatible API (LM Studio, llama.cpp, Groq, etc.).
- 🌐 Web UI – Streamlit-based interface for interactive investigations.
- 💬 Conversational Follow-ups – Ask grounded follow-up questions about an investigation without re-running the search — answered from that investigation's own data.
- 🔀 One-Click Pivots – Suggested follow-up queries surfaced from the findings; click one to launch a fresh investigation.
- 🐳 Docker-Ready – Recommended Docker deployment for clean, isolated usage.
- 📝 Custom Reporting – Save investigation output to file for reporting or further analysis.
- 🧩 Extensible – Easy to plug in new search engines, models, or output formats.
This tool is intended for educational and lawful investigative purposes only. Accessing or interacting with certain dark web content may be illegal depending on your jurisdiction. The author is not responsible for any misuse of this tool or the data gathered using it.
Use responsibly and at your own risk. Ensure you comply with all relevant laws and institutional policies before conducting OSINT investigations.
Additionally, Robin leverages third-party APIs (including LLMs). Be cautious when sending potentially sensitive queries, and review the terms of service for any API or model provider you use.
Note
The tool needs Tor to do the searches. You can install Tor using apt install tor on Linux/Windows(WSL) or brew install tor on Mac. Once installed, confirm if Tor is running in the background.
Tip
You can provide your LLM of choice API key by either creating .env file (refer to sample env file in the repo) or by setting env variables in PATH.
For Ollama, provide http://host.docker.internal:11434 as OLLAMA_BASE_URL in your env if running using docker method or http://127.0.0.1:11434 for other methods. You might need to serve Ollama on 0.0.0.0 depending on your OS. You can do by running OLLAMA_HOST=0.0.0.0 ollama serve & in your terminal.
For any other OpenAI-compatible provider (LM Studio, llama.cpp, Groq, etc.), use the 🔌 Custom API Provider expander in the sidebar — no .env changes required. Enter the base URL, an optional API key, and optionally a model name if the provider doesn't expose /v1/models for auto-discovery.
- Pull the latest Robin docker image
docker pull apurvsg/robin:latest- Run the docker image as:
docker run --rm \
-v "$(pwd)/.env:/app/.env" \
--add-host=host.docker.internal:host-gateway \
-p 8501:8501 \
apurvsg/robin:latestTip
To persist saved investigations across Docker restarts, mount a local directory:
docker run --rm \
-v "$(pwd)/.env:/app/.env" \
-v "$(pwd)/investigations:/app/investigations" \
--add-host=host.docker.internal:host-gateway \
-p 8501:8501 \
apurvsg/robin:latestInvestigations are saved to the investigations/ folder in your working directory and can be loaded from the Past Investigations panel in the sidebar.
- Open your browser and navigate to
http://localhost:8501
- With
Python 3.10+and Tor installed, run the following:
pip install -r requirements.txt
streamlit run ui.py- Open your browser and navigate to
http://localhost:8501
Contributions are welcome! Please feel free to submit a Pull Request if you have major feature updates.
- Fork the repository
- Create your feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add some amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request
Open an Issue for any of these situations:
- If you spot a bug or bad code
- If you have a feature request idea
- If you have questions or doubts about usage
- If you have minor code changes
- Idea inspiration from Thomas Roccia and his demo of Perplexity of the Dark Web.
- Tools inspiration from my OSINT Tools for the Dark Web repository.
- LLM Prompt inspiration from OSINT-Assistant repository.
- Logo Design by my friend Tanishq Rupaal
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- Maintained recently (last push 4 weeks ago).
Who should use it
- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
Strengths
- Recent repository activity
- README present in our index
- Declared license: MIT License
- Substantial fork count (1,311) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.