Dark Web OSINT Has a Signal-to-Noise Problem. Robin Uses LLMs to Solve It.

Dark web intelligence gathering is notoriously inefficient. Security analysts routinely burn dozens of hours wrestling with fragile Boolean operators, navigating unindexed .onion nodes, and parsing megabytes of unstructured, irrelevant text returned by low-fidelity search engines. The problem is rarely finding data; it is extracting signal from an overwhelming torrent of operational noise.

Robin is an open-source, modular AI agent built specifically to attack this bottleneck. Instead of acting as a naive search scraper, Robin injects a multi-model LLM pipeline directly into the dark web OSINT loop to automate query transformation, content filtering, and artifact synthesis.

The Tri-Stage LLM Pipeline

Robin splits the investigation workload into three decoupled, programmatic stages:

Intent-to-Query Refinement: Instead of relying on manual boolean syntax that frequently breaks across different dark web engines, Robin processes natural language investigator prompts through an LLM to generate high-precision, optimized search strings.
Noise Discrimination: Dark web search engines return massive, low-relevance payloads. Robin's extraction layer filters the raw stream against the original investigative scope, discarding garbage data before it hits the analyst's desk.
Structured Synthesis: Once target content is harvested, the model synthesizes the raw findings into an actionable executive summary, cutting manual review time to minutes.

Architecture & Feature Matrix

Robin is engineered for modularity, clean separation of concerns, and local execution.

Feature	Architectural Implementation
Modular Architecture	Decoupled pipeline separating search routing, scraping tasks, and LLM processing layers.
Multi-Model Support	Native support for OpenAI, Claude, Gemini, Ollama, and any OpenAI-compatible API (LM Studio, llama.cpp, Groq).
Interactive Interface	Web UI powered by Streamlit, eliminating the need for custom CLI scripts during live analysis.
Container-First Design	Docker-based isolation ensuring clean runtime environments without dependency conflicts.
Stateful Artifact Logging	Local persistence layer for evidence collection, downstream triage, and client deliverables.
Plugin Extensibility	Open interfaces to mount custom search engines, specialized inference endpoints, or novel output formats.

Operational Security & Legal Boundaries

Operating in dark web spaces requires strict adherence to legal frameworks and operational security (OpSec). Robin is built exclusively for authorized investigations and educational research. Interacting with certain dark web services can violate regional laws; users maintain full liability for what they query and collect.

From an OpSec standpoint, sending indicators of compromise, raw dumps, or target identifiers to commercial cloud APIs introduces significant data leakage risks. When using hosted models like OpenAI, Anthropic, or Google, verify their specific data retention terms. For sensitive investigations, route tasks through local inference engines using Ollama, LM Studio, or llama.cpp.

Prerequisites: Network & Model Routing

Robin relies on two baseline requirements: Python 3.10+ and an active Tor daemon for routing onion traffic.

1. Tor Daemon Setup

Ensure the local Tor proxy is operational:

Linux / WSL:

bash
apt install tor
systemctl start tor

macOS:

bash
brew install tor
brew services start tor

2. LLM Provider Configuration

Credentials can be passed via a .env file (modeled after .env.example) or exported as system environment variables.

If you are running Ollama on your bare-metal host while executing Robin inside a container, bind the host bridge:

Set OLLAMA_BASE_URL to http://host.docker.internal:11434
Ensure Ollama listens across interfaces:

bash
OLLAMA_HOST=0.0.0.0 ollama serve &

For endpoints like LM Studio, llama.cpp, or Groq, navigate to the 🔌 Custom API Provider panel inside the Streamlit sidebar. Enter the base URL, API key, and model name directly. Manual model specification is mandatory if the target provider does not support the /v1/models discovery endpoint.

Deployment Workflows

Option 1: Containerized Execution (Recommended)

Docker isolates the Python runtime, network configurations, and UI services.

Pull the upstream image:

bash
docker pull apurvsg/robin:latest

Launch an ephemeral session:

bash
docker run --rm \
   -v "$(pwd)/.env:/app/.env" \
   --add-host=host.docker.internal:host-gateway \
   -p 8501:8501 \
   apurvsg/robin:latest

To maintain investigation logs across runs, mount the local data directory:

bash
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:latest

Access the dashboard at http://localhost:8501.

Option 2: Bare-Metal Setup

For direct development or debugging:

bash
pip install -r requirements.txt
streamlit run ui.py

Open http://localhost:8501 to access the interface.

Contributions & Upstream Lineage

Robin builds directly on proven security concepts and open tooling:

Concept architecture adapted from Thomas Roccia’s Perplexity of the Dark Web demo.
Core engine mechanics informed by the OSINT Tools for the Dark Web project.
Investigative prompt routines derived from OSINT-Assistant.
Project branding designed by Tanishq Rupaal.

To contribute features, fixes, or custom engine modules, fork the repository, cut a topic branch, and submit a pull request via standard GitHub workflows.

Reference

Project Repository: https://github.com/apurvsinghgautam/robin