Mission: Build an intelligent browser agent that doesn’t just understand the web — it acts on it. Fully model-agnostic, privacy-respecting, and BYOKeys-ready.
Agentic Browser is a next-generation browser extension powered by a Python MCP (Model Context Protocol) server that bridges modern LLM reasoning with real browser interactivity.
Unlike typical AI assistants, this agent:
- Understands complex web content,
- Takes actions (like filling forms, navigating, comparing data),
- And adapts to any preferred model backend — OpenAI, Anthropic, Ollama, local LLaMA, Mistral, etc.
It’s your agent, your browser, your keys.
- Model-Agnostic: Works with any LLM backend that supports API-style calls (OpenAI-compatible, Anthropic, Ollama, LM Studio).
- BYOKeys: No vendor lock-in. Users supply their own API keys via
.envor runtime UI input. - MCP-Compliant: Uses the Model Context Protocol for secure and structured interaction.
- Declarative Action System: The model declares browser actions (e.g.
click,fill_form,extract), and the extension executes them safely.
Create a flexible, LLM-agnostic backend using Python, LangChain, and the Model Context Protocol (MCP) framework.
Allows seamless switching across models (OpenRouter, Ollama, Anthropic, OpenAI, or local inference models).
Design a robust and secure browser extension using the WebExtensions API, ensuring compatibility across Chrome, Firefox, and other Chromium-based browsers.
Support sophisticated agentic workflows through Retrieval Augmented Generation (RAG), persistent memory, and automated multi-step browsing tasks like form filling, search synthesis, and citation retrieval.
Implement strong security and transparency layers:
- User approval before every actionable operation
- Comprehensive activity logs
- Intelligent content filtering
- Safe domain allowlisting and IPI protection
Adopt a modular, community-driven architecture encouraging open innovation and integration of new capabilities, workflows, and extensions over time.
| Component | Functionality | Technologies / Frameworks |
|---|---|---|
| Agent Orchestration | Task planning, retrieval-augmented reasoning, complex multi-step workflows | LangChain, LangGraph |
| Browser Control | DOM inspection, navigation, form filling, input injection, and content extraction | WebExtensions API (Chrome / Firefox) |
| LLM Adapters | Model-agnostic routing, adapter layer for multi-provider compatibility | OpenRouter, Ollama, Anthropic, OpenAI, Hugging Face APIs |
| Backend Agent | Core logic execution, action orchestration, safety and state management | Python MCP Server |
| Retrieval & Citation | Web data extraction, embedding-based retrieval, factual grounding | Vector Databases (FAISS / Pinecone) |
| Safety & Guardrails | Logging, data protection, domain-level security enforcement | Secure Audit System, Activity Logger |
Works with any LLM provider — OpenAI, Anthropic, Mistral, Ollama, LM Studio, or custom deployments — using a unified adapter layer.
No vendor dependency. Users supply their own API keys securely via local .env or UI input; keys never leave local context.
Real-time DOM inspection and manipulation for safe, human-approved automation — including form filling, data extraction, and structured web actions.
Leverages RAG pipelines to incorporate external data and enhance factual grounding, improving contextual accuracy in responses.
Every action is validated, logged, and requires explicit permission, ensuring responsible automation and explainability.
Developers can easily extend agent capabilities by adding Python tools, context managers, or new browser-side actions.
- Add visual DOM debugger panel
- Multi-model round-robin support (reasoning blending)
- Offline LLM embedding-based retrieval
- GUI for managing keys/providers
- Fine-grained content permissions
Contributions are very welcome!
If you’re into LLM orchestration, WebExtension APIs, or intelligent web automation, this project is an open canvas.
Please:
- Fork the repo
- Create a feature branch
- Submit a well-documented PR
Released under the GPL 3 License — free to modify, distribute, and extend with attribution.