Certifiedunder the Multi-Agent System module in the Mastering AI Agents program.Certifiedunder the Multi-Agent System module in the Mastering AI Agents program..png?Expires=1770464047&Key-Pair-Id=K2V2TN6YBJQHTG&Signature=awx9YhliakFZ5exqyHnAEPjc8lf6QjFdNgB~qfkL-ZpwWjDTDBhqBLSwTzY~ZW6n7nn5CdrO5argkVwX~KtmdqGmlS7jRitITycw~CqZmwACX8NgYzej6JgKyMbcwuMJN2swEbeX5F7UJWRjxPAG1EGKxxWSDqxD7bMKEHlmwEb6XncdzX7kax7g~5DxPnW8ppYCl7Xc8T~6oM-1FREZhNt-ewVY6vgVeH-KiTL~l0q23Nk1Q5CF32mLn5BAcm9NnxHI5Pc~NKKpYar3HzVKaj2V5dnLfBL-finuhRc-FJsUUZtIaeqXz0NjqanedTVG88sFyQ3HHnJHm-9nzXnwxQ__)
The Deep Research Agent is a powerful AI-driven Multi-Agent System built with Composio and LangGraph, designed to conduct comprehensive research on user-specified topics within a chosen domain. It generates targeted research questions, performs in-depth analysis using AI-powered tools, and compiles findings into a professional, McKinsey-style HTML report, seamlessly saved to Google Docs. Leveraging the composio_langgraph library for tool integration and langchain_groq for language model interactions, this tool is perfect for researchers, analysts, or anyone seeking structured, high-quality insights. 🚀
With support for follow-up questions, it enables iterative refinement of research, making it a versatile solution for professional and academic use. 📊
COMPOSIO_SEARCH_TAVILY_SEARCH for real-time web searches to gather accurate data.GOOGLEDOCS_CREATE_DOCUMENT_MARKDOWN.├── notebook/deep_research.ipynb #notebook file ├── src/app.py # Main Streamlit application ├── src/graph.py # LangGraph workflow configuration ├── src/state.py # Graph state definition ├── src/nodes/nodes.py # Agent and tool nodes for the workflow ├── src/tools/composio_tools.py # Composio toolset configuration ├── src/tools/llm.py # Language model setup ├── src/prompts.py # System prompt for the research agent ├── .env # Environment variables ├── requirements.txt #dependencies ├── License #license file ├── .gitignore #gitignore for env ├── . gitattributes #text= auto for normalization └── README.md # Project documentation 📖
COMPOSIO_SEARCH_TAVILY_SEARCH) and Google Docs integration (GOOGLEDOCS_CREATE_DOCUMENT_MARKDOWN).The application utilizes a single agent named ResearchAgent, defined in nodes.py as the agent_node function. This agent is powered by the Meta LLaMA model (meta-llama/llama-4-scout-17b-16e-instruct) and is responsible for:
The ResearchAgent operates within a stateful workflow, maintaining context across interactions using the MemorySaver for seamless research continuity.
The application integrates the following tools via the ComposioToolSet from the composio_langgraph library, defined in composio_tools.py:
COMPOSIO_SEARCH_TAVILY_SEARCH 🔍: A web search tool that fetches up-to-date information with configurable parameters (e.g., search_depth, max_results, include_images). Used to gather relevant data, statistics, and sources during research.
GOOGLEDOCS_CREATE_DOCUMENT_MARKDOWN 📑: A tool that saves the compiled HTML report as a markdown document in Google Docs, enabling easy sharing and storage of research outputs.
Report Writing with LLM A tool that uses LLM to write reports from getting answers from web search of given questions.
These tools are bound to the ResearchAgent using llm.bind_tools(tools) in nodes.py to enable function calling within the research workflow.
Input:
Output:
git clone https://github.com/Zeeshier/deep-research-agent.git cd deep-research-agent pip install -r requirements.txt # Set up your environment touch .env # Add your GROQ_API_KEY to the .env file # Run the app streamlit run app.py
This project is actively maintained. For questions, suggestions, or issues, please open an Issue or submit a Pull Request.
For direct inquiries, you can also reach me via:
This project is licensed under the MIT License.
You are free to use, modify, and distribute this software with proper attribution.
For more details, please see the LICENSE file.