🚀 Enhanced fork of gemini-general-llm-providers with expanded LLM provider support

This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. Originally designed for Google's Gemini models, this fork has been enhanced to support multiple LLM providers including OpenAI, Anthropic Claude, Ollama, and Azure OpenAI. The agent performs comprehensive research on user queries by dynamically generating search terms, querying the web, reflecting on results to identify knowledge gaps, and iteratively refining searches until it can provide well-supported answers with citations.

Key Enhancement: While the original project was limited to Gemini models, this fork enables you to use any combination of supported LLM providers for different stages of the research process, providing flexibility in cost optimization and performance tuning.

• 💬 Fullstack application with a React frontend and LangGraph backend.
• 🧠 Powered by a LangGraph agent for advanced research and conversational AI.
• 🔍 Dynamic search query generation using multiple LLM providers (Gemini, OpenAI, Claude).
• 🌐 Integrated web research via Google Search API.
• 🤔 Reflective reasoning to identify knowledge gaps and refine searches.
• 📄 Generates answers with citations from gathered sources.
• ⚡ Multi-provider support for cost optimization and performance tuning.
• 🔄 Hot-reloading for both frontend and backend development during development.

The project is divided into two main directories:

• frontend/: Contains the React application built with Vite.
• backend/: Contains the LangGraph/FastAPI application, including the research agent logic.

Follow these steps to get the application running locally for development and testing.

1. Prerequisites:

• Node.js and npm (or yarn/pnpm)

• Python 3.8+

• API Keys: At least one LLM provider API key is required. Choose from:

  • GEMINI_API_KEY (recommended for web search functionality)
  • OPENAI_API_KEY for OpenAI models
  • ANTHROPIC_API_KEY for Claude models
  • OLLAMA_BASE_URL for Ollama models
  • Azure OpenAI credentials (see Multi-Provider Support section)
  1. Navigate to the backend/ directory.
  2. Create a file named .env by copying the backend/.env.example file.
  3. Open the .env file and add your API key(s). For example: GEMINI_API_KEY="YOUR_ACTUAL_API_KEY"

2. Install Dependencies:

Backend:

cd backend uv venv uv sync # OR uv pip install -e .

Frontend:

cd frontend npm install

3. Run Development Servers:

Backend & Frontend:

make dev

This will run the backend and frontend development servers. Open your browser and navigate to the frontend development server URL (e.g., http://localhost:5173/app).

Alternatively, you can run the backend and frontend development servers separately. For the backend, open a terminal in the backend/ directory and run langgraph dev. The backend API will be available at http://127.0.0.1:2024. It will also open a browser window to the LangGraph UI. For the frontend, open a terminal in the frontend/ directory and run npm run dev. The frontend will be available at http://localhost:5173.

This application now supports multiple LLM providers for enhanced flexibility and cost optimization:

• Google Gemini (default): gemini-2.0-flash, gemini-2.5-flash-preview-04-17, gemini-2.5-pro-preview-05-06
• OpenAI: gpt-4o, gpt-4o-mini, gpt-3.5-turbo
• Azure OpenAI: gpt-4o, gpt-4o-mini, gpt-35-turbo (using your Azure deployments)
• Anthropic Claude: claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022
• Ollama: llama3.1:8b

To add more models, you can update the models.py. And please be aware, the model you added should support structured output.

Add API keys to your .env file:

GEMINI_API_KEY=your_gemini_key # Optional OPENAI_API_KEY=your_openai_key # Optional ANTHROPIC_API_KEY=your_claude_key # Optional OLLAMA_BASE_URL=http://localhost:11434 # Optional # Azure OpenAI (Optional) AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/ AZURE_OPENAI_API_KEY=your_azure_openai_key AZURE_OPENAI_API_VERSION=2024-02-01

The core of the backend is a LangGraph agent defined in backend/src/agent/graph.py. It follows these steps:

1. Generate Initial Queries: Based on your input, it generates a set of initial search queries using a Gemini model.
2. Web Research: For each query, it uses the Gemini model with the Google Search API to find relevant web pages.
3. Reflection & Knowledge Gap Analysis: The agent analyzes the search results to determine if the information is sufficient or if there are knowledge gaps. It uses a Gemini model for this reflection process.
4. Iterative Refinement: If gaps are found or the information is insufficient, it generates follow-up queries and repeats the web research and reflection steps (up to a configured maximum number of loops).
5. Finalize Answer: Once the research is deemed sufficient, the agent synthesizes the gathered information into a coherent answer, including citations from the web sources, using a Gemini model.

In production, the backend server serves the optimized static frontend build. LangGraph requires a Redis instance and a Postgres database. Redis is used as a pub-sub broker to enable streaming real time output from background runs. Postgres is used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics. For more details on how to deploy the backend server, take a look at the LangGraph Documentation. Below is an example of how to build a Docker image that includes the optimized frontend build and the backend server and run it via docker-compose.

Note: For the docker-compose.yml example you need a LangSmith API key, you can get one from LangSmith.

Note: If you are not running the docker-compose.yml example or exposing the backend server to the public internet, you update the apiUrl in the frontend/src/App.tsx file your host. Currently the apiUrl is set to http://localhost:8123 for docker-compose or http://localhost:2024 for development.

1. Build the Docker Image:

Run the following command from the project root directory:

docker build -t gemini-fullstack-langgraph -f Dockerfile .

2. Run the Production Server:

GEMINI_API_KEY=<your_gemini_api_key> LANGSMITH_API_KEY=<your_langsmith_api_key> docker-compose up

Open your browser and navigate to http://localhost:8123/app/ to see the application. The API will be available at http://localhost:8123.

• React (with Vite) - For the frontend user interface.
• Tailwind CSS - For styling.
• Shadcn UI - For components.
• LangGraph - For building the backend research agent.
• Google Gemini - Default LLM provider and required for Google Search.
• OpenAI - Optional LLM provider for query generation, reflection, and answer synthesis.
• Anthropic Claude - Optional LLM provider for query generation, reflection, and answer synthesis.
• Ollama - Optional LLM provider for local development.

This project is licensed under the Apache License 2.0. See the LICENSE file for details.