LangGraph Multi Agent Ollama Application

LangGraph Ollama multi Agent application locally, and how to obtain an API key for LangSmith.

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Prerequisites

• Python 3.11 or higher
• pip (Python package installer)
• A Google Cloud Platform (GCP) account (for Gemini API access) will be good
• A LangSmith account (for tracing and debugging)

Installation

1. Fork and Clone the Repository (if applicable):

   git clone <your_repository_url>
   cd <your_application_directory>

2. Create an .env file:

   Create a file named .env from env_copy in the root directory of your project. This file will store your API keys and other sensitive information.

   GOOGLE_API_KEY=<your_google_api_key>
   LANGCHAIN_API_KEY=<your_langsmith_api_key>
   LANGCHAIN_TRACING_V2="true"
   LANGCHAIN_PROJECT="Your_LangGraph_Project_Name"
   Antyhing else api key etc
   

Obtaining API Keys

1. Google Gemini API Key [optional]

1. Go to Google AI Studio:
   • Google AI Studio
2. Follow the instructions to create a project and obtain an API key.
3. Alternatively, you can obtain a Google Cloud API key from the google cloud console.
   • Google Cloud Platform
4. Enable the Gemini API for your project.
5. Create API credentials.
6. Add the key to your .env file as GOOGLE_API_KEY.

2. LangSmith API Key

1. Sign up for LangSmith:
   • LangSmith
2. Obtain your API key:
   • After creating an account, navigate to your settings to find your API key.
3. Set Environment Variables:
   • Add the API key to your .env file as LANGCHAIN_API_KEY.
   • Also add LANGCHAIN_TRACING_V2="true" to your .env file.
   • Set LANGCHAIN_PROJECT to your desired project name.

Running the Application

1. Navigate to your application directory:

   cd <your_application_directory>
   docker-compose up --build

2. Run the application using the langgraph CLI [optional]:

   langgraph run --config langgraph.json
   
   or 
   
   langgraph dev

   • Ensure that your langgraph.json file is correctly configured to point to your LangGraph definition.

LangGraph and LangSmith Local Development Notes.

• Using Langsmith and setting LANGCHAIN_TRACING_V2="true" in your .env file will enable tracing of your LangGraph runs on your local machine. This is very useful for debugging.

• You can review your LangGraph runs in the LangSmith UI.

• Make sure that your .env file is not committed to version control, as it contains sensitive information. Add .env to your .gitignore file.

• If you encounter any issues, refer to the official LangGraph and LangChain documentation.

  • LangGraph Documentation
  • LangChain Documentation
  • LangSmith Documentation

• 🐳 Running with Docker Compose You can also run the application using Docker Compose, which will spin up both the LangGraph agent and Ollama server.

• Start the services docker-compose up --build

• To stop ctrl + c

and

docker-compose down --remove-orphans

Always check the Docker images and containers. Keep your system and dangling images removed otherwise you system can freeze.

• This will launch:

🚀 agent_service: Your LangGraph agent on http://localhost:5000

🧠 ollama_server: The local model server running on http://localhost:11434

Use this for full isolation and easy multi-service orchestration. Recommended system at least 16 GB or 32 GB RAM, optional GPU, i7 or similar. As LLM will be downloaded into your docker container and it require around 5 GB. Higher system will run faster otherwise slow for response to keep patience.