Llama Streamlit Agent
AI assistant built with Streamlit, NVIDIA NIM (LLaMa 3.3:70B) / Ollama, and Model Control Protocol (MCP).
What is Llama Streamlit Agent?
Llama Streamlit Agent is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to ai assistant built with streamlit, nvidia nim (llama 3.3:70b) / ollama, and model control protocol (mcp).
AI assistant built with Streamlit, NVIDIA NIM (LLaMa 3.3:70B) / Ollama, and Model Control Protocol (MCP).
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- AI assistant built with Streamlit, NVIDIA NIM (LLaMa 3.3:70B
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx llama-mcp-streamlitConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Llama Streamlit Agent
The LLaMa MCP Streamlit project is an AI assistant application that combines a Streamlit web interface, NVIDIA NIM (LLaMa 3.3 70B) or Ollama as the LLM backend, and MCP tool integration into a single runnable demo. It acts as an MCP client rather than a server — it connects to external MCP servers (configured via npx or Docker) and uses them to extend the LLM's capabilities with real-time tool execution. Developers use it as a reference implementation for building chat applications that combine open-weight LLMs with the MCP tool ecosystem.
Prerequisites
- Python 3.9 or higher with Poetry package manager
- NVIDIA NIM API key (for LLaMa 3.3 70B) or a locally running Ollama instance
- An MCP server to connect to (configured in utils/mcp_server.py)
- Node.js (if running MCP servers via npx within the app)
Clone the repository
Clone the LLaMa MCP Streamlit repository to your local machine and enter the project directory.
git clone https://github.com/Nikunj2003/LLaMa-MCP-Streamlit
cd LLaMa-MCP-StreamlitInstall Python dependencies with Poetry
Use Poetry to install all Python dependencies. This sets up the virtual environment and installs Streamlit, the MCP client library, and all other requirements.
poetry installConfigure environment variables
Create a .env file in the project root and set your API endpoint and key. Use the NVIDIA NIM endpoint for cloud inference or the Ollama endpoint for local inference.
# For NVIDIA NIM (LLaMa 3.3 70B):
API_ENDPOINT=https://integrate.api.nvidia.com/v1
API_KEY=your_nvidia_nim_api_key
# For Ollama (local):
API_ENDPOINT=http://localhost:11434/v1/
API_KEY=ollamaConfigure the MCP server connection
Edit utils/mcp_server.py to point to your desired MCP server. You can specify an npx-based server or a Docker-based server. This is the MCP server that the Streamlit app will use for tool execution.
Launch the Streamlit application
Start the application using Poetry and Streamlit. The interface will open in your browser at localhost:8501.
poetry run streamlit run llama_mcp_streamlit/main.pyLlama Streamlit Agent Examples
Client configuration
This project is an MCP client application, not a server. The .env file controls the LLM backend. Here is an example .env configuration for NVIDIA NIM.
{
"API_ENDPOINT": "https://integrate.api.nvidia.com/v1",
"API_KEY": "nvapi-your_nvidia_nim_key_here",
"NOTE": "For Ollama, set API_ENDPOINT to http://localhost:11434/v1/ and API_KEY to ollama"
}Prompts to try
Once the Streamlit app is running, use these prompts in the chat interface. The LLM will automatically invoke MCP tools when needed.
- "What is the current time and date?"
- "Search for recent news about AI model releases"
- "Run a web search for Python asyncio best practices"
- "Use available tools to get information about the weather in New York"
- "Execute a tool to fetch the content of https://example.com"Troubleshooting Llama Streamlit Agent
Poetry install fails with dependency conflicts
Ensure you are using Python 3.9 or higher. Run 'poetry env use python3.11' to explicitly set the Python version, then retry 'poetry install'. Check the pyproject.toml for minimum Python version requirements.
API authentication error with NVIDIA NIM
Verify the API_KEY in your .env file is a valid NVIDIA NIM API key. You can get one at https://integrate.api.nvidia.com. Ensure the API_ENDPOINT matches exactly: https://integrate.api.nvidia.com/v1 (no trailing slash).
MCP tools not executing or returning empty results
Check that the MCP server configured in utils/mcp_server.py is running and accessible. If using an npx-based server, ensure Node.js is installed and the package is available. Verify the server starts without errors before launching the Streamlit app.
Frequently Asked Questions about Llama Streamlit Agent
What is Llama Streamlit Agent?
Llama Streamlit Agent is a Model Context Protocol (MCP) server that ai assistant built with streamlit, nvidia nim (llama 3.3:70b) / ollama, and model control protocol (mcp). It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Llama Streamlit Agent?
Follow the installation instructions on the Llama Streamlit Agent GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Llama Streamlit Agent?
Llama Streamlit Agent works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Llama Streamlit Agent free to use?
Yes, Llama Streamlit Agent is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up Llama Streamlit Agent in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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