🤖 mcp-ollama-beeai

Name: 🤖 mcp-ollama-beeai
Author: tamdilip

@tamdilip

代码仓库

关于 🤖 mcp-ollama-beeai

A minimal agentic app to interact with OLLAMA models leveraging multiple MCP server tools using BeeAI framework.

#machine-learning #mcp #chatbot #mcpe-server #reason-react #ollama #agentic-ai #llama3-1 #beeai

基本信息

分类

AI 与智能体

运行时

node

传输方式

stdio

发布者

tamdilip

配置

暂无标准配置

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代码仓库

工具

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概览

What is 🤖 mcp-ollama-beeai?

A minimal client app that interacts with local Ollama models using multiple MCP (Model Context Protocol) agent tools, built with the BeeAI framework. It provides a chat interface where you can select MCP agents – such as Postgres or Fetch – and let the LLM reason and act on your requests.

How to use 🤖 mcp-ollama-beeai?

Install Ollama, serve it locally, and pull at least one model (default: llama3.1). Configure your MCP servers in mcp-servers.json (defaults include Postgres and Fetch). Clone the repo, run npm i && npm start, then open http://localhost:3000. Use the UI dropdowns to choose a server and tool before asking questions.

Key features of 🤖 mcp-ollama-beeai

Chat interface with LLM‑powered reasoning and action steps
Use any local Ollama model (configurable via .env)
Pre‑configured Postgres and Fetch MCP servers
Switch MCP agents on the fly from the UI
Responses rendered in readable Markdown format
Built on the BeeAI framework for easy ReAct agent setup

Use cases of 🤖 mcp-ollama-beeai

Query a local Postgres database using natural language
Fetch and summarise web pages through a chat interface
Experiment with different Ollama models paired with MCP tools
Quickly test and demo MCP‑based agent workflows locally

FAQ from 🤖 mcp-ollama-beeai

Which Ollama models are supported?

Any model you have pulled locally (default is llama3.1). Override OLLAMA_CHAT_MODEL in .env to use a different one.

What MCP servers are included by default?

Postgres and Fetch are pre‑configured. You can add any MCP server from the official examples by editing mcp-servers.json.

What are the system requirements?

At least 16GB of RAM for models to perform well. Ollama must be installed and running, and Node.js/npm are needed to run the app.

How do I point the app to a different LLM server?

Set OLLAMA_BASE_URL in .env (default: http://localhost:11434/api) to use a remote server instead of your local Ollama.

Does the app require an internet connection?

No. All interactions are local – the Ollama model runs on your machine and the MCP agents connect to local or remote services you configure.

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