# Ollama-Apify-MCP
Bring powerful **local AI** into your Apify workflows.
This project connects **Ollama’s locally-run language models** with the **Model Context Protocol (MCP)** and Apify’s scraping & automation platform. It enables you to process scraped data, extract insights, and generate intelligent responses — all **without external APIs**.
---
## 🧠 Overview
The **Ollama-Apify-MCP Actor** bridges Apify workflows with local LLMs via MCP, allowing AI-driven analysis and reasoning while preserving privacy and reducing costs.
---
## 🚀 Key Features
- 🔗 **Local LLM integration** — Run models like *Llama, Mistral, CodeLlama,* and more using Ollama
- 🧩 **MCP-based communication** — Standards-compliant protocol for tool interaction
- ⚙️ **Automatic context & preprocessing** — Improves model response quality
- 🛠️ **Extensible tool architecture** — Easily add custom MCP tools & resources
- 🔁 **Robust error handling & retries** — Reliable execution in workflows
---
## 📦 Quick Start
Use as in cursor, copilot, claude code or desktop
```
{
"mcpServers": {
"ollama-apify-mcp": {
"url": "https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp?token={YOUR_TOKEN}"
}
}
}
```
### 💻 Run Locally
```bash
pip install -r requirements.txt
APIFY_META_ORIGIN=STANDBY python -m src
```
Server runs at:
```
http://localhost:3000/mcp
```
---
### ☁️ Deploy to Apify
1. Push the repo to GitHub
2. Add it as an Actor in Apify Console
3. Enable **Standby Mode**
4. Deploy
MCP endpoint:
```
https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp
```
Include your API token:
```
Authorization: Bearer <APIFY_TOKEN>
```
## 🎯 Use Cases
- 📊 Analyze & summarize scraped web data
- 🔐 Privacy-first local LLM processing
- ⚡ Low-latency on-device inference
- 🧱 Build AI tools inside Apify workflows
---
## 🧩 Requirements
- Python 3.7+
- Ollama installed locally
- Apify CLI (for deployment)
---
## ❤️ Contributing
PRs and feature ideas are welcome — feel free to extend tools, improve docs, or share sample workflows.
---
## 📄 License
MIT License
