Ollama Local API: Connect to VS Code, Python, and n8n for Private AI Automation
Last updated: 2026-06-15
The Short Answer: Ollama Already Has an API
You do not need an API key. You do not need a cloud account. The moment you run ollama serve, you have a fully functional REST API listening on http://localhost:11434 — compatible with the OpenAI spec, which means almost every AI tool you already use can point at it instead.
This guide shows exactly how to wire that API into three environments where it matters most: VS Code (for AI-assisted coding without sending your code to the cloud), Python (for building your own automation scripts), and n8n (for no-code/low-code workflows that stay on your machine).
Everything here runs locally. Nothing leaves your network unless you explicitly route it out.
What Ollama's API Actually Looks Like
Before connecting anything, run this in your terminal to confirm the server is up:
```bash
curl http://localhost:11434/api/tags
```
You should get JSON listing every model you have pulled. If you see connection refused, run ollama serve in a separate terminal first — on some systems it does not start automatically.
The two endpoints you will use most:
| Endpoint | What it does |
|---|---|
| POST /api/generate | Single-turn completions |
| POST /api/chat | Multi-turn conversations with message history |
| GET /api/tags | List available models |
Ollama also exposes an OpenAI-compatible shim at /v1/chat/completions. This is the one most third-party tools expect, and it is what makes the integrations below so clean.
VS Code: AI Code Assistance With Zero Data Leakage
The fastest path is the Continue extension. It is open source, model-agnostic, and built specifically for local LLM workflows.
Install and configure:
- Install the Continue extension from the VS Code marketplace
- Open the Continue config file — click the gear icon in the Continue sidebar, or open
~/.continue/config.jsondirectly - Replace the default model block with:
```json
{
"models": [
{
"title": "Llama 3.2 (Local)",
"provider": "ollama",
"model": "llama3.2",
"apiBase": "http://localhost:11434"
}
],
"tabAutocompleteModel": {
"title": "Qwen2.5-Coder (Local)",
"provider": "ollama",
"model": "qwen2.5-coder:7b",
"apiBase": "http://localhost:11434"
}
}
```
- Save the file — Continue reconnects automatically
For autocomplete, qwen2.5-coder significantly outperforms general-purpose models on code tasks. Pull it with ollama pull qwen2.5-coder:7b before enabling tab completion.
What you get: inline code suggestions, chat-based refactoring, /edit commands that rewrite selected blocks, and /cmd that translates your intent into shell commands — all running on your own GPU or CPU, with no request ever leaving your machine.
If you want a hardware upgrade to make local models feel instant, a dedicated GPU with at least 12GB VRAM (for 7B-13B models) is the single biggest performance lever. The NVIDIA RTX 4070 Super hits the sweet spot for local LLM inference without going into workstation pricing.
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Model Selection by Use Case
Not every task needs the same model. Running a smaller, faster model for simple classification saves time:
| Task | Recommended Model | Why |
|---|---|---|
| Code completion | qwen2.5-coder:7b | Trained on code, fast on CPU |
| General chat / writing | llama3.2:3b or llama3.2 | Solid reasoning, widely tested |
| Document summarization | mistral:7b | Strong at instruction following |
| Embeddings / RAG | nomic-embed-text | Purpose-built, tiny footprint |
| Long context tasks | qwen2.5:14b | 128K context window |
Pull only what you need. Each model takes 4-8GB of disk space. Keep your active set small and pull others on demand.
Start With One Connection, Expand From There
The Ollama API is intentionally minimal, which is why it plugs into so many tools without adapters or translation layers. The OpenAI compatibility shim does most of the work.
Pick one of the three integrations above based on what you actually need today. Get it working end-to-end before adding the next one. The VS Code setup has the fastest payoff for developers — within fifteen minutes of installing Continue, you stop sending code to the cloud and the workflow feels identical to GitHub Copilot.
The Python path is the right starting point if you have automation scripts in progress or want to build something custom. The n8n integration requires the most setup but unlocks the broadest range of non-code automation once it is running.
All three share the same API. Master the connection pattern once, and the rest is just configuration.
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Last updated: 2026-06-15
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