Ollama

Ollama runs local models on your machine. agentfootprint’s ollama() factory wraps Ollama’s OpenAI-compatible endpoint as an LLMProvider — your agent code is identical between local-Ollama dev and cloud-Anthropic production.

Install

Ollama itself: see ollama.com/download. Pull a model:

ollama pull llama3.1

agentfootprint side: ollama() uses the openai SDK under the hood (via OpenAI-compatible endpoint), so install the OpenAI peer dep:

npm install openai

Use

import { Agent, ollama } from 'agentfootprint';

const provider = ollama({
  model: 'llama3.1',
  baseURL: 'http://localhost:11434/v1',  // default
});

const agent = Agent.create({
  provider,
  model: 'llama3.1',
}).build();

When to use Ollama

• Local development — no API key, no cost, no network.
• Privacy-sensitive workloads — model + data never leave the machine.
• Cost-sensitive batch jobs — local inference for high-volume background processing.
• Edge / offline deployments — bundle the model with your app.

Limitations

• Local models are smaller and less capable than frontier models. Tool-calling reliability varies by model — llama3.1 + qwen2.5 + deepseek work; smaller models may struggle.
• No streaming for some local models (depends on Ollama config).
• Multi-modal not exposed.

Switching to Anthropic / OpenAI for production

Same agent code; swap the provider one line:

const provider = process.env.NODE_ENV === 'production'
  ? anthropic({ apiKey: process.env.ANTHROPIC_API_KEY! })
  : ollama({ model: 'llama3.1' });

Next steps

• OpenAI — baseURL pattern works with any OpenAI-compatible endpoint
• Mocks-first development — mock() for $0 deterministic tests