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How to learn how an agent loop works from a small codebase with Minimal Local Agent

Read and run one model and tool loop with policy and budgets to understand agents end to end.

Short answer: Clone the repo and install it in a venv; then run the demo with python examples/web_demo.py; read the runtime, tools and policy modules. All steps.

Who it is forA developer new to agents who finds big frameworks too opaque to learn from.

The problemLarge agent frameworks hide the core loop under layers of abstraction.

The outcomeA working mental model of runtime, tools, policy and audit state.

What you need first

  • Python 3.11
  • Ollama (README example pulls qwen3.5:9b) or a Chat Completions-compatible API

Steps

  1. Clone the repo and install it in a venv
  2. Run the demo with python examples/web_demo.py
  3. Read the runtime, tools and policy modules
  4. Change a budget in agent.toml and rerun to see the effect

Prompt for your agent

Clone MuzeAnisichael/minimal-local-agent, run `python examples/web_demo.py`, and explain the loop from host to runtime to tools to SQLite state. Verify your explanation against the source files you cite.

Caveat. The web UI is currently in Chinese.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for MuzeAnisichael/minimal-local-agent and "Learn how an agent loop works from a small codebase"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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