Your Local LLM Just Learned to Think: Building an Autonomous ReAct Agent with Ollama + MCP

Your local Ollama model just learned to think for itself. With helix-agent v0.4.0, your local LLM doesn't just answer questions — it reasons step by step, uses tools, and iterates until it solves the problem. All through Claude Code, zero API cost. What Changed helix-agent started as a simple proxy: send a prompt to Ollama, get text back. Now it's an autonomous ReAct agent . Here's what that looks like in practice: Task: "Read pyproject.toml and summarize the project" Step 1: LLM thinks "I need to read the file" -> calls read_file("pyproject.toml") -> gets file contents Step 2: LLM analyzes the contents -> calls finish("v0.4.0, deps: fastmcp + httpx, MIT license") Done. 2 steps. Correct answer. The LLM decided what to do, executed it, observed the result, and formed its answer. No human guidance needed. Built-in Tools The agent has 7 tools it can use autonomously: Tool What it does read_file Read any file (security-guarded) write_file Create or modify files list_files Browse directorie