Local AI Agent Workflow Using OpenCode, FastMCP, and DeepSeek-r1

A developer on r/LocalLLaMA describes moving beyond using LLMs as "glorified autocomplete" by implementing a local agentic workflow with OpenCode, FastMCP, and the DeepSeek-r1 model.

AGENTS.md Standard for Deterministic Prompts

The developer uses an AGENTS.md file as a deterministic manual that injects strict rules into the AI's system prompt. Examples include "Use Python 3.9, format with Ruff, absolutely no global variables." This approach aims to eliminate hallucinations from the start.

Local Subagents with DeepSeek-r1

Instead of using cloud APIs like Claude or GPT-4o for trivial tasks, they set up Ollama with the free deepseek-r1 model. They created specific subagents, such as one for testing defined in a pytest.md file. Key configurations include:

• Temperature set to 0.1
• Tools restricted: "pytest": true and "bash": false

This allows the AI to autonomously run test suites, read tracebacks, and fix syntax errors while being blocked from potentially dangerous commands like rm -rf.

FastMCP for Standardized Local Function Exposure

FastMCP is described as "the 'USB-C' of AI"—similar to FastAPI but for AI agents. With about 5 lines of Python, you can spin up a local server to expose secure local functions (like querying a development database) in a standardized way that any OpenCode agent can consume.

A critical implementation tip: route all Python logs to stderr because the MCP protocol runs over stdio. Leaving a standard print() statement can corrupt the JSON-RPC packet and drop the connection.

The developer notes they recorded a video coding this entire architecture from scratch and setting up the local environment in about 15 minutes.