Reflect MCP Server Implements Reflexion Paper for Persistent Coding Agent Memory

A developer has implemented the Reflexion paper (Shinn et al., NeurIPS 2023) as an MCP server to address a common problem with local coding agents: lack of persistent memory between sessions. The tool, called reflect-mcp, allows agents to remember and avoid repeating mistakes.

How It Works

The system operates through a structured workflow:

• After every test failure, the agent critiques its own work and extracts patterns from the error
• These lessons are stored for future reference
• Before starting new tasks, the agent recalls past lessons using full-text search
• The pattern matching is fully regex-based - no LLM calls are needed for classification

The developer notes that error messages are predictable enough for deterministic matching to work effectively. The agent writes the critique since it has the context, while the server handles structuring and deduplication of the lessons.

Technical Implementation

• Built as an MCP (Model Context Protocol) server
• Uses SQLite with FTS5 for storage and search
• Works with any MCP-compatible client
• Install via: cargo install reflect-mcp

Results After One Week

The developer reported several improvements in their coding agent's behavior:

• Stopped doing the same unwrap() on user input
• Stopped forgetting timezone handling
• Started avoiding previously seen failure patterns automatically
• Pattern tracking made recurring mistakes across the project visible

The project is available on GitHub at https://github.com/rohansx/reflect. The developer is seeking feedback from others who have experimented with persistent memory setups for local coding agents.