Band Memory

Band Memory gives multi-agent systems persistent, shared memory so they stop starting every task with amnesia. The problem: AI agents coordinated through Band.ai handle complex workflows — planning, executing, reviewing — but when the session ends, everything they learned evaporates. The Planner forgets which architecture decisions worked. The Executor re-discovers conventions it already learned. The Reviewer can't reference past findings. Every task starts from zero. Band Memory wires Mimir — a battle-tested persistent memory engine (Rust, SQLite+FTS5, 23 MCP tools) — directly into Band agents. Three agents (Planner, Executor, Reviewer) coordinate through Band rooms and share a common memory backend. Each agent has custom tools (remember, recall, forget) that persist and retrieve context across sessions. In the demo, Session 1 starts cold: the Planner checks for past auth decisions and finds nothing, creates a plan from scratch, the Executor establishes conventions (bcrypt, JWT patterns), and the Reviewer stores findings. In Session 2, the user asks to add OAuth — the Planner instantly recalls the auth architecture, the Executor pulls up the exact conventions, and the Reviewer cross-references past findings. The team compounds knowledge every run. Built with the Band SDK (LangGraph adapter), Mimir MCP server, and GPT-4o for agent reasoning. The skill file in agents/memory_tools.py can be reused by any Band agent. Zero cloud dependency for memory — Mimir runs locally on SQLite. This is what Band agents are missing: memory that survives the session. Not just structured chat history, but searchable, decaying, confidence-scored knowledge that compounds across every interaction.