Provides AI coding agents with persistent memory and institutional knowledge across sessions.
Sponsored
Muninn addresses the fundamental limitation of stateless AI coding agents by providing a persistent, read-write memory loop. It allows agents to retain context, past decisions, known issues, and learned patterns across multiple coding sessions and projects. By intelligently querying and surfacing only the most relevant information within a strict context budget, Muninn prevents agents from making repetitive mistakes, contradicting past choices, or breaking fragile parts of a codebase. It continuously learns and adapts per-codebase and per-developer, transforming individual AI agents into experienced collaborators capable of building like teams.
Key Features
01Multi-machine support via `sqld` for shared memory across environments
02Intelligent context budgeting with a 2000-token limit per tool call
030 GitHub stars
04Persistent memory and institutional knowledge for AI coding agents
05Adaptive intelligence with per-codebase and per-developer learning profiles
06Dynamic fragility scoring based on multiple codebase signals
Use Cases
01Enabling AI agents to retain context, decisions, and learnings across coding sessions
02Preventing AI agents from introducing bugs or breaking fragile parts of a codebase
03Allowing solo developers to manage complex projects with AI agents efficiently, mirroring team-like knowledge accumulation
