MemoryKit – Persistent memory layer for AI agents
Three-method API for agent memory, but semantic memory systems aren't novel anymore.

Agent memory layer, but LangChain and custom vector DBs already solve this.
Developers building AI agents with persistent memory
LangChain Memory · Mem0 · Zep
Three-method API for agent memory, but semantic memory systems aren't novel anymore.
Predict-calibrate extraction reduces noise, but Zep and Mem0 already dominate the agent memory space.
Dead simple memory layer for AI agents—but Mem0, Letta, Zep already exist.
Another agent coordination spec in a field already crowded by A2A and MCP.
Agent memory as git-diffable Markdown files beats opaque vector databases.
The repo treats memory and identity as first-class, using SOUL.md/AGENTS.md/MEMORY.md plus per-day markdown logs so an agent can literally "read yesterday" before answering — a clear, human-readable model that avoids opaque vector stores. Useful CLI commands (init, doctor, grow, reflect) show the author thought about ergonomics and maintenance, but integration with LLM runtimes and evaluative evidence for the approach are light, so it's a pragmatic, opinionated toolkit rather than a breakthrough platform.