OKF Agent Memory – Git-native persistent memory for AI coding agents — SkimNews
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- OKF Agent Memory stores agent knowledge directly in a repository's
knowledge/folder as plain Markdown files with YAML frontmatter, bridging ad-hoc files like CLAUDE.md and AGENTS.md and complex vector databases. - The system uses in-memory BM25 lexical retrieval to deliver search results in under 300µs and graph validation in ~4ms, eliminating recurring vector embedding API costs and network roundtrips.
- OKF Agent Memory enforces a 'Search-Before-Write Principle' requiring agents to query existing memory before authoring, and uses progressive disclosure through hierarchical
index.mdfiles to prevent context bloat and concept duplication. - The toolchain is a zero-dependency single binary with sub-5ms CLI startup, built in Go, and ships with a native Model Context Protocol (MCP) server over stdio compatible with Claude Code, Cursor, and Codex.
- A single bootstrap command scaffolds the full memory stack into any project — generating the OKF bundle, agent skill definitions under
agents/skills/okf-memory/, an AGENTS.md operating file, and Makefile validation tasks. - The project is built on Google's Open Knowledge Format (OKF) v0.2 standard and supports provenance (
sources), trust tiers (generatedvsverified), and lifecycle metadata (status,stale_after). - An included benchmark runner in pure Go verifies Time-To-First-Token speedups and approximately 80% token reduction against local models including Gemma, Qwen, and Llama on LM Studio or Ollama.
Why it matters: AI coding agents currently lose architectural decisions and domain discoveries when context windows close, forcing teams to choose between ad-hoc markdown notes and locked-in vector databases. OKF Agent Memory offers a version-controlled, plain-text alternative with sub-millisecond search and zero API costs, distributed under MIT, that developers can audit with `git diff` and `git log` rather than trust as a black box.
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