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okf-agent-memory
okf-memory
Git-native persistent memory for AI coding agents.
Open okf-memory/okf-agent-memory → Does not match the four rules.
agent-memoryai-agentsbm25coding-agentsdeveloper-toolsgit-nativegogolangknowledge-baseknowledge-graphllm-memorymarkdown
Works with any agent
ships AGENTS.md, the cross-tool instruction file.
In its own words
Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.
A Domain-Neutral, Git-Native Persistent Project Memory for AI Agents based on the Open Knowledge Format (OKF) v0.2.
🌟 Overview
Conversations with AI agents reset when context windows close. Valuable architectural decisions, domain discoveries, and operational facts are lost unless stored persistently.
Traditional approaches suffer from two fatal failure modes:
- The Prompt Monolith: Stuffing all domain knowledge and rules into
AGENTS.mdorCLAUDE.mdcreates massive context bloat and causes attention drift (agents ignore critical instructions). - The RAG Blindspot: Dumping behavioral rules into vector databases fails because agents never semantically search for operational constraints (e.g. formatting or security rules) during general tasks.
OKF Agent Memory resolves this dilemma with the Dual-Memory Agent Architecture (DMAA):
The Universal Composition Model
In DMAA, every agent configuration is structured by a universal composition:
$$\text{AGENTS.md} = \underbrace{\text{Domain Codex (AAG)}}_{\text{Project Invariants, Tone, Guardrails}} + \underbrace{\text{OKF Memory Bridge}}_{\text{Standardized Triggers: Search-Before-Write}}$$
The repository's own description, then 18% of its README. Read the rest on GitHub ↗
Read from the repository's git tree on 2026-08-17.