okf-agent-memory MCP Server
okf-memory/okf-agent-memory
[](https://agentrank-ai.com/tool/okf-memory--okf-agent-memory/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Git-native persistent memory for AI coding agents. 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.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
okf-memory/okf-agent-memory is a Go MCP server licensed under MIT. Git-native persistent memory for AI coding agents. 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. Topics: agent-memory, ai-agents, bm25, coding-agents, developer-tools, git-native, go, golang, knowledge-base, knowledge-graph, llm-memory, markdown, mcp, mcp-server, model-context-protocol, okf, open-knowledge-format, persistent-memory.
Ranked #862 out of 24846 indexed tools.
In the top 4% of all indexed tools.
Ecosystem
Score Breakdown
770 stars → growing community
Last commit 1mo ago → recently updated
11/16 issues closed → decent issue management
6 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# OKF Agent Memory
> **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:
1. **The Prompt Monolith**: Stuffing all domain knowledge and rules into `AGENTS.md` or `CLAUDE.md` creates massive context bloat and causes **attention drift** (agents ignore critical instructions).
2. **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)**:
```mermaid
flowchart TD
subgraph PUSH["1. Normative Working Memory (Push Layer)"]
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