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okf-agent-memory

Git-native agent memory on Google's OKF v0.2: Markdown+YAML concepts in knowledge/, a zero-dep Go CLI/MCP with <300µs BM25 search, provenance and trust tiers.

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Curator's take

Pick this when you want agent memory you can `git diff` and code-review: every concept is a Markdown file with provenance, trust tier and staleness metadata, and progressive-disclosure indexes keep loads small. It is lexical-only (BM25) by design — no embeddings, no API cost — so it wins on decisions, constraints and glossary-style facts, not on fuzzy semantic recall of past conversations. Skip it if you want automatic session capture (it is search-before-write, human/agent-authored) or memory shared across many repos; it is per-project and lives in the repo.

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README.md2 min read

OKF Agent Memory

A Domain-Neutral, Git-Native Persistent Project Memory for AI Agents based on the Open Knowledge Format (OKF) v0.2.

Specification Tooling Protocol License

🌟 Overview

Conversations with AI agents reset when context windows close. Valuable architectural decisions, domain discoveries, and operational facts are lost unless stored persistently.

OKF Agent Memory provides a standardized, vendor-neutral memory layer that lives directly in your repository (knowledge/) as plain Markdown files with YAML frontmatter. It bridges the gap between unstructured ad-hoc markdown files (CLAUDE.md, AGENTS.md) and complex, black-box vector databases.

flowchart TD
    L1["1. OKF v0.2 Specification<br/>(Normative Markdown & YAML Format)"]
    L2["2. Agent Memory Convention<br/>(Behavioral Rules: Search, Review, Trust)"]
    L3["3. Agent Skill<br/>(LLM Prompts & Operational Workflows)"]
    L4["4. Tooling Layer: Go Library & CLI<br/>(Deterministic Parsing, Validation, Search, MCP)"]
    L5["5. Project Knowledge Corpus<br/>(knowledge/ OKF Bundle)"]

    L1 --> L2
    L2 --> L3
    L3 --> L4
    L4 --> L5

⚡ Key Highlights

  • Blazing Fast Performance (<300µs Search, ~4ms Graph Validation): In-memory BM25 retrieval and bundle validation execute in microseconds without VM spin-up or network roundtrips.
  • 100% Git-Native & Zero Vendor Lock-in: Everything is version-controlled plain text. Inspect, audit, and review your agent's memory using standard git diff and git log. No external database required.
  • Zero API Costs for Memory Retrieval: Local lexical BM25 indexing eliminates recurring vector embedding API costs and network roundtrips.
  • Built on Google OKF v0.2: Uses the open standard format for agent knowledge with full support for provenance (sources), trust tiers (generated vs. verified), and lifecycle metadata (status, stale_after).
  • Solves Context Bloat & Memory Rot: Employs Progressive Disclosure (hierarchical index.md files and link graphs) so agents only load the exact concepts they need.
  • Search-Before-Write Principle: Mandates querying existing memory before authoring, preventing concept duplication and hallucinated divergence.
  • Zero-Dependency Go Toolchain: Single binary with zero external dependencies, sub-5ms CLI startup time, and a built-in Model Context Protocol (MCP) server (okf mcp).
  • Truly Domain-Neutral: Designed for Software Engineering, Coaching, Scientific Research, Literature Reviews, and Operations.

📊 Performance Benchmarks

Built in Go with zero external dependencies, okf is engineered for high-frequency agent tool calling loops:

Benchmark Metric Python / Vector DB Runtimes (Mem0, Letta) Deno / Node.js Tooling OKF Agent Memory (Go)
Concept Search Latency 150ms – 800ms (Embedding API + Vector DB) 40ms – 120ms < 300 µs (Microseconds, In-Memory BM25)
Full Corpus Parse & Graph Validation 200ms – 1.5s 80ms – 250ms ~4.0 ms (50+ concepts, bidirectional graph)
Process Cold-Start Overhead 250ms – 600ms (Python VM boot) 80ms – 180ms (V8 / Deno boot) < 4 ms (Compiled Single Binary)
Retrieval Cost per 1,000 Queries ~$0.10 – $0.50 (Embedding tokens) $0.00 $0.00 (Zero API cost, fully local)
Memory Footprint (RSS) ~120 MB – 350 MB ~60 MB – 140 MB < 15 MB

[!TIP] Reproduce Locally with your own LLM: We provide an automated benchmark runner in pure Go to verify Time-To-First-Token (TTFT) speedups and -