OKF Agent Memory

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

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

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

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⚡ 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.
• Governance & Code-to-Knowledge Binding: Bind architecture decisions directly to source files via code_refs and query active constraints/holds via --for-path before modifying code.
• Truly Domain-Neutral: Designed for Software Engineering, Coaching, Scientific Research, Literature Reviews, and Operations.

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📊 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

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