A Domain-Neutral, Git-Native Persistent Project Memory for AI Agents based on the Open Knowledge Format (OKF) v0.2.
๐ 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
โก Key Highlights
- Blazing Fast Performance (<300ยตs 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 diffandgit 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 (generatedvs.verified), and lifecycle metadata (status,stale_after). - Solves Context Bloat & Memory Rot: Employs Progressive Disclosure (hierarchical
index.mdfiles 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.
๐ 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 โ 800ms (Embedding API + Vector DB) | 40ms โ 120ms | < 300 ยตs (Microseconds, In-Memory BM25) |
| Full Corpus Parse & Graph Validation | 200ms โ 1.5s | 80ms โ 250ms | ~4.0 ms (50+ concepts, bidirectional graph) |
| Process Cold-Start Overhead | 250ms โ 600ms (Python VM boot) | 80ms โ 180ms (V8 / Deno boot) | < 4 ms (Compiled Single Binary) |
| Retrieval Cost per 1,000 Queries | ~$0.10 โ $0.50 (Embedding tokens) | $0.00 | $0.00 (Zero API cost, fully local) |
| Memory Footprint (RSS) | ~120 MB โ 350 MB | ~60 MB โ 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 -80% token reduction on your local hardware (LM Studio / Ollama with Gemma, Qwen, Llama). Run make benchmark or explore the Progressive Disclosure Benchmark Suite.
๐ Quickstart
1. Build the Tooling
Clone the repository and compile the standalone okf executable:
This generates the standalone binary at bin/okf.
2. Basic CLI Commands
# Validate bundle conformance, graph connectivity, and description drift ./bin/okf validate knowledge --strict --drift # Search concepts via in-memory BM25 scoring ./bin/okf search "architecture layers" knowledge # Inspect a concept and its relationships (with --json support) ./bin/okf show architecture/layers knowledge --json # Create a new concept with automated log.md and index.md bookkeeping ./bin/okf create decisions/auth-flow knowledge \ --type Decision \ --title "OAuth2 Authorization Flow" \ --desc "Standardized on PKCE for client authentication." # Update an existing concept ./bin/okf update decisions/auth-flow knowledge \ --desc "Updated OAuth2 PKCE token refresh interval." # Bootstrap full agent memory stack into any target project ./bin/okf bootstrap /path/to/project --name "My Project" # Initialize only a bare OKF bundle in any directory ./bin/okf init my-project/knowledge
3. Bootstrapping Agent Memory in Any Project
Scaffold the complete OKF Agent Memory architecture into any new or existing repository with a single command:
# Bootstrap full memory stack into target project ./bin/okf bootstrap /path/to/my-project --name "My Service"
This automatically sets up:
knowledge/โ OKF v0.2 compliant persistent memory bundle (index.md,log.md).agents/skills/okf-memory/โ Embedded agent skill definition and capability guidesAGENTS.mdโ Project-tailored operating instructions for AI coding agentsMakefileโ Convenience tasks for validation (make validate) and search (make search q="...")
4. Running as an MCP Server
okf ships with a native Model Context Protocol (MCP) server over stdio to seamlessly connect with Claude Code, Cursor, Codex, and other agent platforms:
Example MCP Configuration (claude_desktop_config.json or Cursor):
{
"mcpServers": {
"okf-memory": {
"command": "/path/to/okf-agent-memory/bin/okf",
"args": ["mcp", "/path/to/project/knowledge"]
}
}
}๐ Repository Structure
okf-agent-memory/
โโโ benchmarks/ # Progressive disclosure benchmark suite & hardware test data
โ โโโ data/ # Monolith docs vs OKF bundle test fixtures
โ โโโ results/ # Reproducible benchmark logs across 8+ local & cloud LLMs
โโโ cmd/
โ โโโ okf/ # Standalone CLI and embedded MCP server (`stdio`)
โ โโโ okf-benchmark/ # Automated benchmark runner for LLM TTFT & token measurements
โโโ docs/ # Guides, specifications, architecture & release playbook
โ โโโ AGENT_TESTING.md # Multi-agent testing, prompt scenarios & compatibility matrix
โ โโโ ALTERNATIVES.md # Comparison against Mem0, Letta, and ad-hoc markdown
โ โโโ CLI.md # Complete command-line & MCP tool reference
โ โโโ CONVENTION.md # OKF Agent Memory Convention v0.1
โ โโโ GETTING_STARTED.md # Comprehensive onboarding guide
โ โโโ OKF-COMPATIBILITY.md# OKF v0.2 spec compatibility analysis
โ โโโ RELEASE_PLAYBOOK.md # Automated release process & version tagging
โ โโโ ROADMAP.md # Project roadmap & milestones
โ โโโ SECURITY.md # Data governance, secret prevention & PII rules
โโโ examples/ # Domain-neutral reference OKF v0.2 bundles
โ โโโ books/ # Literature & cognitive science knowledge bundle
โ โโโ coaching/ # Executive coaching & client session bundle
โ โโโ software/ # Microservices architecture & ADR bundle
โโโ knowledge/ # Project's own OKF v0.2 persistent memory bundle
โ โโโ index.md # Root progressive disclosure index (okf_version: "0.2")
โ โโโ log.md # Dated change log (ISO 8601 YYYY-MM-DD)
โ โโโ project/ # Overview & value propositions
โ โโโ architecture/ # 5-tier architecture & tooling decisions
โ โโโ convention/ # Principles & lifecycle workflows
โ โโโ roadmap/ # Milestones
โโโ packaging/ # Distribution packaging
โ โโโ homebrew/ # Official Homebrew formula & tap instructions
โโโ pkg/okf/ # Zero-dependency Go core library (parser, validator, BM25, MCP, bootstrap)
โโโ AGENTS.md # Operating instructions for AI coding agents
โโโ CONTRIBUTING.md # Contribution guidelines & development workflow
โโโ Makefile # Build, test, lint, validation & release targets
โโโ LICENSE # MIT License
โโโ README.md # Main repository documentation
โโโ SECURITY.md # Security policy & reporting guidelines
๐งช Testing & Verification
Run the full test suite and validate the repository's self-documenting knowledge bundle:
๐ Further Documentation
- Getting Started Guide โ Comprehensive onboarding guide for agents and humans.
- CLI & MCP Reference โ Complete command-line and protocol tools reference.
- Contributing Guide โ Development setup, quality gates, and pull request standards.
- Security & Privacy Guidelines โ Data governance, secret prevention, and PII protection rules.
- Multi-Agent Testing & Evaluation โ Test scenarios, compatibility matrix, and benchmarks.
- OKF Agent Memory Convention v0.1 โ Behavioral rules and lifecycle specification.
- Project Roadmap & Milestones โ Phased development plan.
- Release Playbook โ Versioning, CI/CD pipeline, and distribution procedures.
- OKF v0.2 Compatibility Matrix โ Specification validation analysis.
- Why OKF Agent Memory? โ Detailed value proposition & differentiators.
- Alternatives & Ecosystem Comparison โ Comparison with Mem0, Letta, and ad-hoc markdown files.
๐ License
MIT License. See LICENSE for details.