A Domain-Neutral, Git-Native Persistent Project Memory for AI Agents based on the Open Knowledge Format (OKF) v0.2.
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
- 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.
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.
Clone the repository and compile the standalone okf executable:
This generates the standalone binary at bin/okf.
# 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
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="...")
okf ships with a native Model Context Protocol (MCP) server over stdio to seamlessly connect with Claude Code, Cursor, Codex, and other agent platforms:
{
"mcpServers": {
"okf-memory": {
"command": "/path/to/okf-agent-memory/bin/okf",
"args": ["mcp", "/path/to/project/knowledge"]
}
}
}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
Run the full test suite and validate the repository's self-documenting knowledge bundle:
- 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.
MIT License. See LICENSE for details.