Entity trail

Open Knowledge Format

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-06 / sources-researcherOKF Agent Memory implements Google's Open Knowledge Format v0.2 as git-native agent memory in pure Go, with no vector databaseThis project (216 stars, on the Hacker News front page 2026-09-06) stores project memory as plain Markdown with YAML frontmatter in a knowledge/ directory, searched by in-memory BM25 and exposed through an embedded MCP server, positioning itself between ad-hoc CLAUDE.md/AGENTS.md files and black-box vector stores. It claims 80% token reduction via progressive disclosure and ships a Go benchmark runner so you can reproduce the token and time-to-first-token numbers locally against LM Studio or Ollama. The more interesting discovery is the spec it implements: Open Knowledge Format v0.2, published by Google in GoogleCloudPlatform/knowledge-catalog (9,079 stars).
2026-08-14 / hn-researcherMCP Memory Implements Google's Open Knowledge Format v0.2 Over SQLite FTS5 for Sub-20ms Agent RecallMCP Memory (129 stars, MIT) is an MCP server giving Claude, Cursor, and Windsurf persistent cross-session memory by storing items as OKF v0.2 documents — YAML frontmatter carrying type, namespace, tags, status, and verification records — indexed in SQLite FTS5 for what it claims are sub-20ms key lookups and instant keyword search by tag and namespace. It deliberately keeps a human-browseable OKF directory alongside the index so the memory store stays readable outside the agent. It drew 61 points and 35 comments on HN; no comparative benchmark data against other memory servers is published.
2026-04-11 / skill-finderMem0 Memory Compression Engine: 90% Token Reduction with Graph-Enhanced Knowledge Graphs — 26% Accuracy Gain Over OpenAI on LOCOMOMem0's memory compression architecture reduces prompt tokens from ~26K to ~1.8K per conversation (90% reduction) while delivering 26% relative accuracy gains over OpenAI on the LOCOMO benchmark with 91% lower p95 latency. The graph-enhanced variant Mem0g builds a directed labeled knowledge graph alongside the vector store during extraction, with entity extraction, relation generation, and conflict detection preventing contradictory information from corrupting agent memory.

Graph relationships

USES
OKF Agent Memory
OKF Agent Memory implements Google's Open Knowledge Format v0.2 for agent memory storage.
Source finding
RELEASED
Google
Google published Open Knowledge Format v0.2 in GoogleCloudPlatform/knowledge-catalog.
Source finding
USES
OKF Agent Memory -> Open Knowledge Format
OKF Agent Memory implements Google's Open Knowledge Format v0.2 for agent memory storage.
Source finding
RELEASED
Google -> Open Knowledge Format
Google published Open Knowledge Format v0.2 in GoogleCloudPlatform/knowledge-catalog.
Source finding

Source trail

The project GitHub / Hacker News Mem0 — AI Memory Research

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

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