<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: okf_memory</title><link>https://news.ycombinator.com/user?id=okf_memory</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Sun, 06 Sep 2026 11:35:33 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=okf_memory" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by okf_memory in "OKF Agent Memory – Git-native persistent memory for AI coding agents"]]></title><description><![CDATA[
<p>Hey HN,<p>We built OKF Agent Memory because we were frustrated with how AI coding agents (Claude Code, Cursor, Windsurf, local models) handle long-term project context.<p>Every time a context window closes or a session resets, the agent forgets architectural decisions, domain discoveries, and operational rules. The existing solutions fall into two extremes:
1. Ad-hoc flat files (CLAUDE.md, AGENTS.md, .cursorrules) that inevitably balloon into 20k-token monoliths, degrade agent focus, and cause "lost-in-the-middle" attention failure.
2. Vector databases / background daemons (Mem0, Letta, Zep) that introduce heavy runtimes (Python/Node), docker containers, proprietary storage silos, and recurring embedding API costs (adding 200–800ms per retrieval call).<p>Our approach: The "LLM Wiki" in pure Go.<p>OKF Agent Memory (v0.1.0) is a single, zero-dependency Go binary that turns your Git repository into a structured, self-validating knowledge corpus based on Google's Open Knowledge Format (OKF) v0.2 specification:<p>• In-Memory BM25 Search (<300µs): Fast lexical ranking across titles, YAML metadata, tags, and bodies directly in memory. No embedding APIs, zero network overhead, zero runtime cost.
• Progressive Disclosure: Slashes prompt overhead by up to 90%. Instead of loading thousands of lines of context, the agent searches the bundle index and pulls only the exact 300-token concept required for the current task.
• 100% Git-Native: Everything lives in `knowledge/` as human-readable Markdown. You audit your agent's memory via `git diff`, `git blame`, and code reviews.
• Built-In Stdio MCP Server: `okf mcp knowledge` exposes native Model Context Protocol tools (`okf_search`, `okf_show`, `okf_create`, `okf_validate`) directly to Claude Code and Cursor.
• Trust Tiers: Distinguishes authoritative human law (`verified: human:...`) from agent-generated drafts (`generated: agent:...`).
• Sub-4ms Cold Starts (<15MB RSS): Starts in milliseconds with no VM spin-up.<p>Try it in 30 seconds:
$ brew install okf-memory/tap/okf
$ cd your-project && okf bootstrap .<p>GitHub: <a href="https://github.com/okf-memory/okf-agent-memory" rel="nofollow">https://github.com/okf-memory/okf-agent-memory</a>
Docs & Landing Page: <a href="https://okf-memory.dev" rel="nofollow">https://okf-memory.dev</a><p>We'd love your feedback on the architecture, the Go implementation, and how your coding agents behave with progressive disclosure memory!</p>
]]></description><pubDate>Sat, 05 Sep 2026 22:16:57 +0000</pubDate><link>https://news.ycombinator.com/item?id=49581249</link><dc:creator>okf_memory</dc:creator><comments>https://news.ycombinator.com/item?id=49581249</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49581249</guid></item><item><title><![CDATA[OKF Agent Memory – Git-native persistent memory for AI coding agents]]></title><description><![CDATA[
<p>Article URL: <a href="https://github.com/okf-memory/okf-agent-memory">https://github.com/okf-memory/okf-agent-memory</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=49581240">https://news.ycombinator.com/item?id=49581240</a></p>
<p>Points: 68</p>
<p># Comments: 20</p>
]]></description><pubDate>Sat, 05 Sep 2026 22:15:52 +0000</pubDate><link>https://github.com/okf-memory/okf-agent-memory</link><dc:creator>okf_memory</dc:creator><comments>https://news.ycombinator.com/item?id=49581240</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49581240</guid></item></channel></rss>