Show HN: PMB – local memory for coding agents that shows if it is used

# PMB · local-first memory for your AI coding agent Source: [https://pmbai.dev/](https://pmbai.dev/) Local\-first memory · MCP\-native · Apache 2\.0 ## Your AI agent forgets every session\.PMB gives it memory, on your disk\. Decisions, lessons and project facts live in one SQLite file you own\. Fed back to Claude Code, Cursor, Codex and Zed through MCP\. Offline, no API keys, no cloud, recall in ~35ms\. Works with your agent · one command **100%**on your machineNo API keysNo cloud, no telemetry**Apache\-2\.0**, open source How it works ## Memory that doesn't wait to be asked Hooks inject the right memory*before*the model thinks, and journal the agent's work after, no LLM call on the read path, no tool the agent has to remember to call\. **1**· Any agent records \_\_claude\_\_Claude Code \_\_cursor\_\_Cursor \_\_openai\_\_Codex writes to memory  PMB · one local memory recalls what matters **2**· Surfaced before it answers lesson0\.94 file0\.88 decision0\.81 01 · Read4–16 ms ### Auto\-recall on every prompt Every message is classified in sub\-millisecond; the matching lessons, decisions and project overview are fetched*for*the agent before it reasons\. 02 · Write< 1 ms ### Sub\-millisecond async writes The MCP tool returns instantly\. SQLite first; the embed and LanceDB vector insert run on a background thread, never blocking the turn\. 03 · Fuse94\.5% recall@10 ### Hybrid recall, ranked BM25 \+ dense vectors \+ entity graph \+ optional rerank, fused with Reciprocal\-Rank\-Fusion\. One call returns the right thing, ranked\. 04 · Learnhonest follow\-rate ### Lessons that earn their place Every rule is scored by whether the agent actually follows it\. Useful ones get starred; ignored ones are flagged dead, so you prune what doesn't help\. The Map · live entity graph ## Your memory, as a graph you can explore Every fact, decision, lesson, file and entity becomes a node, color\-coded by type, sized by importance\. Hover one to dim the rest, light up its neighbors, and read the full memory chunk\. MapTimelineOverviewLessons **0**entities·**0**connections·**8**clusters drag · scroll · hover The Timeline · git\-graph journal ## Every decision, lesson and commit, newest first One lane per project, nodes color\-coded by event type, connected by soft curves\. The same journal that ships in the dashboard, written automatically as you work\. MapTimelineOverviewLessons Not a mockup ## This is the actual dashboard A local web app served from your machine\. The Map and Timeline above are live recreations, here is the real thing, rendering one project's memory\. MapTimeline 5,229 entities  **The Map**· 65,005 connections across 149 clusters, color\-coded by kind Why it matters ## What changes when your agent remembers Not features, outcomes\. This is what persistent memory actually does to your day\. ### Stop re\-explaining your project Every session starts already knowing your decisions, conventions and the bug you hit last Tuesday\. No more pasting the same context into a fresh chat\. ### Switch tools without losing context Claude Code, Cursor, Codex and Zed all read the same memory\. Your context follows you, not your editor, so changing agents costs nothing\. ### Memory you can actually trust PMB scores whether each lesson gets followed and flags the dead ones\. It tells you when a memory*isn't*helping, so your context stays honest, not bloated\. Watch it compoundSession 1 of 5 ### Day one: a blank slate The agent starts with nothing\. You explain your project, your conventions, the bug you hit last Tuesday, and at the end of the session it all evaporates\. S1S2S3S4S5 Quickstart ## Seven commands, then just talk to your agent No account, no keys, nothing leaves your machine\. Inspect everything from the terminal, or open the dashboard\. fix the pricing bug in checkout⏎ L never lower NEGOTIATE/SKIP under 25% lesson · PMB 0\.94 F verdict\-policy\.ts · line 142 file · opened Tue 14:32 0\.88 D use RRF over a learned weight decision · 4 days ago 0\.81 3 of 41 memories · fused in**35ms**hybrid recall Integrate ## One command wires your agent to MCP Everything runs over stdio, the server is a child process of your agent\. No network, no port, no token\. ### Claude Code - Rules appended to your agent's config automatically - Point several agents at one shared workspace - Verify the wiring withpmb doctor Any model · local or hosted ## Bring your own model, or run it offline PMB never calls an LLM on the read path\. The optional summarize and graph\-extract passes run on whatever you point them at, including a fully local Ollama\. Your memory stays yours\. graph\.extractor = llm:ollama·run extraction and summaries on a local model, 100% offline, zero API keys\. Quickstart ## Running in 60 seconds Three commands, no account, no config\. Then just work the way you already do\. 1 ### Install One pip install\. Pure Python, runs on macOS, Linux and Windows\. $`pip install pmb\-ai` 2 ### Connect your agent Wires PMB into your agent over MCP\. Swap in cursor, codex, zed, and more\. $`pmb connect claude\-code` 3 ### Just talk to it Work as usual, PMB records and recalls automatically\. Open the dashboard any time to explore\. $`pmb dashboard` Architecture ## Files on your disk, all the way down Every event lives in SQLite; vectors live in LanceDB next to it\. Copy them anywhere withcp\. No server to trust\. Your agentMCP · stdioMCP server29 tools · prepare\(\)Enginehybrid routerHybrid recallBM25 \+ vector \+ graph · 35msAsync writeembed queue · sub\-ms returnSQLiteLanceDBON YOUR DISK Benchmarks ## Fast, local, and honest about it Every number here is measured on PMB's own engine and reproducible from the repo\. No cloud, no LLM in the read path, no per\-query cost\. 94\.6%recall@10 \(LoCoMo\) 88msp50 recall at 2k memories <1msasync write, never blocks $0per recall, fully local 73\.1%answer accuracy \(J\-score, LoCoMo\) #### Retrieval quality \(recall@k\) MRR 0\.774 · nDCG@10 0\.816 68\.485\.089\.494\.6k=1k=3k=5k=10LoCoMo\-10 · 997 questions · no LLM grader · cache off #### Recall latency vs memory size \(p50 / p95\) p50p95 100 ms101005001k2kWarm daemon, cache off, local CPU\. Real ~100\-memory workspace: p50 24 ms\. Cached: ~0\.15 ms\. Radically honest ## It tells you when a memoryisn't helping Every lesson carries asurface\_id\. PMB tracks whether the agent actually followed it, confirmed or auto\-detected from activity\. Rules that get ignored are flagged dead\. The ones that earn their place are starred\. No vanity metrics\. usefulnever lower NEGOTIATE/SKIP under 25% usefulpmb warmup before first recall unverifiedprefer tree\-sitter for TS indexing deadalways rerun the full suite on edit Under the hood ## Built on boring, durable pieces No exotic infrastructure\. Local files and well\-worn libraries, the kind you can still open in five years\. Memory hygiene ## It tends itself A year in, recall is still sharp\. Memory decays, archives, and dedupes on its own, and never deletes anything behind your back\. Memory flows left to right and tends itself\. Hover a stage to follow the path\. one SQLite file Does my code or data ever leave my machine?No\. Everything lives in a local SQLite file with vectors in LanceDB right next to it\. There are no network calls on the read path, no account and no telemetry, ever\. Unplug the internet and it still works\. How is this different from RAG or a vector database?Two ways\. Recall is hybrid, BM25 plus dense vectors plus an entity graph, fused and ranked\. And it's automatic: the right memory is injected before the model thinks\. You don't build a pipeline or hope the agent remembers to call a tool\. Will it slow my agent down?No\. Recall lands in about 35ms and writes return in under a millisecond, the embedding and vector insert happen on a background thread, so the turn is never blocked\. Which agents and operating systems are supported?Any MCP\-aware agent: Claude Code, Cursor, Codex, Zed, Windsurf and more, wired in with one command\. PMB is pure Python and tested on macOS, Linux and Windows\. What if a memory is wrong or unhelpful?PMB scores whether each lesson actually gets followed and flags the dead ones so you can prune them\. It's the rare tool that tells you when its own memory isn't earning its place\. Is it really free?Yes\. Apache\-2\.0, open source, free forever\. No paid tier, no seats, no telemetry\. You own the file and the code\. Apache 2\.0 · 100% offline ## Give your agent a memory it keeps\.