待翻译：Show HN: Knowl – agent memory that retires stale facts when they change

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

Notifications You must be signed in to change notification settings Fork 1 Star 22 BranchesTags Open more actions menu Latest commit History 1,019 Commits 1,019 Commits Folders and files NameName Last commit message Last commit date .claude-plugin .claude-plugin .github .github benchmarks benchmarks docs docs integrations/cline integrations/cline scripts scripts src src tests tests .gitattributes .gitattributes .gitignore .gitignore AGENTS.md AGENTS.md CHANGELOG.md CHANGELOG.md CLA.md CLA.md CLAUDE.md CLAUDE.md CONTRIBUTING.md CONTRIBUTING.md KNOWL.md KNOWL.md LICENSE LICENSE README.md README.md drizzle.config.ts drizzle.config.ts eslint.config.mjs eslint.config.mjs glama.json glama.json package-lock.json package-lock.json package.json package.json tsconfig.json tsconfig.json tsup.config.ts tsup.config.ts vitest.config.ts vitest.config.ts vitest.mutation.config.ts vitest.mutation.config.ts Repository files navigation Your agent starts every session blank, so you keep a CLAUDE.md. It only grows. Six months in it still names the database you migrated off last spring, and now the agent gets both answers. Knowl is persistent memory for Claude Code, Cursor and Codex, over MCP or the CLI. When a fact is replaced, the old one is retired instead of competing with the new one. No API key needed. When Knowl isn't sure the new fact replaces the old, it leaves both active and hands you the knowl supersede command to say so. Turn that off and retrieval drops from 98% to 47%. End to end, 90 to 73. How it was measured ↓ Forty seconds, one decision, three agents: Quick start Requires Node.js 22 or later. macOS, Linux and Windows. npm install -g @dat999zx/knowl cd your-project knowl init Other package managers The published package is the same one in every case; each of these installs it and puts knowl on your PATH. pnpm add -g @dat999zx/knowl yarn global add @dat999zx/knowl bun add -g @dat999zx/knowl Or run it without installing: npx @dat999zx/knowl init Knowl runs on Node.js in all of these — Bun installs it, Node executes it. It bundles native addons (SQLite, tree-sitter, the embedding runtime), so running the CLI under the Bun or Deno runtime directly is not supported. knowl init creates .knowl/, installs the project guidance files, updates .gitignore, and registers Knowl with whichever agents it detects. It also warms a local embedding model (~53 MB) in the background — init succeeds either way, and without it you still get keyword search. That is the whole setup. You do not record memory by hand: your agent reads and writes it as it works. Connecting an agent Claude Code MCP · lifecycle · gate Codex MCP · lifecycle · gate Copilot MCP · lifecycle · gate Cursor MCP · lifecycle · gate OpenHands MCP · lifecycle · gate Antigravity MCP · lifecycle · gate Windsurf MCP · lifecycle · gate Cline MCP · lifecycle · plugin Zed MCP · capture · ACP JetBrains MCP · capture · ACP OpenCode MCP · manual loop Claude Desktop MCP · manual loop knowl init registers the MCP server for every host it finds. Start a new session afterwards so the agent picks up its guidance, and it will query and write memory on its own. gate means Knowl can refuse an edit that invalidates code another session is holding. Neovim and Kiro work the same way as Zed and JetBrains, through knowl acp. Cline needs one line pointing it at the shipped plugin. Any other MCP client works with no integration at all. → Every host, and what each one can do · How agents use it · MCP tools and resources The idea: memory that retires itself Most memory systems are append-only. Storing "we moved to SQLite" leaves "we use PostgreSQL" active and retrievable, so the agent gets both and picks by rank. Knowl treats a same-subject write as a correction: the predecessor is marked superseded, drops out of normal retrieval, and stays queryable through knowl timeline. That single behavior is most of the accuracy difference. On the MemoryAgentBench Conflict Resolution corpus — 455 facts, 100 questions about which fact is current, top-5 retrieval, no LLM reader: Configuration Top-1 Stale returns Active atoms Supersession ON 98.0% 2 / 100 306 Supersession OFF 47.0% 62 / 100 455 Same corpus, same ranker, same query path. The only variable is whether the outdated fact is still active. This is a retrieval-level measurement in Knowl's own harness: it asks whether the current fact comes back first, with no model in the loop. Verified end-to-end, in the benchmark's own harness Because a number you score yourself is worth less than one somebody else scores, the same claim was re-run inside MemoryAgentBench's harness, scored by its own code, with an LLM reading what Knowl returned — the harder, fully end-to-end setup, at the largest context the task offers: System FactConsolidation-SH @262K Knowl 90 agentmemory 79 GPT-4o (long-context) 60 HippoRAG-v2 54 BM25 48 GPT-4o-mini (long-context) 45 Qwen3-Embedding-4B 29 Cognee 28 MemGPT 28 Mem0 18 MIRIX 14 Zep 7 18,332 facts, 100 questions, substring exact match. Every row uses gpt-4o-mini as the reader, Knowl's included — the paper states it for all RAG and memory agents, so these are like-for-like. Knowl and agentmemory were measured here; every other figure is from the MemoryAgentBench paper, arXiv 2507.05257v4, Table 3. agentmemory is not evaluated in that paper — its published numbers are LongMemEval-S retrieval recall, a different task — so it was run through the same harness with the same config, and both adapters share one reader code path so neither can drift from the paper's own RAG handler. Method, mechanism and reproduction steps: FINDINGS.md. Otherwise shown are every commercial memory system the paper evaluates, plus the highest scorer from each baseline family. The paper's table has changed between versions — BM25 read 56 in v1 and reads 48 in v4 — so the version is cited, not just the table. Knowl's 90 was measured 2026-08-08 and independently reproduced at 89.0 on 2026-08-19 with the checked-in adapter; agentmemory's 79 is a single run. Every figure here is one run at temperature: 0.7, and the ablation gap moved 4 points between two runs of the same 6k cell, so read them to the point rather than the decimal. Switching supersession off in that same harness drops Knowl to 73, and the gap holds across a 40× change in corpus size: Context Supersession ON OFF Gap 262K 90 73 +17 6K 94 78 +16 The two sections measure different things and are not comparable to each other: 98% is retrieval top-1 at 6K with no reader, 90 is end-to-end accuracy at 262K with one. Only the second is comparable to the published systems above. See benchmarks for the protocol, the checked-in results, and what the task does not cover — including multi-hop, where Knowl scores 7 against a 14-point retrieval ceiling. Supersession is a correction, not a delete: the item, its assertions, and its history all survive. Not a mock-up — the same sequence against the published CLI, recorded from demo.tape: Sharing memory across a team: knowl.cloud Everything above is local and needs no account. knowl.cloud is the optional hosted layer for when one machine is not enough: Shared workspaces. Knowledge written in one checkout reaches teammates' agents, with each repository still owning what it publishes. Browser agents. claude.ai and chatgpt.com cannot run a local process, so they connect over a remote MCP endpoint with a token scoped to one workspace. Local-only remains a first-class way to run Knowl. Nothing here is required to use anything above. What gets stored Every atom has exactly one of seven categories: Category Use it for fact Stable project truths, conventions, and verified behavior decision A selected option with reasoning and alternatives goal An intended outcome that guides future work constraint A rule or boundary that must continue to hold architecture How components are arranged and interact state Current progress, readiness, blockers, or operational status skill A reusable procedure or learned workflow description Alongside the content, each atom keeps a status (active, deprecated, rejected, archived, superseded), a freshness flag, confidence, tags, source commit, affected paths, and optional evidence pointing at files, commits, tests, commands, URLs, or indexed code symbols. File and symbol evidence go stale on their own when the code moves, which is how an atom admits it may be out of date instead of asserting a version of the repository that no longer exists. What Knowl deliberately does not store is your conversations. Lifecycle capture records bounded events and summaries — never prompts, transcripts, stdout, or environment variables. Raw transcript search exists as an opt-in, off-by-default index over files the host already wrote. → Knowledge model reference How agents use it knowl serve exposes the store over stdio MCP; knowl init registers it for you. The workflow the installed guidance asks agents to follow is short: Query memory with the words that name the subject before reading repository files. Use an active hit directly; inspect files only on a miss, conflict, or stale result. Store durable findings, stated goals, and recurring diagnoses as you go, and correct contradicted memory rather than duplicating it. In practice that looks like this — a new session, no context, nothing pasted in: You why did we pick SQLite over Postgres? Agent → knowl_query "sqlite postgres database choice" ← decision · Use SQLite · active · fresh "Keeps storage repository-local and simple to operate." alternatives: PostgreSQL, MongoDB tags: database, local-first SQLite keeps the store repository-local and simple to operate. Postgres and MongoDB were both considered and rejected on that basis. The agent answered before opening a single file, and it knew the options you rejected — which the code cannot tell it, because rejected alternatives leave no trace in a codebase. Host MCP Automatic lifecycle Write gate Capture nudge Notes Claude Code Yes Yes Yes Yes Prompt guidance is installed as well Codex CLI Yes Yes Yes Yes Hooks need codex_hooks; not on Windows GitHub Copilot Yes Yes Yes Yes Reuses Claude Code's hook format OpenHands Yes Yes Yes Yes MCP entry is added by hand Antigravity Yes Yes Yes Yes Context rides injectSteps Windsurf Yes Yes Yes Yes Nudge rides MCP; no stop hook Cursor Yes Yes Yes Yes Finalizes per turn Cline Yes Yes No Yes Lifecycle via the shipped plugin Zed, JetBrains, Neovim, Kiro Yes Yes No Yes Via knowl acp -- Claude Desktop, OpenCode, Roo, … Yes No No Yes MCP plus the manual work loop Full detail, and why each gap exists, in docs/hosts.md. Where hooks are available, they own the session lifecycle: bootstrap context, capture, checkpoints, and finalization happen without the agent being asked. Where they are not, knowl task run, task start, task checkpoint, and task finish cover the same ground manually. knowl init writes the MCP registration for every host it detects. To wire one by hand, the entry is the same everywhere: { "mcpServers": { "knowl": { "command": "knowl", "args": ["serve"] } } } Use knowl.cmd as the command on Windows. Codex reads the same entry under mcp_servers. → MCP tools and resources · Lifecycle reference What Knowl is for Knowl does one job: keep a repository's engineering truth accurate for the agents working on it. Not user preferences, not chat history — the decisions, constraints, and architecture of a codebase, and which of them are still true today. Three choices follow from that: Typed, not free text. A decision carries reasoning and the alternatives you rejected. A constraint is a rule that must keep holding. A sta [truncated for AI cost control]