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Local-first, developer-specific, works everywhere.","maintainers":[{"name":"aman_asmuei","email":"amanasmuei@gmail.com"}],"readme":"<p align=\"center\">\n  <img src=\"assets/logo.png\" alt=\"amem\" width=\"140\" />\n</p>\n\n<h1 align=\"center\">amem</h1>\n\n<p align=\"center\">\n  <strong>The memory layer for AI coding tools.</strong><br/>\n  <sub>Tell your AI once — it remembers everywhere.</sub>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://www.npmjs.com/package/@aman_asmuei/amem\"><img src=\"https://img.shields.io/npm/v/@aman_asmuei/amem?style=flat-square&logo=npm&logoColor=white&color=cb3837\" alt=\"npm\" /></a>\n  <a href=\"https://github.com/amanasmuei/amem/actions\"><img src=\"https://img.shields.io/github/actions/workflow/status/amanasmuei/amem/ci.yml?style=flat-square&logo=github&label=CI\" alt=\"CI\" /></a>\n  <a href=\"https://github.com/amanasmuei/amem/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-blue?style=flat-square\" alt=\"MIT\" /></a>\n  <img src=\"https://img.shields.io/badge/MCP-compatible-8A2BE2?style=flat-square\" alt=\"MCP\" />\n  <img src=\"https://img.shields.io/badge/node-%E2%89%A518-brightgreen?style=flat-square&logo=node.js&logoColor=white\" alt=\"Node 18+\" />\n</p>\n\n<br/>\n\n<div align=\"center\">\n\n| 🎯 97.8% R@5 | ⚡ ~14ms p50 | 🛠 33 Tools | 🔒 100% Local |\n|:---:|:---:|:---:|:---:|\n| LongMemEval-S, 500q | Full recall pipeline | Complete memory toolkit | No cloud required |\n\n</div>\n\n<p align=\"center\">\n  <a href=\"#-quick-start\">Quick Start</a> · \n  <a href=\"#-how-it-works\">How It Works</a> · \n  <a href=\"#-benchmarks\">Benchmarks</a> · \n  <a href=\"#%EF%B8%8F-tools-reference\">Tools</a> · \n  <a href=\"#-dashboard--knowledge-graph\">Dashboard</a> · \n  <a href=\"#-architecture\">Architecture</a>\n</p>\n\n---\n\n## 💡 The Problem\n\nEvery AI tool starts from zero. Every session. Every tool.\n\n```diff\n- You: \"Don't use 'any' in TypeScript\"     → told Claude 3 times. Copilot still doesn't know.\n- You: \"We chose PostgreSQL over MongoDB\"   → explained in Cursor. Claude has no idea.\n+ With amem: tell it once, every AI tool remembers — forever.\n```\n\n<details>\n<summary><b>See it in action</b></summary>\n\n```\nYou (in Claude Code):  \"Don't use any type in TypeScript\"\n  └─ amem stores this as a correction (priority 1.0, confidence 100%)\n\nYou (switch to Copilot): starts coding\n  └─ Copilot already knows — amem feeds it the same correction\n\nYou (open Cursor): \"What do you remember about TypeScript?\"\n  └─ Instantly recalls: \"Don't use any type\" + all related preferences\n```\n\n</details>\n\nNo cloud. No API keys. One SQLite file. Everything stays on your machine.\n\n---\n\n## 🚀 Quick Start\n\n<table>\n<tr>\n<td width=\"50%\">\n\n**Claude Code** (recommended)\n\n```bash\n/plugin marketplace add amanasmuei/amem\n/plugin install amem\n```\n\n</td>\n<td width=\"50%\">\n\n**GitHub Copilot CLI**\n\n```bash\ncopilot plugin marketplace add amanasmuei/amem\ncopilot plugin install amem\n```\n\n</td>\n</tr>\n</table>\n\n<details>\n<summary><b>📦 Cursor / Windsurf / Any MCP Client</b></summary>\n\n```bash\nnpm install -g @aman_asmuei/amem\namem-cli init      # Detects & configures all installed AI tools\namem-cli rules     # Generates extraction rules for proactive memory use\n```\n\nOr add to your MCP config manually:\n\n```json\n{\n  \"mcpServers\": {\n    \"amem\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@aman_asmuei/amem\"]\n    }\n  }\n}\n```\n\n</details>\n\n**Verify it works:**\n\n```bash\namem-cli stats     # Should show \"0 memories\" initially\n```\n\n> 💬 Tell your AI: *\"Remember: always use strict TypeScript, never use any type\"*\n>\n> 🔄 Start a **new** session: *\"What do you remember about TypeScript?\"* — it recalls instantly.\n\n---\n\n## 🧬 Powered by `amem-core`\n\n`amem` is the MCP server. The retrieval engine lives in [`@aman_asmuei/amem-core`](https://github.com/amanasmuei/amem-core).\n\n```\n     Claude Code / Copilot / Cursor / any MCP client\n                        │\n                        │ MCP (stdio)\n                        ▼\n        ┌──────────────────────────────────┐\n        │  @aman_asmuei/amem  (this pkg)   │\n        │  33 Tools · 7 Resources · 2 Prompts\n        │  CLI · Hooks · Dashboard         │\n        └───────────────┬──────────────────┘\n                        │ imports\n                        ▼\n        ┌──────────────────────────────────┐\n        │  @aman_asmuei/amem-core          │\n        │  Embeddings · HNSW · Recall      │\n        │  Knowledge Graph · Reflection    │\n        │  97.8% R@5 on LongMemEval-S      │\n        └───────────────┬──────────────────┘\n                        ▼\n              ┌────────────────────┐\n              │  SQLite + WAL      │\n              │  ~/.amem/memory.db │\n              └────────────────────┘\n```\n\n<details>\n<summary><b>Why two packages?</b></summary>\n\n| Package | Role | Install |\n|---|---|---|\n| **`@aman_asmuei/amem`** *(this)* | MCP server + CLI + hooks | `npm i -g @aman_asmuei/amem` |\n| [**`@aman_asmuei/amem-core`**](https://github.com/amanasmuei/amem-core) | Pure TS library, zero MCP deps | `npm i @aman_asmuei/amem-core` |\n\nThe same engine powers `amem` (MCP server), `aman-agent` (CLI), `aman-tg` (Telegram bot), and any Node app you give memory to. Retrieval improvements ship via `amem-core`. MCP-tool changes ship via `amem`. They version independently.\n\n> The **97.8% R@5** headline is the engine quality from `amem-core` (LongMemEval-S, session-level, 500 questions, zero API calls) — exactly what you get whether you call it through MCP or import the library directly.\n\n</details>\n\n---\n\n## ⚙️ How It Works\n\namem captures knowledge in **three layers** — from fully automatic to fully manual:\n\n| Layer | How | What it does |\n|---|---|---|\n| **Automatic** | Lifecycle hooks | Captures tool observations, auto-extracts corrections/decisions/patterns at session end |\n| **AI-driven** | Extraction rules | Your AI proactively calls `memory_store` when you correct it, make decisions, or express preferences |\n| **Manual** | Natural language | *\"Remember: we use PostgreSQL\"* or *\"Forget the Redis memory\"* |\n\n### Memory Types\n\n| Priority | Type | Example |\n|:---:|---|---|\n| **1.0** | **correction** | *\"Don't mock the DB in integration tests\"* |\n| **0.85** | **decision** | *\"Chose Postgres over Mongo for ACID\"* |\n| **0.7** | **pattern** | *\"Prefers early returns over nesting\"* |\n| **0.7** | **preference** | *\"Uses pnpm, not npm\"* |\n| **0.5** | **topology** | *\"Auth module lives in src/auth/\"* |\n| **0.4** | **fact** | *\"API launched January 2025\"* |\n\nCorrections always surface first — they are your AI's hard constraints.\n\n<details>\n<summary><b>🔄 Memory Tiers & Temporal Validity</b></summary>\n\n### Memory Tiers\n\n| Tier | Behavior |\n|---|---|\n| **Core** | Always injected at session start (~500 tokens). Your most critical corrections. |\n| **Working** | Session-scoped, auto-surfaced for current task. |\n| **Archival** | Default. Searchable but not auto-injected. |\n\n### Temporal Validity\n\nMemories aren't forever. When facts change:\n- Old memories get **expired** (not deleted) — preserved for *\"what was true in March?\"*\n- Contradictions are **auto-detected** ��� storing a new decision auto-expires the old one\n- Query any point in time with `memory_since`\n\n</details>\n\n<details>\n<summary><b>🧠 Self-Evolving Memory Loop</b></summary>\n\nYour memory doesn't just store — it **learns from its own structure**. Call `memory_reflect` to trigger the reflection engine:\n\n```\nmemory_reflect → Analyzes your entire memory graph\n  │\n  ├─ Clusters related memories (HNSW neighbor graph)\n  ├─ Detects contradictions (negation pairs, numerical, low-overlap)\n  ├─ Identifies synthesis candidates\n  ├─ Surfaces knowledge gaps (topics with sparse recall)\n  └─ Returns a structured report with suggested actions\n```\n\n**The evolution loop:**\n\n1. **Reflect** — `memory_reflect` clusters your memories and finds patterns\n2. **Synthesize** — AI merges related clusters into higher-order principles via `memory_store`\n3. **Link** — `memory_relate` connects syntheses to source memories (tracked via synthesis lineage)\n4. **Repeat** — each cycle, the graph becomes more coherent and abstract\n\nThe system auto-nudges when reflection is due (>7 days or >50 new memories since last run).\n\n<details>\n<summary><b>📊 What the reflection report looks like</b></summary>\n\n```\n# Memory Reflection Report\nAnalyzed 127 memories in 12ms\nHealth Score: 68/100\n\n## Stats\n- Clusters: 8 (avg size: 4.2)\n- Clustered: 34 | Orphans: 93\n- Contradictions: 2\n- Synthesis candidates: 3\n- Knowledge gaps: 4\n\n## Contradictions Found\n⚠ Opposing language detected (23d apart, 87% similar)\n  A: a1b2c3d4 \"Always use semicolons in JavaScript...\"\n  B: e5f6g7h8 \"Never use semicolons in JavaScript...\"\n  → Expire older memory a1b2c3d4 — newer supersedes it\n\n## Synthesis Candidates\n### cluster-0 (4 patterns)\n  \"These 4 related memories form a cluster about 'typescript, types':\n  [patterns]:\n    - 'Always use strict TypeScript types'\n    - 'Prefer strict null checks'\n    - 'Use unknown instead of any'\n    - 'Enable strictNullChecks in tsconfig'\n\n  Synthesize into a higher-order principle...\"\n\n## Knowledge Gaps\n- \"kubernetes deployment\" — asked 3x, avg 25% confidence\n- \"database migration strategy\" — asked 2x, avg 0% confidence\n```\n\n</details>\n\n---\n\n## 📈 Benchmarks\n\n### Recall Accuracy (LongMemEval)\n\nAll numbers from [`amem-core` v0.5.1](https://github.com/amanasmuei/amem-core) — the retrieval engine powering this MCP server. Zero API calls, all local, fully reproducible.\n\n<table>\n<tr>\n<td>\n\n**LongMemEval-S (session-level) — headline metric**\n\n| Metric | Score |\n|:---:|:---:|\n| **R@1** | **95.0%** |\n| **R@3** | **97.0%** |\n| **R@5** | **🏆 97.8%** |\n| **R@10** | **99.0%** |\n\n500 questions · CPU only · zero API calls\n\n</td>\n<td>\n\n**LongMemEval Oracle (turn-level)**\n\n| Metric | Score |\n|:---:|:---:|\n| **R@1** | **66.2%** |\n| **R@3** | **90.8%** |\n| **R@5** | **94.6%** |\n| **R@10** | **97.5%** |\n\n479 scoreable questions · 301s runtime · Node 22\n\n</td>\n</tr>\n</table>\n\nPipeline: local `bge-small-en-v1.5` bi-encoder + `ms-marco-MiniLM-L-6-v2` cross-encoder (int8, batched, default-on). See [amem-core benchmarks](https://github.com/amanasmuei/amem-core#-benchmarks) for full per-type breakdowns, pipeline evolution, and honest notes.\n\n> **Why this matters for the \"rewrite it in Rust\" question.** The 10.3ms rerank figure above reflects a ~30% speedup over the per-pair implementation it replaced — achieved with ~20 lines of batching plus int8 quantization, no native rewrite. The hot paths were already efficient; the remaining wins came from using them more carefully. We stay on TypeScript.\n\n### Search Latency\n\n<table>\n<tr>\n<td>\n\n**Full recall pipeline (v0.5.1+)**\n\n| Stage | p50 | Share |\n|---|---|---|\n| Embed (bi-encoder) | 3.0ms | 22% |\n| Retrieve (HNSW + multi-strategy) | 0.1ms | 1% |\n| **Rerank (int8 cross-encoder)** | **10.3ms** | **74%** |\n| **Total** | **~14ms** | 100% |\n\n</td>\n<td>\n\n**HNSW index only (vector search)**\n\n| Memories | HNSW | Brute-force | Speedup |\n|---|---|---|---|\n| 100 | 0.05ms | 0.10ms | 2x |\n| 1,000 | 0.06ms | 0.50ms | **8x** |\n| 5,000 | 0.08ms | 2.44ms | **30x** |\n| 10,000 | 0.08ms | 5.35ms | **67x** |\n\n</td>\n<td>\n\nMeasured: 100 searches averaged, 384-dim embeddings, top-10 results.\n\n**Sub-0.1ms at any scale** — effectively O(log n). HNSW is an optional dependency; brute-force is used as fallback when unavailable.\n\n</td>\n</tr>\n</table>\n\n---\n\n## 🛠️ Tools Reference\n\n### Core Memory (7 tools)\n\n| Tool | Description |\n|---|---|\n| `memory_store` | Store a memory with type, tags, confidence. Auto-redacts private content, auto-expires contradictions. |\n| `memory_recall` | Semantic search — compact mode by default (~10x token savings). Use `memory_detail` for full content. |\n| `memory_detail` | Retrieve full content by ID after compact recall. |\n| `memory_context` | Load all relevant context for a topic, organized by type with token budgeting. |\n| `memory_extract` | Batch-save multiple memories from conversation. |\n| `memory_forget` | Delete by ID or query (with confirmation). |\n| `memory_inject` | Surface corrections + decisions + graph neighbors before coding starts. |\n\n<details>\n<summary><strong>Precision, History, Advanced, Admin, Reminders, and Maintenance tools (26 more)</strong></summary>\n\n### Precision & History (5 tools)\n\n| Tool | Description |\n|---|---|\n| `memory_patch` | Surgical field-level edit with auto-snapshot. |\n| `memory_versions` | View full edit history or restore any version. |\n| `memory_search` | Exact full-text search via FTS5 with compact mode. |\n| `memory_since` | Temporal query with natural language ranges (`7d`, `2w`, `1h`). |\n| `memory_relate` | Build a typed knowledge graph between memories. |\n\n### Advanced (6 tools)\n\n| Tool | Description |\n|---|---|\n| `memory_multi_recall` | Multi-strategy search with compact mode: semantic + FTS5 + graph + temporal. |\n| `memory_tier` | Move memories between tiers: core / working / archival. |\n| `memory_expire` | Mark as no longer valid — preserved for history, excluded from recall. |\n| `memory_summarize` | Store structured session summary with decisions, corrections, metrics. |\n| `memory_history` | View past session summaries. |\n| `memory_reflect` | Self-evolving reflection engine — clusters memories, detects contradictions, identifies synthesis candidates, surfaces knowledge gaps. |\n\n### Admin & Sync (4 tools)\n\n| Tool | Description |\n|---|---|\n| `memory_doctor` | Run read-only health diagnostics on the amem database. |\n| `memory_repair` | Perform safe, targeted repairs on the amem database. |\n| `memory_config` | Get or set amem configuration with safety guardrails. |\n| `memory_sync` | Import or export memories between amem and other systems (Claude auto-memory, Copilot instructions). |\n\n### Reminders (4 tools)\n\n| Tool | Description |\n|---|---|\n| `reminder_set` | Create reminder with optional deadline and scope. |\n| `reminder_list` | List active (or all) reminders, filterable by scope. |\n| `reminder_check` | Show overdue, today, and upcoming (7 days). |\n| `reminder_complete` | Mark as done (supports partial ID). |\n\n### Log & Maintenance (7 tools)\n\n| Tool | Description |\n|---|---|\n| `memory_log` | Append raw conversation turns (lossless, append-only). |\n| `memory_log_recall` | Search or replay log by session, keyword, or recency. |\n| `memory_log_cleanup` | Prune old entries with configurable retention. |\n| `memory_stats` | Counts, type breakdown, confidence distribution. |\n| `memory_export` | Export as Markdown or JSON. |\n| `memory_import` | Bulk import from JSON with automatic dedup. |\n| `memory_consolidate` | Merge duplicates, prune stale, promote frequent, decay idle. |\n\n</details>\n\n---\n\n## 📖 Usage Guide\n\n### Storing Memories\n\n<table>\n<tr>\n<td width=\"50%\">\n\n**Natural language** (easiest)\n\n```\n\"Remember: we use PostgreSQL, not MongoDB\"\n\"Store a correction: never use console.log in production\"\n\"Note that the auth module is in src/auth/\"\n```\n\n</td>\n<td width=\"50%\">\n\n**Explicit tool calls**\n\n```js\nmemory_store({\n  content: \"Never use 'any' — define proper interfaces\",\n  type: \"correction\",\n  tags: [\"typescript\"],\n  confidence: 1.0\n})\n```\n\n</td>\n</tr>\n</table>\n\n### Recalling Memories\n\n```js\n// Step 1: Compact index — ~50-100 tokens (default)\nmemory_recall({ query: \"auth decisions\", limit: 5 })\n// -> a1b2c3d4 [decision] Auth service uses JWT tokens... (92%)\n// -> e5f6g7h8 [correction] Never store tokens in localStorage... (100%)\n\n// Step 2: Full details only for what you need\nmemory_detail({ ids: [\"a1b2c3d4\", \"e5f6g7h8\"] })\n```\n\n<details>\n<summary><strong>More search options</strong></summary>\n\n```js\n// Multi-strategy: semantic + FTS5 + graph + temporal\nmemory_multi_recall({\n  query: \"authentication architecture\",\n  limit: 10,\n  weights: { semantic: 0.4, fts: 0.3, graph: 0.15, temporal: 0.15 }\n})\n\n// Exact keyword search (FTS5 syntax)\nmemory_search({ query: \"OAuth PKCE\" })\nmemory_search({ query: '\"event sourcing\"' })     // phrase match\nmemory_search({ query: \"auth* NOT legacy\" })      // boolean\n```\n\n</details>\n\n### Managing Memories\n\n<details>\n<summary><strong>Edit, expire, promote, link</strong></summary>\n\n```js\n// Surgical edit with auto-snapshot for rollback\nmemory_patch({ id: \"a1b2c3d4\", field: \"content\", value: \"Updated text\", reason: \"clarified\" })\n\n// View edit history / restore\nmemory_versions({ memory_id: \"a1b2c3d4\" })\n\n// Expire (preserve for history, exclude from recall)\nmemory_expire({ id: \"a1b2c3d4\", reason: \"Migrated to GraphQL\" })\n\n// Promote to core tier (always loaded at session start)\nmemory_tier({ id: \"a1b2c3d4\", tier: \"core\" })\n\n// Link related memories (graph builds itself, but you can add manual links)\nmemory_relate({ action: \"relate\", from_id: \"abc\", to_id: \"xyz\", relation_type: \"supports\" })\n```\n\nRelation types: `supports`, `contradicts`, `depends_on`, `supersedes`, `related_to`, `caused_by`, `implements` — or define your own.\n\n</details>\n\n### Reminders\n\n<details>\n<summary><strong>Cross-session deadline tracking</strong></summary>\n\n```js\nreminder_set({ content: \"Review PR #42\", due_at: 1743033600000, scope: \"global\" })\n\nreminder_check({})\n// -> [OVERDUE] Review PR #42\n// -> [TODAY] Deploy auth service\n// -> [upcoming] Write quarterly report\n\nreminder_complete({ id: \"a1b2c3d4\" })\n```\n\n</details>\n\n### Privacy\n\n<details>\n<summary><strong>Automatic redaction</strong></summary>\n\n```js\n// Private blocks stripped before storage\nmemory_store({\n  content: \"DB password is <private>hunter2</private>, connect to prod at db.example.com\",\n  type: \"topology\", tags: [\"database\"]\n})\n// Stored: \"DB password is [REDACTED], connect to prod at db.example.com\"\n\n// API keys, tokens, passwords auto-redacted by pattern matching\n// Configure patterns in ~/.amem/config.json\n```\n\n</details>\n\n---\n\n## ⚔️ Honest Comparison: amem vs graphify\n\n<details>\n<summary><b>Click to expand — how amem compares to graphify</b></summary>\n\n[graphify](https://github.com/safishamsi/graphify) is the most common \"what about X?\" when people find amem. They solve **fundamentally different problems** and are genuinely complementary.\n\n### What each tool does\n\n| | **amem** | **graphify** |\n|---|---|---|\n| **One-liner** | Persistent memory across AI sessions | Codebase → knowledge graph |\n| **Core question** | *\"What has my AI learned about me?\"* | *\"What does this codebase look like?\"* |\n| **Input** | Natural language (corrections, decisions, preferences) | Files (code, docs, PDFs, images, video) |\n| **Output** | Recalled memories ranked by relevance | Structural graph + report + interactive HTML |\n| **Persistence** | Always — memory survives across sessions and tools | Snapshot — `graph.json` persists, but doesn't learn over time |\n| **When it runs** | Continuously, every session | On-demand (`/graphify .`) or on commit via git hook |\n\n### Technical comparison\n\n| | **amem** | **graphify** |\n|---|---|---|\n| **Runtime** | TypeScript / Node (≥18) | Python (≥3.10) |\n| **Protocol** | MCP server (33 tools, 7 resources) | AI skill (slash command) + optional MCP server |\n| **Storage** | SQLite + FTS5 + WAL | NetworkX graph → JSON file |\n| **Search** | Semantic embeddings + FTS5 + graph + reranking | Graph traversal (BFS/DFS) + node lookup |\n| **Embeddings** | Local `bge-small-en-v1.5` (384-dim) | None — uses graph topology, not vector similarity |\n| **Code understanding** | None — stores what you tell it | Deep — tree-sitter AST for 25 languages |\n| **Multimodal** | Text only | Code, docs, PDFs, images, video, audio |\n| **LLM required** | No (all local) | Yes for docs/images (code is LLM-free via tree-sitter) |\n| **Benchmark** | 97.8% R@5 on LongMemEval-S | 71.5x token reduction vs raw file reading |\n| **AI tool support** | Claude Code, Copilot, Cursor, any MCP client | Claude Code, Codex, Copilot, Cursor, Gemini, Aider, Kiro, +10 more |\n\n### Where each wins\n\n**amem wins at:**\n- Remembering your preferences, corrections, and decisions **across projects and tools**\n- Semantic recall — finding the right memory from a vague query (97.8% R@5)\n- Temporal intelligence — tracking what was true *when*, auto-expiring contradictions\n- Self-evolution — reflection engine clusters, detects contradictions, identifies gaps\n- Zero LLM dependency — everything runs locally, no API calls\n\n**graphify wins at:**\n- Understanding **code structure** — call graphs, imports, class hierarchies, cross-file relationships\n- Multimodal ingestion — drop in code, papers, screenshots, videos, it graphs them all\n- Token efficiency — 71.5x compression means your AI reads structure, not raw files\n- Breadth of language support — 25 programming languages via tree-sitter AST\n- Breadth of AI tool support — 15+ platforms with dedicated install commands\n\n### Honest takeaways\n\n1. **They don't compete.** amem remembers *your* knowledge (decisions, corrections, preferences). graphify maps *the codebase's* structure (call graphs, dependencies, architecture). Different data, different access patterns.\n\n2. **Use both if you want.** Run `graphify .` to get a structural map of your project. Use amem to remember \"we chose this architecture because X.\" The graph tells your AI *what exists*. The memory tells it *why things are that way*.\n\n3. **graphify has broader platform coverage** (15+ AI tools). amem has deeper integration where it works (MCP protocol with 33 tools, structured resources, prompts).\n\n4. **graphify needs an LLM for non-code files.** amem is fully local — no API calls, no model inference beyond the local embedding model.\n\n5. **The real choice depends on your pain point.** If your AI keeps forgetting your preferences and decisions → amem. If your AI can't navigate your codebase efficiently → graphify. If both → use both.\n\n</details>\n\n---\n\n## 🌐 Platform Compatibility\n\n| Feature | Claude Code | GitHub Copilot CLI | Cursor / Windsurf / Other |\n|---|:---:|:---:|:---:|\n| One-command plugin install | Yes | Yes | -- |\n| 33 MCP tools | Yes | Yes | Yes |\n| AI skills | 14 | 7 | -- |\n| Auto-capture hooks | Yes | Yes | -- |\n| Session auto-summarize | Yes | Yes | -- |\n| Auto-memory sync | Yes | -- | -- |\n| CLI setup (`amem-cli init`) | Yes | Yes | Yes |\n\n**Claude Code** has the deepest integration (plugin + hooks + auto-memory sync). **Copilot CLI** is a close second. **Other MCP clients** get the full 33-tool server via manual config.\n\n### AI Skills\n\n<details>\n<summary><b>Available skills by platform</b></summary>\n\n| What you say | Skill | Claude Code | Copilot CLI |\n|---|---|:---:|:---:|\n| *\"Remember never use any type\"* | `remember` | Yes | Yes |\n| *\"What do you remember about auth?\"* | `recall` | Yes | Yes |\n| *\"Load context for this task\"* | `context` | Yes | Yes |\n| *\"Show memory stats\"* | `stats` | Yes | Yes |\n| *\"Run memory doctor\"* | `doctor` | Yes | Yes |\n| *\"Export my memories\"* | `export` | Yes | Yes |\n| *\"List all corrections\"* | `list` | Yes | Yes |\n| *\"Sync my Claude memory\"* | `sync` | Yes | -- |\n| *\"Open the memory dashboard\"* | `dashboard` | Yes | -- |\n| *\"Install hooks\"* | `hooks` | Yes | -- |\n\n</details>\n\n---\n\n## 🔄 Working with Claude Code Auto-Memory\n\namem complements Claude's built-in auto-memory — it doesn't replace it.\n\n| | Claude auto-memory | amem |\n|---|---|---|\n| **Capture** | Automatic, zero config | Typed with confidence scores |\n| **Storage** | Single markdown file | SQLite with search, graph, temporal |\n| **Recall** | Entire file loaded every session | Only relevant memories surfaced |\n| **History** | Overwritten on update | Versioned, temporal validity |\n| **Search** | None | Semantic + FTS5 + graph + reranking |\n\n**Recommended:** Keep both enabled. Run `amem-cli sync` to import Claude's memories into amem for unified, structured access.\n\n<details>\n<summary><strong>Claude → amem sync</strong></summary>\n\n```bash\namem-cli sync              # Import all projects\namem-cli sync --dry-run    # Preview what would be imported\namem-cli sync --project myapp  # Import specific project\n```\n\n| Claude type | amem type | Confidence |\n|---|---|---|\n| `feedback` | `correction` | 1.0 |\n| `project` | `decision` | 0.85 |\n| `user` | `preference` | 0.8 |\n| `reference` | `topology` | 0.7 |\n\n</details>\n\n<details>\n<summary><strong>amem → Copilot sync</strong></summary>\n\nExport amem memories to `.github/copilot-instructions.md` so Copilot reads them as persistent context:\n\n```bash\namem-cli sync --to copilot              # Export to current project\namem-cli sync --to copilot --dry-run    # Preview without writing\namem-cli sync --to copilot --project /path/to/repo\n```\n\nThis generates structured markdown grouped by priority:\n1. **Corrections** (MUST follow) — hard constraints\n2. **Decisions** — architectural choices\n3. **Preferences** — user preferences\n4. **Patterns** — coding conventions\n5. **Context** — topology + facts\n\nThe amem section is wrapped in `<!-- amem:start/end -->` markers — existing non-amem content in the file is preserved.\n\n**Cross-tool sync:** Decisions made in Claude sessions automatically inform Copilot:\n```\nClaude Code → amem sync → amem DB → amem sync --to copilot → copilot-instructions.md\n```\n\n</details>\n\n---\n\n## 📊 Dashboard & Knowledge Graph\n\n```bash\namem-cli dashboard              # Opens at localhost:3333\namem-cli dashboard --port=8080  # Custom port\n```\n\nFull-featured web dashboard with:\n\n- 🔍 **Memory browser** — search, filter by type/tier/source, inline actions (promote, demote, expire)\n- 🕸️ **Interactive knowledge graph** — zoom, pan, click-to-focus with neighborhood highlighting, detail panel, search, directional edges\n- 📈 **Analytics** — confidence distribution, type breakdown, session timeline\n- ⏰ **Reminders** — view and manage cross-session tasks\n- 📋 **Copilot Preview** — see what would be exported to `copilot-instructions.md`\n\n---\n\n## 💻 CLI Reference\n\n```bash\n# Setup\namem-cli init                          # Auto-configure AI tools\namem-cli rules                         # Generate extraction rules\namem-cli hooks                         # Install hooks for Claude Code\namem-cli hooks --target copilot        # Install hooks for GitHub Copilot CLI\namem-cli hooks --uninstall             # Remove hooks\namem-cli sync                          # Import Claude auto-memory → amem\namem-cli sync --to copilot             # Export amem → copilot-instructions.md\namem-cli doctor                        # Health diagnostics\namem-cli repair                        # Repair corrupted database from backups\n\n# Dashboard\namem-cli dashboard                     # Web dashboard (localhost:3333)\n\n# Memory operations\namem-cli recall \"authentication\"       # Semantic search\namem-cli stats                         # Statistics\namem-cli list --type correction        # List by type\namem-cli export --file memories.md     # Export to file\namem-cli forget abc12345               # Delete by short ID\namem-cli reset --confirm               # Wipe all data\n```\n\n---\n\n## 🏗 Architecture\n\n```\n                        Your AI Tool\n           Claude Code / Copilot CLI / any MCP client\n                    │                │\n                    │ MCP (stdio)    │ Lifecycle Hooks\n                    ▼                ▼\n          ┌─────────────────────────────────┐\n          │   @aman_asmuei/amem             │  ← this package\n          │                                 │\n          │  33 Tools · 7 Resources · 2 Prompts\n          │  Slash commands · CLI · Hooks   │\n          │  Config: ~/.amem/config.json    │\n          └────────────────┬────────────────┘\n                           │ imports\n                           ▼\n          ┌─────────────────────────────────┐\n          │   @aman_asmuei/amem-core        │  ← the engine\n          │                                 │\n          │  Multi-Strategy Retrieval       │\n          │  [HNSW] + [FTS5] + [Graph] + [Temporal]\n          │       + query expansion         │\n          │       + cross-encoder reranker   │\n          │                                 │\n          │  Self-Evolving Reflection       │\n          │  [Clustering] + [Contradictions]│\n          │  + [Synthesis] + [Gap Detection]│\n          │                                 │\n          │  Embeddings: bge-small-en-v1.5  │\n          │  Reranker: ms-marco-MiniLM int8 │\n          │  97.8% R@5 on LongMemEval-S     │\n          └────────────────┬────────────────┘\n                           │\n                           ▼\n          ┌─────────────────────────────────┐\n          │   SQLite + WAL + FTS5           │\n          │   ~/.amem/memory.db             │\n          │                                 │\n          │   memories       (tiered)       │\n          │   conversation_log (raw)        │\n          │   memory_versions (history)     │\n          │   memory_relations (graph)      │\n          │   synthesis_lineage             │\n          │   knowledge_gaps                │\n          │   session_summaries             │\n          │   reminders                     │\n          └─────────────────────────────────┘\n```\n\nThe **`amem` MCP server is a thin wrapper** around `amem-core`. The retrieval engine, embeddings, knowledge graph, reflection — all live in `amem-core` and version independently. Bug in MCP wiring? Republish `amem`. Recall improvement? Republish `amem-core`. No coupling.\n\n### Ranking Formula\n\n```\nscore = relevance x 0.45 + recency x 0.2 + confidence x 0.2 + importance x 0.15\n```\n\n| Factor | How it works |\n|---|---|\n| **Relevance** | Cosine similarity via HNSW index; query-expanded keyword fallback |\n| **Recency** | Exponential decay (`0.995^hours`) |\n| **Confidence** | Reinforced by repeated confirmation (0-1) |\n| **Importance** | Type-based: corrections `1.0` ... facts `0.4` |\n\nAdditive scoring ensures no single low factor kills the ranking.\n\n---\n\n## ⚙️ Configuration\n\n<details>\n<summary><strong>Environment variables</strong></summary>\n\n| Variable | Default | Description |\n|---|---|---|\n| `AMEM_DIR` | `~/.amem` | Storage directory |\n| `AMEM_DB` | `~/.amem/memory.db` | Database path |\n| `AMEM_PROJECT` | *(auto from git)* | Project scope override |\n\n</details>\n\n<details>\n<summary><strong>Config file (~/.amem/config.json)</strong></summary>\n\nCreated automatically with defaults:\n\n```json\n{\n  \"retrieval\": {\n    \"semanticWeight\": 0.4,\n    \"ftsWeight\": 0.3,\n    \"graphWeight\": 0.15,\n    \"temporalWeight\": 0.15,\n    \"rerankerEnabled\": true\n  },\n  \"privacy\": {\n    \"enablePrivateTags\": true,\n    \"redactPatterns\": [\"...\"]\n  },\n  \"tiers\": {\n    \"coreMaxTokens\": 500,\n    \"workingMaxTokens\": 2000\n  },\n  \"hooks\": {\n    \"enabled\": true,\n    \"captureToolUse\": true,\n    \"captureSessionEnd\": true\n  }\n}\n```\n\n</details>\n\n<details>\n<summary><strong>📋 Version history</strong></summary>\n\n### v0.23.0 — Interactive Knowledge Graph Dashboard\nFull-width graph explorer with zoom/pan, click-to-focus neighborhood highlighting, detail panel with relation navigation, search & filter, directional edges, force-directed layout. Admin tools (doctor, repair, config, sync). 255 tests across 18 suites.\n\n### v0.19.0 — Self-Evolving Memory Loop\nReflection engine with HNSW-based clustering, 3-layer contradiction detection (negation + numerical + low-overlap), synthesis candidates with lineage tracking, knowledge gap detection, utility scoring, auto-trigger nudge in `memory_inject`. New DB tables: `synthesis_lineage`, `knowledge_gaps`, `reflection_meta`. Migration v5.\n\n### v0.18.0 — Progressive Disclosure & Scale\nHNSW vector index (67x faster at 10k), compact mode default on recall/search, DB repair CLI, concurrent access safety, heuristic conversation extractor, session-end auto-extraction.\n\n### v0.13.0 — World-Class Recall\nbge-small-en-v1.5 embeddings, additive scoring, query expansion, auto-relate knowledge graph, graph-aware injection, amem doctor, CI benchmarks.\n\n### v0.9.x — Temporal Intelligence\nTemporal validity, auto-expire contradictions, multi-strategy retrieval, cross-encoder reranking, memory tiers, privacy tags, lifecycle hooks, session summaries, dashboard, config system.\n\n### v0.7.0 — v0.8.0\nImport/export, confidence decay, embedding cache, multi-process safety, auto-configure CLI, dashboard.\n\n### v0.1.0 — v0.5.x\nCore store/recall, local embeddings, SQLite + WAL, consolidation, project scoping, reminders, conversation log, knowledge graph, FTS5, progressive disclosure.\n\n</details>\n\n---\n\n## 🧰 Tech Stack\n\n| Layer | Technology |\n|---|---|\n| Protocol | MCP SDK ^1.25 |\n| Language | TypeScript 5.6+, strict mode |\n| Database | SQLite + WAL + FTS5 |\n| Embeddings | HuggingFace bge-small-en-v1.5 (local, 80MB) + HNSW vector index |\n| Reranking | ms-marco-MiniLM-L-6-v2 (default-on, int8, batched, local) |\n| Validation | Zod 3.25+ with `.strict()` schemas |\n| Testing | Vitest — 281 tests across 19 suites + recall benchmarks |\n| CI/CD | GitHub Actions, npm publish on release |\n\n---\n\n## 🤝 Contributing\n\n```bash\ngit clone https://github.com/amanasmuei/amem.git\ncd amem && npm install\nnpm run build   # zero TS errors\nnpm test        # 281 tests pass\n```\n\nPRs must pass CI before merge. See [Issues](https://github.com/amanasmuei/amem/issues) for open tasks.\n\n<br/>\n\n---\n\n<p align=\"center\">\n  <sub>Built with ❤️ in 🇲🇾 Malaysia by <a href=\"https://github.com/amanasmuei\"><strong>Aman Asmuei</strong></a></sub>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://github.com/amanasmuei/amem\"><img src=\"https://img.shields.io/badge/GitHub-repo-181717?style=flat-square&logo=github\" alt=\"GitHub\" /></a>\n  <a href=\"https://www.npmjs.com/package/@aman_asmuei/amem\"><img src=\"https://img.shields.io/badge/npm-package-cb3837?style=flat-square&logo=npm&logoColor=white\" alt=\"npm\" /></a>\n  <a href=\"https://github.com/amanasmuei/amem/issues\"><img src=\"https://img.shields.io/badge/Issues-report-orange?style=flat-square&logo=github\" alt=\"Issues\" /></a>\n</p>\n\n<p align=\"center\">\n  <sub>MIT License · Star ⭐ if amem saves your AI from amnesia</sub>\n</p>\n","readmeFilename":"README.md"}