icm

[English](README.md) | [Français](README_fr.md) | [Español](README_es.md) | [Deutsch](README_de.md) | [Italiano](README_it.md) | [Português](README_pt.md) | [Nederlands](README_nl.md) | [Polski](README_pl.md) | [Русский](README_ru.md) | [日本語](README_ja.md) | [中文](README_zh.md) | [العربية](README_ar.md) | [한국어](README_ko.md) <p align="center"> <img src="assets/banner.png" alt="ICM — Infinite Context Memory" width="600"> </p> <h1 align="center">ICM</h1> <p align="center"> Permanent memory for AI agents. Single binary, zero dependencies, MCP native. </p> <p align="center"> <a href="https://github.com/rtk-ai/icm/actions/workflows/ci.yml"><img src="https://github.com/rtk-ai/icm/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://github.com/rtk-ai/icm/releases/latest"><img src="https://img.shields.io/github/v/release/rtk-ai/icm?color=purple" alt="Release"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache--2.0-blue.svg" alt="Apache-2.0"></a> </p> --- ICM gives your AI agent a real memory — not a note-taking tool, not a context manager, a **memory**. ``` ICM (Infinite Context Memory) ┌──────────────────────┬─────────────────────────┐ │ MEMORIES (Topics) │ MEMOIRS (Knowledge) │ │ │ │ │ Episodic, temporal │ Permanent, structured │ │ │ │ │ ┌───┐ ┌───┐ ┌───┐ │ ┌───┐ │ │ │ m │ │ m │ │ m │ │ │ C │──depends_on──┐ │ │ └─┬─┘ └─┬─┘ └─┬─┘ │ └───┘ │ │ │ │decay │ │ │ │ refines ┌─▼─┐│ │ ▼ ▼ ▼ │ ┌─▼─┐ │ C ││ │ weight decreases │ │ C │──part_of──>└───┘│ │ over time unless │ └───┘ │ │ accessed/critical │ Concepts + Relations │ ├──────────────────────┴─────────────────────────┤ │ SQLite + FTS5 + sqlite-vec │ │ Hybrid search: BM25 (30%) + cosine (70%) │ └─────────────────────────────────────────────────┘ ``` **Two memory models:** - **Memories** — store/recall with temporal decay by importance. Critical memories never fade, low-importance ones decay naturally. Filter by topic or keyword. - **Memoirs** — permanent knowledge graphs. Concepts linked by typed relations (`depends_on`, `contradicts`, `superseded_by`, ...). Filter by label. - **Feedback** — record corrections when AI predictions are wrong. Search past mistakes before making new predictions. Closed-loop learning. ## Install ```bash # Homebrew (macOS / Linux) brew tap rtk-ai/tap && brew install icm # Quick install curl -fsSL https://raw.githubusercontent.com/rtk-ai/icm/main/install.sh | sh # From source cargo install --path crates/icm-cli ``` ## Setup ```bash # Auto-detect and configure all supported tools icm init ``` Configures **14 tools** in one command ([full integration guide](docs/integrations.md)): | Tool | Config file | Format | |------|------------|--------| | Claude Code | `~/.claude.json` | JSON | | Claude Desktop | `~/Library/.../claude_desktop_config.json` | JSON | | Cursor | `~/.cursor/mcp.json` | JSON | | Windsurf | `~/.codeium/windsurf/mcp_config.json` | JSON | | VS Code / Copilot | `~/Library/.../Code/User/mcp.json` | JSON | | Gemini Code Assist | `~/.gemini/settings.json` | JSON | | Zed | `~/.zed/settings.json` | JSON | | Amp | `~/.config/amp/settings.json` | JSON | | Amazon Q | `~/.aws/amazonq/mcp.json` | JSON | | Cline | VS Code globalStorage | JSON | | Roo Code | VS Code globalStorage | JSON | | Kilo Code | VS Code globalStorage | JSON | | OpenAI Codex CLI | `~/.codex/config.toml` | TOML | | OpenCode | `~/.config/opencode/opencode.json` | JSON | Or manually: ```bash # Claude Code claude mcp add icm -- icm serve # Compact mode (shorter responses, saves tokens) claude mcp add icm -- icm serve --compact # Any MCP client: command = "icm", args = ["serve"] ``` ### Skills / rules ```bash icm init --mode skill ``` Installs slash commands and rules for Claude Code (`/recall`, `/remember`), Cursor (`.mdc` rule), Roo Code (`.md` rule), and Amp (`/icm-recall`, `/icm-remember`). ### CLI instructions ```bash icm init --mode cli ``` Injects ICM instructions into each tool's instruction file: | Tool | File | |------|------| | Claude Code | `CLAUDE.md` | | GitHub Copilot | `.github/copilot-instructions.md` | | Windsurf | `.windsurfrules` | | OpenAI Codex | `AGENTS.md` | | Gemini | `~/.gemini/GEMINI.md` | ### Hooks (Claude Code) ```bash icm init --mode hook ``` Installs all 3 extraction layers as Claude Code hooks: **Claude Code** hooks: | Hook | Event | What it does | |------|-------|-------------| | `icm hook pre` | PreToolUse | Auto-allow `icm` CLI commands (no permission prompt) | | `icm hook post` | PostToolUse | Extract facts from tool output every 15 calls | | `icm hook compact` | PreCompact | Extract memories from transcript before context compression | | `icm hook prompt` | UserPromptSubmit | Inject recalled context at the start of each prompt | **OpenCode** plugin (auto-installed to `~/.config/opencode/plugins/icm.js`): | OpenCode event | ICM Layer | What it does | |---------------|-----------|-------------| | `tool.execute.after` | Layer 0 | Extract facts from tool output | | `experimental.session.compacting` | Layer 1 | Extract from conversation before compaction | | `session.created` | Layer 2 | Recall context at session start | ## CLI vs MCP ICM can be used via CLI (`icm` commands) or MCP server (`icm serve`). Both access the same database. | | CLI | MCP | |---|-----|-----| | **Latency** | ~30ms (direct binary) | ~50ms (JSON-RPC stdio) | | **Token cost** | 0 (hook-based, invisible) | ~20-50 tokens/call (tool schema) | | **Setup** | `icm init --mode hook` | `icm init --mode mcp` | | **Works with** | Claude Code, OpenCode (via hooks/plugins) | All 14 MCP-compatible tools | | **Auto-extraction** | Yes (hooks trigger `icm extract`) | Yes (MCP tools call store) | | **Best for** | Power users, token savings | Universal compatibility | ## Dashboard ```bash icm dashboard # or: icm tui ``` Interactive TUI with 5 tabs: Overview, Topics, Memories, Health, Memoirs. Keyboard navigation (vim-style: j/k, g/G, Tab, 1-5), live search (/), auto-refresh. Requires the `tui` feature (enabled by default). Build without: `cargo install --path crates/icm-cli --no-default-features --features embeddings`. ## CLI ### Memories (episodic, with decay) ```bash # Store icm store -t "my-project" -c "Use PostgreSQL for the main DB" -i high -k "db,postgres" # Recall icm recall "database choice" icm recall "auth setup" --topic "my-project" --limit 10 icm recall "architecture" --keyword "postgres" # Manage icm forget <memory-id> icm consolidate --topic "my-project" icm topics icm stats # Extract facts from text (rule-based, zero LLM cost) echo "The parser uses Pratt algorithm" | icm extract -p my-project ``` ### Memoirs (permanent knowledge graphs) ```bash # Create a memoir icm memoir create -n "system-architecture" -d "System design decisions" # Add concepts with labels icm memoir add-concept -m "system-architecture" -n "auth-service" \ -d "Handles JWT tokens and OAuth2 flows" -l "domain:auth,type:service" # Link concepts icm memoir link -m "system-architecture" --from "api-gateway" --to "auth-service" -r depends-on # Search with label filter icm memoir search -m "system-architecture" "authentication" icm memoir search -m "system-architecture" "service" --label "domain:auth" # Inspect neighborhood icm memoir inspect -m "system-architecture" "auth-service" -D 2 # Export graph (formats: json, dot, ascii, ai) icm memoir export -m "system-architecture" -f ascii # Box-drawing with confidence bars icm memoir export -m "system-architecture" -f dot # Graphviz DOT (color = confidence level) icm memoir export -m "system-architecture" -f ai # Markdown optimized for LLM context icm memoir export -m "system-architecture" -f json # Structured JSON with all metadata # Generate SVG visualization icm memoir export -m "system-architecture" -f dot | dot -Tsvg > graph.svg ``` ## MCP Tools (22) ### Memory tools | Tool | Description | |------|-------------| | `icm_memory_store` | Store with auto-dedup (>85% similarity → update instead of duplicate) | | `icm_memory_recall` | Search by query, filter by topic and/or keyword | | `icm_memory_update` | Edit a memory in-place (content, importance, keywords) | | `icm_memory_forget` | Delete a memory by ID | | `icm_memory_consolidate` | Merge all memories of a topic into one summary | | `icm_memory_list_topics` | List all topics with counts | | `icm_memory_stats` | Global memory statistics | | `icm_memory_health` | Per-topic hygiene audit (staleness, consolidation needs) | | `icm_memory_embed_all` | Backfill embeddings for vector search | ### Memoir tools (knowledge graphs) | Tool | Description | |------|-------------| | `icm_memoir_create` | Create a new memoir (knowledge container) | | `icm_memoir_list` | List all memoirs | | `icm_memoir_show` | Show memoir details and all concepts | | `icm_memoir_add_concept` | Add a concept with labels | | `icm_memoir_refine` | Update a concept's definition | | `icm_memoir_search` | Full-text search, optionally filtered by label | | `icm_memoir_search_all` | Search across all memoirs | | `icm_memoir_link` | Create typed relation between concepts | | `icm_memoir_inspect` | Inspect concept and graph neighborhood (BFS) | | `icm_memoir_export` | Export graph (json, dot, ascii, ai) with confidence levels | ### Feedback tools (learning from mistakes) | Tool | Description | |------|-------------| | `icm_feedback_record` | Record a correction when an AI prediction was wrong | | `icm_feedback_search` | Search past corrections to inform future predictions | | `icm_feedback_stats` | Feedback statistics: total count, breakdown by topic, most applied | ### Relation types `part_of` · `depends_on` · `related_to` · `contradicts` · `refines` · `alternative_to` · `caused_by` · `instance_of` · `superseded_by` ## How it works ### Dual memory model **Episodic memory (Topics)** captures decisions, errors, preferences. Each memory has a weight that decays over time based on importance: | Importance | Decay | Prune | Behavior | |-----------|-------|-------|----------| | `critical` | none | never | Never forgotten, never pruned | | `high` | slow (0.5x rate) | never | Fades slowly, never auto-deleted | | `medium` | normal | yes | Standard decay, pruned when weight < threshold | | `low` | fast (2x rate) | yes | Quickly forgotten | Decay is **access-aware**: frequently recalled memories decay slower (`decay / (1 + access_count × 0.1)`). Applied automatically on recall (if >24h since last decay). **Memory hygiene** is built-in: - **Auto-dedup**: storing content >85% similar to an existing memory in the same topic updates it instead of creating a duplicate - **Consolidation hints**: when a topic exceeds 7 entries, `icm_memory_store` warns the caller to consolidate - **Health audit**: `icm_memory_health` reports per-topic entry count, average weight, stale entries, and consolidation needs - **No silent data loss**: critical and high-importance memories are never auto-pruned **Semantic memory (Memoirs)** captures structured knowledge as a graph. Concepts are permanent — they get refined, never decayed. Use `superseded_by` to mark obsolete facts instead of deleting them. ### Hybrid search With embeddings enabled, ICM uses hybrid search: - **FTS5 BM25** (30%) — full-text keyword matching - **Cosine similarity** (70%) — semantic vector search via sqlite-vec Default model: `intfloat/multilingual-e5-base` (768d, 100+ languages). Configurable in your [config file](#configuration): ```toml [embeddings] # enabled = false # Disable entirely (no model download) model = "intfloat/multilingual-e5-base" # 768d, multilingual (default) # model = "intfloat/multilingual-e5-small" # 384d, multilingual (lighter) # model = "intfloat/multilingual-e5-large" # 1024d, multilingual (best accuracy) # model = "Xenova/bge-small-en-v1.5" # 384d, English-only (fastest) # model = "jinaai/jina-embeddings-v2-base-code" # 768d, code-optimized ``` To skip the embedding model download entirely, use any of these: ```bash icm --no-embeddings serve # CLI flag ICM_NO_EMBEDDINGS=1 icm serve # Environment variable ``` Or set `enabled = false` in your config file. ICM will fall back to FTS5 keyword search (still works, just no semantic matching). Changing the model automatically re-creates the vector index (existing embeddings are cleared and can be regenerated with `icm_memory_embed_all`). ### Storage Single SQLite file. No external services, no network dependency. ``` ~/Library/Application Support/dev.icm.icm/memories.db # macOS ~/.local/share/dev.icm.icm/memories.db # Linux C:\Users\<user>\AppData\Local\icm\icm\data\memories.db # Windows ``` ### Configuration ```bash icm config # Show active config ``` Config file location (platform-specific, or `$ICM_CONFIG`): ``` ~/Library/Application Support/dev.icm.icm/config.toml # macOS ~/.config/icm/config.toml # Linux C:\Users\<user>\AppData\Roaming\icm\icm\config\config.toml # Windows ``` See [config/default.toml](config/default.toml) for all options. ## Auto-extraction ICM extracts memories automatically via three layers: ``` Layer 0: Pattern hooks Layer 1: PreCompact Layer 2: UserPromptSubmit (zero LLM cost) (zero LLM cost) (zero LLM cost) ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ PostToolUse hook │ │ PreCompact hook │ │ UserPromptSubmit │ │ │ │ │ │ │ │ • Bash errors │ │ Context about to │ │ User sends prompt │ │ • git commits │ │ be compressed → │ │ → icm recall │ │ • config changes │ │ extract memories │ │ → inject context │ │ • decisions │ │ from transcript │ │ │ │ • preferences │ │ before they're │ │ Agent starts with │ │ • learnings │ │ lost forever │ │ relevant memories │ │ • constraints │ │ │ │ already loaded │ │ │ │ Same patterns + │ │ │ │ Rule-based, no LLM│ │ --store-raw fallbk│ │ │ └──────────────────┘ └──────────────────┘ └──────────────────┘ ``` | Layer | Status | LLM cost | Hook command | Description | |-------|--------|----------|-------------|-------------| | Layer 0 | Implemented | 0 | `icm hook post` | Rule-based keyword extraction from tool output | | Layer 1 | Implemented | 0 | `icm hook compact` | Extract from transcript before context compression | | Layer 2 | Implemented | 0 | `icm hook prompt` | Inject recalled memories on each user prompt | All 3 layers are installed automatically by `icm init --mode hook`. ## Benchmarks ### Storage performance ``` ICM Benchmark (1000 memories, 384d embeddings) ────────────────────────────────────────────────────────── Store (no embeddings) 1000 ops 34.2 ms 34.2 µs/op Store (with embeddings) 1000 ops 51.6 ms 51.6 µs/op FTS5 search 100 ops 4.7 ms 46.6 µs/op Vector search (KNN) 100 ops 59.0 ms 590.0 µs/op Hybrid search 100 ops 95.1 ms 951.1 µs/op Decay (batch) 1 ops 5.8 ms 5.8 ms/op ────────────────────────────────────────────────────────── ``` Apple M1 Pro, in-memory SQLite, single-threaded. `icm bench --count 1000` ### Agent efficiency Multi-session workflow with a real Rust project (12 files, ~550 lines). Sessions 2+ show the biggest gains as ICM recalls instead of re-reading files. ``` ICM Agent Benchmark (10 sessions, model: haiku, 3 runs averaged) ══════════════════════════════════════════════════════════════════ Without ICM With ICM Delta Session 2 (recall) Turns 5.7 4.0 -29% Context (input) 99.9k 67.5k -32% Cost $0.0298 $0.0249 -17% Session 3 (recall) Turns 3.3 2.0 -40% Context (input) 74.7k 41.6k -44% Cost $0.0249 $0.0194 -22% ══════════════════════════════════════════════════════════════════ ``` `icm bench-agent --sessions 10 --model haiku` ### Knowledge retention Agent recalls specific facts from a dense technical document across sessions. Session 1 reads and memorizes; sessions 2+ answer 10 factual questions **without** the source text. ``` ICM Recall Benchmark (10 questions, model: haiku, 5 runs averaged) ══════════════════════════════════════════════════════════════════════ No ICM With ICM ────────────────────────────────────────────────────────────────────── Average score 5% 68% Questions passed 0/10 5/10 ══════════════════════════════════════════════════════════════════════ ``` `icm bench-recall --model haiku` ### Local LLMs (ollama) Same test with local models — pure context injection, no tool use needed. ``` Model Params No ICM With ICM Delta ───────────────────────────────────────────────────────── qwen2.5:14b 14B 4% 97% +93% mistral:7b 7B 4% 93% +89% llama3.1:8b 8B 4% 93% +89% qwen2.5:7b 7B 4% 90% +86% phi4:14b 14B 6% 79% +73% llama3.2:3b 3B 0% 76% +76% gemma2:9b 9B 4% 76% +72% qwen2.5:3b 3B 2% 58% +56% ───────────────────────────────────────────────────────── ``` `scripts/bench-ollama.sh qwen2.5:14b` ### LongMemEval (ICLR 2025) Standard academic benchmark — 500 questions across 6 memory abilities, from the [LongMemEval paper](https://arxiv.org/abs/2410.10813) (ICLR 2025). ``` LongMemEval Results — ICM (oracle variant, 500 questions) ════════════════════════════════════════════════════════════════ Category Retrieval Answer (Sonnet) ──────────────────────────────────────────────────────────────── single-session-user 100.0% 91.4% temporal-reasoning 100.0% 85.0% single-session-assistant 100.0% 83.9% multi-session 100.0% 81.2% knowledge-update 100.0% 80.8% single-session-preference 100.0% 50.0% ──────────────────────────────────────────────────────────────── OVERALL 100.0% 82.0% ════════════════════════════════════════════════════════════════ ``` - **Retrieval** = does ICM find the right information? **100% across all categories.** - **Answer** = can the LLM produce the correct answer from retrieved context? Depends on the LLM, not ICM. - The retrieval score is the ICM benchmark. The answer score reflects the downstream LLM capability. `scripts/bench-longmemeval.py --judge claude --workers 8` ### Test protocol All benchmarks use **real API calls** — no mocks, no simulated responses, no cached answers. - **Agent benchmark**: Creates a real Rust project in a tempdir. Runs N sessions with `claude -p --output-format json`. Without ICM: empty MCP config. With ICM: real MCP server + auto-extraction + context injection. - **Knowledge retention**: Uses a fictional technical document (the "Meridian Protocol"). Scores answers by keyword matching against expected facts. 120s timeout per invocation. - **Isolation**: Each run uses its own tempdir and fresh SQLite DB. No session persistence. ## Documentation | Document | Description | |----------|-------------| | [Integration Guide](docs/integrations.md) | Setup for all 14 tools: Claude Code, Copilot, Cursor, Windsurf, Zed, Amp, etc. | | [Technical Architecture](docs/architecture.md) | Crate structure, search pipeline, decay model, sqlite-vec integration, testing | | [User Guide](docs/guide.md) | Installation, topic organization, consolidation, extraction, troubleshooting | | [Product Overview](docs/product.md) | Use cases, benchmarks, comparison with alternatives | ## License [Apache-2.0](LICENSE)