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Permanent memory for AI agents. Single binary, zero dependencies, MCP native.

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.

# 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

# Auto-detect and configure all supported tools icm init

Configures 14 tools in one command:

Or manually:

# 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"]

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).

icm init --mode hook

Installs all 3 extraction layers as Claude Code hooks:

Claude Code hooks:

OpenCode plugin (auto-installed to ~/.config/opencode/plugins/icm.js):

ICM can be used via CLI (icm commands) or MCP server (icm serve). Both access the same database.

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.

# 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

# 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

part_of · depends_on · related_to · contradicts · refines · alternative_to · caused_by · instance_of · superseded_by

Episodic memory (Topics) captures decisions, errors, preferences. Each memory has a weight that decays over time based on importance:

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.

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:

[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:

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).

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 

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 for all options.

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│ │ │ └──────────────────┘ └──────────────────┘ └──────────────────┘ 

All 3 layers are installed automatically by icm init --mode hook.

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

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

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

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

Standard academic benchmark — 500 questions across 6 memory abilities, from the LongMemEval paper (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

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.

Apache-2.0