# Resource Evaluation: ICM (Infinite Context Memory)

**Date**: 2026-03-14
**URL**: https://github.com/rtk-ai/icm
**Type**: GitHub repository / MCP server
**Score**: 3/5 — Integrated
**Decision**: Added as section after Kairn in `guide/ultimate-guide.md` (~line 11365)

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## Summary

ICM is a persistent memory MCP server from the rtk-ai team (same authors as RTK/Rust Token Killer). It provides a dual memory architecture: "Memories" (episodic, configurable decay) and "Memoirs" (permanent knowledge graph with 9 typed relation types). Distributed as a single Rust binary with zero external dependencies, installable via Homebrew.

### Key Points

- Single Rust binary, SQLite, zero deps — Homebrew install
- Dual architecture: episodic decay + permanent knowledge graph in one tool
- Hybrid search: BM25 30% + vector similarity 70%
- Auto-deduplication (>85% similarity blocked)
- Auto-extraction: pattern hooks, pre-compaction, session-start
- Supports 14 editors/clients (Claude Code, Cursor, VS Code, Windsurf, Zed, Amp, Cline, etc.)
- 52 stars, 55 commits as of March 2026
- License: Source-Available (free for individuals and teams ≤20; enterprise license required above)

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## Scoring

| Criterion | Score |
|-----------|-------|
| Relevance to Claude Code users | 4/5 |
| Differentiation from existing content | 3/5 |
| Maturity / adoption signal | 2/5 |
| License openness | 2/5 |
| **Overall** | **3/5** |

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## Comparison vs Existing Guide Content

| Feature | doobidoo | Kairn | ICM |
|---------|----------|-------|-----|
| Language | Python | Python | Rust (single binary) |
| Install | pip | pip | Homebrew |
| Episodic decay | No | Yes (biological) | Yes (configurable) |
| Permanent knowledge graph | No | Yes | Yes (Memoirs) |
| Auto-extraction | No | No | Yes |
| License | MIT | MIT | Source-Available |

Main differentiator: zero-dependency Rust binary lowers install friction for users who struggle with Python environments. Conceptual overlap with Kairn (knowledge graph + decay) is real but the runtime difference is meaningful.

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## Benchmarks

All benchmarks below are **vendor-reported by rtk-ai** — not independently verified.

**Storage performance (1000 ops, 384d embeddings)**:
- Store (no embeddings): 34.2 µs/op
- Store (with embeddings): 51.6 µs/op
- FTS5 full-text search: 46.6 µs/op
- Vector search (KNN): 590.0 µs/op
- Hybrid search: 951.1 µs/op

**Agent efficiency (Haiku model, multi-session)**:
- Session 2: 29% fewer turns, 32% less input context, 17% cost reduction
- Session 3: 40% fewer turns, 44% less context, 22% cost reduction

**Knowledge retention (10 questions)**:
- Without ICM: 5%
- With ICM: 68%

Note: The knowledge retention benchmark uses a sample of 10 questions on Haiku — too narrow for generalization.

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## Fact-Check

| Claim | Status | Source |
|-------|--------|--------|
| Storage: 34.2 µs/op | ✅ | README benchmarks section |
| Hybrid search: ~951 µs/op | ✅ | README benchmarks section |
| 29-40% turn reduction | ✅ present / ⚠️ vendor-reported | README — rtk-ai self-evaluation |
| 68% vs 5% knowledge retention | ✅ present / ⚠️ vendor-reported, n=10 | README |
| Source-Available license, free ≤20 | ✅ | LICENSE file |
| 9 Memoir relation types | ✅ | README full list |
| 14 supported clients | ✅ | `icm init` documentation |
| 52 stars | ✅ | GitHub as of 2026-03-14 |

No hallucinations detected. All figures present in the source README.

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## License Note

Source-Available license. Free for individuals and teams of up to 20 people. **Enterprise license required for organizations above 20 people.** Contact: license@rtk.ai

This was flagged in the guide entry with an explicit callout. Teams should verify their org size before deploying.

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## Integration Location

- New section: `guide/ultimate-guide.md` after Kairn (~line 11365), before "MCP Memory Stack: Complementarity Patterns"
- Comparison matrix updated: ICM column added with Runtime and License rows

## Upgrade Trigger

Revisit for 4/5 if:
- Benchmarks independently verified by community
- Adoption exceeds 500+ stars with sustained commit activity
- License changes to MIT/Apache