Engram-protocol/engram

ENGRAM: KV Cache Fingerprinting Protocol

You Don't Need Adapters: Cross-Model Document Retrieval via Intrinsic KV Cache Geometry

ENGRAM extracts Fourier fingerprints from LLM KV caches, stores them as compact binary certificates (.eng files, ~800 bytes), and retrieves them via HNSW approximate nearest neighbor search. This enables persistent cross-session memory for large language models with zero training.

By ENIGMA

Key Results

How It Works

KV cache blob --> layer key extraction --> DFT(f0+f1) --> fingerprint (~800 bytes)
                                                              |
Query fingerprint --> HNSW search --> geodesic retrieval --> matched session/document

The Fourier Fingerprint

ENGRAM decomposes per-layer key trajectories using a 2-component DFT:

• —f0 (DC component): captures the mean activation level per layer
• —f1 (first harmonic): captures the dominant oscillation pattern

The resulting fingerprint is a compact, deterministic signature of the KV cache state that is:

• —Model-intrinsic: derived from the model's own geometry, not learned embeddings
• —Cross-model transferable: via Frechet Cross-Domain Bridge (FCDB)
• —Compression-robust: 0.99998 cosine similarity after INT8 quantization

4-Stage Geodesic Retrieval

Stage 0: Prior preemption     (IndexC chronic failure -> skip HNSW)
Stage 1: HNSW search          -> HIGH / MEDIUM confidence
Stage 2: Trajectory correction -> MEDIUM (interpolation w=0.3)
Stage 3: Negative constraints  -> LOW (apophatic layer)
Stage 4: Metadata disambig     -> LOW + stage4_used=True

Install

# Python (core library)
pip install engram-kv

# Node.js (MCP client)
npm install engram-kv-mcp

From source

git clone https://github.com/infraax/engram.git
cd engram
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
KMP_DUPLICATE_LIB_OK=TRUE OMP_NUM_THREADS=1 PYTHONPATH=. pytest tests/ -x -q

Architecture Support

Cross-Model Transfer

9 strategies evaluated. FCDB (Frechet Cross-Domain Bridge) wins:

No adapter training required. The geometry is intrinsic.

MCP Server (Claude Code Integration)

ENGRAM includes an MCP server for persistent session memory in Claude Code:

claude mcp add --global engram-memory \
  -e ENGRAM_SESSIONS_DIR=~/.engram/sessions \
  -- python3 mcp/engram_memory.py

7 tools: write_session_engram, get_last_session, retrieve_relevant_sessions, get_relevant_context, list_indexed, index_knowledge

EIGENGRAM Binary Format (v1.2)

Compact, versioned binary certificates:

Header:  magic(4B) + version(2B) + flags(2B) + dimensions
Vectors: vec_perdoc + vec_fcdb + joint_center + vec_fourier + vec_fourier_v2
Meta:    corpus_hash + model_id + metrics + task_description

~800 bytes per document. Deterministic encoding. Cross-platform portable.

Theoretical Contributions

1. 1.Margin Power Law: margin ~ A * N^alpha where alpha = -0.207 (graceful degradation, no cliff)
2. 2.CKA Manifold Isomorphism: within-family 0.975, cross-family 0.927 (geometry is intrinsic)
3. 3.Frequency Ablation: f0+f1 is the sweet spot (f0-only: -23% recall, f0+f1+f2: -0.3% margin)
4. 4.FCDB Scaling Law: cross-model recall drops from 100% (N<=20) to 0% (N=200) -- adapter-free has limits

Citation

@article{enigma2026engram,
  title={You Don't Need Adapters: Cross-Model Document Retrieval
         via Intrinsic KV Cache Geometry},
  author={ENIGMA},
  year={2026},
  url={https://github.com/infraax/engram}
}

Links

• —GitHub
• —PyPI: engram-kv
• —npm: engram-kv-mcp

License

Apache-2.0