entroly 0.3.2

Information-theoretic context optimization for AI coding agents. Knapsack-optimal token budgeting, Shannon entropy scoring, SimHash dedup, predictive pre-fetch. MCP server.

Entroly

Information-theoretic context compression for AI coding agents.

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Every AI coding tool — Cursor, Copilot, Claude Code, Cody — manages context with dumb heuristics: stuff tokens until the window fills, then cut. Entroly uses mathematics to compress an entire codebase into the optimal context window.

🛑 The Problem

Current AI tools use Cosine Similarity (Vector Search). It's great for finding "things that look like my query," but terrible for coding because:

1. Context Blindness: It finds the "top 5 files" but missed the 6th file that contains the critical interface definition.
2. Boilerplate Waste: 40% of retrieved code is often imports or repetitive boilerplate, wasting expensive tokens.
3. Correlation vs Causation: Vector search finds related code, not causally necessary code.

✅ The Solution: Entroly

Entroly replaces "dumb search" with Information-Theoretic Compression. It treats your context window as a finite resource and uses Knapsack Optimization to pack the most "informative" (highest entropy) and "causally relevant" (dependency-linked) fragments.

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pip install entroly

How It's Different

Sourcegraph Cody does search: "Find 5–10 files that look relevant." Entroly does compression: "Show the LLM the entire codebase at variable resolution."

	Cody / Copilot	Entroly
Approach	Embedding similarity search	Information-theoretic compression
Coverage	5–10 files (the rest is invisible)	100% codebase visible via 3-level hierarchy
Selection	Top-K by cosine distance	Knapsack-optimal with submodular diversity
Dedup	None	SimHash + LSH in O(1)
Learning	Static	Online Wilson-score feedback + autotune
Security	None	Built-in SAST (55 rules, taint-aware)
Temperature	User-set or model default	Auto-calibrated via Fisher information

Architecture

Hybrid Rust + Python. All math runs in Rust via PyO3 (50–100× faster). MCP protocol and orchestration run in Python. Pure Python fallbacks activate automatically if the Rust extension isn't available.

┌─────────────────────────────────────────────────────────┐
│  IDE (Cursor / Claude Code / Cline / Copilot)           │
│                                                         │
│  ┌──── MCP mode ────┐    ┌──── Proxy mode ────┐        │
│  │ entroly MCP server│    │ localhost:9377     │        │
│  │ (JSON-RPC stdio)  │    │ (HTTP reverse proxy)│       │
│  └────────┬──────────┘    └────────┬───────────┘        │
│           │                        │                    │
│  ┌────────▼────────────────────────▼───────────┐        │
│  │          Entroly Engine (Python)             │        │
│  │  ┌─────────────────────────────────────┐     │       │
│  │  │  entroly-core (Rust via PyO3)       │     │       │
│  │  │  14 modules · 330 KB · 93 tests     │     │       │
│  │  └─────────────────────────────────────┘     │       │
│  └──────────────────────────────────────────────┘       │
└─────────────────────────────────────────────────────────┘

Two deployment modes:

• MCP Server — IDE calls remember_fragment, optimize_context, etc. via MCP protocol
• Prompt Compiler Proxy — invisible HTTP proxy at localhost:9377, intercepts every LLM request and auto-optimizes (zero IDE changes beyond API base URL)

Engines

Rust Core (14 modules)

Module	What	How
hierarchical.rs	3-level codebase compression (ECC)	L1: skeleton map of ALL files · L2: dep-graph cluster expansion · L3: knapsack-optimal full fragments with submodular diversity
knapsack.rs	Optimal context subset selection	0/1 Knapsack DP (N ≤ 2000) · greedy with Dantzig 0.5-guarantee (N > 2000)
entropy.rs	Information density scoring	Shannon entropy (40%) + boilerplate detection (30%) + cross-fragment n-gram redundancy (30%)
depgraph.rs	Dependency graph + symbol table	Auto-linking: imports (1.0) · type refs (0.9) · function calls (0.7) · same-module (0.5)
skeleton.rs	AST-lite code skeleton extraction	Preserves signatures, class/struct/trait layouts, strips bodies → 60–80% token reduction
dedup.rs	Near-duplicate detection	64-bit SimHash fingerprints · Hamming threshold ≤ 3 · 4-band LSH buckets
lsh.rs	Semantic recall index	12-table multi-probe LSH · 10-bit sampling · ~3 μs over 100K fragments
sast.rs	Static Application Security Testing	55 rules across 8 CWE categories · taint-flow analysis · severity scoring
health.rs	Codebase health analysis	Clone detection (Type-1/2/3) · dead symbol finder · god file detector · arch violation checker
guardrails.rs	Safety-critical file pinning	Criticality levels (Safety/Critical/Important/Normal) · task-aware budget multipliers
prism.rs	Spectral weight optimizer	Jacobi eigendecomposition on 4×4 covariance matrix · anisotropic gain adaptation
query.rs	Query analysis + refinement	Vagueness scoring · keyword extraction · intent classification
fragment.rs	Core data structure	Content, metadata, scoring dimensions, skeleton, SimHash fingerprint
lib.rs	PyO3 bridge + orchestrator	All modules exposed to Python · 93 tests

Python Layer

Module	What
proxy.py	Invisible HTTP reverse proxy (prompt compiler mode)
proxy_transform.py	Request parsing · context formatting (flat + hierarchical) · EGTC · APA
proxy_config.py	Model context windows · all feature flags · autotune overlay
server.py	MCP server with 10+ tools · pure Python fallbacks
long_term_memory.py	Cross-session memory via hippocampus-sharp-memory integration
multimodal.py	Image OCR · diagram parsing (Mermaid/PlantUML/DOT) · voice transcript extraction
autotune.py	Autonomous hyperparameter optimization (mutate → evaluate → keep/discard)
auto_index.py	File-system crawler for automatic codebase indexing
adaptive_pruner.py	Online RL-based fragment pruning
checkpoint.py	Gzipped JSON state serialization (~100 KB per checkpoint)
prefetch.py	Predictive context pre-loading via import analysis + co-access patterns
provenance.py	Hallucination risk detection via source verification + confidence scoring

Novel Algorithms

Entropic Context Compression (ECC)

Three-level hierarchical codebase compression. The LLM sees everything at variable resolution:

graph TD
    Query["User Query"] --> L1["L1: Skeleton Map (5%)"]
    Query --> L2["L2: Dependency Cluster (25%)"]
    Query --> L3["L3: Full Fragments (70%)"]
    
    L1 --> C1["Signatures of ALL 1000+ files"]
    L2 --> C2["Expanded skeletons of related modules"]
    L3 --> C3["Submodular diversified full code"]
    
    C1 --> Context["Optimal Context Window"]
    C2 --> Context
    C3 --> Context

Novel techniques:

1. Symbol-reachability slicing — BFS through dep graph from query-relevant symbols (cf. NeurIPS 2025)
2. Submodular diversity selection — diminishing returns per module (Nemhauser 1978, 1-1/e guarantee)
3. PageRank centrality — hub files get priority in L2 expansion
4. Entropy-gated budget allocation — complex codebases get more L3 budget

EGTC v2 (Entropy-Gap Temperature Calibration)

Automatically derives the optimal LLM sampling temperature from information-theoretic properties of the selected context. Uses Fisher information scaling with 4 signals:

τ = clip(τ_base + Σ signal_weights × [vagueness, entropy_gap, sufficiency, task_type])

APA (Adaptive Prompt Augmentation)

1. Calibrated token estimation — per-language chars/token ratios (Python: 3.0, Rust: 3.5, ...)
2. Task-aware preamble — conditional hints from security findings, vagueness, and task type
3. Content deduplication — MD5 hash-based dedup saves 10–20% in multi-turn sessions

Setup

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "entroly": {
      "command": "entroly"
    }
  }
}

Claude Code

claude mcp add entroly -- entroly

Prompt Compiler Proxy (any IDE)

Change your IDE's API base URL to http://localhost:9377:

entroly --proxy
# or
ENTROLY_PROXY_PORT=9377 python -m entroly.proxy

Every LLM request is intercepted, optimized with the full pipeline (ECC + EGTC + APA + SAST), and forwarded transparently. < 10ms overhead.

Docker (cross-platform)

docker pull ghcr.io/juyterman1000/entroly:latest
docker run --rm -p 9377:9377 ghcr.io/juyterman1000/entroly:latest

Multi-arch image: linux/amd64 and linux/arm64 (Apple Silicon, AWS Graviton).

MCP Tools

Tool	Purpose
remember_fragment	Store context with auto-dedup, entropy scoring, dep linking, criticality detection
optimize_context	Select optimal context subset for a token budget (knapsack + ECC)
recall_relevant	Sub-linear semantic recall via multi-probe LSH
record_outcome	Feed the Wilson-score feedback loop
explain_context	Per-fragment scoring breakdown with sufficiency analysis
checkpoint_state	Save full session state (gzipped JSON)
resume_state	Restore from checkpoint
prefetch_related	Predict and pre-load likely-needed context
get_stats	Session statistics and cost savings
health_check	Clone detection, dead symbols, god files, arch violations

The Math

Multi-Dimensional Relevance Scoring

r(f) = (w_rec · recency + w_freq · frequency + w_sem · semantic + w_ent · entropy)
       / (w_rec + w_freq + w_sem + w_ent)
       × feedback_multiplier

• Recency: Ebbinghaus forgetting curve — exp(-ln(2) × Δt / half_life)
• Frequency: Normalized access count with spaced repetition boost
• Semantic similarity: SimHash Hamming distance to query, normalized to [0, 1]
• Information density: Shannon entropy + boilerplate + redundancy

Knapsack Context Selection

Maximize:   Σ r(fᵢ) · x(fᵢ)     for selected fragments
Subject to: Σ c(fᵢ) · x(fᵢ) ≤ B  (token budget)

• N ≤ 2000: Exact DP with budget quantization — O(N × 1000)
• N > 2000: Greedy density sort — O(N log N), Dantzig 0.5-optimality guarantee

SAST Security Categories

Category	CWE	Rules
Hardcoded Secrets	CWE-798	API keys, passwords, tokens, private keys
SQL Injection	CWE-89	f-strings, concatenation, raw queries (taint-aware)
Command Injection	CWE-78	os.system, subprocess with shell=True
Path Traversal	CWE-22	open() with user input, os.path.join
XSS	CWE-79	innerHTML, template injection
SSRF	CWE-918	requests with user-controlled URLs
Insecure Crypto	CWE-327	MD5/SHA1 for auth, weak key sizes
Auth Flaws	CWE-287	Hardcoded roles, missing auth checks

Configuration

EntrolyConfig(
    default_token_budget=128_000,     # GPT-4 Turbo equivalent
    max_fragments=10_000,             # session fragment cap
    weight_recency=0.30,              # scoring weights (sum to 1.0)
    weight_frequency=0.25,
    weight_semantic_sim=0.25,
    weight_entropy=0.20,
    decay_half_life_turns=15,         # Ebbinghaus half-life
    enable_hierarchical_compression=True,  # 3-level ECC
    enable_temperature_calibration=True,   # EGTC v2
    enable_prompt_directives=True,         # APA preamble
    enable_security_scan=True,             # SAST
)

References

• Shannon (1948) — Information Theory
• Charikar (2002) — SimHash
• Ebbinghaus (1885) — Forgetting Curve
• Weiser (1981) — Program Slicing
• Nemhauser, Wolsey & Fisher (1978) — Submodular Maximization
• Dantzig (1957) — Greedy Knapsack Approximation
• Wilson (1927) — Score Confidence Intervals
• LLMLingua (EMNLP 2023) — Perplexity-based Token Compression
• LongLLMLingua (ACL 2024) — Query-aware Context Compression
• RepoFormer (ICML 2024 Oral) — Selective Retrieval for Repo-Level Code
• FILM-7B (NeurIPS 2024) — Structure-First Layout
• CodeSage (ICLR 2024) — Code Embedding Representation Learning
• SWE-bench (ICLR 2024) / SWE-agent (NeurIPS 2024) — Evaluation

Part of the Ebbiforge Ecosystem

Entroly integrates with hippocampus-sharp-memory for persistent cross-session memory and Ebbiforge for TF embeddings and RL weight learning. Both are optional.

License

MIT