entroly

Entroly Product Surface

This page maps what Entroly exposes today from the codebase. It is meant to help developers choose the right integration path without guessing from marketing copy.

Entroly is best understood as a local context OS for AI agents:

Context engine — rank, deduplicate, compress, and select evidence under budget.
Recovery ledger — keep omitted originals reachable by handle.
Receipt system — explain selected context, omitted context, risks, hashes, and dependencies.
Verification layer — check model outputs against retained evidence.
Gateway control plane — normalize provider requests, control cost, cache prefixes, and account for usage.
Learning/memory layer — adapt from outcomes, cache useful selections, and preserve agent memory locally.
Security layer — scan code, detect prompt injection, apply redaction policy, and keep file reads contained.
Multi-runtime packaging — Python, Rust native, Node/WASM, Docker, CLI, SDK, MCP, and proxy.
Self-improvement layer — convert real outcomes into safer policies, skills, and weight profiles.
Memory/session/value layer — retain useful working memory, session continuity, checkpoint relevance, and lifetime savings evidence.
Multimodal intake layer — convert diagrams, images, voice, diffs, and structured artifacts into budgetable context.
CogOps/vault layer — compile beliefs, route epistemically, verify knowledge, and sync workspace changes.
Integration/event layer — connect framework helpers and team event gateways into the same local feedback loop.

Installable packages

Package	Registry	Purpose
`entroly`	PyPI	Python CLI, SDK, MCP server, pure-Python engine, optional proxy/native extras
`entroly-core`	PyPI	Rust native engine for Python via PyO3
`entroly`	npm	Node compatibility alias that delegates to the WASM runtime
`entroly-mcp`	npm	NPX bridge for MCP users
`entroly-wasm`	npm	WASM runtime for Node users without a Python-first setup
`ghcr.io/juyterman1000/entroly`	GHCR	Docker image

Recommended first impression:

pip install -U entroly
entroly verify-claims
entroly simulate

Then choose one integration path. Python is the fullest CLI/SDK path. npm/WASM is the no-Python Node path. The MCP bridge exists for MCP clients that prefer npx, and proxy mode is for users who control provider API keys.

CLI surface

Core first-run commands:

entroly verify-claims
entroly simulate
entroly perf
entroly doctor
entroly status

Agent and provider integration:

entroly wrap claude
entroly wrap cursor
entroly wrap codex
entroly wrap aider
entroly proxy
entroly serve
entroly daemon

Context and receipt workflow:

entroly optimize
entroly ingest
entroly select
entroly receipt
entroly explain
entroly batch

Quality, safety, and learning:

entroly witness
entroly verify
entroly verify-code
entroly feedback
entroly learn
entroly benchmark
entroly health
entroly ravs report
entroly cache stats

Operational commands:

entroly config
entroly telemetry
entroly clean
entroly export
entroly import
entroly migrate
entroly completions

SDK surface

Common imports:

from entroly import compress, compress_messages, optimize, verify
from entroly import create_context_receipt, render_context_receipt
from entroly import explain_receipt_omission, context_receipt_from_path

Advanced local control:

from entroly import localize_files, localize_fragments
from entroly import CacheAligner
from entroly import ContextLedger, ProviderPrice, clamp_injected_budget
from entroly import MemoryOS

Verification and trust:

from entroly import WitnessAnalyzer
from entroly import EICVAnalyzer, EICVSuppressor
from entroly import stave_verify, stave_risk
from entroly import acf_scan, acf_sanitize

Compression and recovery:

from entroly import compress_evidence_locked
from entroly import compress_proxy_payload
from entroly import CompressionRetrievalStore
from entroly import answer_with_retrieval_verification

Memory, session, and value intelligence

Entroly includes memory surfaces that make long-running agent work less wasteful and less forgetful.

Area	Code surface	What it supports
Memory OS	`memory.py`, `memory_cli.py`	Budget-aware working, episodic, and semantic recall with safety scanning and receipts
Memory Fabric	`memory_fabric.py`, `long_term_memory.py`, `memory_kernels.py`	Longer-horizon memory, consolidation, and retrieval patterns
Session intelligence	`session_intelligence.py`, `checkpoint.py`	Decision digests, checkpoint relevance, cache-retention forecasting, behavioral-waste detection
Value evidence	`value_tracker.py`, `cost_cortex.py`, `usage_ledger.py`	Lifetime savings, observed provider usage, cache economics, and spend summaries
Agent bridge	`context_bridge.py`, `verified_handoff.py`	Share context between tools while preserving verification and handoff boundaries

The product claim this enables is stronger than “compress this prompt”: Entroly can help a developer keep useful state across a coding session, spend tokens where they are likely to matter, and explain what value was actually measured.

Multimodal and image context

Not all valuable context starts as plain text. Entroly has intake surfaces for turning developer artifacts into structured, budgetable evidence.

Area	Code surface	Notes
Multimodal ingestion	`multimodal.py`, MCP `ingest_diff`, `ingest_diagram`, `ingest_voice`	Converts non-text or semi-structured material into fragments the context engine can rank
Image optimization	`image_optimizer.py`	Estimates provider image token cost and applies compliance-gated optimization where enabled
Smart reads	`semantic_resolution.py`, `context_scaffold.py`, `file_localizer.py`, `query_refiner.py`, `prefetch.py`	Helps agents ask for the right files, symbols, and context slices instead of bulk-reading the repo

This should be described as context intake and budgeting, not as magic vision understanding. The model still receives selected evidence through the configured provider path.

Gateway, provider policy, and accounting

The proxy path is backed by policy and accounting modules, not just URL forwarding.

Area	Code surface	What it supports
Provider adapters	`provider_adapters.py`, `proxy.py`, `compression_proxy.py`, `compression_proxy_live.py`	Anthropic/OpenAI-compatible request handling and provider-shaped responses
Provider policy	`provider_policy.py`, `proxy_config.py`	Capability planning, explicit routing constraints, and gateway redaction policy
Cache routing	`cache_routing.py`, `cache_aligner.py`, `stable_prefix.py`	Prefix stability, cache leases, and cache-aware request planning
Usage accounting	`usage_ledger.py`, `gateway_control_plane.py`, `cost_cortex.py`	Provider-reported token categories, pricing catalogs, spend summaries, and team/project filters
Budget harness	`harness_budget.py`, `adaptive_budget.py`	Subagent budget allocation and cost-aware execution planning

The honest customer story is: Entroly does local context optimization first; when users opt into proxy mode, it also accounts for provider usage and preserves provider protocol boundaries.

CogOps knowledge vault

Several modules support a higher-level knowledge layer for codebases and agent workflows.

Area	Code surface	What it supports
Belief compilation	`belief_compiler.py`, `vault.py`	Build and query local beliefs from workspace evidence
Epistemic routing	`epistemic_router.py`, `flow_orchestrator.py`	Choose flows based on evidence coverage, freshness, and risk
Verification engine	`verification_engine.py`, `verifiers/`	Check claims, code facts, symbols, provenance, type/scope signals, and repair paths
Change awareness	`change_pipeline.py`, `change_listener.py`	Keep local knowledge aligned as files change
Native CogOps	`entroly-core/src/cogops.rs`, `entroly-wasm/js/cogops.js`	Rust/WASM runtime support for the same family of primitives

This is a real differentiator for developer trust: the local runtime can reason over what it knows, what changed, what is stale, and what needs verification.

Framework and event integrations

Entroly also ships integration points for teams and agent ecosystems.

Integration	Code surface	Use
LangChain	`integrations/langchain.py`	Programmatic context optimization in LangChain-style apps
AgentSkills	`integrations/agentskills.py`, `entroly-wasm/js/agentskills_export.js`	Export reusable skills produced or curated by the runtime
Hermes/events	`integrations/hermes.py`	Event bridge for operational workflows
Team gateways	`integrations/slack_gateway.py`, `discord_gateway.py`, `telegram_gateway.py`	Optional feedback/status channels for teams
Dashboard/daemon	`dashboard.py`, `compression_dashboard.py`, `controls_html.py`, `daemon.py`, `control_plane.py`	Local observability, control-plane UX, and supervised runtime processes

These surfaces should be framed as optional integrations. A first-time user should not need them to see value from verify-claims, simulate, MCP, SDK, or proxy mode.

MCP tool families

The MCP server exposes tools across these groups:

Family	Examples
Context memory	`remember_fragment`, `optimize_context`, `recall_relevant`, `entroly_retrieve`
Context Receipts	`create_context_receipt`, `render_context_receipt`, `explain_receipt_omission`, `recover_receipt_omission`
Outcome learning	`record_outcome`, `record_test_result`, `record_command_exit`, `record_ci_result`, `record_edit_outcome`
Explainability	`explain_context`, `entroly_dashboard`, `get_stats`
Security and health	`scan_for_vulnerabilities`, `security_report`, `analyze_codebase_health`, `security_scan`
Multimodal/context intake	`ingest_diff`, `ingest_diagram`, `ingest_voice`, `smart_read`
Knowledge vault	`compile_beliefs`, `verify_beliefs`, `vault_query`, `vault_search`, `sync_workspace_changes`
Response verification	`verify_provenance`, `verify_and_repair`, `verify_response`, `eicv_verify_claim`, `eicv_suppress_hallucinations`

Trust stack

Entroly’s trust layer is intentionally layered:

Context Receipts show selected context, omitted context, ranking reasons, fingerprints, dependency links, warnings, and reproducibility hashes.
Exact recovery gives compressed fragments handles so originals can be fetched later.
WITNESS checks generated answers against supplied evidence.
EICV/STAVE/verifier modules provide additional deterministic and statistical verification signals.
Adversarial Context Firewall scans for prompt injection, encoded payloads, repetition flooding, and integrity issues.
Receipt proof modules provide attestation, disclosure, Merkle, and witness layers.

Self-improvement stack

Entroly’s self-improvement surfaces are deliberately guarded. They adapt from evidence, tests, CI, command exits, verification outcomes, and user acceptance.

Component	Role
`FeedbackJournal` / `autotune.py`	Stores results and evaluates configuration mutations against benchmark cases
`OnlinePrism`	Bayesian online weight adaptation from reward and contribution signals
`RAVS`	Collects honest outcomes from tests, commands, CI, retries, edits, and verification
`OutcomeBridge`	Corrects PRISM posterior weights with delayed honest outcomes
`RewardCrystallizer`	Turns statistically repeated wins into candidate reusable skills
`SkillEngine`	Synthesizes, benchmarks, promotes, merges, or prunes skills
`EvolutionDaemon`	Orchestrates structural synthesis, idle dreaming, archetype-aware evolution, and optional federation
`PromotionGate`	Promotes shadow policies only when non-inferior and supports rollback
`ValueTracker`	Tracks lifetime savings so optional evolution can be budget-gated
`ArchetypeOptimizer`	Detects project shape and loads project-appropriate weight priors

The intended loop:

selection -> model/tool work -> tests/CI/user outcome -> RAVS event
         -> PRISM correction -> safer weights/routes -> promotion gate
         -> repeated wins crystallize into skills

Safety boundaries:

Structural synthesis is attempted before any LLM fallback.
LLM-backed synthesis is budget-gated.
Promotions require non-inferiority checks.
Rollback is available when post-promotion repair/retry/success metrics regress.
Federation is opt-in.

Native Rust/WASM engine

The native engine is not a thin wrapper. It contains the high-throughput context machinery used by Python and Node/WASM paths:

Engine area	Code surface
Retrieval/ranking	`bm25`, `query`, `qccr`, `query_persona`, `localization`
Selection/packing	`knapsack`, `knapsack_sds`, `hierarchical`, `skeleton`, `utilization`
Information scoring	`entropy`, `anomaly`, `resonance`, `rnr`, `semantic_dedup`
Dedup/dependencies	`dedup`, `lsh`, `depgraph`, `causal`
Learning/cache	`learning`, `prism`, `cache`, `trajectory`, `conversation_pruner`
Safety/health	`sast`, `guardrails`, `health`, `compliance`
Verification	`witness`, `eicv`, `eicv_suppressor`
Agent memory	`memory`, `ipc`, `pollination`, `cognitive_bus`, `cogops`
Proxy/runtime	`proxy`, `compress`, `context_receipts`, `entroly-rs` binary

The WASM package mirrors the native engine shape for JavaScript/TypeScript users and adds app-level SDK helpers for OpenAI, Anthropic, Gemini, and Vercel AI SDK-style middleware.

Proof and benchmark assets

The repository includes committed benchmark/proof artifacts rather than only screenshots:

Area	Examples
Accuracy retention	`benchmarks/results/needle_accuracy.json`, `longbench_accuracy.json`, `squad_accuracy.json`, `gsm8k_accuracy.json`, `mmlu_accuracy.json`, `truthfulqa_accuracy.json`, `bfcl_accuracy.json`
Verification	`stave_benchmark.json`, `witness_benchmarks.json`, `halueval_qa_faithful.json`, `fusion4_spectral_benchmark.json`, `epr_benchmark.json`
Compression/recovery	`recovery_policy_benchmark.json`, `compression_proxy_scoreboard.py`, `anchor_compress.py`
Research/proofs	`proofs/knapsack/README.md`, `proofs/bipt/README.md`, `benchmarks/EICV_PREREGISTRATION.md`
Real code workloads	`bench/swebench_real.py`, `bench/repobench_retrieval.py`, `bench/swebench_real_result.json`

When to use each path

Need	Path
Quick confidence check	`entroly verify-claims && entroly simulate`
Claude Code subscription workflow	`claude mcp add entroly -- entroly`
API-key proxy workflow	`entroly proxy`
Programmatic compression	Python SDK
Node/WASM workflow	npm `entroly` / `entroly-wasm`
CI budget enforcement	`entroly batch --budget 8000 --fail-over-budget`
Audit-heavy document/code review	Context Receipts + WITNESS

Honest boundary

Entroly should not claim savings when there is nothing to compress. Small prompts and tiny repos should pass through. simulate estimates token reduction locally and does not judge final answer quality. Quality claims should be tied to committed benchmark JSON, local verification output, or provider-backed evaluation reports.

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