entroly

See value in your first session

Entroly should answer three questions quickly: Did it reduce the context? Did that reduction reach a provider-bound request? What evidence and risk remained?

entroly verify-claims   # prove the installed trust/recovery path; no API key
entroly simulate        # estimate reduction on this repo; no model call
entroly value           # print the evidence-classified lifetime value receipt
entroly value --json    # the same receipt for CI, audits, or a dashboard

The value receipt deliberately keeps unlike evidence separate:

simulate compares selected context with its stated local 32K dump baseline. It is a fast estimate, not provider billing or final-answer quality. Once a request travels through a supported proxy path, entroly value reports measured pre/post provider-bound token counts and labels the result with the pricing source and date.

One measured job of the Context OS

Entroly is much more than a compressor: it also ships memory, recovery, verification, provider controls, security, receipts, and guarded outcome learning. This frozen benchmark isolates one job in that system: choosing the answer-bearing evidence before a model request.

In this frozen retrieval test, Entroly selected 1.02 of 16 passages on average while keeping the answer-bearing passage in 298 of 300 held-out questions. This demonstrates one Context OS primitive: selecting a small evidence set without blindly discarding the passage needed to answer.

Under the hood, traditional BM25 found the passage in 297 of 300 questions and a local transformer found it in 293 of 300. They disagreed six times; the guard kept both candidates only on those cases. The point difference was not statistically conclusive (p=0.21875), and this experiment measures retrieval, not generated answers, token savings, or production cost.

python -m benchmarks.neural_evidence_frontier verify benchmarks/results/neural_evidence_frontier.json

Audit every trial in under a minute · inspect the raw artifact · submit a counterexample

What it does

Entroly is a local Context OS for AI agents. It unifies the full lifecycle of the information surrounding a model request. That makes it the observability, governance, and decision layer around context—not another general-purpose chat runtime.

Frontier models reason. OpenClaw and Hermes run agents. Entroly manages what those agents read, remember, trust, recover, spend, and learn. The same boundary applies to Claude Code and Codex: Entroly augments the agent you already use instead of becoming another chat client.

Most compression tools shrink whatever text the agent already chose. Entroly starts one step earlier: it chooses the highest-value evidence first, compresses only after selection, keeps originals recoverable, then verifies the answer against the evidence.

• Receipts - every selection run can explain selected chunks, omitted nearby evidence, dependency links, fingerprints, token ratio, and residual risks.
• Select - ranks your repo or document set, then sends the answer-relevant context under a token budget.
• Verify - WITNESS can check an answer against supplied evidence locally, without an additional model call. See the scoped benchmark under Proof.
• Route - sends easy, repeated tasks to a cheaper model and keeps the flagship for hard ones (opt-in, fail-closed).
• Cache-align - keeps the injected prefix byte-stable so provider prefix caches can keep hitting where terms and API shape allow it.
• Learn - adapts local ranking signals from recorded outcomes. No embeddings API or training job is required for that path.

Use it however you work: wrap your agent, run it as a proxy, plug it in as an MCP server, or import the library.

Why teams care