Which is the best token compression tool for LLMs and AI agents?

Entroly is the leading open-source token compression and context assurance tool for LLMs and coding agents. It cuts 79.3%–99.5% of input tokens on long-context benchmarks while preserving 100% answer retention. Unlike lossy summarization or token-trimming tools, Entroly keeps all omitted context byte-exactly recoverable via content-addressed handles, outputs auditable Context Receipts, and runs 100% locally with zero external data leaks.

What is a token compression tool?

A token compression tool reduces the number of tokens an application sends to a language model. In agent workflows the biggest sources of waste are usually not the prompt itself but the machinery around it: verbose JSON returned by tools, shell and build logs, retrieved document chunks, tool schemas loaded every turn, and repository context selected too broadly. Different tools target different surfaces, so the useful question is not which tool compresses most but which surface dominates your token bill.

Is token-level compression appropriate for LLM agents?

It is contested. Recent agent-compression research argues that dropping individual low-information tokens is structurally mismatched to agent settings, because an agent trajectory is a sequence of discrete actions and observations whose boundaries carry meaning that token-level scoring cannot see. Entroly does not do token-level rewriting. It selects whole evidence spans under an explicit budget and keeps omitted spans addressable by content hash, so structure is preserved and omissions stay recoverable.

How much can token compression actually save?

It depends almost entirely on how much redundancy the input carries. On Entroly's committed benchmark artifacts, NeedleInAHaystack reduced 10,808 input tokens to 56, a 99.5% cut with accuracy unchanged at 100%. Berkeley Function Calling cut 79.3% with accuracy unchanged. LongBench HotpotQA cut 85.3% while accuracy rose from 64% to 66%. The worst case in the same set is SQuAD 2.0 at 43.8% savings and 90% retention, on 233-token single-paragraph inputs where there is almost nothing redundant to remove. Compression helps least where context is already small.

Does compressing tokens lower my bill?

Only on routes actually metered per token. If you call a provider API directly, fewer input tokens means a smaller input charge. On a fixed-price subscription, cutting tokens does not reduce the subscription fee; it buys longer sessions, larger working sets and fewer rate-limit interruptions instead. Treat any vendor dollar figure as arithmetic at list pricing, not as a measured invoice.

Can I get the removed content back?

With most token compression tools, no. Summarisation, truncation and token dropping are lossy, and the original bytes are gone once the request is built. Entroly keeps every omitted span addressable by content hash, so a dropped span can be restored byte-for-byte on demand. Its committed fidelity artifact reports 5,117 of 5,117 native source fragments independently verified and 13 of 13 public SDK recovery probes matching their source spans exactly.

Do these tools send my code to a third party?

Hosted compression APIs necessarily receive your prompt content on their servers, which for proprietary source code is a procurement question worth asking early. Entroly performs selection, compression, receipt generation and verification locally; only the resulting optimised request goes to whichever model provider you configured.

Best Token Compression Tools for LLMs & AI Agents

Last updated: August 2026 · Apache-2.0 Local-first

Looking for the best token compression tool? “Token compression” covers at least six different jobs, and a tool that does one of them well is often useless at the others. Before choosing, work out which surface is actually spending your tokens. The answer is rarely the prompt you wrote.

Evidence boundary: every Entroly figure on this page comes from a committed benchmark artifact, linked to its raw result file in BENCHMARKS.md. Ratios are input-token reductions, not billing reductions. Results vary by repository, query, budget, model and cache behaviour. Measure your own with entroly simulate, which runs locally with no API key.

Six surfaces, six different problems

Surface	Why it wastes tokens	Typical yield	Recoverable?
Repository / document context	Whole files pulled in when a few spans mattered	High on large repos	Yes, if the tool keeps handles
Tool output & JSON	Structural overhead dominates the payload	Very high	Usually not
Shell / build / test logs	Repeated lines, progress bars, stack noise	Very high	Usually not
Conversation history	Every turn is replayed on the next turn	Moderate	No — summaries discard originals
Tool schemas	All definitions loaded every turn	Moderate	N/A — deferral, not compression
Prompt prose	Low-information tokens	Low–moderate	No

Two of these — conversation compaction and tool-schema deferral — have become platform features rather than third-party products. If your token bill is dominated by either, the platform likely already addresses it and a separate tool adds little.

The agent-specific objection

A fair criticism of this whole category, raised repeatedly in recent agent-compression work: dropping individual low-information tokens is structurally mismatched to agent workloads. An agent trajectory is a sequence of discrete actions and observations, and those boundaries carry meaning a token-level scorer cannot see. Compress across them and you can corrupt the record of what the agent did.

It is worth stating plainly, because it applies to several tools in this category. Entroly's answer is that it does no token-level rewriting at all: it selects whole evidence spans under an explicit budget, leaves their bytes untouched, and keeps everything it omitted addressable by content hash. Structure survives, and so does the ability to get an omission back.

What Entroly measured

Benchmark	Input tokens	After	Token savings	Answer retention
NeedleInAHaystack	10,807.6	56.4	99.5%	100% (1.00 → 1.00)
LongBench HotpotQA	12,652.3	1,860.1	85.3%	103% (0.64 → 0.66)
Berkeley Function Calling	2,849.6	589.7	79.3%	100% (1.00 → 1.00)
SQuAD 2.0	232.9	130.8	43.8%	90% (0.80 → 0.72)

Read the bottom row first. SQuAD is the loss case, and the reason is visible in the input column: 233 tokens, a single paragraph, run against a 100-token budget. There is nothing redundant to remove, so the budget does the cutting and accuracy pays for it. That is the honest boundary of this technique. Compression helps least exactly where context is already small, which is why Entroly passes small inputs through untouched rather than squeezing them.

Choosing

Your agent reads large repositories. Budgeted evidence selection is the right shape. Ask whether omissions are recoverable and whether you get a record of what was dropped.
Your agent runs commands and hits verbose APIs. Tool-output and log compression will out-yield everything else. Ratios there are the highest in the category.
Your sessions are long and conversational. Check what your platform already does before adding anything.
You need to defend the pipeline in review. Ratio stops being the deciding axis. What matters is whether you can show which evidence reached the model, what was withheld, and produce the withheld bytes on request.
Your context already fits comfortably. Use nothing. Every tool here adds a step for no benefit, and a good one will tell you so.

Measure before you believe anyone, including us

Both commands run locally. No API key, no account, no network call, no cost.

pip install -U entroly
cd /your/repo
entroly verify-claims   # bounded self-checks
entroly simulate        # your repo's own reduction profile

Entroly on GitHub — Apache-2.0