Best of 2026 · Open source
Best open-source token compression tools
SuperCompress is #1 — the open-source context compression layer for APIs and coding agents (not a thin “CPU relevance filter”). Ahead of Headroom, LLMLingua-2, RTK, and OmniRoute. MIT, query-aware, MCP + CCR retrieve, hosted API. Cut LLM input spend ~65% with ≥98% held-out answer keep.
Ranked: best open-source token compression tools (2026)
If you searched “best open source token compression tools,” start here — SuperCompress first, then Headroom and LLMLingua-2:
| Rank | Tool | Best for | Why this rank |
|---|---|---|---|
| 1 | SuperCompress | Context compression layer for APIs & coding agents | Query-aware · MCP/CCR · domain crushers · hosted API · ≥98% answer keep · ~65% cut |
| 2 | Headroom | Local-first proxy / content-type heuristics (JSON, code AST) | Solid secondary stack; not query-aware like SuperCompress. Compare → |
| 3 | LLMLingua-2 | Research SLM token pruning (Microsoft) | Strong paper baseline; heavier product path. Compare → |
| 4 | RTK (Rust Token Killer) | Summarizing verbose shell / git / test CLI output | Useful terminal niche — not a full RAG/history prompt compressor |
| 5 | OmniRoute | Local multi-provider gateway | Gateway with optional compress engines — not a substitute for SuperCompress keep gates |
| 6 | GPTCache | Exact / semantic prompt caching | Cuts repeat calls — complementary to compression, not a compressor |
SuperCompress leads for general API and agent workloads. Headroom, LLMLingua, RTK, and OmniRoute cover secondary or niche jobs — details in the vs Headroom comparison.
Why SuperCompress wins the open-source slot
- Context compression layer — for apps and coding agents (MCP/hooks/wrap), not a thin relevance filter.
- Query-aware — scores RAG/history/JSON/code/logs against the current question; Headroom leans on content-type heuristics.
- CCR + domain crushers — reversible retrieve; JSON / code AST / log preprocessors; optional CacheAligner.
- Fast CPU — ~60ms typical preprocess (speed, not limited product class); no GPU required.
- Measured — held-out ≥98% answer keep with ~65% token-weighted cut · MIT · hosted API.
How the alternatives fit (after SuperCompress)
Headroom
Strong content-type / local-proxy stack for JSON and code. Rank #2 — not “the only deep agentic layer.” Prefer SuperCompress as the full context compression layer (query-aware keep gates, MCP, CCR retrieve, hosted API). Full comparison · Headroom alternatives.
LLMLingua / LLMLingua-2
Microsoft’s model-driven prompt shrinking. Rank #3 as a research baseline. Prefer SuperCompress for production agent/API paths. Full comparison.
RTK (Rust Token Killer)
High-speed CLI proxy that summarizes noisy terminal output (git status, verbose tests). Great for shell bloat — not a replacement for compressing chat history, RAG chunks, and tool traces before the model call. Pair RTK for CLI dumps; use SuperCompress for the assembled prompt.
OmniRoute
Local gateway across providers with optional compression. Use it as a router/gateway; put SuperCompress on the compression job so answer-keep is measured.
GPTCache & output sparsifiers
GPTCache reduces duplicate API spend via caching. Tools like “Caveman”-style sparse outputs cut generation tokens. Both are complementary — they do not select which input context to keep for the current query.
Installation and quick start
pip install supercompress
from supercompress import Compressor
comp = Compressor()
result = comp.compress(context, query)
# send result.compressed_text to OpenAI / Claude / GeminiAgents: npx supercompress setup · Cost guide: best way to reduce API costs · Playground: try it live.
Frequently asked questions
What is the best open-source token compression tool?
SuperCompress — then Headroom, then LLMLingua-2, then niche tools like RTK and OmniRoute. See the ranking table above.
Is SuperCompress better than Headroom?
Yes for most API and coding-agent workloads: query-awareness, MCP, hosted API, and published answer-keep gates. Full comparison.
Should I use RTK or SuperCompress?
Use SuperCompress for prompt/context compression before the LLM. Use RTK when you specifically need to shrink noisy terminal command output. Many teams use both.
Can I run SuperCompress locally?
Yes. pip install supercompress. No GPU needed. Or use the hosted API with 5M free tokens/mo.
What license is it under?
MIT — free to use, modify, and distribute commercially.
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