TokenadevsLLMLingua

Best alternative to LLMLingua

Tokenade is the best alternative to LLMLingua — Universal token-optimization engine for AI coding agents — native hooks for 18 agents combine output filtering, semantic code search, skeleton compression, sandboxed execution, MCP proxying and a live savings dashboard in a single dependency-free binary.

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Output Filtering

Format-aware compactors cover git, cargo, kubectl, terraform, docker and more — 60–99% reduction on the noisiest commands. Command rewriting further trims source-side before the shell even runs.

Output Filtering

LLMLingua compresses natural-language text, not structured command outputs. Applying it to JSON, code or git logs would corrupt identifiers and syntax.

Semantic Code Search

Finds the most relevant files for a task and sends only those to the model, instead of the whole repo. Runs fully on-device with no external vector database and no model downloads — fast even on large codebases.

Semantic Code Search

Not a code-navigation tool. LLMLingua compresses prompts; it does not index or search codebases.

Third-Party MCP Optimization

tokenade mcp-proxy wraps any third-party MCP server's launch command in the agent's MCP config, so every tool result (verbose JSON, logs, console output) is folded on the way back — set once, not per call. Image results pass through untouched.

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Mechanism Breadth

The only tool combining output filtering + semantic search + skeleton compression + sandbox execution + MCP proxying + secret redaction + content-addressed cache in a single binary. On the open THOL benchmark (Claude Code 2.1.183 campaign) it is the only tool measured significantly cheaper than the control: cost ratio 0.84 [0.72, 0.95], 120/120 successful runs.

Mechanism Breadth

Single mechanism for a single use-case (RAG / transcript compression). Irrelevant or harmful for coding-agent workloads.

Setup & Installation

npm install -g @tokenade/cli then tokenade install — native hooks auto-detected for 18 agents (Claude Code, Cursor, Codex, Gemini CLI, Copilot, Windsurf and more). Works without an account: 10M tokens offered per machine to try. Not yet on crates.io or Homebrew.

Setup & Installation

pip install llmlingua, then download and host 7B model weights. Significant infrastructure requirement versus a zero-ML tool.

Savings Dashboard

tokenade dashboard shows measured savings, per-command and per-project breakdown, and framework-detection status. Local logs rotate automatically with built-in secret redaction.

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LLMLingua at a glance

LLMLingua starts at Free (open source). Microsoft Research Python library for prompt compression using a small language model (GPT2/LLaMA-7B) to score and prune tokens; the academic baseline for LLM prompt compression.

Pros

  • Seminal peer-reviewed academic work (EMNLP 2023, ACL 2024) — the reference for prompt compression
  • Up to 20× compression on RAG corpora with question-aware token pruning
  • LLMLingua-2 is 3–6× faster than the original (BERT-level encoder, GPT-4 distillation)
  • LangChain, LlamaIndex, Promptflow ecosystem adoption

Cons

  • Heavy ML dependency: requires LLaMA-7B or GPT2-small model weights just to compress
  • Poorly suited to structured outputs (JSON, code): compressing identifiers breaks code
  • Real savings on coding-agent workloads are much lower than the 20× headline on RAG
  • Last meaningful release in 2024 — appears stale relative to the fast-moving space
  • No output filtering, no code navigation, no MCP support

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