gotcontext vs LLMLingua
LLMLingua (Microsoft Research, MIT license) is a Python library that compresses prompts by using a small language model (GPT-2-small or LLaMA-7B class) to score each token and drop the lowest-signal ones. It ships three peer-reviewed variants — LLMLingua, LongLLMLingua, and LLMLingua-2 — and integrates into LangChain and LlamaIndex.
gotcontext is a managed MCP gateway and compression service. One bearer-token URL, no Python install, works inside Claude Code, Cursor, Gemini CLI, and OpenAI Codex CLI. The two tools solve the same underlying problem — too many tokens — by different means and from different deployment shapes. This page maps them honestly.
✓ = supported ✗ = not supported ≈ = partial / varies Amber ✗ marks rows where LLMLingua genuinely wins.
| Feature | gotcontext | LLMLingua |
|---|---|---|
Open source LLMLingua is MIT-licensed with full source on GitHub (~6,353 stars as of June 2026). You can read every line, run locally, fork, and modify without restriction. gotcontext's compression engine (token-saver-5000) is BSL 1.1 (source-available); the hosted service is proprietary. | ||
Data privacy — text stays local LLMLingua runs entirely in your Python process. Your prompts never leave your machine. gotcontext is a hosted service; compressed text transits our servers at api.gotcontext.ai. If your policy requires data-never-leaves-the-building, LLMLingua wins. | ||
Cost to run LLMLingua is free at any volume once you have the Python environment set up. gotcontext charges a monthly fee above the free tier (1,000 compressions/month). For cost-sensitive or high-volume local workloads, LLMLingua has a clear price advantage. | Free / $49 / $99 / $199 / $499 per month | Free (self-hosted) |
Research-backed compression algorithm LLMLingua has three peer-reviewed papers: LLMLingua (EMNLP 2023), LongLLMLingua (ACL 2024), and LLMLingua-2 (ACL 2024 Findings). Its token-classification approach is extensively cited. gotcontext uses ONNX/SBERT semantic compression with internal benchmarks; it does not have ACL/EMNLP peer-review publication as of June 2026. | ≈ | |
How compression works LLMLingua uses a compact language model (e.g., GPT-2 small or LLaMA-7B) to score individual tokens by perplexity and drops the lowest-signal ones — achieving up to 20× compression on prompts. gotcontext builds a semantic skeleton of structural regions (code, prose, tables) and hides low-signal regions rather than removing individual tokens. Different trade-offs: LLMLingua is more aggressive; gotcontext preserves structural shape better for code. | Semantic skeleton — structural sections preserved, low-signal text hidden | Token-level — a small LM scores each token; below-threshold tokens removed |
No install required gotcontext requires only an HTTP client and a bearer token — configure once, works everywhere. LLMLingua requires pip install llmlingua plus a small language model download (GPT-2 small or LLaMA-7B class). LLMLingua-2 also requires a BERT-level encoder. | ||
MCP gateway (works inside Claude Code / Cursor / Codex) gotcontext exposes a Streamable HTTP MCP gateway at api.gotcontext.ai/mcp. One bearer-token URL is all Claude Code, Gemini CLI, or OpenAI Codex CLI needs. LLMLingua is a Python library; it has no MCP server or HTTP endpoint. Wrapping it behind an MCP server requires you to build and host that layer yourself. | ||
REST API (call from any language) gotcontext exposes /v1/compress and related REST endpoints callable from any HTTP client. LLMLingua ships as a Python package with no official hosted REST API. Using it from TypeScript, Go, or Ruby requires a custom wrapper service. | ||
AST-aware code compression gotcontext ships gc_blast_radius for ranked, symbol-aware code context and /v1/compress-code/structural for AST-level structural compression. LLMLingua treats code as text; it has a Code example notebook but does not perform AST-level analysis. Results on code are token-statistical, not syntax-aware. | ≈ | |
Knowledge Hub (cross-agent memory) gotcontext's Knowledge Hub stores documents, chunks them, and retrieves them with compressed semantic search across agent sessions. LLMLingua is a stateless compression library with no persistent storage layer. | ||
Per-user dashboard and team billing gotcontext ships a dashboard with per-user metering, project-level budgets, and team seats. LLMLingua has no billing surface — it runs on your own hardware. | ||
LangChain / LlamaIndex integration LLMLingua has official integrations in LangChain (LLMLinguaRetriever) and LlamaIndex (LongLLMLingua node post-processor), making it drop-in for RAG pipelines already in those frameworks. gotcontext integrates via REST and MCP; native LangChain/LlamaIndex adapters are not yet documented. | ≈ |
Comparison based on publicly documented LLMLingua features and published papers as of June 2026. Source: github.com/microsoft/LLMLingua. Verify current capabilities at the source.
When LLMLingua fits your use case
- Your compliance policy requires all text to stay on your own machine — LLMLingua runs entirely locally, nothing leaves your infrastructure.
- You are already running a Python stack and want a free, MIT-licensed library you can read, modify, and run at unlimited scale.
- You are building inside LangChain or LlamaIndex and want a native retriever or node post-processor with zero extra services.
- You want aggressive token-level compression (up to 20× on prompts) and are comfortable tuning the small language model parameters.
- You need a peer-reviewed, academically cited method for a research or compliance context.
When gotcontext fits your use case
- You want zero install — configure one bearer-token URL and Claude Code, Cursor, Gemini CLI, or Codex CLI connects immediately via the MCP gateway.
- You are working in TypeScript, Go, or any language other than Python — gotcontext exposes a REST API callable from any HTTP client.
- You need AST-aware code compression (gc_blast_radius) or cross-agent Knowledge Hub storage alongside compression.
- Your team needs per-user usage metering, project-level budgets, and a billing dashboard without self-hosting anything.
- You want structural-region compression that preserves code syntax and document shape rather than removing individual tokens.
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