Compresr — RAG Frameworks Platform
RAG FrameworksLayer 2UnknownFounded 2026|San Francisco, CA|2-10
Visit websiteWhat is Compresr?
Compresr provides an API and open-source proxy for compressing LLM context at two levels: coarse-grained (selecting relevant chunks) and fine-grained (token-level compression within chunks). Part of YC W2026, it was founded by a team of four EPFL researchers: Ivan Zakazov (CEO, PhD dropout, published at EMNLP and NeurIPS), Oussama Gabouj (CTO, EMNLP 2025 paper on prompt compression), Berke Argin (CAIO, ex-UBS), and Kamel Charaf (COO, ex-Bell Labs).
The system claims up to 200x compression on aggressive RAG workloads without quality loss, with a default 50% token reduction. Their Context Gateway is an open-source Go proxy that sits between AI agents and LLM providers, compressing tool outputs and conversation history before tokens reach the model. It integrates with Claude Code, OpenClaw, and Codex.
On their SEC filing benchmark (141 questions across 79 filings up to 230K tokens each), Compresr compressed ~106K tokens to ~10.5K while improving accuracy from 72.3% to 74.5% using GPT-5.2 — a 76% cost reduction with better results. The team's peer-reviewed publications at NeurIPS and EMNLP on prompt compression give them the strongest academic credentials in the compression space.
Key Features
- ✓Context compression
- ✓Accuracy preservation
- ✓Long context optimization
- ✓RAG enhancement
Pros & Cons
Pros
- +Strongest academic credentials in compression with NeurIPS and EMNLP publications
- +Four-person founding team from EPFL reduces single-founder risk
- +Open-source Context Gateway creates community adoption funnel
- +Two-level compression (coarse + fine-grained) is more sophisticated than token-only approaches
- +SEC filing benchmark demonstrates real enterprise RAG improvement with measurable results
Cons
- -No disclosed pricing for the paid API tier
- -No named customers or revenue metrics shared publicly
- -Competes directly with The Token Company on overlapping value proposition
- -200x compression claim is for aggressive workloads — default is 50%
Compresr Pricing
Free trial available
Context GatewayFree (open source)
- ✓Go proxy
- ✓Agent integration
- ✓Basic compression
APIContact for pricing
- ✓Coarse + fine-grained compression
- ✓Up to 200x compression
- ✓Python SDK
- ✓Enterprise support
Common Use Cases
Teams building RAG systems with long contexts
- •Long document processing
- •RAG context optimization
- •Token-efficient retrieval
- •Context window management
Using Compresr with Respan
Compresr reduces LLM input costs through context compression while Respan monitors output quality and performance. Together they optimize both sides of the LLM call.
- ✓Verify compression maintains output quality using Respan evaluations
- ✓Track cost savings from Compresr alongside total LLM spend in Respan
- ✓Monitor end-to-end RAG pipeline performance from compression to response via Respan
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Last verified: March 27, 2026