Overview

Rating

10.0 / 10

Rating

Best For

Teams building RAG systems with long contexts

Best For

Enterprises building production RAG applications that need citation-grade answers and rich document understanding

Product Summary

LLM context compression for better accuracy — compresses long contexts to fit more relevant information while maintaining quality.

Product Summary

RAGFlow is Infiniflow's open-source RAG engine that fuses retrieval with agent capabilities. 78.3K+ GitHub stars. Deep document understanding (tables, images, multi-language), hybrid search (vector + BM25 + custom scoring + re-ranking), citation-backed answers, and visual workflow builder. April 2026 release added prebuilt ingestion pipelines, sandbox code execution, and chart generation.

Starting Price

Free (open source)

Starting Price

$0Per forever

Free Trial

Yes

Free Trial

Yes

Free Version

Yes

Website

compresr.ai

Website

ragflow.io

Key features

Core capabilities each platform advertises.

Compresr

Context compression
Accuracy preservation
Long context optimization
RAG enhancement

RAGFlow

Deep document understanding — tables, images, multi-language
Hybrid retrieval: vector + BM25 + custom scoring + re-ranking
Citation-backed answers pointing to source chunks
Visual agent workflow builder with RAG, tools, and MCP
April 2026: prebuilt ingestion pipelines, sandbox code, chart generation

Strengths and tradeoffs

What each tool does well, and the limitations to keep in mind.

Compresr

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%

RAGFlow

Pros

Best document parsing in the OSS RAG space — tables and OCR done right
Citation-grade answers — every output traceable to source chunks
Hybrid retrieval (vector + BM25 + re-ranking) out of the box
Visual agent builder lowers the bar for non-engineers
78.3K stars and active 2026 development

Cons

What people are saying

Compresr

Data not available

RAGFlow

Knightli technical reviewKnightli

RAGFlow's parsing engine uses deep learning to understand document structure — recognizing tables, extracting text from images via OCR, preserving formatting.

Read full review

Compresr or RAGFlow — which should you choose?

Choose Compresr if you wantChoose if you want

Long document processing
RAG context optimization
Token-efficient retrieval
Context window management

Choose RAGFlow if you wantChoose if you want

Enterprise knowledge base with citation-grade answers
Legal and healthcare document Q&A with compliance
Multi-language document retrieval at scale
Visual agent workflows that combine RAG and tools
Research assistants with multi-source data analysis

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Compresr vs RAGFlow (2026)

Overview

Key features

Strengths and tradeoffs

What people are saying

Compresr or RAGFlow — which should you choose?

Compare Compresr and RAGFlow on your own traffic

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