ragflow vs coco-search
ragflow: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that integrates RAG with Agent capabilities. It provides a superior context layer for LLMs and offers a streamlined RAG workflow adaptable to enterprises of any scale.; coco-search: CocoSearch is a local-first hybrid semantic code search tool that combines vector similarity and keyword matching via RRF fusion. It uses CocoIndex for indexing, Tree-sitter for syntax-aware chunking, and PostgreSQL with pgvector for storage. It supports 32 languages, 9 grammars, dependency graphs, cross-repo search, and multiple interfaces.
TL;DR
- Your documents have complex layouts (PDFs, scanned files, tables, slides) and you need layout analysis, OCR, and traceable citations.
- You want an Apache-2.0 all-in-one RAG platform where you can inspect and hand-tune template-based chunks in a web UI.
- Your server has less than 4 CPU cores and 16 GB RAM; RAGFlow's multi-service stack is heavy.
- You want an autonomous web research agent that writes 2,000-word reports from live search rather than indexing a local document corpus; GPT Researcher fits better.
Exploratory/semantic queries about code functionality
Side-by-Side Comparison
ragflowcoco-searchFeatures
ragflowcoco-searchUse Cases
ragflowcoco-searchBest For
ragflowcoco-searchFAQ
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