rag-rat
★ 24
vs
crystalline
★ 17
rag-rat vs crystalline
rag-rat: rag-rat is a local repository intelligence index and MCP server that provides coding agents with deep, source-anchored insights into a codebase. It builds a comprehensive code graph, integrates Git/GitHub history, and stores durable repo memories to offer provenance-backed answers about code rationale and impact.; crystalline: Crystalline provides AI agents with crystallized intelligence, allowing them to accumulate and retain knowledge across sessions, unlike an LLM's fluid intelligence. It acts as a local search index on plain markdown files, enabling agents to onboard curated knowledge and capture new learnings, evolving from a stranger to a peer.
01
TL;DR
rag-ratRAG / Knowledge Base · ★ 24
Code Impact Analysis: Understand the blast radius of a code change by querying callers, callees, tests, recent commits, and associated repo memories before editing.
crystallineRAG / Knowledge Base · ★ 17
Onboard AI agents with curated domain-specific knowledge at the start of each session.
02
Side-by-Side Comparison
Field
rag-ratcrystallineCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 24
★ 17
License
MIT
AGPL-3.0
Install Command
npm install -g @rag-rat/bin && rag-rat init --yes && npx @rag-rat/skills
brew install jordiboehme/tap/crystalline && crystalline install claude-code && mkdir -p ~/knowledge/engineering && crystalline domain init ~/knowledge/engineering --name engineering && crystalline domain add engineering ~/knowledge/engineering
Updated
2w ago
1d ago
Open Source
Yes
Yes
Tags
Repo Intelligence, Code Graph, RAG
AI Agents, Knowledge Base, RAG
03
Features
rag-rat01Provenance-backed Answers: Every result carries confidence labels, coverage warnings, and raw evidence from the code graph, Git history, and repo memories.
02Durable Repo Memories: Source-anchored notes (Invariant, Decision, Risk) persist across refactors and sessions, surfacing automatically during queries to provide context that traditional grep cannot.
03Comprehensive Code Graph: Builds a real code graph across multiple languages (Rust, TypeScript, Python, C/C++, Swift) with optional compiler-grade resolution for enhanced accuracy.
04Git/GitHub History as Evidence: Integrates queryable Git history, lazy blame, and cached GitHub issue/PR rationale for historical context.
05Code Duplication Detection: Warns agents about exact or near-duplicate code during writing, encouraging reuse over re-implementation.
crystalline01Structured knowledge organization using domains (folders) and engrams (markdown files).
02Hybrid search combining full-text and semantic ranking for efficient knowledge retrieval.
03Fluid web UI for human-agent collaboration, enabling reading, editing, and managing knowledge.
04Supports GitHub-based team collaboration with review workflows for shared knowledge.
05Knowledge graph and temporal filtering to understand relationships and time-bounded facts.
04
Use Cases
rag-rat↳Code Impact Analysis: Understand the blast radius of a code change by querying callers, callees, tests, recent commits, and associated repo memories before editing.
↳Semantic Code Search: Find specific code functionalities or concepts across source files and documentation using hybrid BM25 and vector recall.
↳Onboarding & Codebase Understanding: Identify important symbols, code clusters, and architectural decisions to quickly grasp a new repository.
↳Preventing Technical Debt: Detect and prevent code duplication during development and record durable invariants or decisions for future agents.
↳Maintaining Codebase Quality: Use `dream` to find coverage gaps in repo memories, stale references, and verify memory verdicts against current code reality.
crystalline↳Onboard AI agents with curated domain-specific knowledge at the start of each session.
↳Enable AI agents to capture and persist new observations, decisions, and lessons learned from their work.
↳Facilitate team knowledge sharing and collaboration on a continually growing and evolving knowledge base.
↳Reduce redundancy and repeated explanations by providing agents with access to accumulated 'crystallized intelligence'.
↳Efficiently retrieve specific facts or context from a large collection of knowledge units based on task routing.
FAQ
FAQ
What is the difference between rag-rat and crystalline?
Both rag-rat and crystalline are in the RAG / Knowledge Base category. rag-rat has 24 stars, while crystalline has 17 stars.
Which is better, rag-rat or crystalline?
The best choice depends on your use case. Choose rag-rat if Code Impact Analysis: Understand the blast radius of a code change by querying callers, callees, tests, recent commits, and associated repo memories before editing., and crystalline if Onboard AI agents with curated domain-specific knowledge at the start of each session..
Is rag-rat free or open source?
Yes, rag-rat is open source on GitHub (MIT).
Is crystalline free or open source?
Yes, crystalline is open source on GitHub (AGPL-3.0).
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