AgentIndex icon
AgentIndex
kglite logo
kglite
★ 73
vs
crg logo
crg
★ 76

kglite vs crg

kglite: KGLite is an embedded, Cypher-queryable knowledge graph engine for Python and Rust, designed to serve applications, analysts, and LLM agents without an external database. It offers advanced features like schema-aware descriptions for LLMs, temporal querying over historical data, and a declared ontology for data quality.; crg: `crg` (formerly `better-code-review-graph`) is a local-first knowledge graph tool for codebases, enabling token-efficient code reviews through semantic search, call-graph resolution, and structural analysis. It integrates with AI agents via MCP for precise context provision, leveraging local ONNX embeddings or cloud models for enhanced code understanding and security scanning.

01

TL;DR

kglite logo
kgliteRAG / Knowledge Base · ★ 73

Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents.

Read kglite full review →
crg logo
crgRAG / Knowledge Base · ★ 76

Performing token-efficient code reviews with precise context for AI agents

Read crg full review →
02

Side-by-Side Comparison

Field
kglite logokglite
crg logocrg
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 73
★ 76
License
MIT
Apache-2.0
Install Command
pip install kglite
pip install better-code-review-graph
Updated
2d ago
2d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Knowledge Graph, LLM Agents, Cypher Query Language
Knowledge Graph, Code Review, Semantic Search
03

Features

kglite logokglite
01Progressive-disclosure schema (`describe()`) optimized for LLM context windows.
02As-of queries over historical data using `valid_at()` and `valid_during()` functions.
03Declared ontology with data-quality gates and audit scorecards for semantic integrity.
04Hybrid retrieval combining vector similarity (`text_score()`) with Cypher traversal.
05Embedded, in-process graph engine with various storage modes (in-memory, mmap, disk).
crg logocrg
01AND-logic multi-word search for code entities
02Qualified call graph resolution and bare fallback
03Dual-mode fastretrieval ONNX and cloud embeddings (Jina/Gemini/OpenAI/Cohere)
04Paginated and truncated output for large search results
05Integrated security scanning with Semgrep and heuristic rules
04

Use Cases

kglite logokglite
↳Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents.
↳Transforming business data from tabular sources (SQL, CSV, DataFrames) into a queryable graph.
↳Structuring RAG corpora with documents, chunks, entities, and their relationships for hybrid retrieval.
↳Codebase analysis by parsing code into a graph of functions, classes, and modules.
↳Ingesting Markdown knowledge bases (Obsidian vaults, Claude memory) into a queryable graph for analysis and agent memory.
crg logocrg
↳Performing token-efficient code reviews with precise context for AI agents
↳Conducting semantic search and call-graph resolution across a codebase
↳Analyzing the blast radius and impact of changed files within a repository
↳Running local security scans on code using heuristic rules or Semgrep engine
↳Onboarding new repositories and quickly understanding code structure and relationships
FAQ

FAQ

What is the difference between kglite and crg?
Both kglite and crg are in the RAG / Knowledge Base category. kglite has 73 stars, while crg has 76 stars.
Which is better, kglite or crg?
The best choice depends on your use case. Choose kglite if Providing domain knowledge (legal precedents, regulatory rules, medical ontologies) for LLM agents., and crg if Performing token-efficient code reviews with precise context for AI agents.
Is kglite free or open source?
Yes, kglite is open source on GitHub (MIT).
Is crg free or open source?
Yes, crg is open source on GitHub (Apache-2.0).
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