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Tools/txtai vs Graphiti
txtai

txtai

framework
vs
Graphiti

Graphiti

framework

txtai vs Graphiti — Comparison

Overview
What each tool does and who it's for

txtai

txtai is an all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows

txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows. The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases. This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications. Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more. We're also building an easy and secure way to run hosted txtai applications with txtai.cloud.

Graphiti

Build Real-Time Knowledge Graphs for AI Agents. Contribute to getzep/graphiti development by creating an account on GitHub.

Based on the provided content, there are no reviews or social mentions specifically about "Graphiti." All the social media mentions are about GitHub Copilot, Figma, npm registry tools, and other development-related topics, but none reference a tool called "Graphiti." Without actual user feedback about Graphiti, I cannot provide a meaningful summary of user sentiment, strengths, complaints, or pricing opinions for this specific tool.

Key Metrics
—
Avg Rating
—
0
Mentions (30d)
33
12,355
GitHub Stars
24,254
795
GitHub Forks
2,403
—
npm Downloads/wk
—
—
PyPI Downloads/mo
—
Community Sentiment
How developers feel about each tool based on mentions and reviews

txtai

0% positive100% neutral0% negative

Graphiti

0% positive100% neutral0% negative
Pricing

txtai

tiered

Graphiti

per-seat + tiered
Use Cases
When to use each tool

Graphiti (1)

Quick Start
Features

Only in txtai (7)

🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing📄 Create embeddings for text, documents, audio, images and video💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems🔋 Batteries included with defaults to get up and running fast☁️ Run local or scale out with container orchestration

Only in Graphiti (10)

Build context graphs that evolve with every interaction — tracking what's true now and what was true before.Give agents rich, structured context instead of flat document chunks or raw chat history.Query across time, meaning, and relationships with hybrid retrieval (semantic + keyword + graph traversal).Python 3.10 or higherNeo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon OpenSearch Serverless collection (serves as the full text search backend)OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)Connecting to a Neo4j, Amazon Neptune, FalkorDB, or Kuzu databaseInitializing Graphiti indices and constraintsAdding episodes to the graph (both text and structured JSON)
Developer Ecosystem
30
GitHub Repos
11
499
GitHub Followers
417
3
npm Packages
13
30
HuggingFace Models
—
—
SO Reputation
—
Product Screenshots

txtai

txtai screenshot 1

Graphiti

Graphiti screenshot 1Graphiti screenshot 2Graphiti screenshot 3
Company Intel
—
Industry
information technology & services
—
Employees
6,000
—
Funding
$7.9B
—
Stage
Other
Supported Languages & Categories

txtai

AI/MLSecurityDeveloper Tools

Graphiti

AI/MLFinTechDevOpsSecurityAnalytics
View txtai Profile View Graphiti Profile