Graphlit
Graphlit is an API-first platform designed for developers who are building AI-powered applications with unstructured data. It caters to var...
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What is Graphlit?
Graphlit is an API-first, cloud-native platform that provides the context layer for AI agents and applications. It transforms unstructured data from diverse sources—such as Slack, GitHub, Jira, Notion, email, YouTube, PDFs, audio, video, and images—into structured knowledge graphs with semantic memory. The platform handles the entire data pipeline including ingestion, text extraction, audio transcription, entity extraction, vector embeddings, and semantic search, eliminating the need for developers to assemble vector databases, LLM embeddings, cloud storage, and data pipelines themselves.
Key features include RAG and GraphRAG readiness with intelligent text extraction and chunking, built-in vector embeddings, conversation history, and LLM-based entity extraction. Graphlit offers semantic search with vector-based search and metadata filtering, automated content creation including text summarization and social media post generation, multi-modal RAG supporting audio/video/images, and native SDKs for Python, Node.js, and .NET. The platform integrates with major LLMs including OpenAI GPT-4o, Anthropic Sonnet 3.5, Cohere, Google AI, Groq, and Mistral, and includes built-in CrewAI integration for multi-agent collaboration.
Graphlit is designed for developers building AI copilots, chatbots, vertical AI apps, and AI agents who need semantic memory and context for their applications. It is particularly useful for technical teams, data teams, and enterprises managing large datasets and content, as well as organizations building AI-driven knowledge systems. The serverless, cloud-native platform requires no infrastructure deployment, supports multi-tenant apps with RBAC, and includes integrated usage logs with data encrypted-at-rest.
Graphlit pricing
Pricing model: Freemium
Free tier: $0/month with 100 credits, up to 1GB content storage, up to 1000 content items, up to 3 feeds, up to 100 chatbot conversations, includes Deepgram transcription, all vector embeddings and prompt completions, community Discord support. Hobby tier: $49/month + usage at $0.10/credit, up to 10GB storage, up to 10K content items, unlimited feeds, unlimited chatbot conversations, email and Discord support. Starter tier: $199/month + usage at $0.09/credit (10% off), up to 100GB storage, unlimited content items, unlimited feeds, unlimited conversations, priority email and private Slack support. Growth tier: $999/month + usage at $0.08/credit (20% off), unlimited storage, unlimited content items, unlimited feeds, unlimited conversations, priority email, private Slack support, dedicated technical contact. Usage-based pricing starts at $0.10/credit with discounts on higher tiers.
Graphlit pros
- API-first design made for app developers not data scientists
- Serverless cloud-native platform with no infrastructure to deploy
- Ingests any unstructured data format including PDFs, MP3s, MP4, images
- Built-in audio transcription with Deepgram included
- Automatic OCR for extracting text from documents and images
- RAG and GraphRAG ready out of the box
- Built-in vector embeddings included at no extra cost
- Semantic search with metadata filtering capabilities
- Knowledge graph construction via LLM-based entity extraction
- Multi-modal RAG supporting text audio video and images
- Native SDKs for Python Node.js and .NET
- Integrates with 20+ data sources including Slack GitHub Jira Notion
- No file size limits on all tiers unlike OpenAI Assistants API
- Built-in multi-tenancy with RBAC support
- Usage-based pricing with free tier requiring no credit card
- Includes prompt completions and conversation history
- Automated web scraping via sitemap ingestion
- Built-in CrewAI integration for multi-agent workflows
- Model-agnostic supporting OpenAI Anthropic Cohere Mistral Google
Graphlit cons
- Requires technical knowledge and developer expertise
- Learning curve for knowledge graph concepts
- May need additional tools for complete workflow implementation
- Advanced pricing API limits not fully disclosed publicly
- Primarily designed for developers not non-technical users
- SOC 2 compliance still coming soon not yet available
- SLA still coming soon not yet available
- Limited to serverless cloud-native deployment no self-hosting
- Requires understanding of data processing concepts for advanced features
Frequently asked questions about Graphlit
What is Graphlit and what does it do?
Graphlit is an API-first developer platform and context layer for AI agents that transforms unstructured data into structured knowledge graphs. It ingests content from any data source like Slack, GitHub, Jira, Notion, email, YouTube, PDFs, audio, and video, then processes it with intelligent text extraction, entity extraction, vector embeddings, and semantic search. This gives AI agents context that actually works with real-time sync across multiple platforms.
What content types can Graphlit ingest?
Graphlit can ingest any unstructured data format including documents (PDFs, Word), HTML web pages, Markdown text, audio (MP3, podcasts), video (MP4, YouTube), images, Reddit or RSS posts, Slack or Microsoft Teams messages, Microsoft 365 or Google emails, and calendar events. It also supports 3D files and CAD drawings, with automatic OCR for documents and images and Deepgram transcription for audio.
How does Graphlit pricing work?
Graphlit uses usage-based pricing starting at $0.10/credit. There is a free tier with 100 credits and no credit card required. Paid plans include Hobby ($49/month + usage), Starter ($199/month + usage at $0.09/credit), and Growth ($999/month + usage at $0.08/credit). Higher tiers offer discounts on credit usage and increased storage limits, with Growth offering unlimited storage.
What AI models does Graphlit support?
Graphlit is model-agnostic and integrates with top LLMs including OpenAI (GPT-4o, o1-mini, o1-preview), Anthropic (Sonnet 3.5), Cohere, Google AI, Groq, Mistral, and Meta. The platform handles prompted retrieval across these models, and users can configure which AI models Graphlit uses for embeddings, chat, entity extraction, and enrichment through specifications.
What SDKs and programming languages does Graphlit support?
Graphlit provides native SDKs for Python, Node.js, and .NET. For Python, they offer Google Colab Notebooks and Streamlit applications. For Node.js, they have Next.js starter applications deployable to Vercel. For .NET, they provide a .NET CLI for ingesting content. The TypeScript client for Node.js enables GraphQL queries and mutations against the Graphlit service.
How is Graphlit different from LangChain or LlamaIndex?
Unlike LangChain or LlamaIndex which require DIY combination of vector databases, LLM embeddings, cloud storage, and data pipelines, Graphlit is a managed platform that handles all AI and data infrastructure automatically. It requires no assembly of Langchain, Pinecone, or S3, and provides fully automated unstructured data ETL pipelines in a serverless, cloud-native platform with built-in multi-tenancy, semantic search, storage, and workflow automation.
What data sources and integrations does Graphlit support?
Graphlit supports extensive data connectivity including Google Drive, OneDrive, Dropbox, SharePoint, Notion, Intercom, Zendesk, Slack, Teams, GitHub, Jira, Linear, RSS feeds, web pages via sitemap scraping, email (Google Mail and Microsoft Outlook), and cloud storage folders. It offers real-time sync across Slack, GitHub, Jira, and more through automated data feeds.
Does Graphlit support multi-tenant applications?
Yes, Graphlit supports multi-tenant apps with built-in multi-tenancy and RBAC (role-based access control). The Free tier explicitly includes multi-tenant app support. The platform is enterprise-ready with data encrypted-at-rest, and higher tiers offer priority email support, private Slack support, and dedicated technical contacts.
What search capabilities does Graphlit provide?
Graphlit provides three search types: vector search, keyword search, and hybrid search. It offers vector-based semantic search including metadata filtering, allowing users to search content by text or vector similarity. Metadata filters are applied first (such as by date range), then similarity search occurs over the filtered result set. The platform supports advanced filtering and performance optimizations for production-grade search.
What happened to OpenAI Assistants API limitations with Graphlit?
With Graphlit, you are not limited by the OpenAI Assistants API file size limits or storage limitations. Graphlit has no file size limits on all tiers, and the Growth tier offers unlimited content storage. This allows developers to ingest large files and store unlimited content items without the constraints imposed by OpenAI's Assistants API.