Graphlit
Developer API platform that turns unstructured content into knowledge graphs for RAG
- Category
- Data Analysis
- Pricing
- Usage-based pricing with a free developer tier; paid plans scale by content ingestion volume and API usage, with enterprise/custom pricing for higher throughput.
- Best for
- Graphlit is built for developers and AI/ML engineering teams who need to make large volumes of unstructured content (documents, media, messages) usable for LLM applications.
- Official site
- www.graphlit.com
- Last updated
- August 2026
Graphlit is a cloud-native, serverless platform designed to solve the unglamorous but critical problem of getting unstructured data ready for large language models. Rather than developers wiring together separate tools for document parsing, transcription, chunking, embedding, and graph construction, Graphlit exposes a single API that ingests content from a wide range of sources and outputs a structured, queryable knowledge graph.
Under the hood, Graphlit uses LLMs to extract entities and relationships from ingested content, mapping them against a schema.org-inspired data model. This lets applications built on Graphlit support semantic search, contextual RAG-based chat, and automated summarization across mixed content types, including PDFs, web pages, podcasts, videos, and messaging platforms like Slack.
Because the platform is API- and SDK-first, it's aimed squarely at developers and AI engineering teams rather than business users. Graphlit positions itself as infrastructure: the layer between raw unstructured content and LLM-powered features, letting teams focus on their application logic instead of data plumbing.
Graphlit is built for developers and AI/ML engineering teams who need to make large volumes of unstructured content (documents, media, messages) usable for LLM applications. It suits startups and product teams building RAG-based chat, search, or knowledge-assistant features who want to avoid assembling ingestion, extraction, and retrieval infrastructure themselves. It's less suited to non-technical teams or those looking for an out-of-the-box analytics dashboard rather than an API platform.
Key features
Multi-source ingestion
Connects to and ingests PDFs, web pages, images, audio, video, RSS feeds, Slack, and email for unified processing.
Knowledge graph construction
Uses LLMs to extract entities and relationships, building a searchable, conversational knowledge graph from raw content.
Integrated RAG pipeline
Provides retrieval augmented generation out of the box, so ingested content can immediately power LLM-based Q&A and chat.
Audio/video transcription
Automatically transcribes and summarizes audio and video files as part of the ingestion pipeline.
Semantic search
Enables natural-language search across all ingested content regardless of original format.
Webhooks and alerting
Supports event-driven workflows, notifying downstream systems when new knowledge or matching content is ingested.
Pricing breakdown
Free / Developer
- Limited monthly content ingestion
- Access to core API and SDKs
- Community support
Usage-based
- Scales with ingestion volume
- Access to full feature set
- Standard support
Enterprise
- Higher throughput and volume commitments
- Dedicated support and SLAs
- Advanced security/compliance options
Pros and cons
Pros
- Consolidates ingestion, extraction, transcription, and RAG into a single API, cutting the engineering time needed to stand up a custom pipeline
- Broad content-type coverage means teams don't need separate tools for documents versus audio/video versus messaging data
- Serverless architecture removes operational overhead of scaling vector databases or graph stores
- Knowledge-graph approach can surface relationships between entities that pure vector search would miss
- Free tier makes it easy for developers to prototype before committing to paid usage
Cons
- No-code or business-user-facing tooling is minimal; effectively requires an engineering team to adopt
- As an API-based, usage-billed platform, costs can be less predictable than flat-rate SaaS tools at scale
- Being a smaller, newer platform, its ecosystem of integrations and community resources is less mature than larger incumbents
- Teams already invested in a specific vector database or LLM orchestration framework may find some overlap/lock-in tension
- Knowledge graph quality depends on the underlying LLM extraction, which may require tuning for domain-specific content
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Compare →Frequently asked questions
What is Graphlit used for?
Graphlit ingests unstructured content like documents, web pages, audio, and video, and converts it into a searchable knowledge graph that can power RAG-based LLM applications.
Does Graphlit require coding?
Yes, Graphlit is an API/SDK-first developer platform; it's designed to be integrated into applications by engineers rather than used as a standalone no-code tool.
How does Graphlit pricing work?
Graphlit uses usage-based pricing tied to content ingestion volume and API calls, with a free tier available for getting started.
What content types does Graphlit support?
It supports PDFs, web pages, images, audio, video, RSS feeds, Slack messages, and email, among other formats.
Is Graphlit a vector database?
No, Graphlit builds a knowledge graph on top of extracted entities and relationships and provides an integrated RAG pipeline rather than being a standalone vector store.
Who typically uses Graphlit?
Software developers and AI engineering teams building RAG-powered search, chat, or knowledge-management features into their applications.
Ready to try Graphlit?
Head to the official site to explore pricing and start a free trial where available.
Visit Graphlit →