Graphlit
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About
Graphlit serves as a comprehensive context layer for AI agents, moving beyond simple chat memory or basic vector databases. It focuses on modeling organizational knowledge by capturing entities, relationships, and temporal changes. Unlike standard RAG frameworks that require stitching multiple services together, Graphlit provides a production-ready platform that handles the entire pipeline—from data ingestion to enrichment and retrieval—through a single API. This infrastructure enables developers to skip the complexities of building manual ETL pipelines and vector stores, saving weeks of engineering effort. The platform automates the ingestion of diverse content types, including PDFs, audio, video, web pages, and messages from tools like Slack, Notion, or Jira. Once data is ingested, Graphlit uses LLM-powered resolution to extract entities and map relationships, ensuring that AI responses are grounded in operational context rather than just text similarity. It includes built-in capabilities like OCR, transcription, and visual object detection. Developers can integrate it using Python, Node.js, or .NET SDKs and take advantage of its MCP-native integration for tools like Cursor, VS Code, and Claude Desktop. Graphlit is primarily designed for developers and engineering teams building enterprise-grade AI applications or autonomous agents. It is particularly beneficial for organizations that need to maintain multi-tenant environments with strict role-based access control and data encryption at rest. Use cases range from building sophisticated customer support bots that understand complex user histories to creating internal research assistants that synthesize data from disparate sources like S3 buckets, GitHub repositories, and email threads. The platform is designed to scale with a business from initial development to production. What sets Graphlit apart is its "no glue code" philosophy. It eliminates the need for manual infrastructure by providing continuous synchronization and full provenance for every AI-generated answer. By modeling how information evolves over time and identifying who is responsible for specific updates, it significantly reduces the likelihood of hallucinations in AI agents. The platform’s model-agnostic approach also allows users to switch between major providers like OpenAI, Anthropic, xAI, and Google Gemini instantly, ensuring that applications always have access to the latest LLM advancements.
Pros & Cons
The unified API eliminates the need for stitching together separate ETL pipelines and vector databases.
It supports a vast array of integrations including Slack, GitHub, Jira, and major cloud storage providers.
Built-in capabilities for OCR, transcription, and object detection allow for processing multi-modal content seamlessly.
Full provenance tracking ensures that every AI response is traceable back to its original source.
Model-agnostic design allows developers to switch between providers like OpenAI, Anthropic, and Google instantly.
Users are charged a monthly platform fee in addition to usage-based credit costs on all paid plans.
Advanced security features like SOC 2 compliance and formal SLAs are currently listed as coming soon.
The free tier is restricted to 100 credits and a maximum of 1GB of content storage.
Custom integrations and dedicated solutions engineering require a significant starting investment.
Use Cases
Software developers can connect AI agents to GitHub and Jira to track technical debt and project relationships over time.
Enterprise teams can build multi-tenant research assistants with role-based access control and encrypted storage for sensitive documents.
Customer support managers can automate the ingestion of meeting audio and email threads to give support bots full operational context.
Data engineers can replace complex ETL pipelines with a single API that handles document extraction, enrichment, and vector storage.
Platform
Features
• audio/video transcription
• entity resolution
• continuous data sync
• multi-tenant rbac
• mcp-native integration
• model-agnostic api
• source provenance
• automated data feeds
FAQs
What is the difference between Graphlit and vector databases?
Vector databases focus on static embeddings and require manual infrastructure, while Graphlit provides a complete context layer with entity resolution, temporal state, and continuous synchronization in one API.
Which AI models does Graphlit support?
Graphlit is model-agnostic and integrates with major providers including OpenAI, Anthropic, Google, xAI, Deepseek, Groq, Mistral, and Cohere, allowing users to switch models instantly.
How does the credit-based pricing work?
Costs are based on usage averages such as 5 PDF pages per credit or 10 minutes of audio per credit, with tiered pricing reducing the cost per credit as you move to higher plans.
What tools can I connect to Graphlit?
The platform supports a wide range of integrations including Google Drive, Notion, Slack, GitHub, Jira, and Amazon S3, along with MCP-native integration for Cursor and VS Code.
Pricing Plans
Hobby
USD49.00 / per month• Everything in Free
• $0.10 per credit usage
• 10GB content storage
• 10K content items
• Unlimited data feeds
• MCP server access
• Email support
Starter
USD199.00 / per month• Everything in Hobby
• $0.09 per credit usage
• 100GB content storage
• 100K content items
• Private Slack support
Growth
USD999.00 / per month• Everything in Starter
• $0.08 per credit usage
• Unlimited content storage
• Unlimited content items
• Dedicated technical contact
• SLA (coming soon)
Free
Free Plan• 100 credits included
• 1GB content storage
• Up to 1K content items
• Up to 3 data feeds
• Ingest any content type
• Search by text or vector
• Chatbot with RAG
• Community Discord support
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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