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Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.
End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.
Open-source node-based engine for visual AI, giving pros granular control to build image, video, and 3D generation workflows.
Serverless platform for running and fine-tuning image, video, audio and 3D generative models via one fast API.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
No public pricing
- ✦Persistent, structured memory built as a knowledge graph
- ✦Sub-300ms hybrid retrieval (RAG) with reranking
- ✦Native filesystem mount for agent memory access
- ✦Connectors to Slack, Notion, Drive, Gmail, GitHub, S3
- ✦Automatic extraction from PDFs, images, and audio
- ✦User profile and behavior tracking across sessions
- ✦Smart data annotation with SAM-powered one-click masks across six task types
- ✦Cloud training with 22+ GPU configurations from RTX 2000 Ada to B200
- ✦Support for YOLOv5 through YOLO26 model families
- ✦One-click deployment across 43 global regions with auto-scaling
- ✦Export to 18 formats including ONNX, TensorRT, and CoreML
- ✦Live training metrics and experiment comparison dashboard
- ✦Node-based workflow canvas
- ✦Simplified App Mode view
- ✦Community workflow templates and hub
- ✦Comfy Desktop (local) and Comfy Cloud
- ✦Comfy API for production endpoints
- ✦60,000+ nodes and many models
- ✦1,000+ generative model APIs
- ✦Serverless GPU inference engine
- ✦On-demand and dedicated GPU clusters
- ✦Model fine-tuning and custom deployments
- ✦Bring-your-own-weights and private endpoints
- ✦SOC 2 compliance and enterprise features
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- →Developers adding long-term memory to AI agents
- →Teams building agents that need to sync with existing tools
- →Individuals wanting one memory layer shared across multiple AI assistants
- →Building and training custom object detection or segmentation models
- →Labeling large image/video datasets for computer vision projects
- →Deploying vision models to edge or mobile devices
- →Running quality control or defect detection in manufacturing
- →Powering retail, logistics, or agriculture vision applications
- →Building custom image/video/3D pipelines
- →VFX, advertising, gaming, and ecommerce content
- →Running workflows on cloud GPUs
- →Deploying workflows as production APIs
- →Adding image/video generation to an app
- →Running fast diffusion-model inference at scale
- →Training or fine-tuning custom generative models
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals