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Open-source AI coding agent for VS Code, JetBrains, CLI and cloud, with 500+ models at zero inference markup and BYOK.
End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
Free trial available
No public pricing
No public pricing
Free trial available
- ✦500+ AI models at zero inference markup
- ✦Bring-your-own-keys and local model support
- ✦MIT-licensed, fully open source
- ✦Works in VS Code, JetBrains, CLI and cloud
- ✦Agent modes (Code, Architect)
- ✦Parallel isolated worktrees
- ✦Slack code reviewer and gateway
- ✦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
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
- →Writing and refactoring production code with AI
- →Planning features before implementation
- →Running agents across multiple IDEs and the CLI
- →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
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume