AI Frameworks
The scaffolding developers build WITH — agent frameworks like LangChain, CrewAI, and AutoGen, inference engines like vLLM and Ollama, orchestration frameworks, evaluation frameworks, and the SDKs that power production AI applications.
Framework for building LLM apps — chains, agents, RAG, memory. Python & JS/TS. 200+ integrations.
TypeScript toolkit for AI web apps — streaming, tool calling, generative UI. Works with 20+ LLM providers.
OpenAI's official agent framework — agents, handoffs, guardrails, sessions, built-in tracing.
Anthropic's official agent SDK — the Claude Code harness (tools, MCP, subagents, permissions) as a library.
Most-starred open-source browser-agent library — agents drive real browsers via Playwright + any LLM.
Open protocol for connecting AI to external tools and data — universal interface adopted by Claude, Cursor, and more.
Data framework for RAG and agents — 160+ data connectors, vector/keyword/graph indexing, query engines.
Stripe's official agent SDK + MCP — payments, invoices, billing, and usage metering as agent tools.
Open-source realtime voice-agent framework — composable STT/LLM/TTS pipelines, every provider, WebRTC.
LiveKit's realtime agent framework — voice/video agents as WebRTC participants, telephony included.
Microsoft's multi-agent conversation framework — agents collaborate, execute code, with human-in-the-loop.
Multi-agent orchestration framework — define AI agents with roles, organize into collaborative crews.
Microsoft's multi-agent framework — event-driven, typed messages, group chat, AutoGen Studio.
Multi-agent orchestration — role-playing agents with tasks, processes, tools, memory, and delegation.
Microsoft's SDK for integrating LLMs into apps — plugins, planners, and memory in C#/Python/Java.
Revolutionize AI application development, monitoring, and...
Transform enterprise data into powerful LLM applications...
Typescript bindings for langchain
Drag-and-drop LLM flow builder — visual node editor for chains, agents, and RAG with API generation.
Open-source LLM app platform — prompt IDE, RAG, agents, workflows, knowledge base management.
Durable execution for distributed workflows.
Python workflow orchestration — decorators for tasks/flows, retries, caching, scheduling.
ML toolkit for Kubernetes — pipelines, notebooks, training, serving, feature store.
Document preprocessing for RAG — parse PDFs, DOCX, images into clean structured elements.
Framework for training LLM agents on 16K+ real APIs.
Open-source framework for production autonomous agents.
Turn Python scripts into web apps — declarative API, data viz, chat components, free hosting.
AI browser automation — natural language commands for web actions, built on Playwright.
AI framework for Spring/Java — portable LLM API, RAG pipeline, vector stores, function calling.
Visual AI programming environment — node editor for designing and debugging agent workflows.
Type-safe agent framework by Pydantic — structured outputs, dependency injection, model-agnostic.
Agent framework with memory, knowledge, tools — function calling, RAG, multi-agent teams.
Open-source MLOps orchestration with serverless functions and feature store.
TypeScript AI framework — agents, workflows, RAG, and integrations for JS/TS developers.
Modern ChatGPT UI framework — 100+ providers, multimodal, plugins, RAG, Vercel deploy.
Lightning AI's LLM library — pretrain, fine-tune, deploy with clean PyTorch Lightning code.
Unified API for 100+ LLM providers — OpenAI format, load balancing, spend tracking, proxy server.
Visual multi-agent and RAG builder — drag-and-drop flows with Python and LangChain components.
Production NLP/LLM framework for search and RAG pipelines with component-based architecture.
No-code LLM app builder with visual chatflow templates.
Node-based Stable Diffusion UI — visual workflow editor, custom nodes, advanced pipelines.
Python framework for conversational AI UIs — streaming, multi-step visualization, LangChain integration.
Kubernetes-native workflow engine.
TypeScript framework for building production AI agents.
Framework for creating collaborative AI agent swarms.
High-throughput LLM serving engine — PagedAttention, continuous batching, OpenAI-compatible API.
Microsoft's type-safe LLM output validation.
Background jobs framework for TypeScript.
NVIDIA's LLM inference optimizer — quantization, kernel fusion, maximum GPU performance.
Lightweight ML inference for mobile and edge devices.
Microsoft's code-first agent for data analytics.
OpenAI's experimental multi-agent orchestration framework.
PyTorch toolkit for all speech processing tasks.
Industrial-strength NLP library for production use.
Fast LLM/VLM serving — RadixAttention, prefix caching, structured output, automatic parallelism.
Distributed AI framework — Ray Train, Serve, Data, Tune for scaling ML workloads.
PyTorch training framework — distributed training, mixed precision, reproducible research.
Structured text generation — guarantees LLM outputs match JSON schemas or grammars.
Comprehensive computer vision library with 2,500+ algorithms.
Cross-platform ONNX inference for mobile devices.
Cross-platform ML inference accelerator — runs ONNX models on any hardware with optimizations.
NVIDIA's framework for scalable generative AI training.
<p align="center"> <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" /> </p> <h1 align="center">LlamaIndex.TS</h1> <h3 align="center"> Data framework for your LLM application. </h3>
Apple's ML framework for Apple Silicon — NumPy-like API, unified memory, LLM support.
Netflix's ML pipeline framework — Python decorators, auto versioning, multi-cloud deployment.
Programming language for constrained LLM interaction.
Stateful AI agents with long-term memory — virtual context management, self-editing memory.
Python framework for multi-agent LLM applications.
Graph-based framework for stateful multi-agent LLM applications with cycles and persistence.
Multi-backend deep learning API for JAX, TF, and PyTorch.
High-level deep learning API — multi-backend (JAX, TensorFlow, PyTorch), simple model building.
Google's numerical computing library — autodiff, JIT, vectorization, NumPy API for ML research.
Get structured, validated outputs from LLMs using Pydantic models — patches any LLM client.
Event-driven durable workflow engine.
Distributed task queue for AI workloads.
Microsoft's language for efficient LLM control flow.
LLM output validation framework with auto-correction.
Data quality validation framework with declarative expectations.
Google's agent framework — tool use, multi-agent orchestration, Google service integrations.
Google's AI framework — flows, prompts, retrieval, and evaluation with Firebase integration.
High-level deep learning with built-in best practices.
TypeScript framework for autonomous AI agents — multi-platform, plugins, memory, social agents.
Stanford framework that replaces manual prompting with automatically optimized LLM programs.
Python data load tool with automatic schema inference.
Microsoft's distributed training library — ZeRO optimizer, trillion-parameter scale, RLHF.
LLM evaluation framework — 14+ metrics, faithfulness/hallucination detection, Pytest integration.
Data orchestration for ML — software-defined assets, type-checked IO, observability, modern Airflow alternative.
Framework for role-playing cooperative AI agents.
ML model serving framework — package models as Bentos, adaptive batching, GPU, distributed serving.
Industry-standard workflow orchestration.
Lightweight framework for multimodal AI agents.
Easy distributed training — abstracts PyTorch distributed, DeepSpeed, FSDP behind simple API.
Modular CLI for AI-augmented tasks.
OpenTelemetry-based LLM observability with automatic instrumentation.
NVIDIA's programmable guardrails toolkit for conversational AI.
Pythonic LLM toolkit — decorators and type hints for clean, provider-agnostic LLM calls.
Multi-agent software company simulator — PM, architect, engineer roles collaborate on projects.
Google's cross-platform on-device ML framework with pre-built solutions.
Python load testing framework for APIs and AI endpoints.
Open-source LLM input/output security scanner toolkit.
What are AI Frameworks?
AI frameworks and SDKs are the building blocks developers use to create AI applications. They abstract away the complexity of working with LLM APIs, embeddings, vector stores, and retrieval pipelines. The framework landscape includes orchestration layers (LangChain, LlamaIndex), provider SDKs (OpenAI SDK, Anthropic SDK, Vercel AI SDK), agent builders (LangGraph, CrewAI), and specialized toolkits for RAG, fine-tuning, and evaluation.
How to Choose
Match the framework to your application complexity. Simple LLM calls need just a provider SDK (OpenAI SDK, Anthropic SDK). RAG applications benefit from LlamaIndex's data connectors. Complex agent workflows need LangGraph's state machines. Multi-provider applications need Vercel AI SDK's unified interface. The wrong choice is picking a heavy framework for a simple use case — it adds latency, debugging complexity, and coupling.
Key Capabilities to Evaluate
Common Patterns
Sequential processing steps where each step's output feeds the next. The core pattern of LangChain and most orchestration frameworks.
Stateful graph where nodes are processing steps and edges define control flow. LangGraph's approach, better for complex branching logic.
Data flows through transformations in real-time. Vercel AI SDK's approach, optimized for web UI streaming.
Query → embed → retrieve → augment prompt → generate. The fundamental RAG pattern most frameworks implement.
What to Watch Out For
Top Capabilities
Browse all →Analyzes selected code or entire files and generates natural language explanations of what the code does, how it works, and why certain patterns were chosen. The feature can produce documentation in multiple formats (docstrings, comments, markdown) and supports various documentation styles (JSDoc, Sphinx, etc.). Developers can request explanations at different levels of detail (high-level overview, line-by-line breakdown, architectural context) through the chat interface, with responses appearing as formatted text or code comments.
Cody utilizes a context-aware engine that analyzes the current file and project structure to provide relevant code completions. It integrates with the Visual Studio Code API to access the Abstract Syntax Tree (AST) of the code, allowing it to suggest completions that are semantically relevant to the context, rather than relying solely on keyword matching. This approach ensures that the suggestions are not only syntactically correct but also contextually appropriate, enhancing developer productivity.
Converts natural language prompts into executable full-stack web applications by invoking an AI agent that generates React/Next.js frontend code, Node.js backend logic, and database schemas. The agent runs code in-browser via WebContainers to validate syntax and functionality before deployment, iterating on the generated code based on execution feedback. Token consumption scales with project complexity (larger codebases consume more tokens per iteration), and the agent supports design system imports from Figma and GitHub to accelerate UI generation.
Provides six model variants (tiny, base, small, medium, large, turbo) with parameter counts ranging from 39M to 1550M, enabling developers to choose optimal speed-accuracy tradeoffs. Tiny model runs at ~10x speed with 1GB VRAM; large model runs at 1x speed with 10GB VRAM. English-only variants (tiny.en, base.en, small.en) provide higher English accuracy by removing multilingual capacity. Turbo model (809M params) offers 8x speedup over large with minimal accuracy loss but lacks translation support.
Translates non-English speech directly to English text by using a task-specific token in the TextDecoder that signals translation mode, bypassing the need for intermediate transcription-then-translation pipelines. The AudioEncoder processes mel spectrograms identically to transcription, but the decoder generates English tokens directly from audio embeddings, reducing latency and error propagation compared to cascaded systems.
Transcribes audio in 98 languages to text in the original language using a unified Transformer sequence-to-sequence architecture with a shared AudioEncoder that processes mel spectrograms into language-agnostic embeddings, then a TextDecoder that generates tokens autoregressively. The system handles variable-length audio by padding or trimming to 30-second segments and uses task-specific tokens to signal transcription mode, enabling a single model to handle multiple languages without language-specific branches.
Detects the spoken language in audio by processing mel spectrograms through the AudioEncoder and using a language classification head that outputs probability distributions over 98 supported languages. The model leverages 680K hours of multilingual training data to recognize language characteristics from acoustic features alone, without requiring transcription. Language detection occurs as a preliminary step in the transcription pipeline and can be called independently via the language detection task token.
W&B Personal tier (free) and Enterprise tier support self-hosted deployment via Docker, enabling on-premise installation for teams with data residency or security requirements. Self-hosted instances run independently from W&B cloud, with optional integration to W&B cloud for cross-instance features. Supports custom domain configuration, HTTPS, and integration with corporate identity providers (LDAP, SAML, OAuth).
Browse Other Types
Autonomous AI systems that act on your behalf
ModelsFoundation models, fine-tunes, and specialized AI models
MCP ServersModel Context Protocol tools and integrations
RepositoriesOpen-source AI projects on GitHub
APIsProgrammatic endpoints for AI capabilities
ExtensionsBrowser and IDE extensions powered by AI
View all 19 types →Frequently Asked Questions
Do I need an AI framework to build an LLM application?
Not always. For simple use cases (chat, single API calls, basic RAG), direct API calls with the provider SDK are simpler, faster, and easier to debug. Frameworks add value when you need multi-provider support, complex retrieval pipelines, agent loops, or production features like tracing and evaluation.
LangChain vs LlamaIndex — which should I use?
LangChain excels at orchestration and agent workflows with its chain/graph abstractions. LlamaIndex excels at data ingestion and retrieval with its extensive data connectors and indexing strategies. For pure RAG, LlamaIndex. For agent systems, LangChain/LangGraph. Many production apps use both.
What is the Vercel AI SDK and when should I use it?
The Vercel AI SDK is a TypeScript-first framework for building AI-powered web applications. It provides streaming primitives, a unified provider interface, and React hooks for AI UIs. Use it when building Next.js/React applications that need real-time streaming responses and a clean frontend integration.