LLM Technologies
hub Frameworks & SDKs
Comprehensive framework for developing applications powered by language models. Provides modular components for prompt management, chains, agents, memory, and integrations with 100+ LLM providers and data sources.
Extension of LangChain for building stateful, multi-actor applications with LLMs. Enables cyclic graphs, persistence, and human-in-the-loop workflows for complex agentic systems.
Platform for debugging, testing, evaluating, and monitoring LLM applications. Provides tracing, dataset management, and evaluation tools for LangChain applications.
OpenAI's Python SDK for building AI agents with function calling, tool use, and structured outputs. Provides high-level abstractions for creating conversational agents with memory, planning, and execution capabilities.
Google's framework for building AI agents that can interact with tools, services, and APIs. Provides structured approaches to agent reasoning, planning, and execution.
Framework for building multi-agent systems with specialized agents that can collaborate, delegate tasks, and coordinate to solve complex problems.
Evaluation framework for AI agents covering output validation, trajectory analysis, tool usage assessment, and LLM-as-Judge scoring. Includes dynamic simulators for multi-turn testing and automated test suite generation.
Open-source framework for building AI-powered agentic applications with generative UI. Supports AG-UI protocol for agent-to-user communication, MCP for tool integration, and multiple agent frameworks including LangGraph, Strands, and ADK.
storage Local Model Tools
Run large language models locally with a simple command-line interface. Supports Llama, Mistral, Gemma, Qwen, DeepSeek, and many other models with automatic GPU acceleration and model management.
Desktop application for discovering, downloading, and running local LLMs. Features a user-friendly interface, OpenAI-compatible API server, and support for quantized models.
travel_explore Web Agents
Framework for building AI agents that can interact with, scrape, and automate any website. Enables agents to navigate web interfaces, extract data, fill forms, and perform complex web-based tasks.
Web access layer for AI agents providing tools for web search, content extraction, and real-time information retrieval. Optimized for agent workflows with structured outputs and relevance scoring.
Turn websites into clean, LLM-ready data. Crawls and converts any website into markdown or structured data optimized for RAG and fine-tuning pipelines.
Headless browser infrastructure for AI agents. Provides reliable, scalable browser sessions with anti-detection, proxies, and stealth capabilities for web automation.
Neural search engine for AI applications. Returns semantically relevant results with clean extracted content, optimized for agent research and knowledge retrieval.
Extract structured data from any webpage using natural language queries. Converts unstructured HTML into typed data without writing selectors or scrapers.
AI-powered web scraping library that uses LLMs to extract data from websites. Handles dynamic content and complex page structures automatically.
monitoring Observability & Tracing
Open-source LLM observability platform with tracing, prompt management, and cost tracking. Integrates with LangChain, OpenAI SDK, and custom implementations.
ML observability platform with LLM tracing, evaluation, and debugging. Visualizes traces, analyzes embeddings, and identifies performance issues.
LLM evaluation and tracing platform. Provides experiment tracking, prompt versioning, and production monitoring for LLM applications.
LLM proxy with analytics, caching, and rate limiting. Drop-in replacement for OpenAI SDK with cost tracking and usage insights.
Agent observability platform with time-travel debugging, visual agent tracking, and cost monitoring. Supports 400+ LLMs and frameworks including CrewAI, AutoGen, and OpenAI.
security Security & Guardrails
The Open Web Application Security Project's comprehensive list of the top 10 most critical security risks for Large Language Model applications, including prompt injection, insecure output handling, training data poisoning, and model denial of service.
AWS service that provides content filtering, denied topics, word filters, and sensitive information redaction for LLM applications. Integrates with Amazon Bedrock models and supports custom guardrail policies.
Open-source Python framework for adding structure, type safety, and quality guarantees to LLM outputs. Provides validators for data quality, safety, and compliance with customizable rules.
Open-source framework for implementing safety guardrails and content moderation in LLM applications. Provides pre-built validators for toxicity, PII detection, prompt injection, and custom policy enforcement.
data_object Data Formats & Parsing
Token-Oriented Object Notation - A compact, human-readable encoding of the JSON data model for LLM prompts. Optimizes for token efficiency while maintaining readability.
Parse JSON incrementally as it streams in, e.g. from a network request or a language model. Gives you a sequence of increasingly complete values, enabling real-time processing of streaming LLM outputs.
XML's explicit start and end tags make it exceptionally well-suited for LLM structured outputs. Unlike JSON's bracket-matching complexity, XML's self-documenting nature means partial outputs remain parseable, tag names provide semantic context that aids generation, and the format naturally aligns with how LLMs process sequential tokens. Anthropic's Claude notably uses XML tags for prompt structuring.
Tool UI is a React component framework for conversation‑native UIs inside AI chats. Tools return JSON; Tool UI renders it as inline, narrated, referenceable surfaces.