Concept on AI Knowledge Base

Symphony Overview

Mon, 01 Jan 0001 00:00:00 +0000

Symphony turns project work into isolated, autonomous implementation runs. It polls issue trackers, spawns coding agents per issue in isolated workspaces, and manages the full lifecycle from ticket to merged PR.

Orchestration State Machine

Mon, 01 Jan 0001 00:00:00 +0000

Symphony’s state machine for issue lifecycle: polling, dispatch, active run tracking, retry queuing with exponential backoff, and reconciliation of running issues against tracker state changes.

Agent SDK — Overview

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Anthropic’s Agent SDK for building custom AI agents with tool use, MCP, and sub-agents.

Build with Claude — Overview

Mon, 01 Jan 0001 00:00:00 +0000

Central hub for understanding how to build applications with the Claude API.

Introduction to the Anthropic Platform

Mon, 01 Jan 0001 00:00:00 +0000

Overview of the Anthropic API platform, Claude models, and what you can build.

Tool Use — Overview

Mon, 01 Jan 0001 00:00:00 +0000

How Claude calls external tools and functions — the foundation for building agentic systems.

📚 Core Concepts

Mon, 01 Jan 0001 00:00:00 +0000

Adapters

Mon, 01 Jan 0001 00:00:00 +0000

AG-UI Overview

Mon, 01 Jan 0001 00:00:00 +0000

Agent Capabilities

Mon, 01 Jan 0001 00:00:00 +0000

Understand the five ways to extend CrewAI agents: Tools, MCPs, Apps, Skills, and Knowledge.

Agent Framework Overview

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Microsoft Agent Framework: combining AutoGen’s agent abstractions with Semantic Kernel’s enterprise features for production-grade multi-agent orchestration.

Agentic or Tool use

Mon, 01 Jan 0001 00:00:00 +0000

Agents

Mon, 01 Jan 0001 00:00:00 +0000

Learn about agents in the Agent User Interaction Protocol

Agents

Mon, 01 Jan 0001 00:00:00 +0000

Detailed guide on creating and managing agents within the CrewAI framework.

Agents Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Mistral’s agent system — autonomous task execution with tools, state persistence, connectors (code interpreter, web search), and multi-agent collaboration.

Agents Overview

Mon, 01 Jan 0001 00:00:00 +0000

Core agent abstraction in the Microsoft Agent Framework: agent types, providers, skills, and conversation management.

Alias

Mon, 01 Jan 0001 00:00:00 +0000

An Overview of Cohere's Rerank Model

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how Cohere’s Rerank models work.

An Overview of The Cohere Platform

Mon, 01 Jan 0001 00:00:00 +0000

Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications.

An Overview of the Developer Playground

Mon, 01 Jan 0001 00:00:00 +0000

The Cohere Playground is a powerful visual interface for testing Cohere’s generation and embedding language models without coding.

An Overview of Tool Use with Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Learn when to use leverage multi-step tool use in your workflows.

Answer correctness

Mon, 01 Jan 0001 00:00:00 +0000

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

Chroma is designed with a modular architecture that prioritizes performance and ease of use. It scales seamlessly from local development to large-scale production, while exposing a consistent API across all deployment modes.

Architecture overview

Mon, 01 Jan 0001 00:00:00 +0000

The most important mental model here is the three-layer architecture: Host (the application), Client (the protocol handler), and Server (the capability provider). A common mistake is conflating the host and the client – the host is the user-facing application (like Claude Desktop), while the client is an internal protocol component that maintains a 1:1 connection with a single server. Understanding this separation is essential before reading the specification or building anything.

Authorization Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Supplementary authorization mechanisms for the Model Context Protocol

AutoGen Overview

Mon, 01 Jan 0001 00:00:00 +0000

AutoGen is a programming framework for building multi-agent AI systems through conversational agents that collaborate, use tools, and execute code. Now in maintenance mode — see Microsoft Agent Framework.

Backups overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about backups of serverless indexes in Pinecone.

Basic usage of tool use (function calling)

Mon, 01 Jan 0001 00:00:00 +0000

Overview of Cohere’s tool use system for building agentic workflows — with native citation support that links tool results back to generated text.

Basics

Mon, 01 Jan 0001 00:00:00 +0000

Understand the reinforcement learning fundamentals behind RFT

Batch Processing

Mon, 01 Jan 0001 00:00:00 +0000

Build a Phone Voice Agent with Together AI

Mon, 01 Jan 0001 00:00:00 +0000

Build a real-time phone voice agent from scratch with Twilio Media Streams, Together AI realtime STT, chat completions, realtime TTS, and local voice activity detection.

Caching

Mon, 01 Jan 0001 00:00:00 +0000

Capabilities

Mon, 01 Jan 0001 00:00:00 +0000

Dynamic capability discovery for agents in the AG-UI protocol

Case Lifecycle Hooks

Mon, 01 Jan 0001 00:00:00 +0000

Chat

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to query our open-source chat models.

Citations

Mon, 01 Jan 0001 00:00:00 +0000

Claude Code overview

Mon, 01 Jan 0001 00:00:00 +0000

Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. Available in your terminal, IDE, desktop app, and browser.

CLI

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use the CrewAI CLI to interact with CrewAI.

Cloud

Mon, 01 Jan 0001 00:00:00 +0000

Access Mistral AI models via Azure, AWS, Google Cloud, Snowflake, IBM, and Outscale using cloud credits

Cohere's Command A Vision Model

Mon, 01 Jan 0001 00:00:00 +0000

Command A Vision is a powerful visual language model capable of interacting with image inputs. This document contains information about its capabilities.

Collaboration

Mon, 01 Jan 0001 00:00:00 +0000

How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.

Component architecture

Mon, 01 Jan 0001 00:00:00 +0000

Component architecture

Mon, 01 Jan 0001 00:00:00 +0000

Components Guide

Mon, 01 Jan 0001 00:00:00 +0000

Concurrency & Performance

Mon, 01 Jan 0001 00:00:00 +0000

Connectors Overview

Mon, 01 Jan 0001 00:00:00 +0000

Connectors enable Agents and users to access tools like websearch, code interpreter, image generation, and document library on demand

Context Entities Recall

Mon, 01 Jan 0001 00:00:00 +0000

Context overview

Mon, 01 Jan 0001 00:00:00 +0000

Context overview

Mon, 01 Jan 0001 00:00:00 +0000

Context Precision

Mon, 01 Jan 0001 00:00:00 +0000

Context Recall

Mon, 01 Jan 0001 00:00:00 +0000

Context snippets overview

Mon, 01 Jan 0001 00:00:00 +0000

Retrieve context snippets from your assistant’s knowledge base.

Contribute

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to contribute to Mistral AI through docs, code, community, and the Ambassador Program

Contributing

Mon, 01 Jan 0001 00:00:00 +0000

Contributing

Mon, 01 Jan 0001 00:00:00 +0000

Contributing

Mon, 01 Jan 0001 00:00:00 +0000

Core architecture

Mon, 01 Jan 0001 00:00:00 +0000

Understand how AG-UI connects front-end applications to AI agents

Core capabilities overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview of Agent Server core capabilities including streaming, human-in-the-loop, MCP, A2A, distributed tracing, webhooks, and double-texting.

Core Concepts

Mon, 01 Jan 0001 00:00:00 +0000

CrewAI AMP

Mon, 01 Jan 0001 00:00:00 +0000

Deploy, monitor, and scale your AI agent workflows

Crews

Mon, 01 Jan 0001 00:00:00 +0000

Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.

Data controls in the OpenAI platform

Mon, 01 Jan 0001 00:00:00 +0000

Your data is your data. An overview of how OpenAI uses your data, including retention and usage policies.

Data ingestion overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the different ways to ingest data into Pinecone.

Data Preparation

Mon, 01 Jan 0001 00:00:00 +0000

Together Fine-tuning API accepts two data formats for training dataset files: text data and tokenized data (in the form of Parquet files). Below, you can learn about different types of those formats and the scenarios in which they can be most useful.

Data retrieval with GPT Actions

Mon, 01 Jan 0001 00:00:00 +0000

Learn about performing data retrieval using APIs, relational databases, and vector databases with GPT Actions.

Dataset Management

Mon, 01 Jan 0001 00:00:00 +0000

Dataset Serialization

Mon, 01 Jan 0001 00:00:00 +0000

Datasets

Mon, 01 Jan 0001 00:00:00 +0000

Deep Agents CLI

Mon, 01 Jan 0001 00:00:00 +0000

Terminal coding agent built on the Deep Agents SDK

Deep Agents CLI

Mon, 01 Jan 0001 00:00:00 +0000

Terminal coding agent built on the Deep Agents SDK

Deep Agents overview

Mon, 01 Jan 0001 00:00:00 +0000

Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deep Agents overview

Mon, 01 Jan 0001 00:00:00 +0000

Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deployment Options - Overview

Mon, 01 Jan 0001 00:00:00 +0000

This page provides an overview of the available options for deploying Cohere’s models.

Deployment Options Overview

Mon, 01 Jan 0001 00:00:00 +0000

Compare Together AI’s deployment options: fully-managed cloud service vs. secure VPC deployment for enterprises.

Deprecations

Mon, 01 Jan 0001 00:00:00 +0000

Learn about Cohere’s deprecation policies and recommended replacements

Dictionary Operations

Mon, 01 Jan 0001 00:00:00 +0000

Distillation

Mon, 01 Jan 0001 00:00:00 +0000

Distributed/Cloud Performance

Mon, 01 Jan 0001 00:00:00 +0000

How to think about performance in distributed Chroma deployments.

Document AI

Mon, 01 Jan 0001 00:00:00 +0000

Mistral Document AI offers enterprise-grade OCR, structured data extraction, and multilingual support for fast, accurate document processing

Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to get an embedding vector for a given text input.

Embeddings Overview

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s Embeddings API for text and code vector representations — supporting retrieval, clustering, and classification with open-weight models.

Enterprise deployment overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn how Claude Code can integrate with various third-party services and infrastructure to meet enterprise deployment requirements.

Enums

Mon, 01 Jan 0001 00:00:00 +0000

Error codes

Mon, 01 Jan 0001 00:00:00 +0000

An overview of error codes from the OpenAI API and Python library, including solutions and guidance.

Error Codes

Mon, 01 Jan 0001 00:00:00 +0000

An overview on error status codes, causes, and quick fix solutions

Error Handling

Mon, 01 Jan 0001 00:00:00 +0000

Evaluating Use Cases for CrewAI

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to assess your AI application needs and choose the right approach between Crews and Flows based on complexity and precision requirements.

Evaluation concepts

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation Dataset

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn about evaluating the correctness and completeness of assistant responses.

Evaluation Overview

Mon, 01 Jan 0001 00:00:00 +0000

This is the foundational concept page for DSPy’s evaluate-then-optimize workflow, and it is essential reading before you touch any teleprompter. The key insight is that DSPy treats evaluation as a first-class input to compilation rather than an afterthought — your dev set and metric become the signal the optimizer uses to rewrite prompts. Start here, then move to the metrics page to define what good actually means for your task. Watch out for evaluating on the same examples you optimize against, which inflates scores and hides overfitting.

Evaluation Sample

Mon, 01 Jan 0001 00:00:00 +0000

Evaluations overview

Mon, 01 Jan 0001 00:00:00 +0000

Evaluation-driven LLM application development to systematically improve applications

Event Listeners

Mon, 01 Jan 0001 00:00:00 +0000

Tap into CrewAI events to build custom integrations and monitoring

Events

Mon, 01 Jan 0001 00:00:00 +0000

Understanding events in the Agent User Interaction Protocol

Execution Hooks Overview

Mon, 01 Jan 0001 00:00:00 +0000

Understanding and using execution hooks in CrewAI for fine-grained control over agent operations

Experimental

Mon, 01 Jan 0001 00:00:00 +0000

Experimentation

Mon, 01 Jan 0001 00:00:00 +0000

Extend Claude Code

Mon, 01 Jan 0001 00:00:00 +0000

Understand when to use CLAUDE.md, Skills, subagents, hooks, MCP, and plugins.

Extensions Overview

Mon, 01 Jan 0001 00:00:00 +0000

Optional extensions to the Model Context Protocol

Factual Correctness

Mon, 01 Jan 0001 00:00:00 +0000

Faithfulness

Mon, 01 Jan 0001 00:00:00 +0000

FAQs

Mon, 01 Jan 0001 00:00:00 +0000

Frequently asked questions about CrewAI AMP

FAQs

Mon, 01 Jan 0001 00:00:00 +0000

Answers to common questions about Weave tracing

FastAPI

Mon, 01 Jan 0001 00:00:00 +0000

Fields

Mon, 01 Jan 0001 00:00:00 +0000

Files

Mon, 01 Jan 0001 00:00:00 +0000

Pass images, PDFs, audio, video, and text files to your agents for multimodal processing.

Files in Pinecone Assistant

Mon, 01 Jan 0001 00:00:00 +0000

Understand supported file types and metadata in Pinecone Assistant.

Fireworks Agent Overview

Mon, 01 Jan 0001 00:00:00 +0000

Describe what you want, approve the plan and cost, get a deployed fine-tuned model.

Flows

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to create and manage AI workflows using CrewAI Flows.

Frameworks, runtimes, and harnesses

Mon, 01 Jan 0001 00:00:00 +0000

Understand the differences between LangChain, LangGraph, and Deep Agents and when to use each one

Frameworks, runtimes, and harnesses

Mon, 01 Jan 0001 00:00:00 +0000

Understand the differences between LangChain, LangGraph, and Deep Agents and when to use each one

Frequently Asked Questions About Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more.

from_provider

Mon, 01 Jan 0001 00:00:00 +0000

Functional API overview

Mon, 01 Jan 0001 00:00:00 +0000

Functional API overview

Mon, 01 Jan 0001 00:00:00 +0000

General Purpose Metrics

Mon, 01 Jan 0001 00:00:00 +0000

Get Started

Mon, 01 Jan 0001 00:00:00 +0000

DSPy overview and quick start guide

GPT Action authentication

Mon, 01 Jan 0001 00:00:00 +0000

Learn about authentication options for GPT actions, including no authentication, API key, and OAuth methods.

GPT Actions

Mon, 01 Jan 0001 00:00:00 +0000

Learn about GPT Actions for customizing ChatGPT and interacting with external applications via APIs.

GPU Clusters Overview

Mon, 01 Jan 0001 00:00:00 +0000

High-performance GPU clusters for training, fine-tuning, and large-scale AI workloads

Graders

Mon, 01 Jan 0001 00:00:00 +0000

Learn about graders used for evals and fine-tuning.

Graph API overview

Mon, 01 Jan 0001 00:00:00 +0000

Graph API overview

Mon, 01 Jan 0001 00:00:00 +0000

GroupChat Pattern

Mon, 01 Jan 0001 00:00:00 +0000

AutoGen’s GroupChat coordinates multiple agents through managed conversation turns with configurable speaker selection strategies (round-robin, LLM-driven, or custom).

Hooks

Mon, 01 Jan 0001 00:00:00 +0000

How Claude Code works

Mon, 01 Jan 0001 00:00:00 +0000

Understand the agentic loop, built-in tools, and how Claude Code interacts with your project.

How Does Cohere's Pricing Work?

Mon, 01 Jan 0001 00:00:00 +0000

This page details Cohere’s pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads.

How to build a Lovable clone with Kimi K2

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build a full-stack Next.js app that can generate React apps with a single prompt.

How to build an AI audio transcription app with Whisper

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build a real-time AI audio transcription app with Whisper, Next.js, and Together AI.

How to Build Coding Agents

Mon, 01 Jan 0001 00:00:00 +0000

How to build your own simple code editing agent from scratch in 400 lines of code!

How To Implement Contextual RAG From Anthropic

Mon, 01 Jan 0001 00:00:00 +0000

An open source line-by-line implementation and explanation of Contextual RAG from Anthropic!

How To Improve Search With Rerankers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how you can improve semantic search quality with reranker models!

How to use Cline with DeepSeek V3 to build faster

Mon, 01 Jan 0001 00:00:00 +0000

Use Cline (an AI coding agent) with DeepSeek V3 (a powerful open source model) to code faster.

How to use OpenCode with Together AI to build faster

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to combine OpenCode, a powerful terminal-based AI coding agent, with Together AI models like DeepSeek V3 to supercharge your development workflow.

How to use Qwen Code with Together AI for enhanced development workflow

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure Qwen Code, a powerful AI-powered command-line workflow tool, with Together AI models to supercharge your coding workflow with advanced code understanding and automation.

How to work with Support

Mon, 01 Jan 0001 00:00:00 +0000

How Tool Use Works

Mon, 01 Jan 0001 00:00:00 +0000

IA3

Mon, 01 Jan 0001 00:00:00 +0000

Image Generation

Mon, 01 Jan 0001 00:00:00 +0000

Generate high-quality images from text + image prompts.

Indexing overview

Mon, 01 Jan 0001 00:00:00 +0000

Understand key concepts related to indexing data in Pinecone.

Integrations

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Chroma is an open-source search engine for AI. It comes with everything you need to get started built-in.

Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Build AI agent teams that work together to tackle complex tasks

Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Fireworks Training API — custom training loops with full Python control over objectives, while Fireworks handles distributed GPU infrastructure.

Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Deploy custom containers on Together’s managed GPU infrastructure with automatic scaling, job queues, and built-in observability.

Introduction Comparisons

Mon, 01 Jan 0001 00:00:00 +0000

Introduction Design Philosophy

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to A2A

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Aya Vision

Mon, 01 Jan 0001 00:00:00 +0000

In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses.

Introduction to Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

An introduction to Cohere on Azure AI Foundry, a fully managed service by Azure (API v2).

Introduction to Conversational Context: Session, State, and Memory

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Embeddings at Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression.

Introduction to Text Generation at Cohere

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how a large language model generates textual output.

Key concepts

Mon, 01 Jan 0001 00:00:00 +0000

Key concepts to understand when working with the OpenAI API.

Keyboard shortcuts

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the keyboard shortcuts available in W&B.

Knowledge

Mon, 01 Jan 0001 00:00:00 +0000

What is knowledge in CrewAI and how to use it.

La Plateforme

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI’s La Plateforme offers pay-as-you-go API access to its latest models with flexible deployment options

LangChain overview

Mon, 01 Jan 0001 00:00:00 +0000

LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangChain overview

Mon, 01 Jan 0001 00:00:00 +0000

LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangGraph overview

Mon, 01 Jan 0001 00:00:00 +0000

Gain control with LangGraph to design agents that reliably handle complex tasks

LangGraph overview

Mon, 01 Jan 0001 00:00:00 +0000

Gain control with LangGraph to design agents that reliably handle complex tasks

LangSmith shared responsibility model

Mon, 01 Jan 0001 00:00:00 +0000

Overview of how LangChain and customers share security responsibilities for the LangSmith platform.

Learn more about sweeps

Mon, 01 Jan 0001 00:00:00 +0000

Collection of useful sources for Sweeps.

List of available metrics

Mon, 01 Jan 0001 00:00:00 +0000

Lists and Arrays

Mon, 01 Jan 0001 00:00:00 +0000

LLMs

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects

Logfire Integration

Mon, 01 Jan 0001 00:00:00 +0000

Logging

Mon, 01 Jan 0001 00:00:00 +0000

Manage secrets

Mon, 01 Jan 0001 00:00:00 +0000

Overview of W&B secrets, how they work, and how to get started using them.

Managed Fine-Tuning Overview

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune models with Fireworks-managed infrastructure — no custom code required.

MCP Apps

Mon, 01 Jan 0001 00:00:00 +0000

Interactive UI applications that render inside MCP hosts like Claude Desktop

MCP Security Considerations

Mon, 01 Jan 0001 00:00:00 +0000

Learn about important security best practices when integrating MCP servers with your CrewAI agents.

MCP Servers as Tools in CrewAI

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to integrate MCP servers as tools in your CrewAI agents using the crewai-tools library.

Memory

Mon, 01 Jan 0001 00:00:00 +0000

Leveraging the unified memory system in CrewAI to enhance agent capabilities.

Memory overview

Mon, 01 Jan 0001 00:00:00 +0000

Memory overview

Mon, 01 Jan 0001 00:00:00 +0000

Messages

Mon, 01 Jan 0001 00:00:00 +0000

Understanding message structure and communication in AG-UI

Metrics

Mon, 01 Jan 0001 00:00:00 +0000

Metrics & Attributes

Mon, 01 Jan 0001 00:00:00 +0000

Metrics Introduction

Mon, 01 Jan 0001 00:00:00 +0000

This page introduces DeepEval’s fifty-plus metrics, each scored from 0 to 1 with reasoning, and it matters because choosing the right metrics is the whole game in LLM evaluation. The key discipline the docs push is restraint: use no more than about five metrics, roughly two or three generic plus one or two custom to your use case, so you prioritize what truly matters instead of drowning in numbers. Because the metrics are LLM-as-a-judge, expect real cost and some run-to-run variance. This parallels RAGAS’s metric suite; read getting-started first if you have not run an evaluation yet.

Middleware

Mon, 01 Jan 0001 00:00:00 +0000

Transform and intercept events in AG-UI agents

Migration

Mon, 01 Jan 0001 00:00:00 +0000

Migration guides for Chroma version upgrades and schema changes.

Migration Guide

Mon, 01 Jan 0001 00:00:00 +0000

Missing

Mon, 01 Jan 0001 00:00:00 +0000

Mode Migration

Mon, 01 Jan 0001 00:00:00 +0000

Model Gallery

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

Model Lifecycle

Mon, 01 Jan 0001 00:00:00 +0000

Learn about W&B Inference model lifecycle and retirement

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models and providers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to choose models, set defaults, and think about providers and transport in the OpenAI Agents SDK.

Multi modal faithfulness

Mon, 01 Jan 0001 00:00:00 +0000

Multi modal relevance

Mon, 01 Jan 0001 00:00:00 +0000

Multi-agent systems

Mon, 01 Jan 0001 00:00:00 +0000

Build multi-agent systems in ADK by composing LLM agents and workflow agents into hierarchical teams with shared state and tool delegation.

Multi-Run Evaluation

Mon, 01 Jan 0001 00:00:00 +0000

Multimodal

Mon, 01 Jan 0001 00:00:00 +0000

Noise Sensitivity

Mon, 01 Jan 0001 00:00:00 +0000

Nvidia Metrics

Mon, 01 Jan 0001 00:00:00 +0000

Observability concepts

Mon, 01 Jan 0001 00:00:00 +0000

Open Source

Mon, 01 Jan 0001 00:00:00 +0000

Chroma is the open-source AI application database. Contribute to the project or learn about telemetry and privacy.

Optimization Overview

Mon, 01 Jan 0001 00:00:00 +0000

Organizations overview

Mon, 01 Jan 0001 00:00:00 +0000

Understand organization structure, projects, and billing.

Orthogonal Finetuning (OFT and BOFT)

Mon, 01 Jan 0001 00:00:00 +0000

Overview

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Draft changes being considered for the AG-UI protocol

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Client package overview

Overview

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Core concepts in the Agent User Interaction Protocol SDK

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Core concepts in the Agent User Interaction Protocol SDK

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Documentation for encoding Agent User Interaction Protocol events

Overview

Mon, 01 Jan 0001 00:00:00 +0000

This is the essential reference for understanding the Claude model family and should be one of the first pages you read before building anything with the Anthropic API. Focus on the capability differences between model tiers (Haiku, Sonnet, Opus) as this directly impacts your cost, latency, and quality tradeoffs in production. Pay attention to context window sizes and maximum output token limits, since these constraints will shape your prompt design and chunking strategies. When comparing with OpenAI’s model lineup, note that Anthropic’s naming convention signals capability tier rather than generation number.

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Anthropic’s taxonomy of proven AI use cases with implementation guidance for each.

Overview

Mon, 01 Jan 0001 00:00:00 +0000

This overview introduces the agent skills framework, which provides a structured way to package and distribute reusable capabilities that Claude-based agents can invoke. Skills sit between raw tool definitions and full agent architectures, giving you a composable middle layer for common tasks like code review, PR creation, or domain-specific analysis. Start here before reading the individual tool docs to understand how skills orchestrate multiple tools into cohesive workflows. The key architectural decision is which capabilities to implement as standalone tools versus packaged skills, and this doc provides the mental model for making that choice.

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

This overview establishes the conceptual framework for prompt engineering with Claude and should be read before diving into specific techniques or best practices. Focus on how Claude’s instruction-following behavior differs from other models – Claude tends to be more literal in its interpretation of prompts, which means precise wording matters more than with some competitors. The page introduces key concepts like system prompts, role assignment, and output formatting that are referenced throughout all other prompt engineering materials. After reading this, proceed to the best practices guide for actionable techniques you can apply immediately.

Overview

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Overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Leverage AI services, generate images, process vision, and build intelligent systems

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Automate workflows and integrate with external platforms and services

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Interact with cloud services, storage systems, and cloud-based AI platforms

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Connect to databases, vector stores, and data warehouses for comprehensive data access

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Read, write, and search through various file formats with CrewAI’s document processing tools

Overview

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Connect CrewAI agents with external automations and managed AI services

Overview

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Perform web searches, find repositories, and research information across the internet

Overview

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Extract data from websites and automate browser interactions with powerful scraping tools

Overview

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Train models using reinforcement learning in minutes

Overview

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Overview

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Overview

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Overview

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Overview

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Build UIs that display real-time subagent streams, task progress, and sandbox for Deep Agents

Overview

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Stream real-time updates from deep agent runs and subagent execution

Overview

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Build generative UIs with real-time streaming from LangChain agents

Overview

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Control and customize agent execution at every step

Overview

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Stream real-time updates from agent runs

Overview

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Render LangGraph agents to the frontend

Overview

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Build UIs that display real-time subagent streams, task progress, and sandbox for Deep Agents

Overview

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Stream real-time updates from deep agent runs and subagent execution

Overview

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Build generative UIs with real-time streaming from LangChain agents

Overview

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Control and customize agent execution at every step

Overview

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Stream real-time updates from agent runs

Overview

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Render LangGraph agents to the frontend

Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Overview

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Welcome to Together AI’s docs! Together makes it easy to run, finetune, and train open source AI models with transparency and privacy.

Overview

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Learn how to create configuration files for sweeps.

Overview of OpenAI Crawlers

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Parallel Tools

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Patching

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Philosophy

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Pinecone Assistant

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Pinecone Assistant is a service that allow you to build production-grade chat and agent-based applications quickly.

Planning

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Learn how to add planning to your CrewAI Crew and improve their performance.

Platform Overview

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Architecture, deployment lifecycle, and core concepts for Dedicated Container Inference.

Private Deployment Overview

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This page provides an overview of private deployments of Cohere’s models.

Processes

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Detailed guide on workflow management through processes in CrewAI, with updated implementation details.

Production Architecture

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Best practices for building production-ready AI applications with CrewAI

Programming Overview

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DSPy takes a fundamentally different approach to working with LLMs: instead of manually writing and tweaking prompts, you define typed signatures (input/output schemas) and let DSPy’s optimizers compile them into effective prompts automatically. This is a paradigm shift from traditional prompt engineering, and it means the skills you build here are more about program design than wordsmithing. Focus on understanding Modules and Signatures first, as they are the core abstractions everything else builds on. A common gotcha is expecting DSPy to work well without a good set of training examples — the optimizers need representative input-output pairs to produce high-quality compiled prompts, so invest in your dataset before tuning the optimizer.

Projects overview

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Learn about projects, roles, and collaboration.

Prompt

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Prompt Caching

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Prompt engineering concepts

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Prompt Optimization Introduction

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Prompting

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Python v2 SDK Migration Guide

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Migrate from Together Python v1 to v2 - the new Together AI Python SDK with improved type safety and modern architecture.

Rate limit and usage tiers

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Learn about Mistral’s API rate limits, usage tiers, and how to upgrade for higher capacity.’ (99 characters)

Raw Response

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Reasoning

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Support for LLM reasoning visibility and continuity in AG-UI

Reasoning

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Learn how to enable and use agent reasoning to improve task execution.

Reasoning

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Learn how to use reasoning models that think step-by-step before answering.

Reasoning Capabilities

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Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere’s reasoning capalities.

Reasoning models

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Explore the capabilities of OpenAI’s o1 series for complex reasoning and problem-solving. Learn about their features and how they compare to GPT-4o models.

Rerank

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Learn how to improve the relevance of your search and RAG systems with reranking.

Response Relevancy

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Retry Strategies

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Retrying

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Rubric-Based Evaluation

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Run states

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Learn about the different states a W&B run can have.

Runs

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An overview of runs in Agent Server, including how to kick off background runs, stateless runs, and cancel runs.

Sandboxes overview

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Use managed sandboxes to safely execute code and interact with the filesystem in isolated environments.

Schema Overview

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Scoring Overview

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Evaluate AI outputs and return evaluation metrics with Weave Scorers

Search API Overview

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Search overview

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Explore semantic, lexical, and hybrid search options.

Security overview

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Understand Pinecone’s security features, including authentication, encryption, and audit logs.

Security overview

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Understand Pinecone’s security features, including authentication, encryption, and audit logs.

Self-deployment

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Deploy Mistral AI models on your infrastructure using vLLM, TensorRT-LLM, TGI, or tools like SkyPilot and Cerebrium

Semantic Similarity

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Semantic Validation

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Serialization

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Serialize event streams for history restore, branching, and compaction in AG-UI

Serverless Overview

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How Serverless inference works on Fireworks: serving paths, billing, request/response headers, prompt caching, model lifecycle, and when to choose Serverless over On-demand

Skills

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Filesystem-based skill packages that inject domain expertise and instructions into agent prompts.

Soft prompts

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Speaker Selection Strategies

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Speaker selection determines which agent speaks next in a GroupChat: round-robin, random, manual, or auto-selection based on agent descriptions and conversation context.

SQL

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State Management

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Understanding state synchronization between agents and frontends in AG-UI

Stream Iterable

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Stream Partial

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Structured Output

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Learn to generate structured outputs like JSON for LLM agents and pipelines, with custom and flexible formatting options

Structured Outputs for LLMs

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Introduction to structured outputs with LLMs

Summarization

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Synthetic Data Generation Introduction

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Tasks

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Detailed guide on managing and creating tasks within the CrewAI framework.

Templating

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Testing

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Learn how to test your CrewAI Crew and evaluate their performance.

Testset Generation

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Testset Generation for Agents or Tool use cases

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Testset Generation for RAG

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Text generation

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Learn how to use the OpenAI API to generate text from a prompt. Learn about message types and available text formats like JSON and Structured Outputs.

The Cohere Datasets API (and How to Use It)

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Learn about the Dataset API, including its file size limits, data retention, creation, validation, metadata, and more, with provided code snippets.

Thinking in LangGraph

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Learn how to think about building agents with LangGraph

Thinking in LangGraph

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Learn how to think about building agents with LangGraph

Tokenization

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Learn about Mistral AI’s tokenization process, including subword tokenization, control tokens, and Python implementation for LLMs

Tools

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Understanding tools and how they enable human-in-the-loop AI workflows

Tools

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Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.

Tools Overview

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Discover CrewAI’s extensive library of 40+ tools to supercharge your AI agents

Traditional NLP Metrics

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Training

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Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.

Training Overview

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This overview frames Fireworks’ three fine-tuning paths — the autonomous Agent, semi-managed Managed Fine-Tuning, and the custom Training API — so it matters as the decision page before you commit compute. The key heuristic it offers is to reach for supervised fine-tuning when you have more than about a thousand quality labeled examples, and to switch to reinforcement fine-tuning for smaller datasets or reasoning-heavy tasks where ground-truth labels do not exist. A common mistake is defaulting to SFT on too little data. This is the Fireworks counterpart to Together AI’s fine-tuning flow; read the quickstart first if you are new to the platform.

Troubleshooting

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Common issues and solutions when working with Chroma.

Type Adapter

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TypedDicts

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Types

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Understanding MCP clients

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Most developers will build servers rather than clients, but understanding the client side is critical for grasping the full protocol flow. The key insight is that the client acts as a gatekeeper: it decides which server capabilities to expose to the model and enforces security boundaries. Pay attention to how capability negotiation works during initialization, since both sides must agree on what features they support before any real work happens.

Understanding MCP servers

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This page explains the three core primitives that MCP servers expose: Tools, Resources, and Prompts. Focus on understanding when to use each one – Tools let the model take actions, Resources provide read-only data the model can pull in, and Prompts are reusable templates for common interactions. A frequent mistake is implementing everything as a tool when a resource would be more appropriate and give the host application more control over how data is presented to the model.

Understanding pod-based indexes

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Learn about pod-based index architecture and types

Understanding projects

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Learn about projects, environments, and member roles.

Union

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Unions

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Usage Tokens

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Using GPT-5.2

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Learn about how to use and migrate to GPT-5.2 and the GPT-5 model family, the latest models in the OpenAI API.

Utilizing User Feedback

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Validation

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Validators

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Video Generation

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Generate high-quality videos from text and image prompts.

View runs in a project

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Details about customizing how runs are displayed in your project’s runs table

Vision LLMs

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Learn how to use the vision models supported by Together AI.

Voice activity detection (VAD)

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Learn about automatic voice activity detection in the Realtime API.

W&B Self-Managed deployment overview

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Deploy W&B Self-Managed on cloud or on-premises infrastructure

What is Weave?

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Learn about W&B Weave and how it helps you build, evaluate, and improve LLM applications

Workflow Agents

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Workflow agents provide deterministic orchestration without LLM overhead: sequential, parallel, and loop patterns for composing agent execution.

Workflows Overview

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Graph-based workflow orchestration: defining agents as nodes, transitions as edges, and execution strategies (sequential, parallel, conditional).

Zero data retention

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Learn about Zero Data Retention (ZDR) for Claude Code on Claude for Enterprise, including scope, disabled features, and how to request enablement.