vercel/ai

Features

• Generative AI Interfaces - Provides a unified SDK for building conversational interfaces, autonomous agents, and generative UI components.
• Generative User Interfaces - Handle loading indicators, error states, and authentication checks within the generative interface to ensure robust and secure user interactions.
• Agentic Reasoning Loops - Provides a recursive orchestration engine that manages multi-step reasoning by automatically chaining tool invocations and model inputs.
• Model Provider Integrations - Provides a unified interface to abstract and manage multiple AI model providers.
• AI Agent Orchestration Frameworks - Acts as a framework for building autonomous agents that manage tool workflows and conversation state.
• AI Chat Interfaces - Embed conversational chat components into applications to support message persistence, tool calling, and real-time response streaming.
• Autonomous Agent Orchestration - Orchestrates model reasoning and external function calls to create intelligent agents capable of executing custom tools.
• Conversational Interfaces - Manages chat history, conversation state, and real-time streaming to build interactive conversational interfaces.
• Generative AI APIs - Provides a unified interface for generating text, images, and other content through various AI models.
• Generative Content APIs - Produce natural language output from large language models by sending prompts and managing generation settings across various model providers.
• LLM Application Frameworks - Serves as a comprehensive framework for orchestrating complex multi-step reasoning and tool execution in web applications.
• Text Generation APIs - Create and execute language model requests to generate text, stream responses, or produce structured data using specific model identifiers.
• Tool Use and Function Calling - Produce text content and execute tool calls using a unified interface that supports both batch processing and real-time streaming for interactive applications.
• Dynamic Interface Renderers - Maps model-generated tool outputs to interactive UI components in real time.
• Dynamic UI Renderers - Renders dynamic, interactive interface components directly from model outputs within chat streams.
• Generative UI Frameworks - Enables generative user interfaces that render dynamic components directly from model outputs in real time.
• AI Model Integrations - Provides a unified interface for connecting to various generative AI model providers for text, image, and tool execution.
• Chat Interfaces - Provides standardized hooks and utilities for building streaming chat interfaces in frontend applications.
• Chat State Management - Simplifies chat window development by managing user input, message history, and streaming state.
• Conversation State Management - Tracks message history and tool interactions to ensure context is preserved across multi-turn conversations.
• Tool Calling - Integrate streaming responses that include tool invocations to allow for interactive agent workflows during the message generation process.
• AI Chat Interfaces - Provides interactive, real-time chat interfaces with support for message history, streaming, and multi-modal content.
• Interactive AI Interfaces - Manage chat streams, text completions, and structured data updates to create dynamic, real-time AI-driven user experiences.
• Language Model Tooling - Create and configure chat, completion, and response-based language models to process text, manage conversations, and enforce structured output schemas.
• Language Model Response Generators - Streams text responses from language models incrementally to provide real-time user feedback.
• Model Abstractions - Abstracts communication with various language model providers into a unified interface, allowing developers to switch between different AI services.
• Model Provider Abstractions - Normalizes interactions across diverse AI model vendors and local model instances through a standardized interface layer.
• Speech-to-Text Integrations - Sends prompts to various AI model providers through a unified interface to retrieve text responses.
• Structured Data Extraction - Generate schema-validated, typed data objects from model outputs to facilitate information extraction and dynamic interface updates.
• Server-Defined UI Components - Streams rendered UI components from the server to the client during AI generation.
• Server-Side Components - Transmit rendered UI and data values directly from server-side functions to the client to reduce bundle size and improve performance.
• AI Integration Hooks - Provides hooks and utilities for integrating real-time streaming and generative UI into frontend applications.
• Agent Tool Execution - Registers custom functions that models can invoke to perform calculations or interact with external systems.
• Agentic Workflow Construction - Constructs reliable agentic processes using explicit control flow and conditional branching.
• AI Agent Tool Integrations - Connects language models to external APIs, databases, and local functions for real-time data retrieval and task execution.
• AI Tool Execution - Enables models to perform external actions or retrieve data by invoking custom functions during conversation flows.
• MCP Server Integrations - Integrates external Model Context Protocol servers to dynamically discover and execute tools within AI agent workflows.
• Chat Model Integrations - Provides hooks to manage state and communication between user interface elements and language models.
• Conversation Management Systems - Optimize token usage and conversation efficiency by automatically clearing tool uses or reasoning content based on defined conditions.
• Conversation State Persistence - Synchronizes message history and context between backend storage and frontend interface components.
• Dynamic Tool Discovery - Provides mechanisms for runtime introspection and parsing of external tool schemas to enable dynamic agent capabilities.
• Embedding Generators - Convert text into numerical vectors using various model providers to enable semantic search, clustering, and retrieval-augmented generation tasks.
• External Tool Integration - Integrates external services and data sources into AI workflows to enable tool execution and information retrieval.
• Persistent Chat Histories - Persists conversation history to maintain context across user sessions and page reloads.
• Conversation Management - Structures interactions as message arrays to support complex, multi-modal dialogue.
• Retrieval Augmented Generation - Grounds language model responses in external knowledge bases for context-aware generation.
• Multi-Agent Task Orchestrators - Chain multiple model interactions and interface updates into a single cohesive user experience for complex tasks.
• Prompt Caching Strategies - Implements techniques for storing and reusing intermediate model outputs to reduce latency and API costs.
• Execution Step Controllers - Automates multi-step reasoning by chaining tool invocations and model inputs until tasks are completed.
• Structured Output Enforcements - Enforce input and output formats for model interactions using schema libraries to ensure data consistency and reliability.
• Agent State Persistence - Stores and retrieves agent session history and context to maintain continuity across interactions.
• Schema-Validated Data Structures - Enforces type-safe schemas on AI model outputs to ensure generated content conforms to predefined data structures.
• Incremental Structured Streamers - Generate and render complex JSON objects incrementally as they are produced by an AI model, allowing for real-time UI updates based on structured data.
• Contextual Response Objects - Guide users through the generation of structured data objects based on model output using dedicated interface components.
• Debugging and Inspection Tools - Provides interactive tools that allow developers to inspect, monitor, and troubleshoot multi-step AI interaction flows and tool invocations.
• Automation Execution Frameworks - Automates iterative reasoning by executing tools and feeding results back into the generation loop.
• Middleware-Based Request Pipelines - Implements a hook-based pipeline for injecting custom logic into the request and response lifecycle of model calls.
• Streaming Response Architectures - Streams AI-generated content from backend models to frontend interfaces in real time.
• Message Stream Orchestration - Orchestrates bidirectional streaming of message data between AI models and user interfaces.
• External Tool Integrations - Enables language models to execute custom functions and fetch external data to perform tasks beyond native capabilities.
• AI Model Middleware - Define custom logic to transform parameters or wrap generation and streaming methods to create reusable, model-agnostic enhancements.
• Model Call Interceptors - Enhance model behavior by intercepting and modifying requests and responses to implement features like logging, caching, guardrails, or retrieval-augmented generation.
• Request Middleware - Intercepts and modifies model request-response cycles to inject custom logic and streaming behavior.
• Agent Delegation - Invokes autonomous subagents from a parent agent to perform modular, delegated operations.
• Agent Response Streamers - Creates real-time API responses for client-side interfaces by streaming agent output and tool interactions directly to the user.
• Autonomous Agent Definitions - Provides configuration logic to define agent behaviors, stop conditions, and tool-calling parameters.
• Autonomous Agent Loops - Controls agent execution cycles manually using core generation functions for complex iteration control.
• Contextual Data Providers - Fetches and injects external application data to provide relevant background context to AI models during execution.
• Conversational State Managers - Retrieves and updates conversation state on the server to maintain context across interactions.
• Custom Model Adapters - Provides a standardized specification for creating and distributing custom connectors for AI model services.
• Custom Model Integrations - Allows definition of proprietary or third-party model interfaces to integrate non-standard AI services.
• Multi-Modal Input Processors - Processes images and files as part of user messages to support rich content analysis.
• Model Provider Configurations - Sets default provider and model configurations to allow simple identifier-based model referencing throughout the application.
• Pre-built Tool Integrations - Enable models to perform web searches, execute code, search files, generate images, or interact with remote servers and local shell environments.
• Structured Prompting Tools - Structures input instructions for language models to guide output generation and ensure consistent behavior.
• Text Generation - Constructs model inputs using simple text strings or template literals for generation tasks.
• AI and Data Tools - Toolkit for building AI-powered interfaces.
• Model Serving & Deployment - Builds AI-powered applications using modern web frameworks.
• Developer Tooling - Toolkit for building AI-powered applications in TypeScript.
• Extensions and Plugins - Toolkit for building AI-powered applications.
• General Libraries - Toolkit for building AI-powered interfaces and agents.
• Generative UI - SDK for creating generative UI applications.
• Streaming Data Protocols - Pipes incremental model tokens and tool calls from server to client using a chunked data transmission mechanism.
• AI Session Monitoring - Provides dashboards for visualizing and observing live AI interaction streams, including tool calls and token usage.
• AI and Agent Observability - Provides specialized instrumentation and metrics tracking for language model interactions and agent tool execution.
• LLM Performance Monitoring - Tracks token usage, latency, and execution metrics for AI model operations.
• Server-Side Method Invokers - Invokes local functions or external APIs from within the model generation process to perform real-time actions.
• UI State Management - Synchronizes user interface state with generative model outputs to ensure real-time consistency.
• Response Streaming Interfaces - Delivers generated content to the user incrementally as it is produced to reduce perceived latency.
• Agent Context Providers - Enables dynamic injection of external instruction files and domain knowledge into agent contexts.
• Agent Tooling - Forces agents to invoke specific tools at every step to ensure structured workflows.
• Agent Execution Policies - Defines custom stopping conditions and step-by-step settings to manage how autonomous agents process tasks.
• Agent Context Management - Injects structured instructions into agent contexts on demand to provide domain expertise.
• Agent Execution Runtimes - Terminates agent loops based on step counts, tool results, or model outputs to manage resource usage.
• AI Chatbots - Maintains and resumes active AI generation streams across network disconnects or page reloads.
• Response Generation Configurations - Transmits model output to the client incrementally as tokens are generated to provide a responsive user experience.
• Native Tool Call Parsers - Streams tool inputs in real-time as they are generated to improve responsiveness during complex interactions.
• Model Configuration Interfaces - Adjust model behavior through settings like effort levels, speed modes, and task budgets to optimize performance and cost.
• Model Optimization - Enables high-performance modes and token budget adjustments to improve model execution speed and latency.
• Model Response Parsers - Normalize output by extracting reasoning tags, stripping markdown code fences from JSON, or simulating streaming for non-streaming models.
• Model Selection Tools - Provides structured schemas and metadata to guide models in selecting and executing specific functions.
• Observability Tools - Monitors, visualizes, and debugs the execution flow and performance of AI tools and agents.
• Reasoning Transparency Interfaces - Retrieves internal reasoning steps generated by models to provide transparency into output generation.
• Reasoning Depth Controllers - Controls the intensity of model deliberation to balance response speed, cost, and analytical thoroughness.
• Result Reranking - Provides models and algorithms for re-ordering search results to improve precision and relevance.
• Tool Output Processors - Integrate multi-step tool calls with structured data generation to perform actions and return results in specific formats.
• Workflow Branching Logic - Directs workflow logic dynamically by using models to analyze context and determine execution branches.
• Search & Information Retrieval - Enables models to retrieve real-time web content and external information to ground responses.
• Sequential Execution Engines - Chains processing steps in a predefined order where outputs serve as inputs for subsequent operations.
• Selection Constraints - Allows developers to configure whether models must, may, or cannot use specific tools during execution.
• Server-Side Stream Handlers - Ensures AI response streams are fully consumed and saved on the server even if the client disconnects.
• Custom Transport Protocols - Supports custom logic for message transmission and stream processing to integrate with proprietary communication protocols.
• Generation Lifecycle Managers - Provides hooks to manage the lifecycle of AI generation requests, including cancellation and error handling.
• Type Safety - Enforces type safety and data integrity for inputs passed between applications and external tools.
• Execution Confirmation Requirements - Implements human-in-the-loop confirmation requirements for sensitive tool execution and automated actions.
• Error Handling Utilities - Normalizes and transforms error structures from model providers to simplify application-level error reporting and retry logic.
• Exception Handling Strategies - Implements defined patterns for catching, logging, and recovering from runtime errors during model requests to ensure application stability.
• Request Interception Middleware - Provides middleware pipelines to intercept and modify AI generation requests and responses.
• Request Lifecycle Management - Manages request execution flow with support for retries, abort signals, and timeouts.
• Agent Performance Monitoring - Tracks operational metrics and token usage through callback hooks to provide visibility into the internal reasoning process of agents.
• Dynamic Content Streams - Streams dynamic UI components and data values from the server to the client.
• Identifier Matchers - Updates existing data parts in real-time by matching identifiers, enabling dynamic UI elements like progress bars, interactive artifacts, or live status indicators.
• Client-Side State Synchronizers - Expose the current AI and UI state to client-side components to ensure the interface reflects the latest server-side data.
• Concurrent Agent Execution - Executes independent subtasks simultaneously to improve efficiency in agentic workflows.
• Agent Configurations - Injects type-safe structured inputs into agent requests to dynamically modify settings or fetch external context.
• Agent Task Execution - Provides built-in tools for executing system-level tasks like bash commands and file operations.
• In-Process Agent Execution - Invokes agent logic directly within the application process to bypass network overhead during execution.
• Agent Capability Extensions - Adds specialized skills like document processing and data analysis to support complex reasoning tasks.
• Subagent Architectures - Filters information passed between subagents and parent models to maintain relevant context.
• User Input Elicitation - Enables dynamic requests for structured user input or confirmation during tool execution flows.
• Agent Memory Managers - Provides bespoke storage interfaces and retrieval logic for managing agent persistent data.
• Message Validation Utilities - Verifies the structure and integrity of incoming messages and tool calls before processing.
• External Service Integrations - Connects external tools and service marketplaces to language models to extend agent capabilities.
• Image Generation - Generates images from text descriptions with support for batching and provider-specific configurations.
• Mocking Utilities - Simulates language model and embedding provider outputs using deterministic helpers to enable repeatable unit testing.
• Model Configuration - Maps custom names to models and sets default parameters for consistent application behavior.
• Model Configuration Settings - Allows granular control over model behavior by namespacing custom parameters for specific providers.
• Output Processors - Extracts and displays specialized model output data like reasoning steps and web sources.
• Model Parameter Configurations - Provides configurable parameters for adjusting language model behavior, including reasoning effort and caching strategies.
• Multimodal Agent Capabilities - Enables agents to process and generate non-textual data like images, audio, and video.
• Reasoning Parsers - Extracts internal thought processes and rationale from model outputs.
• Self-Hosted AI Models - Connects to locally running or self-hosted model instances for infrastructure control.
• Vectorization Utilities - Transform complex inputs like text or images into dense numerical representations to capture semantic relationships for processing.
• Worker Agent Definitions - Coordinates multiple specialized agents through a central controller to synthesize results.
• Document Processing and Conversion - Reads and processes document content from various file formats directly within message streams.
• Generation Optimizers - Assesses intermediate outputs and triggers corrective actions to ensure robust generation performance.
• Typed Object Parsers - Parses and renders AI-generated content as typed objects in real-time to ensure consistent data structures in the user interface.
• Stream Processing - Transforms raw message chunks into structured streams for real-time AI response processing.
• Enum Membership Validators - Constrain model output to a predefined set of categories by enforcing a specific schema structure during generation.
• Real-Time Data Streaming - Transmits raw data and text tokens in real time to provide immediate feedback during generation.
• Vector Similarity Search - Computes vector similarity to identify related content within datasets for retrieval tasks.
• Tool Exposure Limits - Limits the number of tools exposed to a model to stay within provider constraints.
• Global Defaults - Enforce default configurations or inject tool input examples across model calls to ensure consistent behavior and improved model performance.
• Sandboxed Execution Environments - Executes tools within sandboxed environments to safely run code and manage latency.
• Payload Minimization Strategies - Minimizes network bandwidth usage by transmitting only the most recent message while reconstructing history on the backend.
• Chat Transport Customizations - Enables custom authentication and specialized protocols for chat message transmission between clients and backend services.
• Transport Customizers - Allows customization of the communication path between the application and AI model providers.
• Plain Text Streamers - Sends plain text chunks from the backend to the frontend incrementally to build a complete text response for the user.
• Execution Confirmation Hooks - Adds a manual confirmation step before a tool executes, preventing models from performing sensitive or destructive actions without user authorization.
• Error Contextualization - Wraps errors with source location and custom metadata to improve debugging and failure tracing within interface hooks.
• Lifecycle Event Hooks - Provides hooks that allow developers to execute custom logic at specific stages of the text generation lifecycle.
• Streaming Latency Optimizers - Route streaming requests through persistent WebSocket connections to reduce latency in multi-step agentic workflows.
• Monitoring Integrations - Provides connectors that bridge observability data from AI agents into centralized monitoring and logging platforms.
• Diagnostic and Error Reporting - Categorizes failures into specific types to help developers identify and resolve issues related to model communication and tool execution.
• Event Handling Systems - Triggers custom logic at specific points in the conversation lifecycle, such as response completion or error states.
• Presentation Lifecycle and State Management - Coordinates the lifecycle of AI interactions including loading states, input handling, and event callbacks.

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