{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"chainlit-cookbook","slug":"chainlit-cookbook","name":"Chainlit Cookbook","type":"repo","url":"https://github.com/Chainlit/cookbook","page_url":"https://unfragile.ai/chainlit-cookbook","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"chainlit-cookbook__cap_0","uri":"capability://automation.workflow.decorator.based.message.handler.pattern.for.conversational.flow","name":"decorator-based message handler pattern for conversational flow","description":"Chainlit Cookbook demonstrates a decorator-driven architecture using @cl.on_message, @cl.on_chat_start, and @cl.on_file_upload handlers that define the entire conversational lifecycle. Each handler is a Python async function that receives context objects (cl.Message, cl.User, cl.Session) and can access chat history, user metadata, and file uploads through a unified API. This pattern eliminates boilerplate routing logic and enables hot-reload development with the -w flag for rapid iteration.","intents":["I want to build a chat interface without writing HTTP routing or WebSocket handlers","I need to handle file uploads and process them in the same message flow as text","I want to access chat history and user context automatically in my message handlers","I need to reload my application code without restarting the server during development"],"best_for":["Python developers building LLM chat applications","teams prototyping conversational AI without frontend expertise","developers migrating from REST-based chatbots to event-driven architectures"],"limitations":["Async-only execution model — synchronous code must be wrapped with asyncio.run(), adding complexity for blocking I/O","Handler context is request-scoped — no built-in cross-request state persistence without external storage","Limited to Python backend — frontend customization requires React knowledge for advanced use cases"],"requires":["Python 3.9+","Chainlit framework installed (pip install chainlit)","async/await syntax understanding","LLM API key (OpenAI, Anthropic, or compatible provider)"],"input_types":["text messages","file uploads (PDF, images, documents)","structured metadata from cl.User and cl.Session objects"],"output_types":["cl.Message objects (text, markdown, code blocks)","cl.Element objects (images, files, custom React components)","streaming responses via async generators"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_1","uri":"capability://memory.knowledge.vector.database.integration.for.document.q.a.with.pluggable.retrievers","name":"vector database integration for document q&a with pluggable retrievers","description":"The cookbook provides production-ready patterns for integrating vector databases (Chroma, Pinecone) and retrieval frameworks (LlamaIndex) into Chainlit applications. The architecture uses @cl.on_file_upload to trigger document ingestion, storing embeddings in the vector store, then @cl.on_message to retrieve relevant chunks via semantic search and pass them to an LLM for generation. Examples demonstrate both standalone vector stores (Chroma) and managed services (Pinecone), with LlamaIndex providing abstraction over multiple backends.","intents":["I want to build a Q&A system over uploaded PDFs or documents without managing embeddings manually","I need to switch between vector database providers (Chroma, Pinecone, Weaviate) without rewriting retrieval logic","I want to index documents on upload and retrieve relevant context for LLM responses in real-time","I need to handle large document collections with semantic search instead of keyword matching"],"best_for":["teams building document-centric AI applications (legal, healthcare, enterprise search)","developers prototyping RAG systems before committing to infrastructure","organizations needing multi-provider vector database support for flexibility"],"limitations":["Embedding generation adds latency (typically 1-5 seconds per document depending on size and model)","Vector database costs scale with document volume — Pinecone charges per vector stored, Chroma is local-only","No built-in deduplication or update strategies — re-uploading documents creates duplicate embeddings without cleanup logic","Retrieval quality depends on embedding model choice — cookbook examples use OpenAI embeddings, which may not be optimal for domain-specific content"],"requires":["Python 3.9+","Vector database (Chroma for local development, Pinecone/Weaviate for production)","Embedding API key (OpenAI, Hugging Face, or local model)","LLM API key for generation (OpenAI, Anthropic, etc.)","PyPDF2 or similar for document parsing"],"input_types":["PDF files","text documents","markdown files","user queries (text)"],"output_types":["retrieved document chunks (text)","LLM-generated responses with citations","cl.Message objects with source attribution"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_10","uri":"capability://planning.reasoning.openai.assistants.api.integration.with.persistent.threads.and.file.handling","name":"openai assistants api integration with persistent threads and file handling","description":"The cookbook provides examples of using OpenAI's Assistants API (managed agents with persistent state) integrated with Chainlit. The pattern creates an Assistant with specific instructions and tools, manages conversation threads (persistent across sessions), and handles file uploads for document analysis. This enables developers to build stateful agents without managing conversation history or tool definitions manually.","intents":["I want to create a persistent assistant that remembers conversation context across sessions","I need to give an assistant access to files for analysis without managing embeddings","I want to use OpenAI's managed agent infrastructure instead of building custom agent loops","I need to handle code execution and file retrieval within the assistant"],"best_for":["developers building long-running assistants with persistent state","teams needing file analysis capabilities without custom RAG implementation","applications where conversation history must persist across user sessions"],"limitations":["Assistants API is more expensive than standard completions API due to managed infrastructure","File handling is limited to specific formats and sizes (max 512MB per file)","Code execution is sandboxed — cannot access external systems or install packages","Thread management is manual — developers must track thread IDs and clean up old threads","Tool definitions are limited to OpenAI's built-in tools (code interpreter, retrieval, function calling)"],"requires":["Python 3.9+","OpenAI API key with Assistants API access","Chainlit framework","File storage for uploaded documents (OpenAI manages this)"],"input_types":["text messages","files (documents, code, data files)","assistant instructions (system prompts)"],"output_types":["assistant responses (text)","code execution results","file retrieval results","persistent thread state"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_11","uri":"capability://tool.use.integration.multi.capability.protocol.mcp.server.integration.for.standardized.tool.access","name":"multi-capability protocol (mcp) server integration for standardized tool access","description":"The cookbook demonstrates MCP integration enabling Chainlit applications to discover and invoke tools from MCP servers (e.g., Linear, GitHub, web search). MCP provides a standardized protocol for tool definition and execution, eliminating custom integration code. The pattern uses MCP client libraries to connect to MCP servers, automatically discovers available tools, and routes LLM function calls to the appropriate MCP server. This enables agents to access external systems through a unified interface.","intents":["I want to give an agent access to external systems (Linear, GitHub, web search) without custom integrations","I need a standardized way to define and discover tools across multiple providers","I want to avoid writing custom API client code for each tool integration","I need to support multiple MCP servers with a single agent implementation"],"best_for":["teams building agents that need access to multiple external systems","developers adopting the MCP standard for tool interoperability","organizations standardizing on MCP for AI tool integration"],"limitations":["MCP is a relatively new standard — limited server implementations available (Linear, GitHub, web search, etc.)","Requires running separate MCP server processes — adds deployment complexity","Tool discovery is dynamic — schema changes in MCP servers may break client code","Error handling is MCP server-dependent — no standardized error format across servers","Performance depends on MCP server latency — no built-in caching or optimization"],"requires":["Python 3.9+","MCP client library (pip install mcp)","MCP server implementations (Linear, GitHub, web search, etc.)","API keys for MCP servers (Linear token, GitHub token, etc.)","Chainlit framework for UI integration"],"input_types":["MCP server configurations (host, port, API keys)","tool schemas from MCP servers (JSON)","function call requests from LLM"],"output_types":["discovered tools from MCP servers","tool execution results","error messages from MCP servers","execution traces in Chainlit UI"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_12","uri":"capability://automation.workflow.aws.ecs.deployment.with.docker.containerization.and.environment.configuration","name":"aws ecs deployment with docker containerization and environment configuration","description":"The cookbook includes AWS ECS deployment examples demonstrating how to containerize Chainlit applications with Docker, configure environment variables for production, and deploy to ECS with load balancing. The pattern uses Docker to package the Python application with dependencies, AWS ECS to manage container orchestration, and environment files (.env) to configure API keys and service endpoints. This enables production-grade deployment with auto-scaling and high availability.","intents":["I want to deploy a Chainlit application to AWS ECS for production use","I need to containerize my Chainlit app with Docker for consistent environments","I want to manage secrets and environment variables securely in production","I need auto-scaling and load balancing for high-traffic applications"],"best_for":["teams deploying Chainlit applications to AWS infrastructure","organizations requiring production-grade deployment with auto-scaling","developers familiar with Docker and AWS ECS"],"limitations":["AWS ECS requires AWS account and billing setup — not suitable for free tier deployments","Docker image size can be large (500MB+) — increases deployment time and storage costs","Environment variable management requires careful handling — secrets must not be committed to version control","Stateful applications (persistent chat history) require external storage (DynamoDB, RDS) — adds complexity","Cold starts for serverless alternatives (Lambda) may be slower than ECS for real-time applications"],"requires":["Docker installed locally for building images","AWS account with ECS, ECR (Elastic Container Registry), and IAM permissions","AWS CLI configured with credentials","Dockerfile for Chainlit application","Environment configuration (.env files or AWS Secrets Manager)"],"input_types":["Chainlit application code","requirements.txt with dependencies","Dockerfile with build instructions","environment variables (API keys, service endpoints)"],"output_types":["Docker image in ECR","ECS task definition","running ECS service with load balancer","CloudWatch logs for monitoring"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_13","uri":"capability://automation.workflow.reverse.proxy.configuration.for.production.deployment.and.ssl.tls.termination","name":"reverse proxy configuration for production deployment and ssl/tls termination","description":"The cookbook includes reverse proxy examples (Nginx, Apache) for production Chainlit deployments, demonstrating SSL/TLS termination, request routing, and WebSocket proxying. The pattern uses a reverse proxy to handle HTTPS encryption, route requests to multiple Chainlit instances, and manage WebSocket connections for real-time features. This enables secure, scalable production deployments with proper certificate management and load distribution.","intents":["I want to deploy Chainlit behind a reverse proxy for SSL/TLS encryption","I need to route traffic to multiple Chainlit instances for load balancing","I want to handle WebSocket connections properly in a reverse proxy setup","I need to manage SSL certificates and HTTPS configuration"],"best_for":["teams deploying Chainlit to production with HTTPS requirements","organizations needing load balancing across multiple Chainlit instances","developers familiar with reverse proxy configuration (Nginx, Apache)"],"limitations":["Reverse proxy adds network latency — typically 10-50ms per request","WebSocket proxying requires specific configuration — improper setup breaks real-time features","SSL certificate management requires renewal automation (Let's Encrypt, etc.)","Debugging issues is more complex with an additional network layer","Sticky sessions may be required for stateful applications — complicates load balancing"],"requires":["Reverse proxy server (Nginx, Apache, HAProxy)","SSL certificate (self-signed for testing, Let's Encrypt for production)","Chainlit application running on localhost or internal network","Knowledge of reverse proxy configuration syntax"],"input_types":["HTTP/HTTPS requests from clients","WebSocket upgrade requests","SSL certificates and keys"],"output_types":["proxied requests to Chainlit backend","HTTPS responses to clients","WebSocket connections to backend","access logs and error logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_14","uri":"capability://data.processing.analysis.bigquery.integration.for.data.querying.and.analysis.within.chat","name":"bigquery integration for data querying and analysis within chat","description":"The cookbook includes a BigQuery agent example demonstrating how to query BigQuery datasets from within a Chainlit chat interface. The pattern uses LangChain's BigQuery tool to execute SQL queries based on LLM reasoning, returns results as structured data, and displays them in the chat. This enables natural language querying of large datasets without requiring users to write SQL.","intents":["I want to allow users to query BigQuery datasets using natural language","I need to execute SQL queries based on LLM reasoning without exposing SQL to users","I want to display query results (tables, charts) in the chat interface","I need to control query scope and prevent unauthorized data access"],"best_for":["organizations with BigQuery data warehouses needing natural language access","teams building data exploration tools for non-technical users","applications requiring real-time data analysis within chat"],"limitations":["Query execution latency depends on dataset size and query complexity — large queries may timeout","LLM-generated SQL may be inefficient or incorrect — requires validation and error handling","Data access control is complex — must implement row-level security and query validation","BigQuery costs scale with query volume — high-traffic applications may incur significant costs","Schema understanding is limited — LLM may not understand complex schemas without documentation"],"requires":["Python 3.9+","Google Cloud credentials with BigQuery access","BigQuery dataset with appropriate permissions","LangChain library with BigQuery integration","Chainlit framework"],"input_types":["natural language queries from users","BigQuery schema information","data access credentials"],"output_types":["SQL queries generated by LLM","query results (tables, rows)","error messages for invalid queries","execution time and cost estimates"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_15","uri":"capability://image.visual.vision.and.image.understanding.with.claude.and.gpt.4.vision","name":"vision and image understanding with claude and gpt-4 vision","description":"The cookbook demonstrates multi-modal image analysis using Claude's vision capabilities and OpenAI's GPT-4 Vision. The pattern accepts image uploads in @cl.on_file_upload, passes images to vision models with text prompts, and returns structured analysis (descriptions, object detection, text extraction). This enables applications like document analysis, image captioning, and visual Q&A without custom computer vision models.","intents":["I want to analyze images uploaded by users (descriptions, object detection, text extraction)","I need to extract text from documents or screenshots using OCR","I want to answer questions about images (visual Q&A)","I need to classify or categorize images based on visual content"],"best_for":["developers building document analysis or image understanding applications","teams needing OCR capabilities without custom ML models","applications requiring visual Q&A or image captioning"],"limitations":["Vision model latency is higher than text-only models (2-10 seconds per image)","Image size limits apply — very large images must be resized or compressed","Vision capabilities vary by model — Claude 3 Opus is more capable than Haiku","Accuracy depends on image quality and lighting — poor quality images produce unreliable results","Cost per image is higher than text processing — high-volume applications may be expensive"],"requires":["Python 3.9+","Vision model API key (OpenAI for GPT-4 Vision, Anthropic for Claude 3)","Chainlit framework","PIL/Pillow for image processing"],"input_types":["image files (PNG, JPG, GIF, WebP)","text prompts for image analysis","image metadata (size, format)"],"output_types":["image descriptions (text)","extracted text (OCR results)","structured analysis (JSON with detected objects, labels, etc.)","visual Q&A responses"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_2","uri":"capability://text.generation.language.streaming.response.generation.with.real.time.token.output","name":"streaming response generation with real-time token output","description":"Chainlit Cookbook demonstrates streaming LLM responses using async generators and cl.Message.stream() context manager, enabling real-time token-by-token output to the frontend. The pattern uses LLM client streaming APIs (OpenAI, Anthropic) and pipes tokens into Chainlit's message object, which broadcasts updates to the client via WebSocket. This eliminates the need for manual chunking or polling and provides perceived responsiveness for long-running generations.","intents":["I want to show LLM output token-by-token instead of waiting for the full response","I need to display real-time progress for long-running generations (e.g., code generation, document summarization)","I want to reduce perceived latency by streaming partial results while the LLM is still generating","I need to handle streaming from multiple LLM providers (OpenAI, Anthropic, Ollama) with a unified API"],"best_for":["developers building interactive chat interfaces where perceived latency matters","teams generating long-form content (code, articles, summaries) where partial results are useful","applications requiring real-time feedback (e.g., live coding assistants, interactive tutors)"],"limitations":["Streaming adds WebSocket overhead — not beneficial for very short responses (<100 tokens)","Token-level streaming can expose LLM reasoning or false starts to users, requiring careful prompt engineering","Streaming responses cannot be edited after generation starts — requires full regeneration if user wants changes","Some LLM providers (e.g., local Ollama) have variable streaming latency, causing inconsistent user experience"],"requires":["Python 3.9+ with async/await support","LLM provider with streaming API (OpenAI, Anthropic, Ollama, etc.)","WebSocket support in deployment environment","Client-side WebSocket handling (Chainlit frontend handles this automatically)"],"input_types":["user messages (text)","system prompts","chat history"],"output_types":["streamed text tokens","cl.Message objects with incremental updates","WebSocket events to frontend"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_3","uri":"capability://tool.use.integration.function.calling.and.tool.invocation.with.schema.based.routing","name":"function calling and tool invocation with schema-based routing","description":"The cookbook demonstrates OpenAI function calling and MCP (Multi-Capability Protocol) integration for dynamic tool selection and execution. The pattern uses @cl.step decorator to wrap tool calls with observability, @cl.on_message to intercept LLM responses containing tool_calls, and a tool registry to map function schemas to Python callables. This enables agents to autonomously select and execute tools (web search, database queries, file operations) based on LLM reasoning, with full execution tracing visible in the Chainlit UI.","intents":["I want to give an LLM access to external tools (APIs, databases, file systems) without hardcoding tool selection","I need to trace tool execution and see which tools the LLM chose and why","I want to implement agent loops where the LLM can call tools, receive results, and decide next steps","I need to support multiple tool providers (OpenAI functions, MCP servers, custom Python functions) with a unified interface"],"best_for":["developers building autonomous agents that need external tool access","teams implementing ReAct (Reasoning + Acting) patterns for complex tasks","organizations integrating LLMs with legacy systems via function calling"],"limitations":["Tool calling adds latency — each tool invocation requires an additional LLM round-trip to parse function calls and decide next steps","Schema validation is strict — LLM-generated function calls must match registered schemas exactly, or execution fails","Error handling requires explicit implementation — tool failures don't automatically trigger retries or fallbacks","MCP integration requires running separate MCP servers, adding deployment complexity"],"requires":["Python 3.9+","LLM with function calling support (OpenAI, Anthropic, Claude 3+)","Tool schemas defined as JSON Schema or Pydantic models","For MCP: MCP server implementations (e.g., Linear, GitHub integrations)","API keys for external tools (web search, database access, etc.)"],"input_types":["user messages requesting tool-dependent tasks","tool schemas (JSON Schema or Pydantic)","tool execution results (structured or unstructured)"],"output_types":["tool_calls from LLM (function name, arguments)","tool execution results (JSON, text, or structured data)","final LLM response synthesizing tool results","execution traces in Chainlit UI"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_4","uri":"capability://automation.workflow.real.time.audio.processing.and.streaming.with.openai.realtime.api","name":"real-time audio processing and streaming with openai realtime api","description":"The cookbook provides a complete implementation of OpenAI's Realtime API integration, enabling low-latency voice conversations with streaming audio input/output. The pattern uses WebSocket connections to OpenAI's Realtime endpoint, manages audio buffers for PCM encoding, and integrates with Chainlit's message system to display transcriptions and responses. This enables voice-first conversational interfaces with sub-second latency for both speech recognition and synthesis.","intents":["I want to build a voice-first chatbot with real-time speech-to-text and text-to-speech","I need to handle audio streaming with low latency (<500ms) for natural conversation","I want to display transcriptions and responses in the chat UI while audio is being processed","I need to manage audio encoding (PCM, sample rates) and buffer management automatically"],"best_for":["developers building voice assistants or hands-free interfaces","teams creating accessibility-focused applications (voice-first for users with visual impairments)","applications requiring natural conversation flow where latency significantly impacts UX"],"limitations":["Requires OpenAI Realtime API access (beta feature with limited availability)","Audio encoding/decoding adds CPU overhead — may require optimization for mobile or edge devices","WebSocket connections are stateful — scaling requires sticky sessions or connection pooling","Audio quality depends on microphone hardware and network conditions — no built-in quality detection or fallback","Transcription accuracy varies by language and accent — no fine-tuning available for domain-specific vocabulary"],"requires":["Python 3.9+","OpenAI API key with Realtime API access","WebSocket support in deployment","Audio input device (microphone) on client","Browser with Web Audio API support (Chrome, Firefox, Safari 14.1+)","pyaudio or similar for audio capture (if running on server)"],"input_types":["audio streams (PCM, 16-bit, 24kHz)","user voice input from microphone"],"output_types":["transcribed text","audio responses (PCM stream)","cl.Message objects with transcription and response text","real-time status updates (listening, processing, speaking)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_5","uri":"capability://text.generation.language.multi.modal.message.composition.with.embedded.elements.and.actions","name":"multi-modal message composition with embedded elements and actions","description":"Chainlit Cookbook demonstrates composing rich messages using cl.Element objects for images, files, and custom React components, combined with cl.Action buttons for user interactions. Messages can contain text, code blocks, images, PDFs, and interactive elements in a single response. The pattern uses @cl.on_action to handle button clicks, enabling workflows like document approval, parameter adjustment, or result refinement without leaving the chat interface.","intents":["I want to display images, charts, or PDFs alongside text responses in the chat","I need to add interactive buttons (approve, reject, refine) to chat messages for user feedback","I want to embed code blocks with syntax highlighting and copy functionality","I need to render custom React components (dashboards, forms) inside chat messages"],"best_for":["developers building document review or approval workflows","teams creating data visualization dashboards integrated with chat","applications requiring rich media output (image generation, PDF reports, code generation)"],"limitations":["Custom React components require JavaScript/React knowledge — not suitable for Python-only developers","Element rendering is limited to Chainlit's frontend — cannot embed arbitrary HTML/JavaScript for security reasons","Large files (images, PDFs) increase message size and network latency — no built-in compression or lazy loading","Action handlers are stateless — complex workflows require external state management or database"],"requires":["Python 3.9+","Chainlit framework","For custom React: Node.js 18+, React knowledge, custom-react-frontend template","For images: PIL/Pillow or image generation API (DALL-E, Stable Diffusion)"],"input_types":["text content","image files (PNG, JPG, SVG)","PDF files","code strings with language specification","custom React component props (JSON)"],"output_types":["cl.Message with embedded cl.Element objects","cl.Action buttons with callbacks","rendered HTML/React in chat UI","action event data (button clicks, form submissions)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_6","uri":"capability://automation.workflow.custom.react.frontend.development.with.chainlit.component.library","name":"custom react frontend development with chainlit component library","description":"The cookbook includes a custom-react-frontend example demonstrating how to build a fully custom chat UI using React and Chainlit's JavaScript SDK. Developers can replace the default Chainlit UI with custom layouts, styling, and components while maintaining full integration with the Python backend. The pattern uses the @chainlit/react library to connect to the Chainlit server via WebSocket, enabling custom message rendering, input handling, and UI state management.","intents":["I want to build a chat UI that matches my brand design instead of using the default Chainlit interface","I need custom message rendering (e.g., specialized layouts for different message types)","I want to add custom input controls (file pickers, parameter sliders) beyond the default text input","I need to integrate Chainlit chat with an existing React application"],"best_for":["teams with React expertise building branded chat applications","developers integrating Chainlit into existing React applications","applications requiring highly customized UX (e.g., specialized domain interfaces)"],"limitations":["Requires React and TypeScript knowledge — not suitable for Python-only developers","Custom frontend must handle all UI state management — no automatic persistence of UI state across sessions","WebSocket connection management is developer's responsibility — requires error handling and reconnection logic","Styling must be done manually — no built-in component library beyond basic Chainlit types","Testing custom frontends requires additional tooling (Jest, React Testing Library)"],"requires":["Node.js 18+","React 18+","TypeScript knowledge","@chainlit/react SDK","Vite or similar build tool","CSS framework (Tailwind, Material-UI, etc.)"],"input_types":["Chainlit message objects (JSON via WebSocket)","user interactions (text input, button clicks, file uploads)","session and user metadata"],"output_types":["rendered React components","custom HTML/CSS UI","WebSocket messages to Chainlit backend"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_7","uri":"capability://planning.reasoning.langchain.agent.orchestration.with.react.pattern.and.tool.calling","name":"langchain agent orchestration with react pattern and tool calling","description":"The cookbook demonstrates integrating LangChain agents (ReAct, OpenAI, Tool-using agents) with Chainlit for observability and UI. The pattern uses LangChain's AgentExecutor to manage agent loops (think → act → observe), integrates with Chainlit's @cl.step decorator to trace each step, and uses LangChain callbacks to stream agent reasoning to the UI. This enables developers to build complex multi-step agents with full visibility into the reasoning process.","intents":["I want to build a ReAct agent that reasons about tasks, selects tools, and executes them iteratively","I need to see the agent's reasoning chain and tool selections in the UI for debugging","I want to use LangChain's pre-built agents (OpenAI, Tool-using) without building custom agent loops","I need to integrate agents with Chainlit's chat interface and message system"],"best_for":["developers familiar with LangChain building complex agents","teams needing observability into agent reasoning for debugging and improvement","applications requiring multi-step reasoning (research, planning, code generation)"],"limitations":["Agent loops add latency — each reasoning step requires an LLM call, adding 1-5 seconds per step","Tool selection errors propagate through the loop — agents may get stuck in infinite loops if tools fail","LangChain callbacks add overhead — streaming reasoning to UI increases network traffic and processing","Agent behavior is non-deterministic — same input may produce different tool sequences due to LLM sampling"],"requires":["Python 3.9+","LangChain library (pip install langchain)","LLM with function calling (OpenAI, Anthropic)","Tool implementations (custom Python functions or LangChain tools)","Chainlit framework for UI integration"],"input_types":["user queries (text)","tool schemas (LangChain Tool objects)","chat history"],"output_types":["agent reasoning steps (text)","tool selections and arguments","tool execution results","final agent response","execution traces in Chainlit UI"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_8","uri":"capability://memory.knowledge.llamaindex.document.indexing.and.retrieval.with.multi.format.support","name":"llamaindex document indexing and retrieval with multi-format support","description":"The cookbook provides LlamaIndex integration examples demonstrating document indexing from multiple sources (Google Drive, local files, web pages) and retrieval with query engines. LlamaIndex abstracts embedding and retrieval complexity, supporting 50+ document formats and providing higher-level abstractions (Document, Index, QueryEngine) than raw vector databases. The pattern uses @cl.on_file_upload to trigger indexing and @cl.on_message to query the index, enabling RAG without manual embedding management.","intents":["I want to index documents from multiple sources (Google Drive, local files, URLs) without format-specific parsing","I need to query indexed documents with natural language without writing retrieval logic","I want to use LlamaIndex's query engines (retrieval, summarization, sub-question) for different query types","I need to handle complex documents (tables, code blocks, images) with semantic understanding"],"best_for":["developers building document Q&A systems with diverse input formats","teams needing abstraction over vector database details for faster prototyping","applications requiring multi-source indexing (internal docs, web content, databases)"],"limitations":["LlamaIndex adds abstraction overhead — developers have less control over embedding and retrieval parameters","Query engine selection is manual — no automatic routing to optimal engine for different query types","Index updates require full re-indexing — no incremental updates for large document collections","Memory usage scales with index size — large indexes may exceed available RAM without external storage"],"requires":["Python 3.9+","LlamaIndex library (pip install llama-index)","Embedding API key (OpenAI, Hugging Face, or local model)","LLM API key for query generation","For Google Drive: Google API credentials","Document loaders for specific formats (PyPDF2, python-docx, etc.)"],"input_types":["documents from multiple sources (local files, Google Drive, URLs, databases)","natural language queries","document metadata (titles, authors, dates)"],"output_types":["indexed documents (LlamaIndex Document objects)","retrieved chunks with relevance scores","query engine responses (text, summaries, sub-question answers)","source attribution with document references"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit-cookbook__cap_9","uri":"capability://text.generation.language.anthropic.claude.integration.with.streaming.and.vision.capabilities","name":"anthropic claude integration with streaming and vision capabilities","description":"The cookbook includes examples of integrating Anthropic's Claude models with Chainlit, demonstrating streaming text generation, vision capabilities for image analysis, and tool use. The pattern uses the Anthropic Python SDK to call Claude APIs, integrates streaming responses with Chainlit's message system, and handles vision inputs (images) for multi-modal understanding. This enables developers to build Claude-powered chat applications with full feature support.","intents":["I want to build a chat application using Claude instead of OpenAI","I need to analyze images using Claude's vision capabilities","I want to stream Claude responses for real-time output","I need to use Claude's tool use feature for function calling"],"best_for":["developers preferring Claude's reasoning capabilities or ethical guidelines","teams building vision-enabled applications (image analysis, document understanding)","applications requiring specific Claude features (extended context, tool use)"],"limitations":["Claude API pricing is higher than OpenAI for equivalent token counts","Vision capabilities are limited to specific Claude models (Claude 3 Opus, Sonnet, Haiku)","Tool use requires explicit schema definition — less automatic than OpenAI function calling","Rate limits are stricter than OpenAI for free tier users"],"requires":["Python 3.9+","Anthropic API key (pip install 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