Design RAG systems, chatbots, and AI agent architectures in seconds. Describe your LLM application, get a production-ready architecture diagram.
Try It FreeBuilding production LLM applications involves more than just calling an API. You need retrieval pipelines, vector databases, prompt management, guardrails, evaluation systems, and cost controls, all connected in the right architecture.
InfraSketch generates complete LLM application architectures from natural language. Whether you are building a RAG system, a production chatbot, or a multi-agent workflow, describe what you need and get an architecture diagram in seconds. InfraSketch is itself built on LangGraph and Claude, so it understands LLM architecture patterns from first-hand experience.
Generate complete architectures for LLM-powered applications including RAG pipelines, chatbots, and AI agents from natural language.
Design retrieval-augmented generation systems with document ingestion, embedding pipelines, vector databases, and generation layers.
Diagram multi-agent systems, tool-calling agents, and orchestration workflows with LangGraph, LangChain, or custom frameworks.
Refine your LLM architecture through chat. Add guardrails, caching layers, evaluation pipelines, or cost optimization strategies.
Generate detailed documentation covering system components, data flows, API contracts, prompt strategies, and scaling considerations.
Export LLM architecture diagrams as PNG, PDF, or Markdown for design reviews, technical documentation, or presentations.
Design document Q&A systems, knowledge bases, and semantic search applications with vector databases and retrieval pipelines.
Architect chatbot systems with conversation management, tool integration, guardrails, and observability for production deployment.
Design agentic AI architectures with multi-agent orchestration, tool calling, state management, and human-in-the-loop patterns.
Plan LLM serving infrastructure with prompt caching, model routing, cost management, and evaluation pipelines.
Write a description like "Design a customer support chatbot with RAG over our knowledge base, tool calling for order lookups, and conversation memory"
The AI creates a complete architecture showing your LLM orchestration, data pipelines, vector stores, tools, and infrastructure
Ask the AI to add guardrails, caching, evaluation pipelines, or scale specific components. Your diagram updates in real-time.
Generate comprehensive design docs with component details, data flows, API contracts, and deployment strategies
"Design a RAG system for internal documentation search with PDF ingestion, semantic chunking, Pinecone vector store, and a chat interface with citation tracking"
"Design a multi-agent system where a supervisor agent delegates to specialist agents for code generation, research, and data analysis, with shared memory and tool access"
"Design a production chatbot for e-commerce support with order lookup tools, return processing, FAQ retrieval, human escalation, and conversation analytics"
"Design an AI content pipeline with topic research, outline generation, draft writing, fact-checking, SEO optimization, and human review workflow"
Describe your RAG use case in InfraSketch (e.g., 'Design a RAG system for customer support with document ingestion, vector search, and response generation'). The AI generates a complete architecture with ingestion pipelines, chunking, embedding generation, vector database, retrieval, re-ranking, and generation components. Refine through chat to add specific components like metadata filtering or hybrid search.
InfraSketch is purpose-built for designing AI system architectures. Unlike general diagramming tools, InfraSketch understands LLM-specific components like vector databases, embedding models, prompt chains, and agent orchestration. It generates architectures from natural language and lets you refine through conversation.
Yes. InfraSketch can generate architectures for multi-agent systems including supervisor patterns, peer-to-peer agent collaboration, hierarchical agent teams, and tool-calling workflows. Describe your agent system, and the AI creates a diagram showing agent interactions, shared state, tool integrations, and communication patterns.
Describe your chatbot requirements (e.g., 'Design a customer service chatbot with conversation memory, tool calling for order lookups, content moderation, and analytics'). InfraSketch generates the full architecture including the LLM layer, conversation management, tool integration, guardrails, and monitoring. Then refine through chat to handle edge cases.
Yes. InfraSketch is itself built on LangGraph, so it deeply understands graph-based LLM orchestration patterns. You can design LangGraph state machines, LangChain pipelines, and custom orchestration architectures. The AI generates appropriate components for nodes, edges, conditional routing, and tool execution.
Complete guide to building production LLM applications with RAG, agents, and scaling patterns.
Design multi-agent systems with supervisor patterns, tool calling, and state management.
Understand vector database architecture, indexing strategies, and integration patterns.
Create your first LLM architecture diagram in seconds. No signup required.
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