> ## Documentation Index > Fetch the complete documentation index at: https://mcp-server-langgraph.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # QuickStart Presets Guide > Rapid agent prototyping with zero infrastructure using pre-configured presets # QuickStart Presets MCP Server LangGraph provides pre-configured presets for different deployment scenarios: | Preset | Setup Time | Infrastructure | Use Case | | -------------- | ------------ | -------------- | --------------------- | | **QuickStart** | \< 2 minutes | None | Learning, prototyping | | Development | \~15 minutes | Docker Compose | Local development | | Production | 1-2 hours | Kubernetes | Enterprise deployment | This guide covers the **QuickStart** preset for rapid agent development. *** ## QuickStart Preset The QuickStart preset provides: * **In-memory checkpointing** (LangGraph MemorySaver) * **Free LLM defaults** (Gemini Flash) * **No Docker required** * **No authentication needed** * **Simple agent creation** * **FastAPI server ready** ### Quick Start ```python theme={null} from mcp_server_langgraph.presets import QuickStart # Create an agent in one line agent = QuickStart.create("Research Assistant") # Chat with the agent result = agent.chat("What is LangGraph?") print(result) ``` *** ## Creating Agents ### Basic Agent ```python theme={null} from mcp_server_langgraph.presets import QuickStart # Create agent with default settings agent = QuickStart.create("My Agent") # Chat response = agent.chat("Hello, how can you help me?") print(response) ``` ### Agent with Tools ```python theme={null} from mcp_server_langgraph.presets import QuickStart # Create agent with tools agent = QuickStart.create( name="Calculator Agent", tools=["calculator", "search"], llm="gemini-flash", ) # Use the agent result = agent.chat("What is the square root of 144?") ``` ### Agent with Custom Settings ```python theme={null} from mcp_server_langgraph.presets import QuickStart agent = QuickStart.create( name="Creative Writer", llm="claude-haiku", system_prompt="You are a creative writing assistant specialized in short stories.", temperature=0.9, # Higher creativity ) result = agent.chat("Write a short story about a robot learning to paint.") ``` *** ## Available LLMs The QuickStart preset supports these free-tier friendly models: | Model | Provider | Best For | | -------------- | --------- | ------------------------------- | | `gemini-flash` | Google | Fast, general purpose (default) | | `gemini-pro` | Google | Complex reasoning | | `claude-haiku` | Anthropic | Concise responses | | `gpt-5-mini` | OpenAI | Balanced performance | ```python theme={null} # Use a specific LLM agent = QuickStart.create( name="Assistant", llm="gemini-pro", # Use Gemini Pro for complex tasks ) ``` *** ## Creating a FastAPI Application Generate a complete REST API for your agent: ```python theme={null} from mcp_server_langgraph.presets import QuickStart # Create FastAPI app app = QuickStart.create_app( name="Customer Support Bot", tools=["search", "knowledge_base"], llm="gemini-flash", port=8000, ) # Run with: uvicorn app:app --reload ``` ### API Endpoints The generated app includes: | Endpoint | Method | Description | | --------- | ------ | ---------------------------- | | `/` | GET | Health check with agent info | | `/chat` | POST | Chat with the agent | | `/health` | GET | Simple health check | ### Example Request ```bash theme={null} curl -X POST "http://localhost:8000/chat" \ -H "Content-Type: application/json" \ -d '{"query": "What can you help me with?", "thread_id": "user123"}' ``` Response: ```json theme={null} { "query": "What can you help me with?", "response": "I'm Customer Support Bot, a helpful AI assistant...", "thread_id": "user123" } ``` *** ## Conversation Threading QuickStart supports conversation history with thread IDs: ```python theme={null} agent = QuickStart.create("Assistant") # First message in thread response1 = agent.chat("My name is Alice", thread_id="user-123") # Continuing the conversation response2 = agent.chat("What's my name?", thread_id="user-123") # Response will remember the context ``` *** ## Streaming Responses For real-time output: ```python theme={null} agent = QuickStart.create("Streaming Agent") # Stream the response for chunk in agent.stream_chat("Tell me a long story"): print(chunk, end="", flush=True) ``` *** ## Configuration Options The `QuickStartConfig` model defines all options: ```python theme={null} from mcp_server_langgraph.presets.quickstart import QuickStartConfig config = QuickStartConfig( name="Custom Agent", tools=["search", "calculator"], llm="gemini-flash", system_prompt="You are a helpful research assistant.", temperature=0.7, ) ``` | Option | Type | Default | Description | | --------------- | ---------- | ------------ | ------------------------- | | `name` | str | required | Agent name | | `tools` | list\[str] | \[] | Tools to include | | `llm` | str | gemini-flash | LLM model | | `system_prompt` | str | None | Custom system prompt | | `temperature` | float | 0.7 | LLM temperature (0.0-1.0) | *** ## Migrating to Production When ready for production, migrate from QuickStart to the full deployment: ### Step 1: Add Persistence ```python theme={null} # QuickStart uses in-memory (data lost on restart) # For production, use PostgreSQL checkpointing # See: deployment/postgresql-checkpointing.mdx ``` ### Step 2: Add Authentication ```python theme={null} # QuickStart has no authentication # For production, enable Keycloak JWT auth # See: guides/authentication.mdx ``` ### Step 3: Add Observability ```python theme={null} # QuickStart has basic logging # For production, add OpenTelemetry + LangSmith # See: getting-started/langsmith-tracing.mdx ``` ### Step 4: Deploy to Kubernetes ```bash theme={null} # QuickStart runs locally # For production, use Helm charts helm install my-agent ./charts/mcp-server-langgraph ``` *** ## Limitations The QuickStart preset is designed for learning and prototyping. It has these limitations: | Feature | QuickStart | Production | | ------------------ | --------------------------- | ------------------------- | | State persistence | In-memory (lost on restart) | PostgreSQL | | Authentication | None | Keycloak JWT | | Authorization | None | OpenFGA | | Observability | Basic logging | OpenTelemetry + LangSmith | | Scaling | Single instance | Kubernetes HPA | | Secrets management | Environment variables | Infisical | *** ## Examples ### Research Assistant ```python theme={null} from mcp_server_langgraph.presets import QuickStart agent = QuickStart.create( name="Research Assistant", tools=["search", "summarize"], llm="gemini-pro", system_prompt="You are a research assistant. Provide detailed, accurate information with sources.", ) result = agent.chat("What are the latest developments in quantum computing?") ``` ### Code Helper ```python theme={null} agent = QuickStart.create( name="Code Helper", llm="claude-haiku", system_prompt="You are a Python coding assistant. Provide concise, working code examples.", temperature=0.3, # Lower temperature for more deterministic code ) result = agent.chat("Write a function to calculate Fibonacci numbers") ``` ### Customer Support Bot ```python theme={null} app = QuickStart.create_app( name="Support Bot", tools=["knowledge_base", "ticket_system"], llm="gemini-flash", ) # Deploy with: uvicorn app:app --host 0.0.0.0 --port 8000 ``` *** ## Related Documentation * [LangGraph Functional API](/architecture/adr-0010-langgraph-functional-api) * [Agent Architecture](/guides/agent-architecture) * [Deployment Overview](/deployment/overview)