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You use GenAI Launchpad to start client projects on a solid production foundation without spending weeks on setup. It bridges the gap between proof‑of‑concept AI integrations and production systems by providing a robust, scalable architecture so you can focus on delivering outcomes instead of rebuilding infrastructure. The Launchpad is designed for solo developers and freelancers who need production‑ready patterns they can reuse across multiple client engagements.
What’s New in v3.3.0
  • SSE Streaming with OpenAI-compatible /v1/chat/completions endpoint
  • Native Langfuse integration for observability and tracing
  • New AgentStreamingNode for streaming LLM responses
  • Modular Docker compose files for flexible deployments
  • Python 3.13.7+ requirement
View full changelog →

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Core Concept: Workflows and Nodes

Everything in GenAI Launchpad is built around one pattern: Workflows execute Nodes that pass data through a TaskContext. 
class MyWorkflow(Workflow):
    workflow_schema = WorkflowSchema(
        event_schema=MyEventSchema,
        start=AnalyzeNode,
        nodes=[
            NodeConfig(node=AnalyzeNode, connections=[GenerateNode]),
            NodeConfig(node=GenerateNode, connections=[]),
        ],
    )

# Run it
workflow = MyWorkflow(enable_tracing=True)
result = workflow.run({"message": "Hello, world!"})
  • Workflow: Orchestrates execution of connected nodes
  • Node: A processing unit (fetch data, call an LLM, route logic)
  • TaskContext: Pydantic model passed between nodes containing event data and outputs
  • WorkflowSchema: Defines the structure—which node starts, how they connect

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Multi-Provider LLM Support

GenAI Launchpad uses PydanticAI for LLM access. Switch providers by changing one line: 
class MyAgentNode(AgentNode):
    def get_agent_config(self) -> AgentConfig:
        return AgentConfig(
            model_provider=ModelProvider.OPENAI,  # or ANTHROPIC, BEDROCK, OLLAMA, etc.
            model_name="gpt-4.1",
            output_type=MyOutputSchema,
        )
Supported providers: OpenAI, Azure OpenAI, Anthropic, Google Gemini, Google Vertex AI, AWS Bedrock, Ollama, Mistral.

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Production Infrastructure

The stack is pre-configured and ready to deploy:
  • FastAPI - API endpoints that receive events
  • Celery + Redis - Background task processing
  • PostgreSQL - Event persistence and results storage
  • Supabase - Auth, realtime, and storage (optional)
  • Langfuse - LLM observability and tracing
  • Alembic - Database migrations
  • Docker + Caddy - Containerized deployment with automatic HTTPS
Events flow through: API → Database → Celery Worker → Workflow → Results stored.

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What GenAI Launchpad Is Not

Not an Agent Framework: While you can build agent-like systems using our workflow architecture, GenAI Launchpad isn’t primarily an agent framework like AutoGPT, CrewAI, or LangGraph. Instead, it provides the infrastructure to build any type of AI application. These frameworks can be integrated into the Launchpad alongside or instead of PydanticAI. Not Opinionated About AI Logic: We don’t dictate how you implement your AI logic:
  • Use our built-in workflow system
  • Integrate LangChain or LlamaIndex
  • Build custom solutions
Not a Closed System: Every component is replaceable:
  • Swap Redis for RabbitMQ
  • Use different model providers
  • Implement custom workflow processors

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Use Cases

The workflow/node pattern works well for:
  • Document Processing - Chain nodes: extract → analyze → summarize → store
  • Customer Support - Route node determines intent, specialized nodes handle responses
  • Content Generation - Sequential nodes for research → outline → draft → refine
  • Data Pipelines - Concurrent nodes process multiple data sources in parallel
Each use case maps naturally to a workflow with connected nodes, giving you traceability and the ability to modify individual steps without rewriting everything.