Step-by-step solutions that show you how to build reliable, production-ready AI systems with Temporal. Learn practical paradigms for prompts, tools, retries, and Workflow design.
Simple example demonstrating how to call an LLM from Temporal using the OpenAI Python API library.
Use Temporal and OpenAI Responses API to reliably request output conforming to a specific data structure.
Integrate LiteLLM into a Temporal Workflow in Python.
Extract retry information from HTTP response headers and make it available to Temporal's retry mechanisms.
A basic agentic loop that invokes a dynamic set of tools.
A durable MCP server that uses Temporal workflows for reliable execution of weather tools.
Build a simple, non-looping agent that gives agency to the LLM to choose tools, and then invokes chosen tools.
Build a simple deep research system embodying the standard deep research architecture.
Build a durable AI agent with OpenAI Agents SDK and Temporal that can intelligently choose tools to answer user questions