Step-by-step recipes for building reliable AI systems with Temporal, covering LLM integrations, agentic loops, tool calling, and production patterns.
Call an LLM from a durable Temporal Workflow in Python using the OpenAI API library.
Use Temporal and the OpenAI Responses API to reliably request output conforming to a specific data structure.
Integrate LiteLLM into a durable Temporal Workflow in Python to call and switch between LLM providers.
Extract retry information from HTTP response headers and pass it to Temporal's retry mechanisms in Python.
Build a durable agentic loop in Python with Claude tool calling and Temporal.
Build a durable agentic loop in Python that calls a dynamic set of tools with Temporal and the OpenAI Responses API.
Build a durable MCP server in Python that runs weather tools reliably with Temporal Workflows.
Build a simple, non-looping Python agent that lets the LLM choose tools and then invokes the chosen tools with Temporal and OpenAI.
Add human-in-the-loop approval to a durable AI agent using Temporal Signals in Python.
Build a durable AI agent with the OpenAI Agents SDK and Temporal that chooses tools to answer user questions.
Build a durable AI agent in Python with Temporal and the Strands Agents SDK plugin, combining an MCP server tool with an Activity-backed tool that calls a live HTTP feed.
Use the Claim Check pattern with Temporal to keep large payloads out of Event History by offloading them to S3.
Build a multi-agent deep research system in Python with Temporal and the OpenAI Responses API.