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.
Build a durable AI agent with the AI SDK by Vercel and Temporal that chooses tools to answer user questions.
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.
Build a durable content-moderation guardrail in Python with Temporal and Claude that layers deterministic hard rules over an LLM's verdict for auditable overrides.
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.