Search posts, papers, and topics
21 posts · temporal.io
Temporal Cloud now offers a pay‑go plan with no base monthly fee, charging $50 per million Actions and storage per GB‑hour, plus 10% of usage for developer support. New users also get $150 in credits for 90 days, and existing customers can switch via the billing center.
Temporal announced a $550M Series E at a $12.55B valuation, citing >200% YoY revenue growth, 1.9 trillion billable actions processed, and 4.3k paying customers. The round funds further work on its durable execution platform for long‑running AI workflows.
Temporal’s CEO reports a year of rapid growth—200% YoY revenue, 1.9 trillion monthly actions, and 570 staff—while positioning durable execution as the reliability layer for AI agents and announcing GA of OpenAI and Vercel SDK integrations.
The post shows how to wire Temporal's Worker Controller with GitHub Actions to automatically build, version, and roll out worker images via Kubernetes. A demo repo illustrates the full CI pipeline and safe version upgrades.
Temporal’s Serverless Workers let you replace always‑on Kubernetes Workers with on‑demand Lambda invocations. The guide shows how to add a Lambda handler, configure worker versioning, tune shutdown timeouts for long‑running activities, package dependencies for Lambda, and deploy the function, keeping workflow and activity code unchanged.
Temporal announced it is expanding its sponsorship of Crystal Palace Football Club to include the Palace Women's team as front-of-shirt sponsor. This move aims to address gender inequality in sports, drawing parallels to diversity challenges within the tech industry.
This article proposes using Temporal to orchestrate large, flaky end-to-end test suites, treating each test execution as a durable unit of work. This approach allows retrying only failed individual tests or browser configurations, significantly reducing wasted CI time from transient failures.
Manetu's AgentVisor uses Temporal Workflows to provide durable execution for AI agents, allowing them to survive crashes and long-running interactions without modifying the agent's core code. It maps agent threads to Temporal Workflows, managing state and recovery transparently while also integrating security features.
Coding agents proved most effective for cleaning up temporary code and legacy configurations during a migration, rather than for initial code construction. This effectiveness stemmed from three specific practices: documenting cleanup needs upfront, co-locating rules with code and their enforcement, and actively managing instruction staleness.
LlamaIndex migrated from RabbitMQ to Temporal for orchestrating complex, multi-stage document processing workflows, scaling to tens of millions of pages daily. This shift enabled them to replace ad-hoc state management and concurrency controls with Temporal's durable execution and workflow primitives, improving reliability and resource efficiency.
Temporal added a public‑preview integration with Google’s Gen AI Python SDK, letting Gemini calls survive crashes, and launched Temporal Cloud on Google Cloud Marketplace with pay‑as‑you‑go pricing and a $1,000 starter credit. The updates aim to simplify AI agent orchestration and procurement for teams already on Google Cloud.
Temporal’s August digest rolls out a suite of new features: Serverless Workers for AWS Lambda (public preview), Projects for organizing Cloud resources, Standalone Nexus Operations, Google GenAI and Deep Agents integrations, Activity Operations and Workflow Pause controls, poller autoscaling GA, and .NET/TypeScript Nexus SDKs. It also adds a Web UI news feed, new tutorials, validated patterns, an…
Use three Temporal‑specific resources to give LLM agents the right context and guardrails: the Temporal Developer Skill (curated instructions), the official SDK samples repos (executable recipes), and the Temporal Knowledge Base MCP (RAG‑powered, citation‑rich Q&A). Explicitly load the skill, add the sample directories, and wire the MCP so the agent can validate designs before emitting code. Keep…
Temporal’s Agent Harness wraps existing LLM agent SDKs in a Temporal workflow, giving production‑grade durability, policy‑driven tool approval, and typed interfaces. It lets you keep your preferred inner harness while adding reliable execution and composability.
Temporal adds a durability layer on top of AWS’s agentic stack (Bedrock, Strands, Lambda) so AI agents can survive crashes and long human‑in‑the‑loop pauses. The loan‑underwriting demo shows a workflow that resumes after a worker crash without losing state.
The author attempted to use an AI agent to convert STL files into editable CAD, but discovered that rigorous validation and objective measurement of output quality were far more effective. The project succeeded by focusing on generating and inspecting candidates against clear metrics, rather than relying on the agent's 'cleverness'.
This article describes how MongoDB Atlas and Temporal combine to build durable RAG and agent systems. MongoDB handles data, embeddings, and vector search, while Temporal ensures reliable data ingestion pipelines and resilient, long-running agent execution, preventing data loss and re-computation.
This post demonstrates how to use Temporal's `summary` fields for Activities, Timers, and Workflows to enhance observability. These labels make it significantly easier to debug and understand the execution flow of complex, especially agentic, workflows directly in the Temporal UI without needing to inspect full payloads.
This post details how Temporal Activities orchestrate an RAG pipeline, from text embedding to vector storage, ensuring PII compliance and idempotency. It covers using an on-premise embedding model, caching vectors in Redis, and upserting to Pinecone with robust retry mechanisms.
Temporal Cloud migrated its billing store to ClickHouse, gaining 3-5 years of headroom and reducing costs by 70%. The migration focused heavily on a six-week shadowing period to validate data correctness, which uncovered an integer overflow bug in ClickHouse for large accounts.
Xgrid used Temporal to re-architect field operations for a construction company, modeling each shift as a durable workflow. This approach improved payroll accuracy, reduced tool loss, and simplified audits by handling unreliable networks, offline-first needs, and complex state transitions in harsh physical environments.
