Debug your Render services in Claude Code and Cursor.

Render Workflows

Run thousands of parallel tasks with zero ops overhead. Scale seamlessly, retry automatically, and debug with full visibility.

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@task(concurrency=50)
async def analyze_documents(file_paths):
  files = await get_files(file_paths)
  for path in files:
    summary = await summarize_with_llm(path)
    await save_summary(path, summary)

@task(retries=3, retry_backoff_factor=2)
async def summarize_with_llm(path, text):
  return await call_llm_for_summary(text)

@task
async def save_summary(path, summary):
  await db.upsert_summary(path, summary)

Smarter background jobs, right on queue

Model your batch processes and agentic workloads as composable tasks. Branch, retry, and fan out with ease.

Lightweight SDKs

Define tasks and subtasks right in your code with clean, declarative syntax. No DSLs or YAML required.

Queue-based autoscaling

Add workers in seconds to handle “bursty” workloads. Scale down to zero when the task queue is empty.

Durable execution

Automatically retry failed tasks and surface failure patterns. Restore from stateful checkpoints without losing work.

Complete observability

Track, debug, and monitor runs in real time. Visualize bottlenecks in parallel runs with built-in instrumentation.

Your stack's elastic execution engine

Workflow steps are queued and scheduled on workers that spin up on demand. You define the jobs, and Render’s engine handles the distribution.

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Engineered for your most demanding workloads

Dispatch entire fleets of long-lived, stateful compute—perfect for AI agents, ETL pipelines, and event-driven architectures.

Durable

Automatic retries

Avoid hitting rate limits with exponential backoff.

Stateful checkpointing

Restore to your last healthy state after an interruption.

Idempotent execution

Avoid duplicated work, even across retries.

Scalable

Fan out in seconds

Spin up hundreds or even thousands of
workers when your queue spikes.

Run for hours

Go beyond 15-minute serverless limits—tasks
can stay active for a day or more.

Scale to zero

Workers automatically spin down when there’s
nothing to work on. Only pay for what you use.

Effortless

Drop-in libraries

Start running with just a few lines of code—
no heavy frameworks or steep learning curve.

Local dev support

Iterate on your machine, then scale on ours.

First-class observability

View per-task logs, retries, and timelines.

Declaratively distributed

Express tasks as functions in Python, TypeScript, or Go. Render takes it from there: provisioning, monitoring, retries, and more.

@task(concurrency=50)
async def query_llms_and_evaluate(prompt):
  # Query 3 LLMs in parallel using model names
  model_configs = [
    {"provider": "openai", "model": "gpt-5"},
    {"provider": "anthropic", "model": "claude-opus-4"},
    {"provider": "google", "model": "gemini-2.5-pro"},
  ]
  queries = [query_llm(cfg["provider"], cfg["model"], prompt) for cfg in model_configs]
  responses = await asyncio.gather(*queries)

  # Have a 4th LLM evaluate and pick the best response
  return await select_best_result(responses)

@task(retries=3, retry_wait_duration=5000)
async def query_llm(provider, model_name, prompt):
  providers = {
    "openai": ChatOpenAI,
    "anthropic": ChatAnthropic,
    "google": ChatGoogleGenerativeAI,
  }
  # Construct the appropriate LLM client based on provider
  llm = providers[provider](model=model_name)

  return call_llm(llm, [HumanMessage(content=prompt)])

@task(retries=2)
async def select_best_result(responses):
  evaluator = ChatOpenAI(model="gpt-5")
  eval_prompt = (
    "Which response is best?

    f'{"
1}: {r}" for i, r in enumerate(responses))}'
  )
  evaluation = await evaluator.ainvoke([HumanMessage(content=eval_prompt)])
  return evaluation.content

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Workflows are launching soon. Request early access and help shape the future of background orchestration.

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