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AWS Lambda Durable Functions

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides guidance and patterns for implementing durable, multi-step applications on AWS Lambda. It focuses on the replay model, which allows functions to maintain state across long-running executions—up to one year—by automatically checkpointing progress. The skill covers essential architectural patterns such as the saga pattern for error handling, wait/callback mechanisms for external system integration, and testing strategies using the local test runner. It emphasizes strict adherence to deterministic coding practices required by the replay model to ensure reliable execution.

What it does

  • Enables state persistence for long-running Lambda functions.
  • Provides patterns for implementing the saga pattern and compensating transactions.
  • Supports wait/callback patterns for integrating with external systems.
  • Offers guidance on testing durable functions locally and in the cloud.
  • Defines rules for deterministic code to prevent state corruption during replays.
  • Outlines requirements for qualified ARN invocation and IAM permissions.

When to use it

  • When building workflows that require execution times longer than standard Lambda timeouts.
  • When orchestrating multi-step processes that need to maintain state across interruptions.
  • When implementing reliable, fault-tolerant serverless integrations.
  • When managing complex state machines or human-in-the-loop approval processes.
  • When you need automatic retry logic and checkpointing for critical business processes.

Advantages

  • Resilient execution that survives infrastructure interruptions.
  • Simplified state management without external databases for simple workflows.
  • Native support for complex orchestration patterns like sagas and parallel execution.
  • Built-in replay-aware logging and error handling.
  • Local testing capabilities to validate complex workflow logic.

Usage examples

“How do I implement a multi-step workflow with a wait period in AWS Lambda?”
The agent provides a code example using context.step for logic and context.wait for the delay, ensuring the execution state is persisted correctly.
“Why is my durable function failing during replay?”
The agent helps diagnose non-deterministic code (like Date.now() or Math.random()) placed outside of steps, which causes state corruption.
“How do I handle errors in a long-running durable function?”
The agent explains the saga pattern and how to implement compensating transactions within steps to roll back state if a step fails.
“What are the requirements for invoking a durable Lambda function?”
The agent clarifies that durable functions must be invoked using a qualified ARN (version, alias, or $LATEST) and provides the corresponding AWS CLI command.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/aws-lambda-durable-functions/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip aws-lambda-durable-functions.zip -d .agents/skills

Documentation by AWS

AWS Lambda durable functions

Build resilient multi-step applications and AI workflows that can execute for up to 1 year while maintaining reliable progress despite interruptions.

Works best with the AWS MCP server but is not required. All AWS interactions in this skill use standard AWS CLI commands that work in any environment with configured AWS credentials.

Critical Rules

Read these before writing any code. Each one is a constraint that will silently break a function if violated.

  1. Durable execution must be enabled at function creation time — it cannot be retrofitted. A new Lambda function must be created with durable execution turned on. Migrate the logic into the new function; do not attempt to install the SDK and wrap the handler of the existing function and expect it to work.

  2. Durable functions must be invoked with a qualified ARN — a specific version, an alias, or the literal $LATEST suffix. An unqualified function name will fail. See the Invocation Requirements section below for examples.

  3. Durable operations cannot be nested. You cannot call context.step(), context.wait(), or context.invoke() from inside another step's callback. Use context.runInChildContext() to group operations instead.

  4. All non-deterministic code must run inside steps. Date.now(), Math.random(), UUID generation, API calls, and database queries outside a step will produce different values on replay and corrupt execution state.

  5. Closure mutations are lost on replay - return values from steps

  6. Side effects outside steps repeat - use context.logger (replay-aware)

When to Load Reference Files

Load the appropriate reference file based on what the user is working on:

  • Getting started, basic setup, example, ESLint, or Jest setup -> see getting-started.md

  • Understanding replay model, determinism, or non-deterministic errors -> see replay-model-rules.md

  • Creating steps, atomic operations, or retry logic -> see step-operations.md

  • Waiting, delays, callbacks, external systems, or polling -> see wait-operations.md

  • Parallel execution, map operations, batch processing, or concurrency -> see concurrent-operations.md

  • Error handling, retry strategies, saga pattern, or compensating transactions -> see error-handling.md

  • Advanced error handling, timeout handling, circuit breakers, or conditional retries -> see advanced-error-handling.md

  • Testing, local testing, cloud testing, test runner, or flaky tests -> see testing-patterns.md

  • Deployment, CloudFormation, CDK, SAM, log groups, deploy, or infrastructure -> see deployment-iac.md

  • Advanced patterns, GenAI agents, completion policies, step semantics, or custom serialization -> see advanced-patterns.md

  • troubleshooting, stuck execution, failed execution, debug execution ID, execution history, execution error, why did my execution fail, execution timed out, callback not received, diagnose execution, or root cause execution -> see troubleshooting-executions.md

Quick Reference

Basic Handler Pattern

TypeScript:

import { withDurableExecution, DurableContext } from '@aws/durable-execution-sdk-js';

export const handler = withDurableExecution(async (event, context: DurableContext) => {
  const result = await context.step('process', async () => processData(event));
  return result;
});

Python:

from aws_durable_execution_sdk_python import durable_execution, DurableContext

@durable_execution
def handler(event: dict, context: DurableContext) -> dict:
    result = context.step(lambda _: process_data(event), name='process')
    return result
Python API Differences

The Python SDK differs from TypeScript in several key areas:

  • Steps: Use @durable_step decorator + context.step(my_step(args)), or inline context.step(lambda _: ..., name='...'). Prefer the decorator for automatic step naming.

  • Wait: context.wait(duration=Duration.from_seconds(n), name='...')

  • Exceptions: ExecutionError (permanent), InvocationError (transient), CallbackError (callback failures)

  • Testing: Use DurableFunctionTestRunner class directly - instantiate with handler, use context manager, call run(input=...)

Invocation Requirements

Durable functions require qualified ARNs (version, alias, or $LATEST):

# Valid
aws lambda invoke --function-name my-function:1 output.json
aws lambda invoke --function-name my-function:live output.json

# Invalid - will fail
aws lambda invoke --function-name my-function output.json

IAM Permissions

Your Lambda execution role MUST have the AWSLambdaBasicDurableExecutionRolePolicy managed policy attached. This includes:

  • lambda:CheckpointDurableExecution - Persist execution state

  • lambda:GetDurableExecutionState - Retrieve execution state

  • CloudWatch Logs permissions

Additional permissions needed for:

  • Durable invokes: lambda:InvokeFunction on target function ARNs

  • External callbacks: Systems need lambda:SendDurableExecutionCallbackSuccess and lambda:SendDurableExecutionCallbackFailure

Validation Guidelines

When writing or reviewing durable function code, ALWAYS check for these replay model violations:

  1. Non-deterministic code outside steps: Date.now(), Math.random(), UUID generation, API calls, database queries must all be inside steps

  2. Nested durable operations in step functions: Cannot call context.step(), context.wait(), or context.invoke() inside a step function — use context.runInChildContext() instead

  3. Closure mutations that won't persist: Variables mutated inside steps are NOT preserved across replays — return values from steps instead

  4. Side effects outside steps that repeat on replay: Use context.logger for logging (it is replay-aware and deduplicates automatically)

When implementing or modifying tests for durable functions, ALWAYS verify:

  1. All operations have descriptive names

  2. Tests get operations by NAME, never by index

  3. Replay behavior is tested with multiple invocations

  4. Use LocalDurableTestRunner for local testing

Security Considerations

  • Checkpoint data encryption: Execution state is persisted automatically. Enable KMS encryption on associated CloudWatch Log Groups to protect checkpointed data at rest.

  • Sensitive data in step results: Step return values are checkpointed and persisted. Do not return secrets, raw credentials, or PII from steps — store sensitive data in Secrets Manager or SSM Parameter Store and return references instead.

  • Input validation: Validate and sanitize event payloads at the handler entry point before passing data to steps.

  • Credential management: Retrieve secrets from AWS Secrets Manager or SSM Parameter Store within steps.

  • Callback payload validation: Data received via waitForCallback originates from external systems — validate and sanitize before processing.

  • Logging: Avoid DEBUG log level in non-development environments as it may expose step results and execution state. Enable CloudWatch Logs encryption with KMS.

Resources

Files

references/advanced-error-handling.md

Advanced Error Handling

Advanced error handling patterns for durable functions, including timeout handling, circuit breakers, and conditional retry strategies.

Timeout Handling with Callbacks

Pattern: Wait for an external callback with a timeout, and implement fallback logic if the timeout is reached.

Implementation approach:

  1. Use waitForCallback (TypeScript) or wait_for_callback (Python) with a timeout configuration set in the config argument

  2. Wrap in try-catch to handle timeout errors

  3. Check if the error is a timeout

  4. Implement fallback logic in a step (e.g., escalate to manager, use default value, retry with different parameters)

  5. Return appropriate status indicating timeout occurred

Key considerations:

  • Timeout errors are thrown when the callback doesn't complete within the specified duration

  • Fallback logic should be in a step to ensure it's checkpointed

  • Log timeout events for monitoring and debugging

Local Timeout with Promise.race in Typescript SDK

Pattern: Implement a timeout for a step operation within a single Lambda invocation.

Implementation approach:

  1. Use Promise.race() to race the step operation against a timeout promise

  2. The timeout promise rejects after the specified duration

  3. Catch the timeout error and implement fallback logic

  4. Execute fallback operation in a separate step

Important limitation: In TypeScript, native setTimeout (and patterns like Promise.race using it) will fail during execution replays. To create a reliable timeout that persists across execution (expands over multi invocations), always use the timeout parameter provided by waitForCallback

Conditional Retry Based on Error Type

Pattern: Retry operations selectively based on the type of error encountered.

Implementation approach:

  1. Define a custom retry strategy function that examines the error

  2. For client errors (4xx): Don't retry - these are permanent failures

  3. For server errors (5xx): Retry with exponential backoff

  4. For network errors: Retry with fixed delay

  5. For unknown errors: Don't retry by default

Key considerations:

  • Client errors (400-499) typically indicate bad input and shouldn't be retried

  • Server errors (500-599) are often transient and benefit from retry

  • Network errors (connection refused, timeout) should retry with reasonable limits

  • Use exponential backoff for server errors to avoid overwhelming the service

  • Set maximum retry attempts to prevent infinite loops

Circuit Breaker Pattern

Pattern: Temporarily stop making requests to a failing external service to prevent cascading failures.

Implementation approach:

  1. Track failure count and last failure time (note: these reset on replay due to closure mutations)

  2. Check if circuit is "open" (too many recent failures)

  3. If open, throw a circuit breaker error and wait before retrying

  4. If closed, attempt the operation

  5. On success, reset failure count

  6. On failure, increment failure count and record timestamp

  7. Configure retry strategy to wait longer when circuit is open

Important caveat: The example implementations use closure variables (failureCount, lastFailureTime) which reset on replay. For production use, store circuit breaker state in:

  • A step return value that persists across replays

  • An external store like DynamoDB

  • A durable variable pattern

Key considerations:

  • Circuit breaker prevents cascading failures to downstream services

  • The "open" duration should be long enough for the service to recover

  • Reset the circuit on successful operations

  • Log circuit state changes for monitoring

Error Handling Best Practices

  1. Timeout Handling: Always implement fallback logic for callback timeouts - don't let executions fail silently

  2. Conditional Retries: Classify errors as transient vs permanent, only retry transient errors

  3. Circuit Breakers: Protect against cascading failures to external services, especially for high-volume operations

  4. Structured Logging: Log error context (error type, attempt count, operation name) for debugging

  5. Graceful Degradation: Return partial results when possible rather than failing completely

  6. Error Classification: Distinguish between client errors (don't retry), server errors (retry with backoff), and network errors (retry with fixed delay)

Common Error Patterns

Transient Errors (Should Retry)
  • Network timeouts

  • Service unavailable (503)

  • Rate limiting (429)

  • Database connection failures

  • Temporary infrastructure issues

Permanent Errors (Should Not Retry)
  • Invalid input (400)

  • Authentication failures (401, 403)

  • Resource not found (404)

  • Business logic violations

  • Validation errors

Timeout Errors (Need Fallback)
  • Callback timeouts - external system didn't respond in time

  • External system delays - service is slow or unresponsive

  • Long-running operations - operation exceeded expected duration

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs a command-line tool, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/serverless-skills/aws-lambda-durable-functions · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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