AWS Lambda Durable Functions
AWSOfficialPublished by AWS
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Low risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/aws-lambda-durable-functions/downloadUnzip 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.
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.
Durable functions must be invoked with a qualified ARN — a specific version, an alias, or the literal
$LATESTsuffix. An unqualified function name will fail. See the Invocation Requirements section below for examples.Durable operations cannot be nested. You cannot call
context.step(),context.wait(), orcontext.invoke()from inside another step's callback. Usecontext.runInChildContext()to group operations instead.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.Closure mutations are lost on replay - return values from steps
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 resultPython API Differences
The Python SDK differs from TypeScript in several key areas:
Steps: Use
@durable_stepdecorator +context.step(my_step(args)), or inlinecontext.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
DurableFunctionTestRunnerclass directly - instantiate with handler, use context manager, callrun(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.jsonIAM Permissions
Your Lambda execution role MUST have the AWSLambdaBasicDurableExecutionRolePolicy managed policy attached. This includes:
lambda:CheckpointDurableExecution- Persist execution statelambda:GetDurableExecutionState- Retrieve execution stateCloudWatch Logs permissions
Additional permissions needed for:
Durable invokes:
lambda:InvokeFunctionon target function ARNsExternal callbacks: Systems need
lambda:SendDurableExecutionCallbackSuccessandlambda:SendDurableExecutionCallbackFailure
Validation Guidelines
When writing or reviewing durable function code, ALWAYS check for these replay model violations:
Non-deterministic code outside steps:
Date.now(),Math.random(), UUID generation, API calls, database queries must all be inside stepsNested durable operations in step functions: Cannot call
context.step(),context.wait(), orcontext.invoke()inside a step function — usecontext.runInChildContext()insteadClosure mutations that won't persist: Variables mutated inside steps are NOT preserved across replays — return values from steps instead
Side effects outside steps that repeat on replay: Use
context.loggerfor logging (it is replay-aware and deduplicates automatically)
When implementing or modifying tests for durable functions, ALWAYS verify:
All operations have descriptive names
Tests get operations by NAME, never by index
Replay behavior is tested with multiple invocations
Use
LocalDurableTestRunnerfor 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
waitForCallbackoriginates from external systems — validate and sanitize before processing.Logging: Avoid
DEBUGlog level in non-development environments as it may expose step results and execution state. Enable CloudWatch Logs encryption with KMS.
Resources
Files
- ATTRIBUTION.mdAdded by AutoGineer946 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md8.9 KiB
- references/advanced-error-handling.md4.9 KiB
- references/advanced-patterns.md10.4 KiB
- references/concurrent-operations.md8.4 KiB
- references/deployment-iac.md14.0 KiB
- references/error-handling.md10.5 KiB
- references/getting-started.md12.6 KiB
- references/replay-model-rules.md8.3 KiB
- references/step-operations.md8.3 KiB
- references/testing-patterns.md17.4 KiB
- references/troubleshooting-executions.md8.5 KiB
- references/wait-operations.md9.5 KiB
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:
Use
waitForCallback(TypeScript) orwait_for_callback(Python) with a timeout configuration set in the config argumentWrap in try-catch to handle timeout errors
Check if the error is a timeout
Implement fallback logic in a step (e.g., escalate to manager, use default value, retry with different parameters)
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:
Use
Promise.race()to race the step operation against a timeout promiseThe timeout promise rejects after the specified duration
Catch the timeout error and implement fallback logic
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:
Define a custom retry strategy function that examines the error
For client errors (4xx): Don't retry - these are permanent failures
For server errors (5xx): Retry with exponential backoff
For network errors: Retry with fixed delay
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:
Track failure count and last failure time (note: these reset on replay due to closure mutations)
Check if circuit is "open" (too many recent failures)
If open, throw a circuit breaker error and wait before retrying
If closed, attempt the operation
On success, reset failure count
On failure, increment failure count and record timestamp
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
Timeout Handling: Always implement fallback logic for callback timeouts - don't let executions fail silently
Conditional Retries: Classify errors as transient vs permanent, only retry transient errors
Circuit Breakers: Protect against cascading failures to external services, especially for high-volume operations
Structured Logging: Log error context (error type, attempt count, operation name) for debugging
Graceful Degradation: Return partial results when possible rather than failing completely
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)
- Synced
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