Aws Lambda Durable Functions logo

Aws Lambda Durable Functions

Organization
awslabs
aws-lambda-durable-functions

Build 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, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill nameaws-lambda-durable-functions
Stars
917
Forks
162
Bundled files
11
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Aws Lambda Durable Functions AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-lambda-durable-functions
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/aws-serverless/skills/aws-lambda-durable-functions .claude/skills/aws-lambda-durable-functions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Lambda Durable Functions in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Lambda Durable Functions on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Lambda Durable Functions is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Onboarding

Step 1: Validate Prerequisites

Before using AWS Lambda durable functions, verify:

  1. AWS CLI is installed (2.33.22 or higher) and configured:

    bash
    aws --version
    aws sts get-caller-identity
  2. Runtime environment is ready:

    • For TypeScript/JavaScript: Node.js 22+ (node --version)
    • For Python: Python 3.11+ (python --version. Note that currently only Lambda runtime environments 3.13+ come with the Durable Execution SDK pre-installed. 3.11 is the min supported Python version by the Durable SDK itself, however, you could use OCI to bring your own container image with your own Python runtime + Durable SDK.)
  3. Deployment capability exists (one of):

    • AWS SAM CLI (sam --version) 1.153.1 or higher
    • AWS CDK (cdk --version) v2.237.1 or higher
    • Direct Lambda deployment access

Step 2: Select language and IaC framework

Language Selection

Default: TypeScript

Override syntax:

  • "use Python" → Generate Python code
  • "use JavaScript" → Generate JavaScript code

When not specified, ALWAYS use TypeScript

IaC framework selection

Default: CDK

Override syntax:

  • "use CloudFormation" → Generate YAML templates
  • "use SAM" → Generate YAML templates

When not specified, ALWAYS use CDK

Error Scenarios

Unsupported Language
  • List detected language
  • State: "Durable Execution SDK is not yet available for [framework]"
  • Suggest supported languages as alternatives
Unsupported IaC Framework
  • List detected framework
  • State: "[framework] might not support Lambda durable functions yet"
  • Suggest supported frameworks as alternatives

Serverless MCP Server Unavailable

  • Inform user: "AWS Serverless MCP not responding"
  • Ask: "Proceed without MCP support?"
  • DO NOT continue without user confirmation

Step 3: Install SDK

For TypeScript/JavaScript:

bash
npm install @aws/durable-execution-sdk-js
npm install --save-dev @aws/durable-execution-sdk-js-testing

For Python:

bash
pip install aws-durable-execution-sdk-python
pip install aws-durable-execution-sdk-python-testing

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:

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:

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

Critical Rules

  1. All non-deterministic code MUST be in steps (Date.now, Math.random, API calls)
  2. Cannot nest durable operations - use runInChildContext to group operations
  3. Closure mutations are lost on replay - return values from steps
  4. Side effects outside steps repeat - use context.logger (replay-aware)

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):

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

MCP Server Configuration

Write access is enabled by default. The plugin ships with --allow-write in .mcp.json, so the MCP server can create projects, generate IaC, and deploy on behalf of the user.

Access to sensitive data (like Lambda and API Gateway logs) is not enabled by default. To grant it, add --allow-sensitive-data-access to .mcp.json.

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Lambda Durable Functions AI skill do?

Build 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, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.

Why use Aws Lambda Durable Functions on TypingMind?

Because you install it once and use it with any model. Aws Lambda Durable Functions is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Lambda Durable Functions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-lambda-durable-functions. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Lambda Durable Functions?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Lambda Durable Functions?

As many as you like. As long as a model supports skills, you can use Aws Lambda Durable Functions with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Lambda Durable Functions AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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