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Saga Orchestration

CommunityPopular
wshobson
saga-orchestration

Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building event-driven saga coordinators for travel booking systems that must roll back hotel, flight, and car rental reservations atomically, or debugging stuck saga states in production where compensation steps never complete.

Overview

Publisherwshobson
Repositoryagents
Skill namesaga-orchestration
Stars
39.8K
Forks
4.2K
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Saga Orchestration 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.
  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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/backend-development/skills/saga-orchestration .claude/skills/saga-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Saga Orchestration 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 Saga Orchestration 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 Saga Orchestration 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.

Saga Orchestration

Patterns for managing distributed transactions and long-running business processes without two-phase commit.

Inputs and Outputs

What you provide:

  • Service boundaries and ownership (which service owns which step)
  • Transaction requirements (which steps must be atomic, which can be eventual)
  • Failure modes for each step (transient vs. permanent, retry policy)
  • SLA requirements per step (informs timeout configuration)
  • Existing event/messaging infrastructure (Kafka, RabbitMQ, SQS, etc.)

What this skill produces:

  • Saga definition with ordered steps, action commands, and compensation commands
  • Orchestrator or choreography implementation for your chosen pattern
  • Compensation logic for each participant service (idempotent, always-succeeds)
  • Step timeout configuration with per-step deadlines
  • Monitoring setup: state machine metrics, stuck saga detection, DLQ recovery

When to Use This Skill

  • Coordinating multi-service transactions without distributed locks
  • Implementing compensating transactions for partial failures
  • Managing long-running business workflows (minutes to hours)
  • Handling failures in distributed systems where atomicity is required
  • Building order fulfillment, approval, or booking processes
  • Replacing fragile two-phase commit with async compensation

Detailed section: Core Concepts

Moved to references/details.md.

Detailed section: Templates

Moved to references/details.md.

Best Practices

Do's

  • Make every step idempotent — Commands may be replayed on broker reconnect
  • Design compensations carefully — They are the most critical code path
  • Use correlation IDs — The saga_id must flow through every event and log
  • Implement per-step timeouts — Never wait indefinitely for a participant reply
  • Log state transitionssaga_id, step_name, old_state → new_state on every change
  • Test compensation paths explicitly — Inject failures at each step index in integration tests

Don'ts

  • Don't assume instant completion — Sagas are async and may take minutes
  • Don't skip compensation testing — The rollback path is the hardest to get right
  • Don't couple services directly — Use async messaging, never synchronous calls inside a saga step
  • Don't ignore partial failures — A step that partially executed still needs compensation
  • Don't use a global timeout — Each step has different latency characteristics

Troubleshooting

Saga stuck in COMPENSATING state

A saga enters compensation but never reaches FAILED. This means a compensation handler is throwing an unhandled exception and never publishing SagaCompensationCompleted. Add dead-letter queue (DLQ) handling to compensation consumers and ensure every compensation action publishes a result event even when the underlying operation was already rolled back.

python
async def handle_release_reservation(self, command: Dict):
    try:
        await self.release_reservation(command["original_result"]["reservation_id"])
    except ReservationNotFoundError:
        pass  # Already released — treat as success
    # Always publish completion, regardless of outcome
    await self.event_publisher.publish("SagaCompensationCompleted", {
        "saga_id": command["saga_id"],
        "step_name": "reserve_inventory"
    })

Duplicate saga executions on restart

If your orchestrator service restarts mid-saga, it may replay events and re-execute already-completed steps. Guard every step action with an idempotency key — see Template 3 above.

Choreography saga losing events

In a choreography-based saga, a downstream service may miss an event if it was offline when published. Use a durable message broker (Kafka with replication, RabbitMQ with persistence) and store the current saga state in a dedicated saga_log table so you can replay from the last known good step.

Timeout firing before a slow-but-valid step completes

A step like create_shipment might take up to 15 minutes during peak load but your global timeout is 5 minutes, causing spurious compensation. Make step timeouts configurable per step type — see references/advanced-patterns.md for the TimeoutSagaOrchestrator implementation and the STEP_TIMEOUTS dict pattern.

Compensation order not matching execution order

When two steps both complete before a failure is detected, compensation must run in strict reverse order or you leave data in an inconsistent state. Verify that _compensate() iterates from current_step - 1 down to 0, and add an integration test that deliberately fails at each step index to confirm correct rollback order.


Advanced Patterns

The references/ directory contains production-grade implementations not needed for most sagas:

  • references/advanced-patterns.md — Full SagaOrchestrator abstract base class, TimeoutSagaOrchestrator with per-step deadlines, detailed bank transfer compensating transaction chain, Prometheus instrumentation, stuck saga PromQL alerts, and DLQ recovery worker.

Related Skills

  • cqrs-implementation — Pair sagas with CQRS for read-model updates after each step completes
  • event-store-design — Store saga events in an event store for full audit trail and replay capability
  • workflow-orchestration-patterns — Higher-level workflow engines (Temporal, Conductor) that build on saga concepts

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 Saga Orchestration AI skill do?

Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building event-driven saga coordinators for travel booking systems that must roll back hotel, flight, and car rental reservations atomically, or debugging stuck saga states in production where compensation steps never complete.

Why use Saga Orchestration on TypingMind?

Because you install it once and use it with any model. Saga Orchestration 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 Saga Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/backend-development/skills/saga-orchestration. 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 Saga Orchestration?

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 Saga Orchestration?

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

Is the Saga Orchestration AI skill free?

Yes. It is published on GitHub by wshobson under the MIT 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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