---
title: Multi Agent Orchestration Langgraph
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/multi-agent-orchestration-langgraph/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/multi-agent-orchestration-langgraph/
last_updated: 2026-06-20
---

# Multi Agent Orchestration Langgraph

> LangGraph ile production multi-agent orkestrasyon state machine (nodes + edges + state)…

A production patterns library for orchestrating multiple AI agents with LangGraph state machines. It replaces fragile sequential dispatch with explicit nodes, edges, and shared state, adding supervisor routing, parallel map-reduce, saga rollback, and checkpoint-resume so long-running multi-agent pipelines stay coordinated and recoverable.

## Use cases
- Coordinating three or more agents where sequential handoff causes context drift
- Running a fleet of agents in parallel for an audit, then synthesizing one result
- Rolling back an entire commit-deploy-test chain when one step fails
- Resuming a long batch job from its checkpoint after a crash or restart
- Building a supervisor agent that dynamically routes work to specialists
- Mixing models per role to control multi-agent cost without losing quality

## Benefits
- Eliminate context drift by carrying shared state across every agent transition
- Cut wall-clock time by running independent agents in parallel instead of in sequence
- Recover long-running jobs without restarting from scratch after a failure
- Keep multi-agent chains consistent and rollback-safe under partial failure

## What’s included
- A LangGraph state-machine skeleton with typed shared state and conditional routing
- A supervisor pattern using structured LLM output for dynamic agent routing
- A parallel map-reduce pattern with fan-out workers and a synthesizer node
- A saga pattern with compensating actions for multi-step rollback
- A checkpoint-resume pattern with cycle detection for long jobs
- A ten-item anti-pattern table covering state mutation, missing checkpoints, and tool boundaries

## Who it’s for
AI engineers building coordinated, long-running multi-agent systems who have outgrown simple sequential dispatch and need resilience, parallelism, and rollback.

## How it runs
Five agents running in parallel cut a 150 second audit to about 30. The catch is shared state, failure rollback and routing, which is exactly what this state-machine build handles.
1. Starts with four gating questions before any code: how many agents and what is the dependency shape (sequential A to B to C, or parallel fan-out into a synthesizer), what does the shared state carry, what happens when one agent fails (saga rollback, skip and log, or retry), and is the run long enough to require persistent checkpoints.
2. Defines the shared state as a typed schema (problem, customer slug, findings list, decision trace, failure flag, checkpoint id) with an immutability rule: every agent returns a copy-and-update of the state, in-place mutation is banned because parallel branches race on it.
3. Builds the graph from the agent registry: each node is an agent with its own model assignment, strict allowed-tools whitelist and isolated system prompt that the parent state cannot override, which is both a drift guard and a prompt-injection boundary.
4. Wires the edges by pattern: a supervisor node makes dynamic routing decisions via structured output (next agent plus reason plus priority) instead of static if-else, parallel map-reduce fans 5 workers out simultaneously and merges them in an synthesizer node, cutting a 150 second sequential audit to about 30 seconds.
5. Adds the failure machinery: saga compensating edges so a failed downstream step (deploy test fails) walks back the chain (revert deploy, revert commit), plus state-hash cycle detection that halts the graph when the supervisor keeps returning to the same state three times.
6. Compiles with a Postgres checkpoint backend for long runs: every node completion persists state under a thread id, so a 4-hour overnight batch that crashes at hour 2 resumes from the last completed step instead of restarting, and each transition stays visible in the trace for debugging.

## FAQ
### We only run two agents with a simple handoff. Do we need a state machine?
Probably not yet. The patterns earn their keep at three or more agents, where sequential handoff starts causing context drift, or when jobs run long enough to need checkpoint-resume. A two-step chain that finishes in one pass is fine without a graph.

### How does a crashed batch job resume without starting over?
State is persisted at checkpoints through a Postgres-backed saver, so after a crash or restart the graph picks up from the last checkpoint instead of step one. Cycle detection keeps a resumed run from looping on the node that failed.

### Can I use these patterns with a different framework, like CrewAI?
No, not directly. The skeletons are written as LangGraph nodes, edges, and typed shared state, and the checkpoint layer assumes its saver interface. The concepts port; the code does not.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI and LLM engineering](https://forgehouse.ai/guides/ai-llm-engineering/)