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Moving an n8n prototype into a LangGraph production agent means rebuilding its behavior in code, not importing its workflow file. The public LangGraph and n8n documentation does not describe a converter that turns an n8n workflow into a LangGraph graph, so the working prototype becomes your specification. Inventory what it does, decide which steps must stay deterministic, then implement state, tools, persistence, approvals and deployment explicitly. You gain control over state, pauses for human decisions, and a deployment route you choose on purpose, but only if the prototype’s behavior is mapped before the first node is written.
What does not carry over automatically
An n8n workflow is a visual graph of trigger and action nodes, with credentials and execution history managed by the n8n platform. A LangGraph agent is a program: a graph of nodes that read and write a typed state, with a runtime that saves that state between steps. Nothing in the documentation maps one onto the other node by node. The n8n documentation covers workflow export, credentials, executions, queue mode and deployment, but it does not establish whether an export keeps every setting and credential reference in a form LangGraph can consume. Treat the export as a backup and reference copy, not as input.
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That makes the migration a behavioral comparison. For each thing the prototype does, define what the new system must do, where that behavior will live, and how you will prove it works.
Step 1: Inventory the prototype’s behavior
Start with the n8n documentation index to locate the current pages for workflows, credentials and executions. Then record the n8n version you run, your hosting mode and the plan features you depend on, including queue mode if it is enabled. Settings in those areas can differ by version and plan, so confirm them against your own installation rather than a generic guide.
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The inventory is the work that makes the rebuild safe. Record each item below for every workflow path.
| Behavior | What to record | Why it matters in the rebuild |
|---|---|---|
| Trigger and input | Source (webhook, schedule, chat message, form), input schema, validation, authentication | Defines the API contract and rejects bad input before any model call |
| Branches and transformations | Conditions, data types, handling of null and empty values | Becomes deterministic graph edges and code |
| Model calls | Model, prompt version, expected output format, parsing and failure handling | Determines which nodes call a model and how their output is validated |
| Tool calls | Which tools the model may call, their arguments, and the side effects they allow | Defines tool schemas and the limit of agent autonomy |
| Integrations and credentials | External API, owning account, permissions, rate limits, expected errors | Drives credential design and per-integration retry policy |
| State scope | Per invocation, per thread, per user, or shared long-term | Decides whether data goes into the checkpointer, a store, or nowhere |
| Side effects | Emails sent, records created, payments, messages posted; idempotency keys; compensation steps | Identifies what must never run twice and where approvals belong |
| Retries, timeouts and cancellation | Current settings and what the user sees when a run fails or is stopped | Sets retry, timeout and cancellation behavior in the new graph |
| Output and audit | Response shape, logs, and any audit records users or teams rely on | Defines the output contract and observability requirements |
Step 2: Define the contract and the state
Input and output contract
Write the request and response shapes before any node exists. Include required fields, types, allowed values, error responses, and the identifiers that bind a request to a thread or user. If the n8n workflow currently returns whatever its last node produced, fix that first. Production callers need a stable schema, and the new agent should return that schema even when a model step fails.
If callers will reach the agent through an API, the Agent Protocol documentation organizes serving around runs, threads and stores, and describes persistent thread state and concurrency controls. It supplies useful vocabulary for designing that API, but it does not require you to adopt the protocol.
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State schema
Give every field in the graph state one owner and one lifetime. Per-invocation values, such as the raw request and intermediate tool results, should not outlive the run that produced them. Per-thread values, such as conversation history and a pending approval, belong in the thread’s saved state. Values shared across conversations, such as a user’s stated preferences, belong in a store. Each field you add without a lifetime becomes a retention and privacy question later.
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Step 3: Decide what the model decides
LangGraph is described as an orchestration framework for long-running, stateful agents and for customized combinations of deterministic and agentic workflows, according to its reference documentation. That combination is the reason to rebuild in it, but it does not mean every n8n node should become a model decision. Keep logic in code when the rule is already known:
- Schema validation, authentication and authorization checks.
- Arithmetic, date handling, and branching on fixed fields such as status codes.
- Policy thresholds, such as whether a refund exceeds a set limit.
- Writes to external systems through fixed code paths with defined arguments.
Use a model only where the prototype already relied on interpretation or open-ended choice: classifying free-form text, selecting among several tools for a request, or drafting a response. A model should decide only what the prototype already let it decide.
Step 4: Rebuild each integration as a tool or service
Reimplement every n8n integration as a LangGraph tool or a service call with an explicit input schema, output schema and error semantics. Mark each error as retryable or terminal. A timeout from a payment provider and a validation failure in your own input check need different handling. For any write, derive an idempotency key from the thread and step, so a repeated call does not duplicate the effect, provided the target API accepts one.
Keep secrets in the secret configuration your deployment target provides, not in source code, prompts, graph state or logs. The LangGraph CLI documentation shows API keys supplied through environment variables or a .env file for its own commands. That pattern suits local work. For production, follow the provider-specific secret guidance for the environment you deploy into.
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Step 5: Recreate credential access on purpose
n8n’s workflow sharing documentation says that editors of a shared workflow can use the credentials that workflow uses, even when those credentials were never shared with them separately. A code review alone will miss that access path, so the migration needs its own access audit. Before cutover:
- List each workflow and every credential it references.
- List everyone who can edit or run that workflow in your n8n project or sharing setup. Confirm this against the sharing model in your installed version, because the permission rules are the part most likely to differ from what you remember.
- For each credential, decide who should be able to use the underlying account in the new system.
- Issue new credentials for the target service, scoped to the minimum permissions each tool needs. If a secret must move, transfer it through your secret manager, not through a document, ticket or chat message.
Step 6: Choose persistence by the lifetime of the data
LangGraph separates two persistence roles. A checkpointer saves graph state for a thread, which is what lets a run continue, recover or wait for review. A store holds application data that must survive across threads. The LangGraph guide to cross-thread persistence describes this split. For production, both need durable backing; in-memory state is suitable only for development. The documentation leaves the database choice, retention period, encryption and deletion policy to you.
| Data role | Scope | Typical content | Decision you must make |
|---|---|---|---|
| Checkpointer | One thread | Graph state at each step, including pending interrupts | Durable backend, and how long completed threads are kept |
| Store | Across threads | User preferences, reusable facts, shared reference data | Which fields persist, who can read them, and how one user’s data is deleted |
| Single run only | One invocation | Temporary parsing results, raw API responses | Let it expire with the run, and avoid persisting raw payloads you do not need |
Step 7: Make approvals and retries safe
Where the prototype waits for a person, use an interrupt. The LangGraph human-in-the-loop guide describes the pattern, which runs in this order:
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- LangGraph saves the thread’s state while the run waits.
- The interface or API caller shows the payload to the reviewer and collects the decision.
- The caller resumes the run with the same thread identifier and the decision.
- The interrupted node restarts from its beginning, not from the line where the interrupt was raised.
That final point determines where side effects go. Code that runs before the interrupt inside the same node will run again on resume. Keep that code free of external writes, or move the write into a later node that runs only after approval. A write that cannot be moved needs an idempotency key so that a rerun cannot duplicate it.
Choosing a deployment route
The LangGraph CLI documentation describes the routes below. They differ mainly in who operates the runtime, and that choice drives most of the other trade-offs.
| Route | What the CLI documentation describes | Who operates the runtime | Main trade-off to check |
|---|---|---|---|
| Local development | langgraph dev starts a local development server |
You, on your own machine | Fast iteration; not a production target |
| Docker image | langgraph build builds a Docker image |
You build the image and run it where you choose | Control over infrastructure, network placement and data location |
| LangSmith deployment | langgraph deploy deploys to LangSmith |
Deployment is through LangSmith; confirm the hosting and data-handling terms in current LangSmith documentation | Operational ownership and data terms; cost not stated in the CLI documentation |
| Team-managed registry | Pushes a built or existing image to a registry your team manages, for self-hosted or listener-based deployment | Your team, for the registry and the environment that runs the image | Registry control and fit with existing infrastructure |
Compare the production routes on operational ownership, network and data constraints, deployment lifecycle, authentication, monitoring, concurrency needs, and current commercial terms. The CLI documentation does not say which route is cheaper or better for a given team. Deployment type names and pricing terms change, so verify them when you implement.
Cut over in stages
Neither vendor’s documentation prescribes a testing or rollout method, so the sequence below is a practical one. Keep the n8n workflow running until the new agent meets the criteria you set.
- Build a replay set from representative prototype inputs, including malformed, empty and edge-case inputs. If your n8n plan and version retain execution history, use it as a source.
- Run the new agent against the replay set with integrations pointed at sandbox or test accounts. Compare the output contract, the tools selected and the branches taken with the prototype’s results.
- Inject failures: a timeout, a tool error, malformed model output, and a rate-limit response. Confirm the retry and error routes behave as specified and that no write happens twice.
- Test authorization and isolation. One user must not be able to read another user’s thread or store data.
- Interrupt a run, restart the service, and resume it. Confirm the thread state survives and that the interrupted node does not repeat an unsafe write.
- Run the new agent in shadow mode on live inputs with external writes disabled, then compare its outputs with the n8n results. Shadow mode cannot confirm that writes behave correctly, so write behavior still needs the sandbox tests above.
- Route a small share of production traffic to the new agent, and measure latency, error rate and duplicate-side-effect incidents at the concurrency you expect.
- Define the rollback before you start. Name who can route traffic back to n8n, how in-flight threads are handled, and which writes must be reconciled if you switch back.
The documentation contains no migration success rates or benchmarks, so the criteria for each stage have to come from your own workload and risk tolerance.
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