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Connect n8n to Ollama by running an Ollama model, creating an Ollama credential in n8n, entering the address where Ollama listens, and selecting the exact model tag you pulled. Use http://localhost:11434 when both applications run on the same machine. A containerized n8n normally needs http://host.docker.internal:11434 (plus a Linux host-gateway mapping), while a Compose deployment can route to the Ollama service by its container hostname.
This guide covers native and alternative nodes, Docker layouts, verification commands, model-name matching, secure remote access, and the failure messages that most often stop a local workflow.
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What the n8n–Ollama connection actually does
Ollama runs a language model on your own computer or server and exposes an HTTP API. n8n supplies prompts and workflow data to that API, then passes the generated text to later nodes such as a database, email action, ticketing system, or webhook response. The model files and inference stay under your control instead of being sent to a hosted AI provider.
There are three practical ways to call Ollama from n8n:
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| Method | Best fit | Trade-off |
|---|---|---|
| Native Ollama Chat Model node | New workflows that need the shortest setup | Uses the n8n integration and its supported chat-model options |
| OpenAI Chat Model node pointed at Ollama | Teams already standardized on OpenAI-compatible n8n nodes | Requires Ollama’s compatible /v1 endpoint and matching fields |
| HTTP Request node | Custom payloads, non-chat endpoints, or maximum control | You must build the request and parse the response yourself |
The native node is the usual starting point. Keep the other two methods in reserve when an existing workflow or an Ollama API feature does not fit the native integration.
Before you configure n8n
Install Ollama and download a model
- Install Ollama for your operating system and start the Ollama service.
- Download a model, for example:
ollama pull llama3.1 - Run a direct test before opening n8n:
ollama run llama3.1
Type a short prompt and confirm that Ollama returns an answer. This separates an Ollama installation problem from an n8n networking or credential problem. The tag in this example is llama3.1; if you pull a variant such as a quantized or size-specific tag, copy that complete tag when you configure n8n.
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Ollama normally listens on port 11434. For a same-machine installation, n8n can usually reach http://localhost:11434. If you have set the OLLAMA_HOST environment variable, use that value instead of assuming the default. Include the scheme, host, and port exactly as n8n expects; do not add an API path to the credential’s base URL.
Know where n8n is running
The correct address depends on the network namespace of n8n:
| n8n location | Ollama location | Base URL to try | Important condition |
|---|---|---|---|
| Native process | Same computer | http://localhost:11434 |
Use the OLLAMA_HOST value instead if it is set. |
| Native process | Same computer, localhost resolution issue | http://127.0.0.1:11434 |
Useful when localhost resolves unexpectedly on the local n8n server. |
| Docker container | Ollama on the Docker host | http://host.docker.internal:11434 |
Linux requires an explicit host-gateway mapping. |
| Docker Compose | Ollama in another service | http://ollama:11434 |
ollama is the service name in the shared network example; use your actual service name. |
Configure the native Ollama credential
Same-machine n8n and Ollama
- Open n8n and go to Credentials.
- Create a credential whose type is Ollama. n8n’s wording can vary slightly by release.
- Enter
http://localhost:11434in Base URL. IfOLLAMA_HOSTis configured for Ollama, enter that address instead. - Save the credential and run its test. A successful test reports Connection tested successfully.
- If the test fails on a local n8n installation, replace
localhostwith127.0.0.1and test again.
A successful credential test proves that n8n can reach the Ollama service. It does not prove that the model name is correct; model selection is checked when the model node makes its request.
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Select the model tag exactly
- Add an Ollama Chat Model node to the workflow.
- Choose the Ollama credential you just created.
- Enter the model name exactly as shown by the model you pulled, such as
llama3.1. - Save the workflow and run a minimal prompt before adding complex branching or external integrations.
Ollama model identifiers are tags, not broad product names. A name that looks close but differs in punctuation, version, size, or quantization is a different identifier and can produce a “model not found” response.
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Build a first local AI workflow
Start with the smallest workflow that proves the complete path: input, model call, and visible output. Node labels change between n8n releases, so use the current equivalent when a label differs.
- Create a new workflow and add a Manual Trigger (or the current manual-test trigger).
- Add a text input node, such as Set or Edit Fields, with a field named
prompt. Enter a sentence such asSummarize this local test in one sentence.. - Add the Ollama Chat Model node and attach the Ollama credential.
- Set its model field to the exact pulled tag.
- Connect the trigger/input path to the AI operation that accepts the chat model. Depending on your n8n version, this may be an AI chain, an agent, or another node that exposes a chat-model input.
- Map the input field into the user message. In an expression, select the incoming
promptvalue rather than typing a fixed string. - Execute the workflow and inspect the output item. Confirm that the response text is present before connecting downstream actions.
Keep the first prompt short. Large prompts make it harder to distinguish a connection failure from a context-length, memory, or generation-time problem. Once the minimal run succeeds, add your real data source and output actions one at a time.
Use a deterministic output when another node parses it
If a later node expects JSON or a fixed set of labels, instruct the model to return only that structure and validate the result before writing it to a database. Local models can still add commentary or produce malformed JSON. A validation branch that catches invalid output is safer than allowing the first response to trigger an irreversible action.
Docker: connect n8n to Ollama on the host
A container’s localhost points to the container itself, not to the computer running Ollama. Use the host gateway address instead.
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Docker Desktop and similar environments
- In the n8n Ollama credential, set the base URL to
http://host.docker.internal:11434. - Save and test the credential.
- If the test fails, confirm that Ollama is running on the host and that the host firewall permits the connection from Docker.
Linux Docker Engine
On Linux, host.docker.internal is not automatically defined in many Docker Engine setups. Add a host-gateway mapping when you start n8n:
docker run --add-host=host.docker.internal:host-gateway ...With Docker Compose, add the equivalent host entry to the n8n service:
extra_hosts:
- "host.docker.internal:host-gateway"Keep the indentation and service placement valid for your Compose file. Recreate the n8n container after changing the mapping; restarting only the workflow does not alter container networking.
Make Ollama reachable from the container
Ollama may be bound only to the host loopback interface. A container cannot reach a service that accepts connections exclusively from that loopback interface. Some deployment guides recommend setting OLLAMA_HOST=0.0.0.0 so Ollama listens on available interfaces. This broadens exposure, so apply it only on a protected network and add firewall controls; do not publish an unauthenticated Ollama API directly to the internet.
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Docker Compose: run both services on one network
When n8n and Ollama are separate Compose services, placing them on the same Compose network avoids host-gateway routing. Set the credential base URL to the Ollama service hostname and port, for example http://ollama:11434, where ollama is the service name in your Compose file.
- Define n8n and Ollama under the same Compose project or explicitly attach both services to the same network.
- Use the Ollama service name as the hostname; do not use
localhostfrom inside the n8n container. - Ensure the Ollama service is listening on the container interface, not only its own loopback interface.
- Start or recreate both services so the network and environment changes take effect.
- Test the Ollama credential from n8n, then run the minimal workflow.
Service-name routing is specific to the network shared by those containers. A hostname that works in one Compose project will not automatically resolve from an unrelated Docker network.
Alternative connection methods
OpenAI-compatible Chat Model node
Ollama provides an OpenAI-compatible endpoint under /v1. If your n8n workflow already uses an OpenAI Chat Model node, point its base URL at the Ollama-compatible endpoint and configure the model tag that exists locally. The node may still request an API key field even when your local Ollama instance does not enforce one; use the value and credential format required by your n8n version, and keep the endpoint private.
This route is useful for reusing existing prompt, agent, or structured-output configurations. It adds another compatibility layer, so the native Ollama node is easier to diagnose when you are starting from zero.
HTTP Request node and /api/generate
An HTTP Request node gives direct control over Ollama’s native API. A basic request posts JSON to /api/generate with a model tag and prompt. For n8n parsing, one tutorial recommends disabling streaming:
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{
"model": "llama3.1",
"prompt": "Summarize the incoming text.",
"stream": false
}Set the request URL to the Ollama base address followed by /api/generate, send the JSON body, and map the returned response field into the next node. Streaming can deliver token-by-token chunks that require extra assembly; a non-streaming response is simpler for ordinary n8n item processing.
Verify the connection outside the workflow
When n8n reports a refusal, test the same address from the environment where n8n runs. Testing from your host terminal is not equivalent to testing from a container.
Test the Ollama API on the same machine
curl http://localhost:11434/api/generate
-H 'Content-Type: application/json'
-d '{"model":"llama3.1","prompt":"Reply with OK","stream":false}'Replace the model tag with the one you actually pulled. A JSON response confirms that Ollama is answering requests at that address. If this command fails, fix Ollama first; n8n cannot compensate for a stopped service or an incorrect bind address.
Test from an n8n container
Run an equivalent request from a shell inside the n8n container, using host.docker.internal or the Compose service hostname. If the request works on the host but fails inside the container, the problem is Docker DNS, routing, host-gateway mapping, firewall policy, or Ollama’s listening interface—not the n8n credential form.
Troubleshoot common errors
“Connection refused”
- Confirm that Ollama is running and that the service is listening on port
11434. - Check the scheme, hostname, and port in the credential. A typo or an unnecessary path can prevent the test.
- For native n8n, try
127.0.0.1instead oflocalhost. - For Docker, replace
localhostwithhost.docker.internalor the Compose service name. - On Linux, add the
host-gatewaymapping and recreate the container. - If containers reach the host but not Ollama, review the Ollama bind address and firewall.
“Model not found”
- List the models available to the Ollama installation and compare the spelling character by character with n8n.
- Use the full tag, including version, size, or variant suffixes.
- Pull the model into the same Ollama instance that n8n is contacting. A model installed on your laptop is not available to an Ollama container or a different server unless you installed it there too.
The credential tests successfully, but the model node fails
- Recheck the selected model tag; credential testing checks reachability, not model availability.
- Run the direct
/api/generatetest with the same tag. - Reduce the prompt and remove optional output-format instructions to rule out a request-size or generation issue.
- Inspect the node’s raw input and output so you can see whether an expression supplied an empty prompt.
The workflow hangs or returns partial text
- Check whether the request is using streaming. Token streams require a node or code step that collects chunks before later nodes can parse one response.
- For a straightforward item-based workflow, set
streamtofalsein an HTTP Request payload. - Allow enough execution time for the local model to load and generate; avoid treating a slow first response as proof that networking is broken.
n8n Cloud cannot reach local Ollama
A cloud-hosted n8n instance is outside your private network. A local-only address such as localhost refers to the cloud worker, not your computer. A guide for this arrangement proposes exposing Ollama through an internet-reachable address with HTTPS and authentication. Treat that as a network architecture requirement, not a reason to publish an open Ollama port: place it behind an authenticated reverse proxy, restrict source addresses where possible, and use encrypted transport. If you cannot provide that protected endpoint, run n8n self-hosted near Ollama instead.
Security and operational safeguards
Keep the API private by default
Ollama is designed for local use. Binding it to 0.0.0.0 can make it reachable by other machines on the network, and an internet-facing port without authentication can allow unauthorized prompts and resource consumption. Prefer same-host or private Compose networking, host firewalls, and a reverse proxy with authentication for any remote access.
Separate test and production models
Use a small, known-good model tag for connectivity tests, then switch to the model your workflow requires. Store the chosen tag in one place in the workflow so an upgrade does not silently change several nodes. Retest after pulling a new tag because the credential can remain healthy while a model-specific request fails.
Control what downstream nodes can do
Local inference does not remove workflow risk. Treat model output as untrusted text: validate JSON, constrain allowed labels, and require a human approval step before destructive actions. Log the prompt and model tag when your privacy policy permits, so a failed run can be reproduced without guessing which model answered.
Quick Recap
A practical decision checklist
- Choose the native Ollama Chat Model node unless an existing design requires the OpenAI-compatible or raw HTTP route.
- Install and test Ollama directly before configuring n8n.
- Use
http://localhost:11434for same-machine processes, or the configuredOLLAMA_HOSTvalue. - Use
http://127.0.0.1:11434when local n8n cannot resolve localhost correctly. - Use
http://host.docker.internal:11434from Docker on supported environments; addhost-gatewayon Linux. - Use the Ollama Compose service hostname when both containers share a network.
- Match the n8n model field to the pulled Ollama tag exactly.
- Run a direct API test and a minimal n8n workflow before adding production actions.
- Keep any externally reachable Ollama endpoint behind authentication, HTTPS, and network restrictions.
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