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Contact details pulled from chat messages by a local LLM

by Mindketn8n workflowProductivity
Newversion 1 · updated
Nodes7Apps1Like

Overview

  1. When chat message received
  2. Ollama Chat Model
  3. Auto-fixing Output Parser
  4. Structured Output Parser
  5. Basic LLM Chain
  6. Extract JSON Output
  7. On Error
Contact details pulled from chat messages by a local LLM, picture 1
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An n8n workflow with a chat box: type a message and Mistral NeMo, running in your own Ollama, returns the name, contact details, topic and time as JSON. The text stays between n8n and your Ollama.

Contact details pulled from chat messages by a local LLM is an n8n workflow in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n.

At a glance

Runs in
  • n8n Cloud: possible only when your Ollama has an address Cloud can reach
  • Your own n8n server: the natural fit, with Ollama on the same machine or network
  • Docker: when n8n and Ollama run in containers, put both on one user-defined network
  • Other sources: swap the chat trigger for a webhook or a mailbox, then update the chain’s prompt source

Platform

n8nWorkflow automation, in n8n’s cloud or on your own server
Connects 1 app

How it works

Trigger

Chat message

It starts when someone sends a message in n8n’s chat, which gives it:

  • The message text, in the chatInput field

Actions

  1. The message goes to a Basic LLM Chain with a system message that asks for details by a JSON schema, and gives today’s date
  2. Mistral NeMo in Ollama answers
  3. A structured parser checks the answer, and an auto-fixing parser asks the model again when it fails
  4. A Set node returns the parsed JSON as the run’s output

Result

A JSON object with the person’s name, surname, how they reached out, the contact details, a timestamp and a subject, made by a model on your own machine

Integrations

What you need before you start.

Ollama

Local language model

Runs mistral-nemo:latest on your own server, at temperature 0.1, to fill the schema from each message.

NeedsAn Ollama server, with its base URL in an Ollama credential

Workflow check

Checked by Mindket on 13 Nov 2026

Steps
4 steps, starting on a chat message
AI
1 step calls an AI model: Basic LLM Chain
Sign-ins
Signs in to: Ollama
API keys
No API key is left in the file
Step types
Mindket’s checker reads all 4 of its step types

Read from the workflow’s file, not from a run.

About

What it is

This n8n workflow pulls personal details out of free text with a model on your own machine. A chat trigger takes a message, and a Basic LLM Chain sends it to mistral-nemo in Ollama with a strict JSON schema. The schema asks for a name, a surname, how to reach the person, the contact details, a timestamp and a subject. An auto-fixing parser asks the model again when an answer breaks the schema. Seven nodes do the work, beside six sticky notes.

Who it suits

Teams that handle messages with personal data and do not want to send them to a hosted AI service, and developers who need a small extraction step with a fixed output shape. Skip it if the computer that runs Ollama is too small for a 7 GB model.

Who uses it, and how
IndustryWho uses itHow they use itExample
Customer supportSupport leadsTurn a pasted message into a record with a name, a contact and a subjectA lead pastes an email and gets a name, an address and a one line subject back.
RecruitingRecruitersPull a candidate’s details out of a messageA recruiter gets a name and a phone number from a forwarded text.
SalesAccount managersLog who wrote, how to reach them and what aboutA manager turns a chat note into a JSON record for the CRM.
Health and legalSmall practicesKeep the extraction on a machine they controlA practice runs Ollama on its own server and keeps messages off hosted AI services.

You need some n8n and a working Ollama: import a file, point a credential at Ollama and pull one model. No account or key is needed.

Where to use it

Any n8n that can reach your Ollama server will run it. Keep both on one network if the messages are private.

  • n8n Cloud: possible only when your Ollama has an address Cloud can reach
  • Your own n8n server: the natural fit, with Ollama on the same machine or network
  • Docker: when n8n and Ollama run in containers, put both on one user-defined network
  • Other sources: swap the chat trigger for a webhook or a mailbox, then update the chain’s prompt source

Setup

Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. No account or API key is needed.

What you need first
NeedDetail
n8nA server of your own, or n8n Cloud with an Ollama address it can reach
OllamaInstalled and running, with mistral-nemo:latest pulled. The download is about 7 GB
An Ollama credentialThe base URL of your Ollama server, http://localhost:11434 by default
  1. In n8n, create a workflow, open its menu, choose Import from File and pick workflow.json.
  2. On the machine that runs Ollama, pull the model with ollama pull mistral-nemo.
  3. Add an Ollama credential with your server’s base URL and select it in Ollama Chat Model.
  4. Check the model name in that node. The file uses mistral-nemo:latest.
  5. Save, open the chat from When chat message received and send a test message.
  6. Open Extract JSON Output to read the result.

How to use it

Open the workflow’s chat and send a message that mentions a person, a way to reach them and a topic. The chain asks the model for the fields in the schema and checks the answer. On success, Extract JSON Output holds the fields as plain JSON. If the chain fails, the run ends at On Error.

What to change for a different result
To changeWhere
The fieldsThe JSON schema in Structured Output Parser
The instructionsThe message in Basic LLM Chain, which also gives the model today’s date
The modelmodel in Ollama Chat Model, mistral-nemo:latest in the file
The model’s settingstemperature 0.1, keepAlive 2h and useMLock in the same node
Where the text comes fromThe trigger, and the Prompt Source (User Message) setting in Basic LLM Chain
Where the result goesThe nodes you add after Extract JSON Output

Example

A message in the chat box says that Maria Tan can be reached on 555 0142 about last Tuesday’s invoice.

The model answers with a JSON object: name Maria, surname Tan, commtype phone, contacts the number and subject the invoice. It also fills timestamp, which it works out from the date the prompt gives it. Extract JSON Output passes that object on.

For newcomers and experts

If you are new. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. The model and the two parsers hang under the chain as small side nodes. Send a message with no contact in it to see what the model returns.

For experts. One Ollama node feeds both the chain and the auto-fixing parser, so a repair call uses the same model.

Where to look closer
NodeWhat it doesWhat to know
Basic LLM ChainChain 1.5 with a system message and an output parserIts error output leads to On Error, which does nothing, so a failed answer ends the run without a message
Auto-fixing Output ParserCalls the model again when an answer breaks the schemaThe new prompt quotes the error, and it costs one more pass on a local model
Structured Output ParserA manual JSON schema with name and commtype requiredcontacts is meant only when given, so an empty field is fine
Ollama Chat Modelmistral-nemo:latest at temperature 0.1keepAlive 2h keeps the model in memory for two hours after the last call

Good to know

The file runs as it is, with two things to watch.

  • Size: the model is a download of about 7 GB, and Ollama’s page for it lists 7.1GB. A small computer needs a smaller model, and the schema may need simpler wording to match it
  • Accuracy: a small local model can miss a field or make one up, so check names and numbers before they reach a CRM
  • Errors: On Error does nothing, so add a node there if you want a message when a run fails
  • Your data: the messages stay between n8n and your Ollama server, and nothing goes to a hosted AI service
  • Credentials: the JSON holds none

README and files

README

Contact details pulled from chat messages by a local LLM

An n8n workflow with a chat box. A message goes to Mistral NeMo, running in your own Ollama, together with a strict JSON
schema. The model answers with a name, a surname, how to reach the person, the contact details, a timestamp and a subject.
The text stays between n8n and your Ollama server.

What is inside

  • workflow.json: the workflow as n8n exports it, 13 nodes. Seven do the work and six are sticky notes that label the
    steps. It holds no credential.
  • README.md: these notes.

What we changed from the original copy

  • The Ollama model node was linked to a credential in its author's n8n, and the chat trigger carried that n8n's webhook
    id. Both are gone, so select your own credential. n8n gives the trigger a fresh id when you import the file.
  • Nothing else. The prompt, the schema, the model and its settings, and the sticky notes are the file's own.

Before you start

  • An n8n instance, on n8n Cloud or your own server, that can reach your Ollama server.
  • Ollama installed and running, with the model pulled: ollama pull mistral-nemo. The download is about 7 GB.
  • An Ollama credential in n8n. No key is needed. The base URL is http://localhost:11434 by default.

Set it up

  1. In n8n, create a workflow, open its menu and choose Import from File. Pick workflow.json.
  2. Pull the model on the machine that runs Ollama: ollama pull mistral-nemo.
  3. Add an Ollama credential with your server's base URL and select it in Ollama Chat Model. If localhost fails from a
    local n8n, try 127.0.0.1. When n8n and Ollama run in separate Docker containers, put both on one user-defined
    network and use the Ollama container's name in the URL.
  4. Check the model name in Ollama Chat Model. The file uses mistral-nemo:latest.
  5. Save, open the chat from When chat message received and send a test message.
  6. Open Extract JSON Output to read the result.

How a run goes

  1. When chat message received (Chat Trigger 1.1) starts the run with the message in chatInput.
  2. Basic LLM Chain (1.5) sends the message to Ollama Chat Model with a system message: analyse the request, extract
    the information by the JSON schema, and today's date.
  3. Structured Output Parser holds the schema. name and commtype are required. commtype is one of email,
    phone or other. contacts is for the details, only when they were given.
  4. Auto-fixing Output Parser wraps the schema. When an answer breaks it, the same model is called again with the
    error and asked to correct itself.
  5. On success, Extract JSON Output (Set 3.4, raw mode) returns the parsed object as the item. If the chain fails,
    its error output leads to On Error, a No Operation node, and the run ends there.

Change the look

  • Fields: the JSON schema in Structured Output Parser.
  • Instructions: the message in Basic LLM Chain.
  • Model and its settings: Ollama Chat Model, with temperature 0.1, keepAlive 2h and useMLock in the file.
  • Where the text comes from: replace the chat trigger with a webhook, a mailbox or a form, then update the
    Prompt Source (User Message) setting in Basic LLM Chain.
  • Where the result goes: add a node after Extract JSON Output, such as a Google Sheets row or an HTTP Request to your
    CRM. Add one after On Error if you want a message when a run fails.

Good to know

  • The model is a download of about 7 GB. Ollama's page for it lists 7.1GB, so a small computer needs a smaller model,
    and the schema may need simpler words to match it.
  • A small local model can miss a field or make one up, so check names and numbers before they reach a CRM.
  • On Error does nothing, so a failed run ends without a message.
  • The messages stay between n8n and your Ollama server. Nothing is sent to a hosted AI service.
  • The file holds no credential and no key.
Files
NOTICE.md  644 B
README.md  4 KB
workflow.json  8 KB

3 files, 12 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

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