Build Custom AI Agent with LangChain & Gemini (Self-Hosted) — n8n Workflow

Niedrig Komplexität⚡ Auslöser4 Knoten🏷️ Miscellaneous👁 4,410 Aufrufevon shepard

Übersicht

Overview
This workflow leverages the LangChain code node to implement a fully customizable conversational agent. Ideal for users who need granular control over their agent's prompts while reducing unnecessary token consumption from reserved tool-calling functionality (compared to n8n's built-in Conversation Agent).

Setup Instructions
Configure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes.
Interacti

Verwendete Knoten

LangChain CodeSimple MemoryGoogle Gemini Chat Model

Workflow-Vorschau

👇 Prompt Engineering
- Define agent personality and conversation structur
- ⚠️ Template must preserve `{chat_history}` and
Setup Instructions
1. Configure Gemini Credentials: Set up your Google Gem
👆 Interface Settings
Configure chat UI elements (e.g., title) in the `When C
👆 Model Selection
Swap language models through the `language model` input
👆 Memory Control
Adjust conversation history length in the `Store Conver
modelmemorymemory
⚡
W
When chat message received
Google Gemini Chat Model
Store conversation history
Construct & Execute LLM …
4 nodes4 edges

So funktioniert es

  1. 1

    Auslöser

    Der Workflow startet mit einem auslöser-Auslöser.

  2. 2

    Verarbeitung

    Die Daten fließen durch 4 Knoten, connecting chattrigger, code, lmchatgooglegemini.

  3. 3

    Ausgabe

    Der Workflow schließt seine Automatisierung ab und liefert das Ergebnis an das konfigurierte Ziel.

Knotendetails (4)

LA

LangChain Code

n8n-nodes-langchain.code

#1
SI

Simple Memory

n8n-nodes-langchain.memoryBufferWindow

#2
GO

Google Gemini Chat Model

n8n-nodes-langchain.lmChatGoogleGemini

#3

So importieren Sie diesen Workflow

  1. 1Klicken Sie rechts auf die Schaltfläche JSON herunterladen, um die Workflow-Datei zu speichern.
  2. 2Öffnen Sie Ihre n8n-Instanz. Gehen Sie zu Workflows → Neu → Aus Datei importieren.
  3. 3Wählen Sie die heruntergeladene Datei build-custom-ai-agent-with-langchain-gemini-self-hosted und klicken Sie auf Importieren.
  4. 4Richten Sie Anmeldedaten für jeden Dienstknoten ein (API-Schlüssel, OAuth usw.).
  5. 5Klicken Sie auf Workflow testen, um zu überprüfen, ob alles funktioniert, und aktivieren Sie es dann.

Oder direkt in n8n → Aus JSON importieren einfügen:

{ "name": "Build Custom AI Agent with LangChain & Gemini (Self-Hosted)", "nodes": [...], ...}

Integrationen

chattriggercodelmchatgooglegeminimemorybufferwindow

Diesen Workflow holen

Herunterladen und mit einem Klick importieren

JSON herunterladenAuf n8n.io ansehen
Knoten4
Komplexitätlow
Auslösertrigger
Aufrufe4,410
KategorieMiscellaneous

Erstellt von

shepard

shepard

@shepard

Tags

chattriggercodelmchatgooglegeminimemorybufferwindow
⚡

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