n8n-nodes-agent-langfuse

An n8n community node that brings AI Agent execution and Langfuse observability together in a single node. Select prompts from Langfuse, override models dynamically, and get full tracing — all without extra nodes in your workflow.

First n8n node to combine Agent V3 architecture with native Langfuse prompt management and tracing.

Why this node?

If you use n8n's AI Agent with Langfuse, you currently need:

• An HTTP Request node to fetch prompts from Langfuse API
• A Code node to extract the prompt content and model config
• The AI Agent node itself
• Manual configuration to pass tracing callbacks

This node replaces all of that with one node. Select your Langfuse prompt from a dropdown, and the node handles everything: prompt injection, model override, tracing, and metadata.

vs. other Langfuse nodes

Feature	This node	Official Langfuse nodes	Wistron DXLab
Agent execution	Yes	No (separate nodes)	Yes
Prompt selector dropdown	Yes	Yes (separate node)	No
Prompt variable substitution (auto-loaded fields)	Yes	No	No
Generation linked to prompt version	Yes	No	No
Model override from prompt config	Yes	N/A	No
Auto metadata (execution, workflow, node, project, prompt)	Yes	No	No
Agent V3 architecture	Yes	N/A	No (V2)
Streaming support	Yes	N/A	Limited
Fallback model	Yes	N/A	Yes
Batching	Yes	N/A	Yes

Quick Start

1. Install the node: Settings > Community Nodes > Install > n8n-nodes-agent-langfuse
2. Create a Langfuse API credential with your Base URL, Public Key, and Secret Key
3. Add "AI Agent + Langfuse" to your workflow and connect a Chat Model
4. Select a Langfuse prompt from the dropdown
5. Execute — the agent runs with your prompt and traces to Langfuse automatically

Features

• Langfuse Prompt Selector — Browse and select production prompts from Langfuse directly in the node UI. No HTTP Request nodes needed.
• Model Override — Use the model and temperature defined in your Langfuse prompt config, or override manually. Switch models by changing Langfuse config — no workflow edits required.
• Prompt Variable Substitution — {{variables}} in your Langfuse prompt auto-load as editable fields in the node. Values support n8n expressions and are validated before any LLM call.
• Prompt-Linked Generations — Each generation is linked to the Langfuse prompt version, so it appears under the prompt's Generations tab and feeds its metrics (cost, latency by version).
• Automatic Tracing — Every execution is traced to Langfuse with full LLM call details, tool usage, and intermediate steps. The trace name defaults to <workflow name> - <node name>, so traces are easy to disambiguate when you reuse a node across workflows.
• Auto Metadata — Execution ID, workflow info, node name, project, and prompt name/version are automatically included in every trace. Add your own custom metadata on top (reserved keys are listed in the Langfuse Metadata section).
• Streaming — Full streaming support for real-time responses.
• Fallback Model — Configure a backup model that activates if the primary fails.
• Batch Processing — Process multiple items with configurable batch size and delay.
• Output Parser — Connect structured output parsers for typed responses.
• Memory — Connect memory nodes for conversational agents.

Installation

Community Nodes (Recommended)

1. Go to Settings > Community Nodes
2. Click Install
3. Enter n8n-nodes-agent-langfuse
4. Click Install

Manual Installation

cd ~/.n8n/nodes
npm install n8n-nodes-agent-langfuse
# Restart n8n

Setup

1. Create Langfuse Credentials

1. In n8n, go to Credentials > New Credential
2. Search for Langfuse API
3. Fill in:
   • Base URL: Your Langfuse instance URL (e.g., https://cloud.langfuse.com or your self-hosted URL)
   • Public Key: Your Langfuse project public key
   • Secret Key: Your Langfuse project secret key
4. Click Test to verify the connection — you should see "Connection tested successfully"

Tip: Find your keys in Langfuse under Settings > Projects > [Your Project] > API Keys

2. Configure the Node

1. Search for "AI Agent + Langfuse" in the node panel
2. Drag it into your workflow
3. Connect a Chat Model (OpenAI, OpenRouter, Anthropic, etc.) to the "Chat Model" input
4. Select your Langfuse credential
5. Choose your prompt from the dropdown
6. Optionally connect Tools, Memory, or Output Parser

Configuration

Prompt Source

Option	Description
Langfuse Prompt	Select a production prompt from your Langfuse project. The dropdown shows all chat-type prompts.
Manual	Write the system message directly in the node (standard behavior).

Langfuse Prompt Selector

The dropdown fetches all production chat-type prompts from your Langfuse project. Select one, and the node automatically:

• Injects the prompt content as the system message
• Reads the model and temperature from the prompt config (if Model Source = "From Langfuse")
• Includes the prompt name and version in the trace metadata

Model Source (when using Langfuse Prompt)

Option	Description
From Langfuse	Uses the model and temperature from your Langfuse prompt's config. The connected Chat Model provides the provider and API key — only the model name is overridden.
Manual Override	Uses the model exactly as configured in the connected Chat Model node.

How model override works: When you select "From Langfuse", the node creates a new LLM instance using the same provider and API key from your connected Chat Model, but with the model name from Langfuse. For example, if your Chat Model is configured with OpenRouter and gpt-4.1-mini, but your Langfuse prompt has model: "openai/gpt-5-nano", the node will call OpenRouter with gpt-5-nano. Change models in Langfuse — no workflow changes needed.

Prompt Variables

Langfuse chat prompts can contain {{variable}} placeholders in their system and user messages. The node reads the selected prompt and auto-populates one input field per {{variable}} — no need to type variable names by hand.

• Select a Langfuse prompt, then open the Prompt Variables mapper. It lists every {{var}} referenced by that prompt's system and user messages.
• Each value supports full n8n expression syntax (e.g. {{ $json.customer }}).
• If you change the selected prompt, click the mapper's refresh icon to reload the field list for the new prompt.
• If the prompt has no {{variables}}, the mapper shows a notice and there's nothing to fill in.

Missing variables throw a NodeOperationError before any LLM call is made, listing exactly which names need values. Empty-string values count as missing — this runtime check is the real guard (the mapper marks fields required but won't hard-block execution).

How the Langfuse user message interacts with Text / chatInput

Langfuse prompt contains...	Result
system only	Compiled system message is used. Prompt Type / Text (or chatInput) drives the human turn — existing behaviour.
system + user	Compiled system message is used. Compiled user message replaces the human turn; Prompt Type / Text field is ignored. Map any free-form input via a variable instead (e.g. set the question field to {{ $json.chatInput }}).

Example A — parameterised system prompt, free-form user input:

Langfuse prompt:
  system: "You help customers of {{company}}."

Node config:
  Prompt Variables:  company = Acme
  Prompt Type: Auto

Result:
  system → "You help customers of Acme."
  human  → chatInput from previous node (unchanged)

Example B — fully parameterised prompt:

Langfuse prompt:
  system: "You are a support agent."
  user:   "Ticket {{ticket_id}}: {{question}}"

Node config:
  Prompt Variables:
    ticket_id = {{ $json.ticketId }}
    question  = {{ $json.message  }}

Result (chatInput ignored):
  system → "You are a support agent."
  human  → "Ticket 4821: Where is my order?"

Prompt Type (User Input)

Option	Description
Auto (From Previous Node)	Reads the chatInput field from the previous node's output. Works automatically with Chat Trigger and other AI nodes.
Define Below	Write a fixed prompt text in the node.

Ignored when the selected Langfuse prompt defines a user-role message — see Prompt Variables above.

Langfuse Metadata

Field	Description
Session ID	Groups related traces in Langfuse. Supports n8n expressions (e.g., {{ $json.sessionId }}).
User ID	Identifies the end user. Supports expressions.
Trace Name	Custom name for the trace. Defaults to <workflow name> - <node name> — e.g. a node named "AI Agent - Selector" in the workflow "Customer Support" produces the trace name "Customer Support - AI Agent - Selector".
Custom Metadata (JSON)	Any additional metadata you want to attach to traces.

Automatic Metadata

The following fields are automatically included in every trace — no configuration needed:

Field	Value	Source
execution_id	The n8n execution ID	n8n
workflow.id	The n8n workflow ID	n8n
workflow.name	The n8n workflow name	n8n
workflow.active	Whether the workflow is active	n8n
node	The node name	n8n
project	Your Langfuse project name	Langfuse API
prompt.name	The selected prompt name	Langfuse prompt
prompt.version	The production version number	Langfuse prompt

Reserved keys: execution_id, workflow, node, project, and prompt are reserved for the auto-populated values above. These fields are factual and always win — if your Custom Metadata JSON includes any of them, those keys are dropped and a warning listing the ignored keys is written to the n8n log.

Example Custom Metadata:

{
  "env": "prod",
  "tenant": "{{ $json.tenantId }}"
}

Resulting trace metadata:

{
  "execution_id": "1234",
  "workflow": { "id": "aB3dE5fG", "name": "Customer Support Agent", "active": true },
  "node": "AI Agent - Selector",
  "project": "my-project",
  "prompt": { "name": "my-agent", "version": 3 },
  "env": "prod",
  "tenant": "acme-corp"
}

Langfuse Trace Output

Every execution produces a full trace in Langfuse showing:

• The complete LLM call chain (agent, prompt template, model call, output parsing)
• Token usage and cost
• The output with structured fields
• All metadata (automatic + custom)

Options

Option	Default	Description
System Message	"You are a helpful assistant"	System prompt (only when Prompt Source = Manual)
Max Iterations	10	Maximum agent reasoning loops
Return Intermediate Steps	false	Include tool calls and reasoning in output
Enable Streaming	true	Stream responses in real-time
Passthrough Binary Images	true	Forward images from input to the LLM
Batch Size	1	Items to process in parallel
Delay Between Batches	0 ms	Wait time between batches

Canvas Inputs

Input	Type	Required	Description
Chat Model	Language Model	Yes	Any LangChain-compatible model (OpenAI, OpenRouter, Anthropic, etc.). Provides the LLM provider and API key.
Tools	Tool	No	Connect one or more tools for the agent to use.
Memory	Memory	No	Conversation memory for multi-turn agents.
Output Parser	Output Parser	No	Structured output format (enable "Require Specific Output Format" first).
Fallback Model	Language Model	No	Backup model (enable "Enable Fallback Model" first).

Examples

Basic: Agent with Langfuse Prompt

Manual Trigger → AI Agent + Langfuse → Output
                      ↑
                 Chat Model (OpenRouter)

1. Set Prompt Source = "Langfuse Prompt"
2. Select your prompt from the dropdown
3. Set Model Source = "From Langfuse"
4. Set Prompt Type = "Define Below" and enter a test message

Sub-workflow: Reusable Agent

Sub-workflow Trigger → AI Agent + Langfuse → Return
                            ↑        ↑
                       Chat Model   Tool (HTTP)

Perfect for n8n pipelines where multiple workflows call the same agent. Each gets its own Langfuse trace with the correct prompt and model.

With Tools and Memory

Chat Trigger → AI Agent + Langfuse → Response
                    ↑    ↑    ↑
              Chat Model Tool Memory

Full conversational agent with tool access and conversation history, all traced to Langfuse.

Multiple Langfuse Projects

Each Langfuse API key pair belongs to one project. To use prompts from different projects:

1. Create a separate Langfuse API credential for each project
2. Select the appropriate credential in each node
3. The project name in trace metadata updates automatically

Compatibility

Component	Version
n8n	>= 1.70.0
Node.js	>= 20.15
Langfuse	Any version with API v2 (>= 2.0)

Works with any LangChain-compatible Chat Model: OpenAI, OpenRouter, Anthropic, Azure OpenAI, Google Vertex AI, Ollama, and more.

Troubleshooting

"Cannot connect to Langfuse"

• Verify your Base URL, Public Key, and Secret Key in the credential
• Click "Test" in the credential editor to check connectivity
• For self-hosted Langfuse: ensure n8n can reach the URL (check Docker networking, firewalls)

Prompt dropdown is empty

• Check that your Langfuse project has chat-type prompts (not text-type)
• Verify the credential has access to the correct project

Model from Langfuse not being used

• The model field in your Langfuse prompt config must match your provider's model naming (e.g., openai/gpt-5-nano for OpenRouter, gpt-4o for OpenAI direct)
• Check the Langfuse trace to confirm which model was actually called

"Failed to receive response" with Chat Trigger

• This is usually a CORS issue with your n8n reverse proxy, not this node
• Test with a Manual Trigger first to isolate the issue

Development

git clone https://github.com/Diward/n8n-nodes-agent-langfuse.git
cd n8n-nodes-agent-langfuse
npm install
npm run build

# Install locally in n8n for testing
cd ~/.n8n/nodes
npm install /path/to/n8n-nodes-agent-langfuse
# Restart n8n

Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

License

MIT - (c) 2026 diward

Credits

• Built on LangChain and Langfuse
• Icon: Lucide bot icon + Langfuse logo
• Inspired by n8n-nodes-ai-agent-langfuse by Wistron DXLab