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 | | Model override from prompt config | Yes | N/A | No | | Auto metadata (project, prompt, version) | 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.
• Automatic Tracing — Every execution is traced to Langfuse with full LLM call details, tool usage, and intermediate steps. The trace name defaults to the n8n node name, so naming your node descriptively (e.g., "AI Agent - Selector") makes traces easy to find in Langfuse.
• Auto Metadata — Project name, prompt name, and prompt version are automatically included in every trace. Add your own custom metadata on top.
• 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 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. |

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 the n8n node name — so naming your node "AI Agent - Selector" will make the trace appear as "AI Agent - Selector" in Langfuse. | | 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 | |-------|-------|--------| | project | Your Langfuse project name | Langfuse API | | prompt.name | The selected prompt name | Langfuse prompt | | prompt.version | The production version number | Langfuse prompt |

Your custom metadata is merged on top of the automatic fields. You can override any automatic field by including it in your Custom Metadata JSON.

Example Custom Metadata:

{
  "env": "prod",
  "workflow": "{{ $workflow.name }}",
  "n8n_exec_id": "{{ $execution.id }}"
}

Resulting trace metadata:

{
  "project": "my-project",
  "prompt": { "name": "my-agent", "version": 3 },
  "env": "prod",
  "workflow": "Customer Support Agent",
  "n8n_exec_id": 1234
}

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