Chat Trigger¶
Trigger
The Chat Trigger provides a built-in web chat interface for interacting with workflows directly from the Pipelit frontend. Users type messages in the ChatPanel, and each message fires the trigger to start a new workflow execution.
Component type: trigger_chat
Ports¶
Outputs¶
| Port | Type | Description |
|---|---|---|
text | STRING | The message text sent by the user |
payload | OBJECT | Full trigger payload including text and metadata |
Inputs¶
This component has no input ports. It is an entry point.
Configuration¶
The Chat Trigger requires no configuration. Add it to the canvas and connect it to downstream nodes.
Usage¶
- Drag a Chat Trigger node onto the canvas from the Node Palette (under Triggers).
- Connect its output to an Agent, Code, or any other downstream node.
- Connect an AI Model sub-component to your agent.
- Open the ChatPanel in the workflow editor (bottom panel).
- Type a message and press Enter.
Each message sent through the ChatPanel creates a new workflow execution scoped to the Chat Trigger. The user's message text is available on the text output port, and the full payload (including metadata) on the payload port.
Accessing Chat Input Downstream¶
In your agent's system prompt or any node's configuration, reference the chat input using Jinja2 expressions:
Or reference the specific trigger node by its ID:
Conversation Memory¶
When the Chat Trigger connects to an Agent with Conversation Memory enabled, the agent maintains conversation history across multiple chat messages. This gives the agent continuity -- it remembers what was said in previous messages within the same workflow.
The thread ID for conversation memory is constructed from the user's profile ID and the workflow ID, so each user gets their own conversation thread per workflow.
Example¶
A minimal chat-based workflow:
graph LR
CT[Chat Trigger] --> AG[Agent]
AM[AI Model] -.-> AG
style CT fill:#f97316,color:white
style AM fill:#3b82f6,color:white - Chat Trigger receives the user's message.
- Agent processes the message using the connected AI Model, reasons through the response, and produces output.
- The response is displayed back in the ChatPanel.
Multi-trigger workflow¶
A Chat Trigger and a Telegram Trigger can both feed the same agent:
graph LR
CT[Chat Trigger] --> AG[Agent]
TT[Telegram Trigger] --> AG
AM[AI Model] -.-> AG
style CT fill:#f97316,color:white
style TT fill:#f97316,color:white
style AM fill:#3b82f6,color:white In this layout, the same agent handles messages from both the web chat and Telegram. The {{ trigger.text }} expression works regardless of which trigger fired.
API Endpoint¶
Chat messages are sent via:
Request body:
The trigger_node_id field is optional. If omitted, Pipelit uses the first trigger_chat node found in the workflow.