Tool
A tool the LLM can call. It declares a name, a description and its parameters. When the model calls it, call carries the arguments and trigger fires the body you wired; when the body fires result_trigger, the Tool reads result and hands it back to the model.
Called by the LLM. When the model calls the tool, call carries the arguments and trigger fires the body you wired. When the body fires result_trigger, the Tool reads result and hands it back to the LLM as the tool’s answer, so the Tool never runs as a step of its own.
Inputs
Section titled “Inputs”| Port | Type | Notes |
|---|---|---|
result_trigger |
event | Fires when the body’s answer is ready |
result |
text | The tool’s answer, read when result_trigger fires |
Outputs
Section titled “Outputs”| Port | Type | Notes |
|---|---|---|
call |
tool-call | Right-click the node to add a wired Tool Args |
trigger |
event |
Right-click a knob and pick Convert to input to drive it from a wire.
| Knob | Kind | Default | Notes |
|---|---|---|---|
namename |
text | my_tool |
|
description |
text block | Describe what this tool does for the model. |
|
schema_mode |
choice | auto |
auto, manual |
schema |
text block | {"type":"object","properties":{},"required":[]} |
only when schema_mode is manual |
Example
Section titled “Example”Tells the model it can call something. Give it a name and a description the model reads, wire call into the LLM tools, and right-click the node to add its Tool Args, wired by call and trigger. When the model calls the tool, trigger fires the Tool Args with the arguments on call. At the end of the branch, wire the answer into result and the trigger of the node that made it into result_trigger: when that fires, the answer goes to the model. No answer within two minutes, and the model gets an error for that call.
In auto mode the parameters come from the Tool Args fields; manual takes a JSON schema. The model finds a tool by its name, so two tools with one name keep the graph from turning On.
More in AI
Section titled “More in AI”- Embed: Turn text into an embedding vector with the embed model you pick, the way the LLM picks its model.
- LLM: A chat / multimodal model.
- Rerank: Reorder candidates by how well each one answers the query, and keep the best.
- STT: Speech to text.
- Tool Args: Where a tool’s body starts.
- TTS: Text to speech.