Agents

An agent is an executable unit that processes input and produces output. Agentron supports two kinds:

Kind	Description
node	Graph-based. Nodes are LLM, decision, tool, context_read, context_write, input, output. Execution follows edges.
code	Custom script (JavaScript, Python, TypeScript) run in a sandbox.

Node agents

Node agents run a graph of nodes connected by edges.

Decision layer

The decision layer lets the agent decide per request whether to call a tool or respond directly.

LLM node with toolIds: Add toolIds to the agent; the LLM receives tool definitions and may return tool calls.
Decision node: A dedicated node type with llmConfigId, toolIds, and systemPrompt. Each decision node can use a different LLM and tool set.

The agent can only decide on tools defined for it (agent-level toolIds or node-level toolIds for decision nodes).

Node types

Type	Parameters	Purpose
llm	`systemPrompt` (required), `llmConfigId` (optional)	Calls the LLM. Optional per-node LLM config.
decision	`systemPrompt`, `llmConfigId` (required), `toolIds`	Decision layer: LLM decides whether to call tools or respond.
tool	`toolId`	Calls a tool by ID.
context_read	`key`	Reads from shared context.
context_write	`key`	Writes to shared context.
input	`transform.expression`	Entry point; optional transform (e.g. `{{ $input }}`).
output	`transform.expression`	Exit point; optional transform.

LLM and decision nodes can set llmConfigId to use a different LLM than the agent default. When omitted, the agent’s defaultLlmConfigId is used (or the first available config).

Graph structure (canvas format)


{
  "graph": {
    "nodes": [
      { "id": "n1", "type": "llm", "position": [100, 100], "parameters": { "systemPrompt": "You are a helpful assistant.", "llmConfigId": "cfg-1" } },
      { "id": "n2", "type": "decision", "position": [100, 220], "parameters": { "systemPrompt": "Decide whether to use tools.", "llmConfigId": "cfg-1", "toolIds": ["std-weather"] } }
    ],
    "edges": [
      { "id": "e1", "source": "n1", "target": "n2" }
    ]
  },
  "defaultLlmConfigId": "cfg-1",
  "toolIds": ["std-weather"]
}

Execution flows along edges; the output of one node becomes input to the next.

Required fields for node agents

Field	Purpose
name	Display name
description	What the agent does
llmConfigId / defaultLlmConfigId	ID from `list_llm_providers`. Required when using LLM or decision nodes.
graphNodes	At least one `llm` or `decision` node for AI responses
graphEdges	Connect nodes (`source`, `target`)
toolIds (optional)	Array of tool IDs for the decision layer

Code agents

Code agents execute custom source code in a sandbox. They have source and entrypoint.

Suggested user actions

User wants…	Action
”Create an agent”	`list_llm_providers` first if LLM needed. Then `create_agent` with name, description, llmConfigId, graphNodes, graphEdges, toolIds.
”Fix an agent”	`get_agent(id)` first, then `update_agent` with corrected fields.
”Populate agent settings”	`get_agent`, `list_tools`, `list_llm_providers`, then `update_agent` with toolIds, llmConfigId, systemPrompt, description.
”Add tools to my agent”	`list_tools` to get IDs, then `update_agent` with `toolIds` array.
”Delete an agent”	`delete_agent(id)`.