LLM Agent node

LLM Agent (llm) is where intelligence happens: it reasons over its input with a language model, optionally calls tools, and emits its answer. One node type covers every AI use case — from a one-shot summarizer to a multi-tool agent.

Configuration

Open the node (double-click) to edit it. Key fields:

• Node ID / Name — the identifier used in variables and the display name on the canvas.
• Provider & Model — OpenAI, Anthropic, Gemini, Vertex, Azure, Bedrock, Groq, Mistral, HuggingFace, Friendli, Perplexity… Managed by HiveFlow (credits) or your own keys.
• Objective — the system prompt: who the agent is and what it must achieve.
• Prompt — the per-run input, usually {{message}}.
• Function Calling — enable tools wired into the node; temperature / max tokens tune the output.

Inputs & outputs

It receives its prompt (plus chat context when fed by a Chat Input, and Skills in scope) and emits the model's response — with tool-call traces in its Console. Downstream nodes reference its output with {{node_id.field}} — see the variables reference.

Example

Chat Input → LLM Agent ⇄ CRM (tool)
                     ⇄ MCP Tool (Airtable)
            → Chat Output

See also: Adding tools · What is an AI agent? · Creating agents