Agent

The Agent block connects your workflow to Large Language Models (LLMs). It processes natural language inputs, calls external tools, and generates structured or unstructured outputs.

The system prompt establishes the agent's operational parameters and behavioral constraints. This configuration defines the agent's role, response methodology, and processing boundaries for all incoming requests.

You are a helpful assistant that specializes in financial analysis.
Always provide clear explanations and cite sources when possible.
When responding to questions about investments, include risk disclaimers.

User Prompt

The user prompt represents the primary input data for inference processing. This parameter accepts natural language text or structured data that the agent will analyze and respond to. Input sources include:

Static Configuration: Direct text input specified in the block configuration
Dynamic Input: Data passed from upstream blocks through connection interfaces
Runtime Generation: Programmatically generated content during workflow execution

Model Selection

The Agent block supports multiple LLM providers through a unified inference interface. Available models include:

OpenAI: GPT-5.1, GPT-5, GPT-4o, o1, o3, o4-mini, gpt-4.1
Anthropic: Claude 4.5 Sonnet, Claude Opus 4.1
Google: Gemini 2.5 Pro, Gemini 2.0 Flash
Other Providers: Groq, Cerebras, xAI, Azure OpenAI, OpenRouter
Local Models: Ollama or VLLM compatible models

Temperature

Controls response randomness and creativity:

Low (0-0.3): Deterministic and focused. Best for factual tasks and accuracy.
Medium (0.3-0.7): Balanced creativity and focus. Good for general use.
High (0.7-2.0): Creative and varied. Ideal for brainstorming and content generation.

API Key

Your API key for the selected LLM provider. This is securely stored and used for authentication.

Tools

Extend agent capabilities with external integrations. Select from 60+ pre-built tools or define custom functions.

Available Categories:

Communication: Gmail, Slack, Telegram, WhatsApp, Microsoft Teams
Data Sources: Notion, Google Sheets, Airtable, Supabase, Pinecone
Web Services: Firecrawl, Google Search, Exa AI, browser automation
Development: GitHub, Jira, Linear
AI Services: OpenAI, Perplexity, Hugging Face, ElevenLabs

Execution Modes:

Auto: Model decides when to use tools based on context
Required: Tool must be called in every request
None: Tool available but not suggested to model

Response Format

The Response Format parameter enforces structured output generation through JSON Schema validation. This ensures consistent, machine-readable responses that conform to predefined data structures:

{
  "name": "user_analysis",
  "schema": {
    "type": "object",
    "properties": {
      "sentiment": {
        "type": "string",
        "enum": ["positive", "negative", "neutral"]
      },
      "confidence": {
        "type": "number",
        "minimum": 0,
        "maximum": 1
      }
    },
    "required": ["sentiment", "confidence"]
  }
}

This configuration constrains the model's output to comply with the specified schema, preventing free-form text responses and ensuring structured data generation.

Accessing Results

After an agent completes, you can access its outputs:

<agent.content>: The agent's response text or structured data
<agent.tokens>: Token usage statistics (prompt, completion, total)
<agent.tool_calls>: Details of any tools the agent used during execution
<agent.cost>: Estimated cost of the API call (if available)

Advanced Features

Memory + Agent: Conversation History

Use a Memory block with a consistent id (for example, chat) to persist messages between runs, and include that history in the Agent's prompt.

Add the user's message before the Agent
Read the conversation history for context
Append the Agent's reply after it runs

See the Memory block reference for details.

Outputs

<agent.content>: Agent's response text
<agent.tokens>: Token usage statistics
<agent.tool_calls>: Tool execution details
<agent.cost>: Estimated API call cost

Example Use Cases

Customer Support Automation - Handle inquiries with database and tool access

API (Ticket) → Agent (Postgres, KB, Linear) → Gmail (Reply) → Memory (Save)

Multi-Model Content Analysis - Analyze content with different AI models

Function (Process) → Agent (GPT-4o Technical) → Agent (Claude Sentiment) → Function (Report)

Tool-Powered Research Assistant - Research with web search and document access

Input → Agent (Google Search, Notion) → Function (Compile Report)

Best Practices

Be specific in system prompts: Clearly define the agent's role, tone, and limitations. The more specific your instructions are, the better the agent will be able to fulfill its intended purpose.
Choose the right temperature setting: Use lower temperature settings (0-0.3) when accuracy is important, or increase temperature (0.7-2.0) for more creative or varied responses
Leverage tools effectively: Integrate tools that complement the agent's purpose and enhance its capabilities. Be selective about which tools you provide to avoid overwhelming the agent. For tasks with little overlap, use another Agent block for the best results.