Artifact Components

Artifacts are structured components that capture and store source information when agents interact with tools or other agents. They act as a record-keeping system, automatically documenting where information comes from during tool and agent interactions.

How Artifacts Work

When an agent uses a tool (whether that's another agent or a utility tool), the response is automatically parsed to create artifacts. These artifacts store:

• The source of the information (tool/agent used)
• The relevant content from the response
• Metadata about the interaction

How to create an artifact component

1. Go to the Artifact Components tab in the left sidebar. Then select "New artifact component".
2. Add in an id, name, description, and enter in a JSON schema for both summary props and full props. These are required fields.
3. Click "Submit".

To visually add the artifact component to the graph, see the Graphs page for details.

Artifact Name & Description Generation

Important: Artifact names and descriptions are automatically generated at the agent level using that specific agent's configured summarizer model.

How It Works

When an agent creates an artifact during tool execution or data processing:

1. Agent Creates Artifact: During tool execution or data processing
2. Summarizer Analysis: The agent's summarizer model analyzes:
   • The artifact content and structure
   • Recent conversation context
   • The user's original question or request
3. Name Generation: Creates a concise, descriptive name (max 50 characters)
4. Description Generation: Provides context about relevance and content (max 150 characters)

Model Settings for Artifacts

Each agent uses its configured summarizer model for artifact generation:

// Agent with custom summarizer for artifact generation
const researchAgent = agent({
  id: "research-agent",
  models: {
    base: { model: "claude-4-sonnet" },
    summarizer: {
      model: "gpt-4o-mini", // This model generates artifact names/descriptions
      providerOptions: {
        temperature: 0.2, // Lower temperature for consistent naming
        maxTokens: 512,
      },
    },
  },
});

Inheritance and Fallback

Artifact generation follows the same model inheritance rules:

• Default: Agent uses graph's summarizer model (if configured)
• Fallback: If no summarizer configured at project/graph level, falls back to agent's base model
• Override: Agent can specify its own summarizer model

Best Practices

• Consistent Models: Use the same summarizer model across related agents for consistent artifact naming
• Appropriate Models: Choose models good at summarization (GPT-4o-mini, Claude-3-Haiku work well)
• Temperature Settings: Lower temperatures (0.1-0.3) provide more consistent naming patterns