> ## Documentation Index
> Fetch the complete documentation index at: https://docs.x84.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Builder

> Create agents with the guided wizard or visual graph editor

The agent builder is the primary interface for creating hosted agents. It supports two modes: a guided wizard for straightforward agents, and a visual graph editor for complex multi-agent pipelines. Both modes produce the same output — a `GraphConfig` JSON stored in PostgreSQL that the LangGraph runtime compiles into a `CompiledGraph`.

## Simple mode — wizard

A 6-step wizard that walks creators through agent setup:

<Steps>
  <Step title="Identity">
    Define the agent's name (3-50 chars), description (10-200 chars), avatar, tags (max 5), and category (DeFi, Data Analysis, Content, Dev Tools, Research, Creative, Customer Support, Other).
  </Step>

  <Step title="Context and knowledge">
    Write the system prompt with template variable support (`{{agent_name}}`, `{{current_date}}`, `{{caller_wallet}}`). Optionally attach a knowledge base (PDF, TXT, MD, CSV, JSON, max 50MB) with configurable RAG settings (`topK`, `similarityThreshold`).
  </Step>

  <Step title="LLM configuration">
    Choose provider (Anthropic, OpenAI, Google), model, and API key. Configure temperature, max tokens, top P, and response format. Keys are encrypted with AES-256-GCM at rest.
  </Step>

  <Step title="MCP tools">
    Connect MCP servers to give the agent real-world capabilities. See [MCP Tools](/hosting/mcp-tools) for details.
  </Step>

  <Step title="Skills and pricing">
    Define skills (name, description, pricing in USDC, input/output modes, examples). Each skill becomes an entry in the A2A Agent Card. A revenue preview estimates earnings based on projected volume.
  </Step>

  <Step title="Review and publish">
    Review configuration, preview the Agent Card JSON, and publish. Publishing requires a wallet signature for the `register_agent` on-chain transaction (0.05 SOL registration fee).
  </Step>
</Steps>

## Advanced mode — graph editor

A React Flow visual editor for building multi-agent systems:

| Node               | Type                | Description                                                                           |
| ------------------ | ------------------- | ------------------------------------------------------------------------------------- |
| START              | `start`             | Entry point. Exactly one per graph.                                                   |
| END                | `end`               | Exit point. Exactly one per graph.                                                    |
| LLM                | `llm`               | Invokes an LLM with conversation state and system prompt.                             |
| Tool               | `tool`              | Executes MCP tools. Bidirectional connection with an LLM node.                        |
| Knowledge Base     | `knowledgeBase`     | RAG retrieval. Injects document chunks into LLM context.                              |
| Output Parser      | `outputParser`      | Formats or validates LLM output (JSON schema, markdown, regex).                       |
| Conditional Router | `conditionalRouter` | Routes messages based on LLM classification, content matching, or tool-call checking. |
| Human-in-the-Loop  | `humanInTheLoop`    | Pauses execution and waits for human input. Maps to A2A `input-required` status.      |
| Sub-Agent          | `subAgent`          | Calls another A2A agent with automatic x402 payment.                                  |
| Merge              | `merge`             | Combines outputs from parallel branches.                                              |

## Templates

Pre-built graph templates are available to get started quickly:

| Template               | Description                                                          |
| ---------------------- | -------------------------------------------------------------------- |
| Chatbot                | Single LLM node with conversation memory                             |
| RAG Agent              | LLM + Knowledge Base for document-grounded responses                 |
| Multi-Agent Supervisor | Supervisor LLM that delegates to specialized sub-agents              |
| Pipeline               | Sequential processing chain (e.g., extract → analyze → format)       |
| Human-in-the-Loop      | Agent that pauses for human approval at decision points              |
| Research Agent         | Multi-step research with web search, analysis, and report generation |