Agent Builder

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:

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).

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).

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.

MCP tools

Connect MCP servers to give the agent real-world capabilities. See MCP Tools for details.

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.

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).

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

Getting Started

Protocol

Agent Hosting

Simple mode — wizard

Advanced mode — graph editor

Templates

​Simple mode — wizard

​Advanced mode — graph editor

​Templates

Simple mode — wizard

Advanced mode — graph editor

Templates