RevSetter AI Agent Builder Features

A dedicated and guided environment for constructing AI agents from the ground up.

A structured process (Basic Information, Agent Pattern, AI Model, etc.) that walks users through agent creation, clearly indicating progress and required inputs.

Define an agent's name and core purpose using natural language. Leverage AI assistance to craft or refine descriptions and system instructions.

Choose how your agent processes tasks by selecting from various patterns (Multi-Agent Supervisor, ReAct, Plan and Execute, Hierarchical), each with detailed guidance on use cases, best practices, and configuration.

• Multi-Agent Supervisor: Orchestrate multiple specialized worker agents.
• ReAct (Reasoning & Acting): Build agents that iteratively think, act, and adapt.
• Plan and Execute: Create agents that develop a plan first, then execute systematically.
• Hierarchical: Design multi-level agent systems for complex task delegation.

Select from various LLMs (OpenRouter, Anthropic, Google, OpenAI, etc.) with filtering and sorting. View key model details like token limits and features.

Equip agents with pre-built functional 'Tools' (e.g., web search, page fetch) and integrate other specialized 'Agents' as sub-components from a searchable library.

Graphically map out interactions for multi-agent systems using a node-based 'Workflow Graph Builder' with various node types and management tools.

Input detailed system messages (prompts) to precisely define an agent's behavior, tone, operational constraints, and response style.

Enable persistent memory for agents to retain information across conversations. Configure memory schemas and update modes, supporting various memory types (UserProfile, ConversationPreferences, etc.).

Utilize an embedded 'Test Your Agent' chat panel for real-time interaction and verification during creation and editing, with conversation management.

Clear 'Finish' and 'Deploy Agent' actions to make agents operational.

Deploy agents within a separate 'Orchestrator' module for trigger-based automation or as contextual 'Co-Pilots' embedded within other applications (e.g., CRM).