Introduction
AgentOpera is an open-source, graph-based framework enabling multi-agent teams to collaboratively tackle complex tasks. Its intelligent Router automatically directs user intents to specialized agents, orchestrating seamless coordination across a dynamic network, and supporting MCP-compatible AI agents, zero-code tools, and hybrid cloud-edge execution.
💡 Why AgentOpera?
Developers can leverage AgentOpera's end-to-end ecosystem to design, deploy, and manage sophisticated multi-agent AI systems with integrated frameworks and tooling.
The AgentOpera framework adopts a hierarchical and modular architecture where each layer serves a distinct purpose and builds upon the capabilities of underlying layers. This structured approach allows developers to engage with the system at their preferred level of abstraction.
Engine Layer: The foundation providing core execution capabilities, including agent definition interfaces, function calling mechanisms, telemetry tracking, and runtime environments (both local and distributed). This layer establishes the type system and communication protocols that enable agent interoperability.
ChatFlow layer: Implements streaming computing primitives that manage conversation flows between agents. Components include basic message handling, state management, termination conditions, agent handoffs, team coordination, and task execution.
Single-Agent layer: Contains specialized worker agents with domain-specific capabilities including Video Agents for media processing, File Agents for document operations, Browser Agents for web interactions, and extensible frameworks for custom agent development.
Multi-Agent Systems Layer: Consists of various agent design patterns: Router intelligently analyzes user prompts and directs them to appropriate domain-specific agents; Swarm implements collaborative group intelligence; and other patterns enable complex agent interactions for sophisticated problem-solving.
Supporting Components: Integration modules that connect the framework to diverse AI models, specialized tools, memory systems, and Model Context Protocol (MCP) for standardized interactions.
Agent ECO Link: Provides adapter components for integrating with external agent frameworks (LangGraph/CrewAI), and Langflow for flow-based programming. ZeroCode interfaces enable no-code agent development.
Additional Capabilities: Includes some key elements: (1) Deployment capabilities for efficiently delivering AgentOpera-based agent services to production environments; (2) Agent-to-agent (A2A) Protocol establishing standardized communication pathways between diverse agent systems; and (3) Edge computing support with Hybrid AI Agents that balance local and cloud processing for enhanced performance and privacy.
⛏️ Features
Graph-Based Multi-Agent System: Intelligently transforms user intents into executable task plans distributed across an AI agent network.
Smart Orchestrator & Dynamic Router: Converts natural language inputs into structured action plans and intelligently routes to optimal agents.
Anywhere Agent Network: Implements swarm intelligence through unified routing across devices, edge nodes, and cloud.
Distributed Agent Runtime: Facilitates seamless agent-to-agent communication and collaborative problem-solving.
Ecosystem-Ready Platform: Includes MCP, framework adapters, zero-code plugins, and seamless API integrations.
Unified AI Workspace: An all-in-one interface compliant with industry standards (e.g. Vercel AI SDK).
Zero-Code Agent Builder: Enables custom agent creation through an intuitive UI and AI-assisted development ("AI for AI").
Universal Multimodal Support: Natively compatible with diverse model types and input modalities.
Hybrid AI Agents: Combine on-device processing with cloud intelligence for personalized experiences while preserving privacy.
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