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Basic Information

agentUniverse is a developer-focused multi-agent framework built on large language models for constructing, orchestrating, and deploying collaborating intelligent agents. It provides a library of reusable components and multi-agent collaboration pattern factories so teams can compose agents with distinct responsibilities and domain expertise. The project emphasizes the injection of domain experience, support for configurable LLM backends, and validated collaboration patterns such as PEER (Plan, Execute, Express, Review) and DOE (Data-finding, Opinion-inject, Express). The repository includes example apps, standard project scaffolding, a visual agentic workflow platform, observability integration, and documentation covering quick start, API reference, templates, MCP server usage, and deployment guidance. It targets developers and enterprises building domain-expert agent applications, with origins in real-world financial business practices and usage in commercial products.

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Features
agentUniverse bundles flexible, extensible building blocks for single agents and multi-agent systems, enabling customization of agents, tools, prompts, memory modules, and knowledge management. It ships industry-validated multi-agent collaboration models including PEER and DOE and supports creation and publication of custom collaboration patterns and MCP servers. The project integrates many LLM vendors and models via simple configuration, offers a visual canvas platform for designing agent workflows, and provides observability based on OpenTelemetry for lifecycle tracing of agents, LLMs, and tools. Additional features include project scaffolding, agent templates, sample applications (legal advice, code execution agent, financial event analysis), a user guide, API reference, and examples for memory, RAG, and prompt management.
Use Cases
This framework accelerates development of complex agent applications by providing prebuilt collaboration patterns, standard project structure, and integration points for domain prompts and knowledge so teams can encode expert workflows into agents. It lowers operational friction by supporting many LLM providers through configuration, supplying a visual workflow platform for design and testing, and offering observability for monitoring and debugging agent behavior. The repository includes runnable examples and templates to shorten onboarding, guidance for publishing and using MCP servers, and validated patterns tested in financial scenarios; a commercial assistant built on the framework demonstrates real-world applicability. Documentation, API references, and community support channels help teams iterate and deploy domain-specialized multi-agent solutions.

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