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Comprehensive papéis de agentes personalizáveis Tools for Every Need

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papéis de agentes personalizáveis

  • LLM Coordination

    LLM Coordination is a Python framework orchestrating multiple LLM-based agents through dynamic planning, retrieval, and execution pipelines.
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    What is LLM Coordination?
    LLM Coordination is a developer-focused framework that orchestrates interactions between multiple large language models to solve complex tasks. It provides a planning component that breaks down high-level goals into sub-tasks, a retrieval module that sources context from external knowledge bases, and an execution engine that dispatches tasks to specialized LLM agents. Results are aggregated with feedback loops to refine outcomes. By abstracting communication, state management, and pipeline configuration, it enables rapid prototyping of multi-agent AI workflows for applications like automated customer support, data analysis, report generation, and multi-step reasoning. Users can customize planners, define agent roles, and integrate their own models seamlessly.
    LLM Coordination Core Features
    • Task decomposition and planning
    • Retrieval-augmented context sourcing
    • Multi-agent execution engine
    • Feedback loops for iterative refinement
    • Configurable agent roles and pipelines
    • Logging and monitoring
    LLM Coordination Pro & Cons

    The Cons

    Overall accuracy on coordination reasoning, especially joint planning, remains relatively low, indicating significant room for improvement.
    Focuses mainly on research and benchmarking rather than a commercial product or tool for end-users.
    Limited information on pricing model or availability beyond research code and benchmarks.

    The Pros

    Provides a novel benchmark specifically for evaluating multi-agent coordination abilities of LLMs.
    Introduces a plug-and-play Cognitive Architecture for Coordination facilitating integration of various LLMs.
    Demonstrates strong performance of LLMs like GPT-4-turbo in coordination tasks compared to reinforcement learning methods.
    Enables detailed analysis of key reasoning skills such as Theory of Mind and joint planning within multi-agent collaboration.
    LLM Coordination Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://eric-ai-lab.github.io/llm_coordination/

  • AgentInteraction

    AgentInteraction is a Python framework enabling multi-agent LLM collaboration and competition to solve tasks with custom conversational flows.
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    What is AgentInteraction?
    AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.
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