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Comprehensive anpassbare Agentenrollen Tools for Every Need

Get access to anpassbare Agentenrollen solutions that address multiple requirements. One-stop resources for streamlined workflows.

anpassbare Agentenrollen

  • 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/

  • Agent2Agent

    Agent2Agent is a multi-agent orchestration platform enabling AI agents to collaborate efficiently on complex tasks.
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    What is Agent2Agent?
    Agent2Agent provides a unified web interface and API to define, configure, and orchestrate teams of AI agents. Each agent can be assigned unique roles such as researcher, analyst, or summarizer, and agents communicate through built-in channels to share data and delegate subtasks. The platform supports function calling, memory storage, and webhook integrations for external services. Administrators can monitor workflow progress, inspect agent logs, and adjust parameters dynamically for scalable, parallelized task execution and advanced workflow automation.

  • Duet GPT

    Duet GPT is a multi-agent orchestration framework enabling dual OpenAI GPT agents to collaboratively solve complex tasks.
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    What is Duet GPT?
    Duet GPT is a Python-based open source framework for orchestrating multi-agent conversations between two GPT models. You define distinct agent roles, customized with system prompts, and the framework manages turn-taking, message passing, and conversation history automatically. This cooperative structure accelerates complex task resolution, enabling comparative reasoning, critique cycles, and iterative refinement through back-and-forth exchanges. Its seamless OpenAI API integration, simple configuration, and built-in logging make it ideal for research, prototyping, and production workflows in coding assistance, decision support, and creative ideation. Developers can extend the core classes to integrate new LLM services, adjust the iterator logic, and export transcripts in JSON or Markdown formats for post-analysis.
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