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Comprehensive наблюдаемость в AI Tools for Every Need

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  • UnitMesh Framework

    An open-source AI agent framework orchestrating multi-LLM agents, dynamic tool integration, memory management, and workflow automation.
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    What is UnitMesh Framework?
    UnitMesh Framework provides a flexible, modular environment for defining, managing, and executing chains of AI agents. It allows seamless integration with OpenAI, Anthropic, and custom models, supports Python and Node.js SDKs, and offers built-in memory stores, tool connectors, and plugin architecture. Developers can orchestrate parallel or sequential agent workflows, track execution logs, and extend functionality via custom modules. Its event-driven design ensures high performance and scalability across cloud and on-premise deployments.

  • Disco

    Disco is an open-source AWS framework for developing AI agents by orchestrating LLM calls, function executions, and event-driven workflows.
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    What is Disco?
    Disco streamlines AI agent development on AWS by providing an event-driven orchestration framework that connects language model responses to serverless functions, message queues, and external APIs. It offers pre-built connectors for AWS Lambda, Step Functions, SNS, SQS, and EventBridge, enabling easy routing of messages and action triggers based on LLM outputs. Disco’s modular design supports custom task definitions, retry logic, error handling, and real-time monitoring through CloudWatch. It leverages AWS IAM roles for secure access and provides built-in logging and tracing for observability. Ideal for chatbots, automated workflows, and agent-driven analytics pipelines, Disco delivers scalable, cost-efficient AI agent solutions.

  • JKStack Agents Server

    Backend framework providing REST and WebSocket APIs to manage, execute, and stream AI agents with plugin extensibility.
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    What is JKStack Agents Server?
    JKStack Agents Server serves as a centralized orchestration layer for AI agent deployments. It offers REST endpoints to define namespaces, register new agents, and initiate agent runs with custom prompts, memory settings, and tool configurations. For real-time interactions, the server supports WebSocket streaming, sending partial outputs as they are generated by underlying language models. Developers can extend core functionalities through a plugin manager to integrate custom tools, LLM providers, and vector stores. The server also tracks run histories, statuses, and logs, enabling observability and debugging. With built-in support for asynchronous processing and horizontal scaling, JKStack Agents Server simplifies deploying robust AI-powered workflows in production.
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