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Comprehensive observabilidad Tools for Every Need

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

observabilidad

  • Playbooks AI

    Playbooks AI is an open-source low-code framework to design, deploy, and manage custom AI agents with modular workflows.
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    What is Playbooks AI?
    Playbooks AI is a developer framework for building AI agents through a declarative playbook DSL. It enables integration with various LLMs, custom tools, and memory stores. With a CLI and web UI, users can define agent behavior, orchestrate multi-step workflows, and monitor execution. Features include tool routing, stateful memory, version control, analytics, and multi-agent collaboration, making it easy to prototype and deploy production-ready AI assistants.
    Playbooks AI Core Features
    • Declarative playbook DSL for agent workflows
    • Modular memory stores for stateful interactions
    • Custom tool and API integration
    • Multi-agent orchestration and routing
    • CLI and web UI interfaces
    • Built-in observability and analytics
    • Version control and registry support
    • Plugin marketplace for extensions
    Playbooks AI Pro & Cons

    The Cons

    The Pros

    Supports natural language programming with an English-like language
    Seamlessly integrates natural language workflows with Python
    Native multi-agent system architecture for agent communication
    Event-driven programming with dynamic triggers
    Strong execution observability with verifiable and auditable execution
    State and artifact management for persistent data handling

  • Tongui Agent

    A lightweight JavaScript framework for building AI agents with memory management and tool integration.
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    What is Tongui Agent?
    Tongui Agent provides a modular architecture for creating AI agents that can maintain conversation state, leverage external tools, and coordinate multiple sub-agents. Developers configure LLM backends, define custom actions, and attach memory modules to store context. The framework includes an SDK, CLI, and middleware hooks for observability, making it easy to integrate into web or Node.js applications. Supported LLMs include OpenAI, Azure OpenAI, and open-source models.

  • FastAPI LangGraph Agent Template

    Production-ready FastAPI template using LangGraph for building scalable LLM agents with customizable pipelines and memory integration.
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    What is FastAPI LangGraph Agent Template?
    FastAPI LangGraph Agent Template offers a comprehensive foundation for developing LLM-driven agents within a FastAPI application. It includes predefined LangGraph nodes for common tasks like text completion, embedding, and vector similarity search while allowing developers to create custom nodes and pipelines. The template manages conversation history via memory modules that persist context across sessions and supports environment-based configuration for different deployment stages. Built-in Docker files and CI/CD-friendly structure ensure seamless containerization and deployment. Logging and error-handling middleware enhance observability, while the modular codebase simplifies extending functionality. By combining FastAPI's high-performance web framework with LangGraph's orchestration capabilities, this template streamlines the agent development lifecycle from prototyping to production.
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