Ultimate connectivité API Solutions for Everyone

Discover all-in-one connectivité API tools that adapt to your needs. Reach new heights of productivity with ease.

connectivité API

  • Taiga is an open-source AI agent framework enabling creation of autonomous LLM agents with plugin extensibility, memory, and tool integration.
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    What is Taiga?
    Taiga is a Python-based open-source AI agent framework designed to streamline the creation, orchestration, and deployment of autonomous large language model (LLM) agents. The framework includes a flexible plugin system for integrating custom tools and external APIs, a configurable memory module for managing long-term and short-term conversational context, and a task chaining mechanism to sequence multi-step workflows. Taiga also offers built-in logging, metrics, and error handling for production readiness. Developers can quickly scaffold agents with templates, extend functionality via SDK, and deploy across platforms. By abstracting complex orchestration logic, Taiga enables teams to focus on building intelligent assistants that can research, plan, and execute actions without manual intervention.
  • Hyperbolic Time Chamber enables developers to build modular AI agents with advanced memory management, prompt chaining, and custom tool integration.
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    What is Hyperbolic Time Chamber?
    Hyperbolic Time Chamber provides a flexible environment for constructing AI agents by offering components for memory management, context window orchestration, prompt chaining, tool integration, and execution control. Developers define agent behaviors via modular building blocks, configure custom memories (short- and long-term), and link external APIs or local tools. The framework includes async support, logging, and debugging utilities, enabling rapid iteration and deployment of sophisticated conversational or task-oriented agents in Python projects.
  • An AI framework combining hierarchical planning and meta-reasoning to orchestrate multi-step tasks with dynamic sub-agent delegation.
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    What is Plan Agent with Meta-Agent?
    Plan Agent with Meta-Agent provides a layered AI agent architecture: the Plan Agent generates structured strategies to achieve high-level goals, while the Meta-Agent oversees execution, adjusts plans in real-time, and delegates subtasks to specialized sub-agents. It features plug-and-play tool connectors (e.g., web APIs, databases), persistent memory for context retention, and configurable logging for performance analysis. Users can extend the framework with custom modules to suit diverse automation scenarios, from data processing to content generation and decision support.
  • SuperBot is a Python-based AI Agent framework offering CLI interface, plugin support, function calling, and memory management.
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    What is SuperBot?
    SuperBot is a comprehensive AI Agent framework enabling developers to deploy autonomous, context-aware assistants via Python and the command line. It integrates OpenAI’s chat models with a memory system, function-calling features, and plugin architecture. Agents can execute shell commands, run code, interact with files, perform web searches, and maintain conversation state. SuperBot supports multi-agent orchestration for complex workflows, all configurable through simple Python scripts and CLI commands. Its extensible design allows you to add custom tools, automate tasks, and integrate external APIs to build robust AI-driven applications.
  • Agent Protocol is an open web3 protocol for creating autonomous AI Agents that execute tasks, transact on-chain, interact with APIs.
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    What is Agent Protocol?
    Agent Protocol is a decentralized framework that allows users to build AI Agents capable of interacting with smart contracts, external APIs, and other agents. It offers a no-code Agent Studio for visual workflow design, a Marketplace to publish and monetize agents, and an SDK for programmatic integration. Agents can initiate token payments, perform cross-chain operations, and dynamically adapt to real-time data, making them ideal for DeFi, NFT automation, and oracle services.
  • A Python-based autonomous AI Agent framework providing memory, reasoning, and tool integration for multi-step task automation.
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    What is CereBro?
    CereBro offers a modular architecture for creating AI agents capable of self-directed task decomposition, persistent memory, and dynamic tool usage. It includes a Brain core managing thoughts, actions, and memory, supports custom plugins for external APIs, and provides a CLI interface for orchestration. Users can define agent goals, configure reasoning strategies, and integrate functions such as web search, file operations, or domain-specific tools to execute tasks end-to-end without manual intervention.
  • A CLI toolkit to scaffold, test, and deploy autonomous AI agents with built-in workflows and LLM integrations.
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    What is Build with ADK?
    Build with ADK streamlines the creation of AI agents by providing a CLI scaffolding tool, workflow definitions, LLM integration modules, testing utilities, logging, and deployment support. Developers can initialize agent projects, select AI models, configure prompts, connect external tools or APIs, run local tests, and push their agents to production or container platforms—all with simple commands. The modular architecture allows easy extension with plugins and supports multiple programming languages for maximum flexibility.
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