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.

mcp-agent-graph provides a graph-based orchestration layer for AI agents, enabling developers to map out complex multi-step workflows as directed graphs. Each node in the graph corresponds to an agent task or function, capturing inputs, outputs, and dependencies. Edges define the flow of data between agents, ensuring correct execution order. The engine supports sequential and parallel execution modes, automatic dependency resolution, and integrates with custom Python functions or external services. Built-in visualization allows users to inspect graph topology and debug workflows. This framework streamlines the development of modular, scalable multi-agent systems for data processing, natural language workflows, or combined AI model pipelines.

AgenticSearch is an open-source Python toolkit for building autonomous AI agents that perform web searches, aggregate data, and produce structured answers. It integrates with large language models and search APIs to orchestrate multi-step workflows: issuing queries, scraping results, ranking relevant links, extracting key passages, and summarizing findings. Developers can customize agent behavior, chain actions, and monitor execution to build research assistants, competitive intelligence tools, or domain-specific data gatherers without manual browsing.

AI-Agent is an extensible Python framework designed to create autonomous agents powered by OpenAI's GPT models and LangChain. It includes modules for web searching, Wikipedia lookup, calculator functions, and custom tool integrations, enabling automated research, data analysis, and script execution. Users can configure agents to plan multi-step tasks, interact with APIs, generate reports, and perform complex workflows without manual intervention, streamlining productivity across development, data science, and business processes.

AI-Agent-Framework offers a comprehensive foundation for building AI-powered agents in Python. It includes modules for managing conversation memory, integrating external tools, and constructing prompt templates. Developers can connect to various LLM providers, equip agents with custom plugins, and orchestrate multiple agents in coordinated workflows. Built-in logging and monitoring tools help track agent performance and debug behaviors. The framework's extensible design allows seamless addition of new connectors or domain-specific capabilities, making it ideal for rapid prototyping, research projects, and production-grade automation.

The Kurtosis AutoGPT Package is an AI Agent framework packaged as a Kurtosis module that delivers a fully configured AutoGPT environment with minimal effort. It provisions and wires up services such as PostgreSQL, Redis, and a vector store, then injects your API keys and agent scripts into the network. Using Docker and Kurtosis CLI, you can spin up isolated agent instances, view logs, adjust budgets, and manage network policies. This package removes infrastructure friction so teams can rapidly develop, test, and scale autonomous GPT-driven workflows in a reproducible manner.

Besser Agentic Framework offers a modular toolkit for defining, coordinating, and scaling AI agents. It allows you to configure agent behaviors, integrate external tools and APIs, manage agent memory and state, and monitor execution. Built on Python, it supports extensible plugin interfaces, multi-agent collaboration, and built-in logging. Developers can rapidly prototype and deploy agents for tasks like data extraction, automated research, and conversational assistants, all within a unified framework.

Swarms is a developer-focused framework enabling the creation, orchestration, and execution of multi-agent AI workflows. You define agents with specific roles, configure their behavior via LLM prompts, and link them to external tools or APIs. Swarms manages inter-agent communication, task planning, and memory persistence. Its plugin architecture allows seamless integration of custom modules—such as retrievers, databases, or monitoring dashboards—while built-in connectors support popular LLM providers. Whether you need coordinated data analysis, automated customer support, or complex decision-making pipelines, Swarms provides the building blocks to deploy scalable, autonomous agent ecosystems.

ModelScope Agent provides a modular, Python‐based framework to orchestrate autonomous AI agents. It features plugin integration for external tools (APIs, databases, search), conversation memory for context preservation, and customizable agent chains to handle complex tasks such as knowledge retrieval, document processing, and decision support. Developers can configure agent roles, behaviors, and prompts, as well as leverage multiple LLM backends to optimize performance and reliability in production.

Dialogflow CX Chatbot is an AI-driven conversational agent built on Google's Dialogflow CX framework. It processes natural language inputs, identifies user intents, and extracts entities to maintain context-aware dialogues across multi-turn interactions. With features like slot filling, conditional flows, and webhook integrations, it can dynamically fetch external data and trigger backend services during conversations. The chatbot supports custom event handling, fallback strategies for unrecognized queries, and multilingual setups, providing consistent responses. Developers can design visual state machines in the Dialogflow CX console, mapping conversation paths and testing interactions in real time. Easily deployed via webhooks or client SDKs, this chatbot integrates with websites, messaging platforms, and voice channels to streamline customer service, automate FAQs, and drive user engagement.

Layra is designed to simplify developing LLM-powered agents by providing a modular architecture that integrates with various tools and memory stores. It features a planner that breaks down tasks into subgoals, a memory module for storing conversation and context, and a plugin system to connect external APIs or custom functions. Layra also supports orchestrating multiple agent instances to collaborate on complex workflows, enabling parallel execution and task delegation. With clear abstractions for tools, memory, and policy definitions, developers can rapidly prototype and deploy intelligent agents for customer support, data analysis, RAG, and more. It is framework-agnostic toward modeling backends, supporting OpenAI, Hugging Face, and local LLMs.

MCP Crew AI is a developer-focused framework that simplifies the creation and coordination of GPT-based AI agents in collaborative teams. By defining manager, worker, and monitor agent roles, it automates task delegation, execution, and oversight. The package offers built-in support for OpenAI’s API, a modular architecture for custom agent plugins, and a CLI for running and monitoring your Crew. MCP Crew AI accelerates multi-agent system development, making it easier to build scalable, transparent, and maintainable AI-driven workflows.

NagaAgent is an open-source Python library designed to simplify the creation, orchestration, and scaling of AI agents. It provides a plug-and-play tool integration system, persistent conversational memory objects, and an asynchronous multi-agent controller. Developers can register custom tools as functions, manage agent state, and choreograph interactions between multiple agents. The framework includes logging, error-handling hooks, and configuration presets for rapid prototyping. NagaAgent is ideal for building complex workflows—customer support bots, data processing pipelines, or research assistants—without infrastructure overhead.

Nuzon-AI provides a Python-based agent framework that lets you define tasks, manage conversational memory, and extend capabilities via plugins. It supports integration with major LLMs (OpenAI, local models), enabling agents to perform web interactions, data analysis, and automated workflows. The architecture includes a skill registry, tool invocation system, and multi-agent orchestration layer, allowing you to compose agents for customer support, research assistance, and personal productivity. With configuration files, you can tailor each agent’s behavior, memory retention policy, and logging for debugging or audit purposes.

OpenAI Agent Swarm is a modular framework designed to streamline the coordination of multiple GPT-powered agents across diverse tasks. Each agent operates independently with customizable prompts and role definitions, while the Swarm core manages agent lifecycle, message passing, and task scheduling. The platform includes tools for defining complex workflows, monitoring agent interactions in real time, and aggregating results into coherent outputs. By distributing workloads across specialized agents, users can tackle complex problem-solving scenarios, from content generation and research analysis to automated debugging and data summarization. OpenAI Agent Swarm integrates seamlessly with the OpenAI API, allowing developers to rapidly deploy multi-agent systems without building orchestration infrastructure from scratch.

With AI Agents is a code-first framework in TypeScript that helps you define and orchestrate multiple AI Agents, each with distinct roles such as planner, executor, and memory. It provides built-in memory management to persist context, a function-calling subsystem to integrate external APIs, and a CLI interface for interactive sessions. By composing agents in pipelines or hierarchies, you can automate complex tasks—like data analysis pipelines or customer support flows—while ensuring modularity, scalability, and easy customization.

Devon provides a comprehensive suite of tools for defining, orchestrating, and running autonomous agents within Python applications. Users can outline agent goals, specify callable tasks, and chain actions based on conditional logic. Through seamless integration with language models like GPT and local vector stores, agents ingest and interpret user inputs, retrieve contextual knowledge, and generate plans. The framework supports long-term memory via pluggable storage backends, enabling agents to recall past interactions. Built-in monitoring and logging components allow real-time tracking of agent performance, while a CLI and SDK facilitate rapid development and deployment. Suitable for automating customer support, data analysis pipelines, and routine business operations, Devon accelerates the creation of scalable digital workers.