Bot-With-Plan provides a modular Python template for building AI agents that first generate a detailed plan before execution. It uses OpenAI GPT to parse user instructions, decompose tasks into sequential steps, validate the plan, and then execute each step through external tools like web search or calculators. The framework includes prompt management, plan parsing, execution orchestration, and error handling. By separating planning and execution phases, it offers better oversight, easier debugging, and a clear structure for extending with new tools or capabilities.

At its core, DAGent represents agent workflows as a directed acyclic graph of nodes, where each node can encapsulate an LLM call, custom function, or external tool. Developers define task dependencies explicitly, enabling parallel execution and conditional logic, while the framework manages scheduling, data passing, and error recovery. DAGent also provides built-in visualization tools to inspect the DAG structure and execution flow, improving debugging and auditability. With extensible node types, plugin support, and seamless integration with popular LLM providers, DAGent empowers teams to build complex, multi-step AI applications such as data pipelines, conversational agents, and automated research assistants with minimal boilerplate. The library's focus on modularity and transparency makes it ideal for scalable agent orchestration in both experimental and production environments.

Team of AI Agents provides a modular architecture to build and deploy multi-agent systems. Each agent operates with distinct roles, utilizing a global memory store and local contexts for knowledge retention. The framework supports asynchronous messaging, tool usage via adapters, and dynamic task reassignment based on agent outcomes. Developers configure agents through YAML or Python scripts, enabling topic specialization, goal hierarchy, and priority handling. It includes built-in metrics for performance evaluation and debugging, facilitating rapid iteration. With extensible plugin architecture, users can integrate custom NLP models, databases, or external APIs. Team of AI Agents accelerates complex workflows by leveraging collective intelligence of specialized agents, making it ideal for research, automation, and simulation environments.

AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.

Autonomous Economic Agents (AEA) by Fetch.ai is a versatile framework that empowers developers to design, implement, and orchestrate autonomous software agents capable of interacting with each other, external environments, and digital ledgers. Leveraging a plugin-based architecture, AEA provides pre-built modules for communication protocols, cryptographic ledger APIs, decentralized identity, and customizable decision-making skills. Agents can discover and transact within decentralized marketplaces, perform goal-driven behaviors, and adapt through real-time data feeds. The framework supports simulation tools for testing and debugging multi-agent scenarios, as well as deployment onto live blockchains or peer-to-peer networks. With built-in interoperability and agent-to-agent messaging, AEA streamlines the development of complex autonomous economic applications such as energy trading, supply chain optimization, and smart IoT coordination.

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.

Copilot Studio is an AI platform & framework enabling rapid creation of domain-specific copilots. Through its drag-and-drop interface, users design prompt templates, set up tool integrations (e.g., APIs, databases), manage model selection, and orchestrate deployment. It supports version control, testing consoles, and multi-model routing, abstracting infrastructure complexity. Teams can prototype, iterate, and launch intelligent agents for customer service, developer assistance, or personal productivity in minutes.

Restack Python SDK Examples offer a comprehensive set of demonstration projects illustrating how to leverage the Restack platform to build AI agents. Included are templates for chatbots, document analysis agents, and task automation workflows. Examples cover API configuration, tool integration (e.g., web search, memory storage), agent orchestration, error handling, and deployment scenarios. Developers can clone the repository, configure their API keys, and extend sample agents to suit custom use cases.

ggfai provides a unified interface to define goals, manage multi-step reasoning, and maintain conversational context with memory modules. It supports customizable tool integrations for calling external services or APIs, asynchronous execution flows, and abstractions over OpenAI GPT models. The framework’s plugin architecture lets you swap memory backends, knowledge stores, and action templates, simplifying agent orchestration across tasks like customer support, data retrieval, or personal assistants.