The 12-Factor Agents framework adapts the proven 12-factor app principles to the unique demands of AI Agent development. It prescribes a single codebase with version control, explicit dependency declaration, environment-agnostic configuration, and seamless integration with external services. It defines clear build and release stages, supports stateless processes, port-based binding, process concurrency, graceful shutdowns, and parity between development and production. Centralized logging and scripted administrative tasks are also emphasized. By following these structured guidelines, development teams can create AI Agents that are modular, scalable, and resilient, simplifying deployment, enhancing observability, and reducing operational complexity.

AChat.dev is a developer-centric platform that allows users to create, test and deploy AI chat agents with advanced capabilities. It supports persistent conversation memory so agents remember past interactions, dynamic function calls to external APIs for real-time data retrieval, and role-based multi-agent collaboration. Built on Python and Node.js SDKs, it includes templating for quick setup, plugin architecture for extensibility, and monitoring dashboards to track agent performance. AChat.dev ensures GDPR-compliant data handling and can scale across cloud and on-premise environments.

AI-Agents provides a modular architecture for defining and running Python-based AI agents. Developers can configure agent behaviors, integrate external APIs or tools, and manage agent memory across sessions. It leverages popular LLMs, supports multi-agent collaboration, and enables plugin-based extensions for complex workflows like data analysis, automated support, and personalized assistants.

AgentBridge is a comprehensive AI agent orchestration platform enabling teams to create intelligent assistants without deep coding expertise. It provides drag-and-drop workflow design, integration adapters for REST APIs, databases, and messaging platforms, error-handling rules, and real-time logging. Agents can be scheduled, triggered by events, or run on demand. The platform includes monitoring dashboards, usage analytics, version management, and team collaboration tools. Security features such as role-based access and audit trails ensure compliance. AgentBridge scales horizontally, allowing enterprises to deploy multiple agents in parallel and integrate them seamlessly into existing infrastructures.

Aegix provides a robust SDK for orchestrating AI Agents capable of handling complex workflows through multi-step reasoning. With support for various LLM providers, it lets developers integrate custom tools—from database connectors to web scrapers—and maintain conversation state with memory modules such as vector stores. Aegix’s flexible agent loop architecture allows the specification of planning, execution, and review phases, enabling agents to refine outputs iteratively. Whether building document question-answering bots, code assistants, or automated support agents, Aegix simplifies development with clear abstractions, configuration-driven pipelines, and easy extension points. It’s designed to scale from prototypes to production, ensuring reliable performance and maintainable codebases for AI-driven applications.

Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

Agenite is a Python-centric AI agent framework designed to streamline the creation, orchestration, and management of autonomous agents. It offers modular components such as memory stores, task schedulers, and event-driven communication channels, enabling developers to build agents capable of stateful interactions, multi-step reasoning, and asynchronous workflows. The platform provides adapters for connecting to external APIs, databases, and message queues, while its pluggable architecture supports custom modules for natural language processing, data retrieval, and decision-making. With built-in storage backends for Redis, SQL, and in-memory caches, Agenite ensures persistent agent state and enables scalable deployments. It also includes a command-line interface and JSON-RPC server for remote control, facilitating integration into CI/CD pipelines and real-time monitoring dashboards.

Agent-Baba provides a comprehensive toolkit for creating and managing autonomous AI agents tailored to specific tasks. It offers a plugin architecture for extending capabilities, a memory system to retain conversational context, and workflow automation for sequential task execution. Developers can integrate tools like web scrapers, databases, and custom APIs into agents. The framework simplifies configuration through declarative YAML or JSON schemas, supports multi-agent collaboration, and provides monitoring dashboards to track agent performance and logs, enabling iterative improvement and seamless deployment across environments.

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.

AgentForge provides a structured environment for defining, combining, and orchestrating individual AI skills into cohesive autonomous agents. It supports conversation memory for context retention, plugin integration for external services, multi-agent communication, task scheduling, and error handling. Developers can configure custom skill handlers, leverage built-in modules for natural language understanding, and integrate with popular LLMs like OpenAI’s GPT series. AgentForge’s modular design accelerates development cycles, facilitates testing, and simplifies deployment of chatbots, virtual assistants, data analysis agents, and domain-specific automation bots.

Agentic-AI is an open-source Python framework that streamlines building autonomous agents leveraging large language models such as OpenAI GPT. It provides core modules for task planning, memory persistence, and tool integration, allowing agents to decompose high-level goals into executable steps. The framework supports plugin-based custom tools—APIs, web scraping, database queries—enabling agents to interact with external systems. It features a chain-of-thought reasoning engine coordinating planning and execution loops, context-aware memory recalls, and dynamic decision-making. Developers can easily configure agent behaviors, monitor action logs, and extend functionality, achieving scalable, adaptable AI-driven automation for diverse applications.

Agentic AI Workflow is an extensible Python library designed to orchestrate multiple AI agents for complex task automation. It includes a planning agent to break down objectives into actionable steps, execution agents to perform those steps via connected LLMs, and a reflection agent to review outcomes and refine strategies. Developers can customize prompt templates, memory modules, and connector integrations for any major language model. The framework provides reusable components, logging, and performance metrics to streamline the creation of autonomous research assistants, content pipelines, and data processing workflows.

AWS Agentic Workflows is a serverless orchestration framework that lets you chain AI tasks into end-to-end workflows. Using Amazon Bedrock foundation models, you can invoke AI agents to perform natural language processing, classification, or custom tasks. AWS Step Functions manages state transitions, retries, and parallel execution. Lambda functions can preprocess inputs and post-process outputs. CloudWatch provides logs and metrics for real-time monitoring and debugging. This enables developers to build reliable, scalable AI pipelines without managing servers or infrastructure.

AI Refinery provides businesses with a suite of tools to facilitate the integration of artificial intelligence into existing processes. It streamlines the adoption of AI technologies, allowing organizations to improve operational efficiency, enhance customer experiences, and drive innovation. The platform includes features for automating workflows, optimizing decision-making processes, and enabling smarter data analysis, all tailored to specific business needs.

AI Agent with MCP is a comprehensive framework designed to streamline the development of advanced AI agents capable of maintaining long-term context, performing multi-step reasoning, and adapting strategies based on memory. It leverages a modular design comprising Memory Manager, Conditional Planner, and Prompt Manager, allowing custom integrations and extension with various LLMs. The Memory Manager persistently stores past interactions, ensuring context retention. The Conditional Planner evaluates conditions at each step and dynamically selects the next action. The Prompt Manager formats inputs and chains tasks seamlessly. Built in Python, it integrates with OpenAI GPT models via API, supports retrieval-augmented generation, and facilitates conversational agents, task automation, or decision support systems. Extensive documentation and examples guide users through setup and customization.

AnYi AI Agent Framework helps developers integrate autonomous AI agents into their applications. Agents can plan and execute multi-step tasks, leverage external tools and APIs, and maintain conversation context through configurable memory modules. The framework abstracts interactions with various LLM providers and supports custom tool and memory backends. With built-in logging, monitoring, and asynchronous execution, AnYi accelerates deployment of intelligent assistants for research, customer support, data analysis, or any workflow requiring automated reasoning and action.

AMOD provides a platform to deploy advanced large language models, such as Meta Llama, Anthropic Claude, and Amazon Titan, within seconds. Users can select from multiple API schemas for their integrations, ensuring compatibility and ease of migration from other service providers like OpenAI. The platform supports automatic scaling, making it ideal for businesses seeking robust and scalable AI solutions with minimal setup time.

AskAtlasAI-Agent empowers developers to deploy AI agents that answer natural language queries against any document set stored in MongoDB Atlas. It orchestrates LLM calls for embedding, search, and response generation, handles conversational context, and offers configurable prompt chains. Built on JavaScript/TypeScript, it requires minimal setup: connect your Atlas cluster, supply OpenAI credentials, ingest or reference your documents, and start querying via a simple API. It also supports extension with custom ranking functions, memory backends, and multi-model orchestration.

Assistants Hub is a platform that enables the creation, integration, and deployment of personalized AI assistants in minutes. This user-friendly platform democratizes AI, allowing even non-tech-savvy users to build and deploy AI assistants. The service boasts scalability and ease of use, aiming to enhance productivity and innovation in various environments such as business, education, and personal use cases.

autogen_multiagent provides a structured way to instantiate, configure, and coordinate multiple AI agents in Python. It offers dynamic agent creation, inter-agent messaging channels, task planning, execution loops, and monitoring utilities. By integrating seamlessly with the OpenAI API, it allows you to assign specialized roles—such as planner, executor, summarizer—to each agent and orchestrate their interactions. This framework is ideal for scenarios requiring modular, scalable AI workflows, such as automated document analysis, customer support orchestration, and multi-step code generation.