Lila delivers a complete AI agent framework tailored for multi-step reasoning and autonomous task execution. Developers can define custom tools (APIs, databases, webhooks) and configure Lila to call them dynamically during runtime. It offers memory modules to store conversation history and facts, a planning component to sequence sub-tasks, and chain-of-thought prompting for transparent decision paths. Its plugin system allows seamless extension with new capabilities, while built-in monitoring tracks agent actions and outputs. Lila’s modular design makes it easy to integrate into existing Python projects or deploy as a hosted service for real-time agent workflows.

Minerva is an extensible AI agent framework designed to automate complex workflows using large language models. Developers can integrate external tools—such as web search, API calls, or file processors—define custom planning strategies, and manage conversational or persistent memory. Minerva supports both synchronous and asynchronous task execution, configurable logging, and a plugin architecture, making it easy to prototype, test, and deploy intelligent agents capable of reasoning, planning, and tool use in real-world scenarios.

Python AGNO Memory Agent provides a structured approach to agent memory by organizing memories via an AGNO framework. It leverages embedding models to convert textual memories into vector representations and stores them in configurable vector stores like ChromaDB, FAISS, or SQLite. Agents can add new memories, query relevant past events, update outdated entries, or delete irrelevant data. The library offers timeline tracking, namespaced memory stores for multi-agent scenarios, and customizable similarity thresholds. It integrates easily with popular LLM frameworks and can be extended with custom embedding models to suit diverse AI agent applications.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.

sma-begin sets up a streamlined codebase to create AI-driven agents by abstracting common components like input processing, decision logic, and output generation. At its core, it implements an agent loop that queries an LLM, interprets the response, and optionally executes integrated tools, such as HTTP clients, file handlers, or custom scripts. Memory modules allow the agent to recall previous interactions or context, while prompt chaining supports multi-step workflows. Error handling catches API failures or invalid tool outputs. Developers only need to define the prompts, tools, and desired behaviors. With minimal boilerplate, sma-begin accelerates prototyping of chatbots, automation scripts, or domain-specific assistants on any Python-supported platform.

Alibaba Smart VSCode Extension is an open-source Visual Studio Code plugin that transforms the IDE into an interactive AI agent environment. By abstracting communication with bot frameworks like ChatGPT, it provides developers with a chat widget, customizable triggers, and code action integrations. Users define agent roles, pipeline steps, and plugins via a simple configuration file, while the extension handles session management, API requests, and UI rendering. This enables rapid prototyping of chat-driven features, on-the-fly code generation, and contextual knowledge retrieval from internal docs, all within VSCode. Teams can extend the extension with custom connectors, event hooks, and middleware, making it a versatile framework for building AI assistants directly in the editor.

Stella Framework empowers developers to build robust AI agents that can maintain context, perform tool-assisted actions, and deliver dynamic conversational experiences. By abstracting the complexities of LLM integrations, Stella offers provider-agnostic adapters for OpenAI, Hugging Face, and self-hosted models. Agents can leverage customizable memory stores to recall user data and conversation history, and plugins enable interactions with external APIs, databases, or services. The built-in orchestration engine manages decision loops, while a concise DSL allows defining actions, tool calls, and response handling. Whether creating customer support bots, research assistants, or workflow automators, Stella provides a scalable foundation for deploying production-grade AI agents.

X AI Agent is a developer-focused framework that simplifies building custom AI agents using large language models. It provides native support for function calling, memory storage, tool and plugin integration, chain-of-thought reasoning, and orchestration of multi-step tasks. Users can define custom actions, connect external APIs, and maintain conversational context across sessions. The framework’s modular design ensures extensibility and allows seamless integration with popular LLM providers, enabling robust automation and decision-making workflows.

AgentScript is an AI agent framework that lets users visually compose workflows, integrate external APIs, and configure autonomous agents. With built-in debugging, monitoring dashboards, and version control, teams can quickly prototype, test, and deploy agents to handle tasks like data analysis, customer support, and process automation. Agents can be scheduled, triggered by events, or run continuously, and you can extend them via custom code or third-party plugins.

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.

AgentLLM is a web-based AI agent framework that lets users create, configure, and run autonomous agents through a graphical interface or JSON definitions. Agents can plan multi-step workflows by reasoning over tasks, invoke code via Python tools or external APIs, maintain conversation and memory, and adapt based on results. The platform supports OpenAI, Azure, or self-hosted models, offering built-in tool integrations for web search, file handling, mathematical computation, and custom plugins. Designed for experimentation and rapid prototyping, AgentLLM streamlines building intelligent agents capable of automating complex business processes, data analysis, customer support, and personalized recommendations.

AgentReader is a developer-friendly AI agent framework that enables you to load and index various data sources such as PDFs, text files, markdown documents, and web pages. It integrates seamlessly with major LLM providers to power interactive chat sessions and question-answering over your knowledge base. Features include real-time streaming of model responses, customizable retrieval pipelines, web scraping via headless browser, and a plugin architecture for extending ingestion and processing capabilities.

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.

autogen4j is a lightweight Java library designed to abstract the complexity of building autonomous AI agents. It offers core modules for planning, memory storage, and action execution, letting agents decompose high-level goals into sequential sub-tasks. The framework integrates with LLM providers (e.g., OpenAI, Anthropic) and allows registration of custom tools (HTTP clients, database connectors, file I/O). Developers define agents through a fluent DSL or annotations, quickly assembling pipelines for data enrichment, automated reporting, and conversational bots. An extensible plugin system ensures flexibility, enabling fine-tuned behaviors across diverse applications.

Continuum is an open-source Python framework that enables developers to construct intelligent agents by defining tasks, tools, and memory in a composable manner. Agents built with Continuum follow a plan-execute-observe loop, allowing interleaving of LLM reasoning with external API calls or scripts. Its pluggable architecture supports multiple memory stores (e.g., Redis, SQLite), custom tool libraries, and asynchronous execution. With a focus on flexibility, users can write custom agent policies, integrate third-party services like databases or webhooks, and deploy agents across environments. Continuum’s event-driven orchestration logs agent actions, facilitating debugging and performance tuning. Whether automating data ingestion, building conversational assistants, or orchestrating DevOps pipelines, Continuum provides a scalable foundation for production-grade AI agent workflows.

Dev-Agent is an open-source AI agent framework that empowers developers to rapidly build and deploy autonomous agents. It combines a modular plugin architecture with easy-to-configure tool invocation, including HTTP endpoints, database queries, and custom scripts. Agents can leverage a persistent memory layer to reference past interactions, and orchestrate multi-step reasoning flows for complex tasks. With built-in support for OpenAI GPT models, users define agent behavior via simple JSON or YAML specs. The CLI tool manages authentication, session state, and logging. Whether creating customer support bots, data retrieval assistants, or automated CI/CD helpers, Dev-Agent reduces development overhead and enables seamless extension through community-driven plugins, offering flexibility and scalability for diverse AI-driven applications.

CamelAGI is an open-source framework designed to simplify the creation of autonomous AI agents. It features a plugin architecture for custom tools, long-term memory integration for context persistence, and support for multiple large language models such as GPT-4 and Llama 2. Through explicit planning and execution modules, agents can decompose tasks, call external APIs, and adapt over time. CamelAGI’s extensibility and community-driven approach make it suitable for research prototypes, production systems, and educational projects alike.

IntelliConnect is a versatile AI agent framework that enables developers to build intelligent agents by connecting LLMs (e.g., GPT-4) with various external APIs and services. It supports multi-step reasoning, context-aware tool selection, and error handling, making it ideal for automating complex workflows such as customer support, data extraction from web or documents, scheduling, and more. Its plugin-based design allows easy extension, while built-in logging and observability help monitor agent performance and refine capabilities over time.

Kaizen is an advanced AI agent framework designed to simplify creation and management of autonomous LLM-driven agents. It provides a modular architecture for defining multi-step workflows, integrating external tools via APIs, and storing context in memory buffers to maintain stateful conversations. Kaizen's pipeline builder enables chaining prompts, executing code, and querying databases within a single orchestrated run. Built-in logging and monitoring dashboards offer real-time insights into agent performance and resource usage. Developers can deploy agents on cloud or on-premise environments with autoscaling support. By abstracting LLM interactions and operational concerns, Kaizen empowers teams to rapidly prototype, test, and scale AI-driven automation across domains like customer support, research, and DevOps.

LangChain is a developer-focused framework designed to streamline the creation of intelligent AI agents and applications. It provides abstractions for chains of LLM calls, agentic behavior with tool integrations, memory management for context persistence, and customizable prompt templates. With built-in support for document loaders, vector stores, and various model providers, LangChain allows you to construct retrieval-augmented generation pipelines, autonomous agents, and conversational assistants that can interact with APIs, databases, and external systems in a unified workflow.