Java-Action-Datetime is an add-on module for the LightJason multi-agent system framework, designed to handle all temporal operations within your agents. It provides actions to retrieve the current timestamp, parse date/time strings into Java temporal objects, apply custom formatting patterns, perform arithmetic operations such as adding or subtracting durations, compute differences between datetimes, and convert between timezones. These actions seamlessly integrate into LightJason agent code, reducing boilerplate and enabling reliable, consistent temporal reasoning across distributed agent deployments.

Java Action Interpolate is a specialized Java library designed to integrate with the LightJason multi-agent framework. It provides a suite of interpolation algorithms, including linear, polynomial, and spline methods, enabling agents to transition between states and actions fluidly. The module offers configurable interpolation parameters, hooks into the LightJason action lifecycle, and supports custom data types. By incorporating Java Action Interpolate, developers can eliminate jarring behavior jumps, enhance simulation fidelity, and simplify the implementation of smooth agent movements and decision-driven behaviors within distributed or simulation environments.

ProficientAI Agent Framework is an end-to-end solution for designing and deploying advanced AI agents. It allows users to define custom agent behaviors through modular tool definitions and function specifications, ensuring seamless integration with external APIs and services. The framework’s memory management subsystem provides short-term and long-term context storage, enabling coherent multi-turn conversations. Developers can easily switch between different language models or combine them for specialized tasks. Built-in monitoring and logging tools offer insights into agent performance and usage metrics. Whether you’re building customer support bots, knowledge base search assistants, or task automation workflows, ProficientAI simplifies the entire pipeline from prototype to production, ensuring scalability and reliability.

openai-agents-python is a comprehensive Python package designed to help developers construct fully autonomous AI agents. It provides abstractions for agent planning, tool integration, memory states, and execution loops. Users can register custom tools, specify agent goals, and let the framework orchestrate step-by-step reasoning. The library also includes utilities for testing and logging agent actions, making it easier to iterate on behaviors and troubleshoot complex multi-step tasks.

Toolhouse is a developer platform designed to build and deploy AI agents and workflows without the hassle of boilerplate code. It comes with pre-built agentic frameworks like RAG, evals, API integrations, memory, cache, prompts, and tools, enabling developers to quickly build and ship functional AI products. With robust support for third-party app integrations, Toolhouse offers a seamless development and debugging experience, significantly accelerating the production lifecycle.

AgenticRAG provides a modular architecture for creating autonomous agents that leverage retrieval-augmented generation (RAG). It offers components to index documents in vector stores, retrieve relevant context, and feed it into LLMs to generate context-aware responses. Users can integrate external APIs and tools, configure memory stores to track conversation history, and define custom workflows to orchestrate multi-step decision-making processes. The framework supports popular vector databases like Pinecone and FAISS, and LLM providers such as OpenAI, allowing seamless switching or multi-model setups. With built-in abstractions for agent loops and tool management, AgenticRAG simplifies development of agents capable of tasks like document QA, automated research, and knowledge-driven automation, reducing boilerplate code and accelerating time to deployment.

AgentScope is a developer-focused framework designed to simplify the creation of intelligent agents by providing modular components for dynamic planning, contextual memory storage, and tool/API integration. It supports multiple LLM backends (OpenAI, Anthropic, Hugging Face) and offers customizable pipelines for task execution, answer synthesis, and data retrieval. AgentScope’s architecture enables rapid prototyping of conversational bots, workflow automation agents, and research assistants, all while maintaining extensibility and scalability.

Agent Transparency Tool offers a comprehensive framework for instrumenting AI agents with transparency features. It provides logging interfaces to record state transitions and decisions, modules to compute key transparency metrics (e.g., confidence scores, decision lineage), and visualization dashboards to explore agent behavior over time. By integrating seamlessly with popular agent frameworks, it generates structured transparency logs, supports export to JSON or CSV formats, and includes utilities to plot transparency curves for audit and performance analysis. This toolkit empowers teams to identify biases, debug workflows, and demonstrate responsible AI practices.

Agentic Framework is a versatile, open-source framework designed to streamline the creation of autonomous AI agents that leverage large language models and MongoDB. It equips developers with modular components for managing agent memory, defining toolsets, orchestrating multi-step workflows, and templating prompts. The integrated MongoDB-backed memory store enables agents to maintain persistent context across sessions, while pluggable tool interfaces allow seamless interaction with external APIs and data sources. Built on Node.js, the framework includes logging, monitoring hooks, and deployment examples to rapidly prototype and scale intelligent agents. With customizable configuration, developers can tailor agents for tasks such as knowledge retrieval, automated customer support, data analysis, and process automation, reducing development overhead and accelerating time-to-production.

AI Agents Tools is a comprehensive Python framework enabling developers to rapidly compose AI agents by leveraging OpenAI function calling. The library encapsulates a suite of modular tools, including web search, browser-based browsing, Wikipedia retrieval, Python REPL execution, and vector memory integration. By defining agent templates—such as single-tool agents, toolbox-driven agents, and callback-managed workflows—developers can orchestrate multi-step reasoning pipelines. The toolkit abstracts the complexity of function serialization and response handling, offering seamless integration with OpenAI LLMs. It supports dynamic tool registration and memory state tracking, allowing agents to recall past interactions. Suitable for building chatbots, autonomous research assistants, and task automation agents, AI Agents Tools accelerates experimentation and deployment of custom AI-driven workflows.

AnyAgent is a flexible agent framework that lets developers construct intelligent agents capable of reasoning, planning, and executing tasks across diverse domains. It offers a built-in planner for chaining actions, configurable memory stores for long-term context, and easy hookups to external tools and APIs. Through a simple declarative DSL, you can define custom skills, embed event logging, and swap between LLM backends seamlessly. Whether for customer support bots, data analysis assistants, or research prototypes, AnyAgent accelerates agent creation with robust architecture, modular components, and extensibility for real-world automation scenarios.

Blue Agent serves as a comprehensive toolkit for constructing AI-driven agents in Node.js. It enables developers to implement chain-of-thought prompting to improve reasoning, integrate external tools and APIs for enriched functionality, and maintain conversation memory for context retention. The framework features a planning engine that sequences tasks, an execution module to perform actions, and built-in logging to track agent decisions. Developers can define custom tool interfaces, orchestrate multi-step workflows, and leverage function calling to interact with services. Blue Agent's modular architecture allows seamless extension with plugins and supports debugging tools for observing agent behaviors, making it ideal for building advanced chatbots, autonomous assistants, and automated pipelines.

JADE is a Java-based agent development framework that simplifies the creation of distributed multi-agent systems. It provides FIPA-compliant infrastructure including a runtime environment, message transport, directory facilitator, and agent management. Developers write agent classes in Java, deploy them in containers, and use graphical tools like RMA and Sniffer for debugging and monitoring. JADE supports agent mobility, behavior scheduling, and lifecycle operations, enabling scalable and modular designs for research, IoT coordination, simulations, and enterprise automation.

LangChain is an open-source Python framework designed to accelerate development of AI-powered applications. It provides abstractions for chaining multiple language model calls (chains), building agents that interact with external tools, and managing conversation memory. Developers can define prompts, output parsers, and run end-to-end workflows. Integrations include vector stores, databases, APIs, and hosting platforms, enabling production-ready chatbots, document analysis, code assistants, and custom AI pipelines.

Bitte AI Agents is an end-to-end agent development framework designed to simplify the creation of autonomous AI assistants. It allows you to define agent roles, configure memory stores, integrate external APIs or custom tools, and orchestrate multi-step workflows. Developers can use the platform SDK to build, test, and deploy agents on any environment. The framework handles context management, conversation histories, and security controls out of the box, enabling rapid iteration and scalable deployment of intelligent agents across use cases such as customer service automation, data insights, and content generation.

MCP Agent Proxy acts as a middleware service between your applications and the OpenAI API. It transparently forwards ChatCompletion and Embedding calls, handles streaming responses to clients, caches results to improve performance and reduce costs, logs request and response metadata for debugging, and allows on-the-fly customization of API parameters. Developers can integrate it into existing agent frameworks to simplify multi-channel processing and maintain a single managed endpoint for all AI interactions.

Julep AI Responses is an AI agent framework delivered as a Node.js SDK and cloud platform. Developers initialize an Agent object, define onMessage handlers for custom responses, manage session state for context-aware conversations, and integrate plugins or external APIs. The platform handles hosting and scaling, enabling rapid prototyping and deployment of chatbots, customer support agents, or internal assistants with minimal setup.

Wise Agents offers a comprehensive, searchable catalog of AI agent frameworks and platforms. It features filtering by category, programming language, license type, and more to help users zero in on the right tool. Each agent entry includes a detailed profile, key capabilities, GitHub and documentation links, and community ratings. The site is regularly updated through community contributions, ensuring the latest agent releases and developments are always available in one centralized resource.

echoOLlama leverages the Ollama ecosystem to provide a minimal agent framework: it reads user input from the terminal, sends it to a configured local LLM, and streams back responses in real time. Users can script sequences of interactions, chain prompts, and experiment with prompt engineering without modifying underlying model code. This makes echoOLlama ideal for testing conversational patterns, building simple command-driven tools, and handling iterative agent tasks while preserving data privacy.