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.

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.

Agent Adapters is designed to provide developers with a consistent interface for connecting AI agents to external services and frameworks. Through its pluggable adapter architecture, it offers prebuilt adapters for HTTP APIs, messaging platforms like Slack and Teams, and custom tool endpoints. Each adapter handles request parsing, response mapping, error handling, and optional logging or monitoring hooks. Developers can also register custom adapters by implementing a defined interface and configuring adapter parameters in their agent settings. This streamlined approach reduces boilerplate code, ensures uniform workflow execution, and accelerates the deployment of agents across multiple environments without rewriting integration logic.

CoderAssistants is an AI Agent framework designed to streamline software development workflows by embedding intelligent coding assistance directly into popular development environments and pipelines. At its core, CoderAssistants orchestrates large language models to generate boilerplate code, suggest improvements, automatically refactor legacy code, diagnose bugs based on error messages, and produce contextual documentation. Its modular plugin system allows teams to tailor agents for specific languages, frameworks, or compliance requirements, including custom prompt templates, workflow hooks, and automated testing integrations. By providing interactive chat-like assistants, CLI tools, and API endpoints, CoderAssistants ensures developers can interactively refine code, automate repetitive tasks, and maintain high code quality across projects.

CrewAI Agent Generator leverages a command-line interface to let you initialize a new AI agent project with opinionated folder structures, sample prompt templates, tool definitions, and testing stubs. You can configure connections to OpenAI, Azure, or custom LLM endpoints; manage agent memory using vector stores; orchestrate multiple agents in collaborative workflows; view detailed conversation logs; and deploy your agents to Vercel, AWS Lambda, or Docker with built-in scripts. It accelerates development and ensures consistent architecture across AI agent projects.

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.

ExampleAgent is a developer-focused toolkit designed to accelerate the creation of AI-driven assistants. It integrates directly with OpenAI’s GPT models to handle natural language understanding and generation, and offers a pluggable system for adding custom tools or APIs. The framework manages conversation context, memory, and error handling, enabling agents to perform information retrieval, task automation, and decision-making workflows. With clear code templates, documentation, and examples, teams can rapidly prototype domain-specific agents for chatbots, data extraction, scheduling, and more.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.