Agent Starter Pack is a developer toolkit that scaffolds intelligent, interactive agents on Google Cloud. It offers templates in Node.js and Python to manage conversation flows, maintain long-term memory, and perform tool and API invocations. Built on Vertex AI and Cloud Functions or Cloud Run, it supports multi-step planning, dynamic routing, observability, and logging. Developers can extend connectors to custom services, build domain-specific assistants, and deploy scalable agents in minutes.

Thousand Birds is an extensible AI agent framework allowing developers to define and configure agent behaviors using a Python SDK and CLI. Agents can plan multi-step workflows, integrate web search, interact with browser sessions, read and write files, call external APIs, and manage stateful memory. It supports plugin modules to add custom tools and data connectors. The built-in orchestration engine schedules tasks, handles retries, and logs execution details. Developers can chain agents, enable parallel execution, and monitor performance through structured outputs. Thousand Birds accelerates deployment of autonomous assistants for research, data extraction, automation, and experimental prototypes.

LAWLIA provides a structured interface to define agent behaviors, plugin tools, and memory management for conversational or autonomous workflows. Developers can integrate with major LLM APIs, configure prompt templates, and register custom tools like search, calculators, or database connectors. Through its Agent class, LAWLIA handles planning, action execution, and response interpretation, allowing multi-turn interactions and dynamic tool invocation. Its modular design supports extending capabilities via plugins, enabling agents for customer support, data analysis, code assistance, or content generation. The framework streamlines agent development by managing context, memory, and error handling under a unified API.

MDLR is an innovative, open-source framework that utilizes AI to analyze and organize unstructured content like comments and notes. This powerful platform helps transform scattered data into actionable, real-time, and continually evolving summaries. Ideal for personal diaries and collaborative efforts, MDLR provides users with AI-driven summaries that adapt as new data is introduced, ensuring insights remain current and relevant. Easily integrated into various platforms, MDLR enhances project management with auto-updating notes and summaries, perfect for both individual and group usage.

PerfAgents is an AI-powered synthetic monitoring and testing platform designed to simplify the setup and management of monitoring your critical applications. It supports multiple open-source frameworks like Selenium, Puppeteer, Cypress, and Playwright. By leveraging your existing scripts or effortlessly creating new ones, PerfAgents provides continuous testing, reducing downtime and ensuring optimal application performance. It seamlessly integrates with tools like Slack, Microsoft Teams, Jira, and PagerDuty for real-time alerts and notifications.

SoccerAgent is a specialized AI framework designed for developing and training autonomous soccer agents using state-of-the-art multi-agent reinforcement learning (MARL) techniques. It simulates realistic soccer matches in 2D or 3D environments, offering tools to define reward functions, customize player attributes, and implement tactical strategies. Users can integrate popular RL algorithms (such as PPO, DDPG, and MADDPG) via built-in modules, monitor training progress through dashboards, and visualize agent behaviors in real time. The framework supports scenario-based training for offense, defense, and coordination protocols. With an extensible codebase and detailed documentation, SoccerAgent empowers researchers and developers to analyze team dynamics and refine AI-driven gameplay strategies for academic and commercial projects.

Step Functions Agent is an open-source toolkit enabling developers to construct intelligent serverless workflows on AWS. By leveraging Large Language Models like OpenAI's GPT, this agent dynamically generates AWS Step Functions state machine definitions based on natural language prompts or structured instructions. It supports invoking Lambda functions, passing context between steps, implementing conditional branching, parallelization, retries, and error handling. The framework abstracts AWS service integrations, automatically provisions resources, and offers observability through CloudWatch. Users can customize prompts, integrate custom functions, and monitor workflow executions. With built-in fallback strategies and audit logging, Step Functions Agent streamlines building scalable, resilient AI-driven automation pipelines, accelerating development for data processing, ETL, and decision support applications.