AmongA and researching multi-agent AI systems. Through a simple Python API, users instantiate any number of AI agents, each equipped with tailored personas, prompts, and memory buffers. Agents engage in configurable conversation loops, supporting debates, brainstorming, decision-making, or game simulations. The framework seamlessly integrates with major LLM APIs (e.g., OpenAI, Anthropic), enabling message-based interaction and transcript logging. Developers can extend behavior by customizing agent roles, controlling turn-taking logic, and plugging in external data sources. AmongAIs also provides utilities for sentiment analysis, score-based evaluation, and session replay. Ideal for teams exploring emergent communication, collaborative ideation, and testing digital worker coordination in research and production settings.

AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.

demo_smolagents is a reference implementation of SmolAgents, a Python-based microframework for creating autonomous AI agents powered by large language models. This demo includes examples of how to configure individual agents with specific toolkits, establish communication channels between agents, and manage task handoffs dynamically. It showcases LLM integration, tool invocation, prompt management, and agent orchestration patterns for building multi-agent systems that can perform coordinated actions based on user input and intermediate results.