Avalon-LLM is a Python-based multi-agent AI framework that allows users to orchestrate multiple LLM-driven agents in a coordinated environment. Each agent can be configured with specific tools—including web search, file operations, and custom APIs—to perform specialized tasks. The framework supports memory modules for storing conversation context and long-term knowledge, chain-of-thought reasoning to improve decision making, and built-in evaluation pipelines to benchmark agent performance. Avalon-LLM provides a modular plugin system, enabling developers to easily add or replace components such as model providers, toolkits, and memory stores. With simple configuration files and command-line interfaces, users can deploy, monitor, and extend autonomous AI workflows tailored to research, development, and production use cases.

llm-ReAct implements the ReAct (Reasoning and Acting) architecture for large language models, enabling seamless integration of chain-of-thought reasoning with external tool execution and memory storage. Developers can configure a toolkit of custom tools—such as web search, database queries, file operations, and calculators—and instruct the agent to plan multi-step tasks, invoking tools as needed to retrieve or process information. The built-in memory module preserves conversational state and past actions, supporting more context-aware agent behaviors. With modular Python code and support for OpenAI APIs, llm-ReAct simplifies experimentation and deployment of intelligent agents that can adaptively solve problems, automate workflows, and provide context-rich responses.

Easy-Agent accelerates AI agent development by providing a modular framework that integrates LLMs with external tools, in-memory session tracking, and configurable action flows. Developers start by defining a set of tool wrappers that expose APIs or executables, then instantiate an agent with desired reasoning strategies—such as single-step, multi-step chain-of-thought, or custom prompts. The framework manages context, invokes tools dynamically based on model output, and tracks conversation history through session memory. It supports asynchronous execution for parallel tasks and solid error handling to ensure robust agent performance. By abstracting complex orchestration, Easy-Agent empowers teams to deploy intelligent assistants for use cases like automated research, customer support bots, data extraction pipelines, and scheduling assistants with minimal setup.