LangGraph Agent combines LLMs with a graph-structured memory to build autonomous agents that can remember facts, reason over relationships, and call external functions or tools when needed. Developers define memory schemas as graph nodes and edges, plug in custom tools or APIs, and orchestrate agent workflows through configurable planners and executors. This approach enhances context retention, enables knowledge-driven decision making, and supports dynamic tool invocation in diverse applications.

ToolAgents is a modular open-source AI agent framework that integrates large language models with external tools to automate complex workflows. Developers register tools via a centralized registry, defining endpoints for tasks such as API calls, database queries, code execution, and document analysis. Agents can plan multi-step operations, dynamically invoking or chaining tools based on LLM outputs. The framework supports both sequential and parallel task execution, error handling, and extensible plug-ins for custom tool integrations. With Python-based APIs, ToolAgents simplifies building, testing, and deploying intelligent agents that fetch data, generate content, execute scripts, and process documents, enabling rapid prototyping and scalable automation across analytics, research, and business operations.