mcp-agent-graph provides a graph-based orchestration layer for AI agents, enabling developers to map out complex multi-step workflows as directed graphs. Each node in the graph corresponds to an agent task or function, capturing inputs, outputs, and dependencies. Edges define the flow of data between agents, ensuring correct execution order. The engine supports sequential and parallel execution modes, automatic dependency resolution, and integrates with custom Python functions or external services. Built-in visualization allows users to inspect graph topology and debug workflows. This framework streamlines the development of modular, scalable multi-agent systems for data processing, natural language workflows, or combined AI model pipelines.

LangChain is a developer-focused framework designed to streamline the creation of intelligent AI agents and applications. It provides abstractions for chains of LLM calls, agentic behavior with tool integrations, memory management for context persistence, and customizable prompt templates. With built-in support for document loaders, vector stores, and various model providers, LangChain allows you to construct retrieval-augmented generation pipelines, autonomous agents, and conversational assistants that can interact with APIs, databases, and external systems in a unified workflow.

Serena is designed to automate complex workflows through autonomous planning and execution. It interacts with web search engines, databases, and APIs to gather information, summarizes results, and carries out tasks according to user-defined goals. Built as a Python library, Serena maintains stateful memory across sessions, dynamically loads plugins for extended capabilities, and uses large language models to generate structured plans. Developers can customize tool integrations for code execution, file management, and analytics, making Serena a versatile framework for research, data processing, content generation, and beyond.