by agentailor · MCP Server · ★ 95
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LangGraph.js AI Agent Template A production-ready Next.js template for building AI agents with LangGraph.js, featuring Model Context Protocol (MCP) integration, human-in-the-loop tool approval, and persistent memory. Complete agent workflow: user input → tool approval → execution → streaming response Need help taking this to production? I help teams design and optimize LangGraph-based AI agents (RAG, memory, latency, architecture). If you're building something serious on top of this template and want hands-on help: → DM me on LinkedIn Happy to jump on a short call. Features Dynamic Tool Loading with MCP Model Context Protocol integration for dynamic tool management Add tools via web UI - no code changes required Support for both stdio and HTTP MCP servers Tool name prefixing to prevent conflicts #
| Stars | 95 |
| Forks | 28 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 38.75/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-12 |
| Created | 2025-09-28 |
| Platforms | mcp, node |
| Est. Tokens | ~379k |
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fullstack-langgraph-nextjs-agent is Production-ready Next.js template for building AI agents with LangGraph.js. Features MCP integration for dynamic tool loading, human-in-the-loop tool approval, persistent conversation memory with Po. It is categorized as a MCP Server with 95 GitHub stars.
fullstack-langgraph-nextjs-agent is primarily written in TypeScript. It covers topics such as agent-framework, ai-agent, langchain.
You can find installation instructions and usage details in the fullstack-langgraph-nextjs-agent GitHub repository at github.com/agentailor/fullstack-langgraph-nextjs-agent. The project has 95 stars and 28 forks, indicating an active community.
fullstack-langgraph-nextjs-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fullstack-langgraph-nextjs-agent on Agent Skills Hub include oreilly-ai-agents, PolyMCP, browser-operator-core. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.