How to Build a Multi-Agent Research Assistant: A Step-by-Step Tutorial Using LangGraph 0.5 and Qwen 3.8 Max

Building a Multi-Agent Research Assistant with LangGraph 0.5 and Qwen 3.8 Max for B2B Teams As of May 24, 2026, enterprises evaluating AI for operations are increasingly turning to multi-agent systems to automate complex research workflows. While many tutorials focus on single-agent chatbots, a clear gap exists for a practical, open-source blueprint that combines LangGraph 0.5's orchestration capabilities with Qwen 3.8 Max's advanced tool calling—tailored specifically for B2B research teams. This step-by-step tutorial guides you through building a multi-agent research assistant that handles literature searches, database queries, and citation management, all while remaining vendor-neutral and deployable in your own infrastructure. Why a Multi-Agent Research Assistant for B2B Teams? B2B research teams face a common challenge: gathering, synthesizing, and citing information from multiple so

urces—industry reports, academic papers, competitor websites, internal databases—often under tight deadlines. A single-agent chatbot can handle simple Q&A but struggles with complex, multi-step tasks that require context switching between tools. A multi-agent architecture solves this by decomposing the research process into specialized agents: Orchestrator Agent : routes queries, manages state, and aggregates responses. Search Agent : handles web searches (e.g., via Tavily or Brave). Database Agent : queries internal knowledge bases or APIs. Citation Agent : tracks sources and formats references. This design mirrors how human research teams operate—assigning subtasks to experts—and enables parallel execution, better error isolation, and transparent source attribution. For B2B leaders, it also means reduced manual effort, consistent output quality, and full auditability of information sou

rces. Core Components: LangGraph 0.5 and Qwen 3.8 Max Two open-source technologies form the foundation of our assistant: LangGraph 0.5 (released April 2026): A graph-based framework for building stateful, multi-agent applications. Key features include , nodes, edges, and persistent channels that allow agents to share context and coordinate actions. It is available at under the MIT license. Qwen 3.8 Max (model ID: ): Alibaba Cloud's latest large language model optimized for tool calling and long-context reasoning. It supports parallel function calling, a 128k token context window, and is accessible via the DashScope API. For self-hosted deployments, the model is available on Hugging Face ( ) under a Qwen license. Together, they provide a robust, scalable stack for building research agents without vendor lock-in. Architecture Overview: Orchestrating Research Agents The multi-agent system f

ollows a hierarchical design: The orchestrator uses a to manage conversation state, routing each sub-task to the appropriate agent via edges. After agents complete their tasks, the orchestrator collects results and passes them to the citation agent for formatting. This pattern ensures each agent stays focused while the orchestrator maintains overall coherence. Step 1: Setting Up Your Environment and Dependencies Start by installing LangGraph 0.5 and the DashScope SDK for Qwen 3.8 Max: Next, obtain API keys: Qwen 3.8 Max : Sign up at and create an API key. Set it as environment variable . Web search : Register for a Tavily API key (or use a custom search API). Set . Optionally, for internal database integration, set connection strings for your SQL or vector database. Step 2: Implementing the Research Orchestrator Agent The orchestrator is the brain of the system. Using LangGraph's , defin

e a state that holds the user query, intermediate results, and citation list: Add nodes for each phase: Define edges to control flow. For parallel execution, use or simply sequence them. For a research assistant, you may run search and database queries in parallel, then combine results: Finally, compile the graph: The orchestrator now routes queries through the pipeline. Step 3: Integrating External Tools (Web Search, Databases) Now implement the tool-calling functions using Qwen 3.8 Max's native function calling capabilities. First, define a tool schema for web search: In the node, use Qwen 3.8 Max to decide whether to call the web search tool. Provide the tool description as part of the system prompt: Similarly, implement a function that calls your internal database via API or direct SQL. Use Qwen to generate SQL queries or API parameters based on the user's natural language request. S

tep 4: Adding Citation Management and Source Tracking One of the biggest pain points in B2B research is manual citation. Our system automates this through a dedicated citation agent that processes each source and formats it consistently. After search and database nodes complete, the node collects al