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Building Agentic Flows with LangGraph and Model Context Protocol
Learn how Qodo Gen leverages LangGraph & MCP to build agentic AI workflows for coding, improving automation & tool integration.
To support this on-demand context retrieval, we shifted to a more task-oriented architecture to let the LLM dynamically request exactly what it needs through tools based on the specific task and session state. LangGraph’s graph-based structure allowed us to define complex workflows where each node can do something different—from calling the LLM to fetching data from a tool—without forcing us into a single linear chain of steps. This integration unlocked access to previously inaccessible information sources, allowing agents to leverage both native IDE capabilities and external resources such as company-specific knowledge bases provided through user-created tools.
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