SQL optimization process via LangGraph Supervisor - Need Help

[mraguth]

I've built an SQL optimization process using a single agent. However, I would like to transition this into an agentic process involving multiple agents.

Specifically, I'm interested in adopting a supervisor-agent architecture. Could anyone advise on how to decompose the existing single-agent system into multiple specialized agents under a supervisor framework?

Any guidance or best practices would be greatly appreciated!

[dosubot[bot]]

To move from a single-agent SQL optimization process to a supervisor-agent architecture in LangGraph, the best practice is to decompose your workflow into specialized agents (e.g., query parsing, cost estimation, rewriting, validation) and use LangGraph's routing logic to coordinate them. In LangGraph, the "supervisor" is typically implemented as routing or decision logic within the graph, not as a dedicated agent class. You can use constructs like RunnableLambda (recommended) or RunnableBranch to conditionally delegate tasks to the right agent based on the current state or output of previous steps. This modular approach lets you wire agents as nodes and supervisor logic as graph edges, enabling flexible orchestration and specialization example.

LangGraph also supports state management (via persistence/checkpointing) and recursion limits for complex, multi-step workflows migration guide. For multi-agent orchestration, you might also look at the OpenAIMultiFunctionsAgent class, which lets you build agents from a language model and a set of tools (each tool can represent a specialized agent) source.

If you share more about your current single-agent process or the specific subtasks you want to split out, I can help with a more tailored decomposition or example.

To reply, just mention @dosu.

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[mraguth]

I am using Delegation tasks that suggested in the supervisor agent link https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/?h=supervisor+agent#4-create-delegation-tasks. Is it same what you talking RunnableLambda (recommended) or RunnableBranch?

Plus having query parsing, cost estimation, rewriting, validation agents are good enough? or I need to create a agents for nested joins, cartesion joins etc? thoughts? Also can you give me a example if possible?

[dosubot[bot]]

Great questions! The "delegation tasks" pattern from the supervisor agent tutorial is not a specific class or API in LangGraph—it's an architectural approach where the supervisor explicitly assigns tasks to specialist agents. In practice, this delegation is implemented using primitives like RunnableLambda (recommended) or RunnableBranch (legacy). RunnableLambda lets you write flexible routing/controller logic that decides which agent to invoke at runtime, while RunnableBranch is a more rigid, condition-based router. Both are used to encode supervisor logic, but RunnableLambda is preferred for new workflows because it's more composable and expressive. There is no "DelegationTask" object—it's just a pattern you implement with these tools. See routing docs.

As for agent decomposition: there are no prescribed requirements in LangChain for creating agents for nested joins, Cartesian joins, etc. The standard breakdown—query parsing, cost estimation, rewriting, validation—covers most SQL optimization flows. If you encounter queries whose complexity or performance needs aren't well handled by your current agents, you can always introduce more specialized agents for those cases. But unless you see a clear need, your current setup is a solid starting point. Agent specialization is left to your discretion based on your workflow's complexity and bottlenecks.

To reply, just mention @dosu.

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[mraguth]

@dosu RunnableLambda is Langchain class. Can I use that in LangGraph? I want to build my agentic system in the LangGraph. Please advise.