I’m working on a project where I need to implement monitoring and tracking for my ADK agent setup. I’ve been looking into Langsmith as a solution for observability but I’m having trouble figuring out how to connect everything together.
Has anyone in the community successfully configured Langsmith to work with ADK agents? I’m particularly interested in understanding the setup process and any potential issues I might run into. If you have experience with this integration, could you share some guidance on how to get started?
I’ve tried looking through the documentation but it’s not entirely clear how these two systems should work together. Any code examples or step-by-step instructions would be really helpful.
Been running this combo in production for about a year. Callback order matters way more than most people realize.
I built a middleware layer between ADK and Langsmith instead of forcing ADK callbacks directly into Langsmith traces. Buffer the events first, then batch them.
My basic flow:
Set up an event collector in your ADK agent
Queue agent actions and tool calls locally
Push to Langsmith in batches every few seconds
This fixes the threading issues Pete mentioned and keeps you under rate limits. Better error handling too - failed Langsmith calls won’t break your agent.
One gotcha: ADK’s internal state changes fast during complex reasoning chains. Snapshot the relevant context when each event happens, not when you send it to Langsmith.
I implemented the integration you’re working on about six months ago and faced quite a few challenges before successfully configuring it. One major hurdle was that the standard Langsmith examples tend to focus on basic LLM chains, which don’t align well with the unique callback mechanisms of ADK agents.
To get things working, I developed a custom tracer to link the ADK agent callbacks with Langsmith’s tracing system. It’s crucial to initialize the Langsmith client first, then correctly wrap your agent’s execution calls with appropriate tracing decorators. You’ll need to ensure that you capture both the planning steps of the agent and the specific tool executions.
A significant point to be aware of is that ADK agents operate with multiple concurrent threads, which can disrupt the trace context if not managed properly. Be diligent about propagating the trace context across thread boundaries to avoid issues. Also, keep in mind the Langsmith API rate limits, as your agent could generate numerous calls during development and testing.
the langsmith + adk combo’s tricky but solid once you nail it. First thing - double-check your enviroment variables are set right. I wasted hours on that mistake. Also verify your adk version works with langsmith’s current features since older versions break with newer stuff.
Just wrapped up something similar last month - hit some authentication issues that aren’t documented anywhere. ADK agents don’t play nice with Langsmith’s session management out of the box. ADK’s session lifecycle is completely different from what Langsmith expects. I built a session bridge that keeps the Langsmith session alive during the entire agent execution. Catch the agent init event, establish your Langsmith session there, then make sure it survives all the decision loops. Watch for memory leaks on long runs. Langsmith hoards trace data references and they pile up fast with long-running ADK agents. Had to add periodic cleanup for finished traces or my overnight runs would crash.
This integration taught me that handling ADK’s event serialization is everything. The default serialization completely breaks when you try sending complex agent state objects to Langsmith. I had to write custom serializers for ADK’s internal data structures before sending them as traces. The agent’s decision tree objects and tool response formats need special handling or you’ll get JSON encoding errors on Langsmith’s side. I also ran into timing issues - ADK considers an action complete way before you want to close the trace in Langsmith. ADK agents keep processing internally after what looks like the final output, so if you close traces too early, you’ll truncate important execution data. Add a small delay before finalizing traces to catch any delayed internal operations.
I’ve hit this integration nightmare tons of times. The problem? ADK agents and Langsmith just don’t play nice together.
Skip the custom tracers and session bridges - they’re a pain. I automated the whole monitoring setup instead. Built a workflow that grabs ADK agent events as they happen, converts them to Langsmith format, and handles sessions automatically.
It fixes the threading mess, batches API calls properly, and retries when Langsmith acts up. Everything buffers locally so your agents don’t get slowed down by monitoring.
Best part? Different monitoring rules for different agents. Some need detailed tracing, others just want basic metrics. The automation knows what to send based on how each agent’s configured.
First setup took an afternoon. Now I just copy the template for new projects. Beats maintaining custom middleware.