Hi everyone! I’m currently working on developing AI agents within my Kubernetes environment and I need a monitoring solution that can run locally in my cluster. I’m looking for something that works like Langsmith but can be deployed on-premises. I’ve experimented with both Opik and Langfuse and they work okay, but their user interfaces aren’t great when you’re trying to track sessions across multiple agent interactions. The workflow becomes pretty messy when dealing with complex multi-agent scenarios. Has anyone found better alternatives that handle agent session tracking more elegantly? Any suggestions would be really helpful!
we built something custom on jaeger after months of fighting with these tools. existing solutions don’t understand agent workflows - they’re designed for single llm calls, not complex handoffs. jaeger’s trace view shows the full conversation flow naturally and it’s already running in most k8s setups. more work upfront but way cleaner than forcing langfuse into something it wasn’t meant for.
I’ve had this exact problem for a year at work. We run complex multi-agent setups in K8s clusters and hit the same issues with Langfuse and Opik.
AgentOps saved us. It’s built specifically for agent monitoring and crushes session tracking compared to LLM tools that tack on agent support later. The UI actually makes sense when you’re tracking conversations between multiple agents.
We deploy it via Docker in our cluster and it plays nice with our existing observability stack. Session correlation works great - you can actually see what happens when agents hand off tasks.
Phoenix by Arize is another solid option. Open source with decent agent tracing. Less polished than AgentOps but free with full deployment control.
Both handle multi-agent workflow visualization way better than forcing Langfuse into something it wasn’t designed for.
Phoenix by Arize is effective for multi-agent deployments, but session correlation must be set up manually. Establish proper trace hierarchies from the outset; it’s crucial. Many monitoring tools struggle with agent handoffs, treating each interaction as an isolated event. We addressed this with custom correlation IDs that remain intact during task handoffs, allowing us to track entire dialogues regardless of the active agent. Helicone’s self-hosted solution is also worthwhile, as it offers solid distributed tracing, though it’s not specifically designed for agents. Its easy setup in Kubernetes and robust query features can help with debugging complex workflows. However, if you’re managing significant multi-agent interactions, be prepared to create custom dashboards, as stock visualizations often fail to capture the comprehensive dynamics of agent interactions in a live environment.
The Problem:
You’re working with a Kubernetes environment and multiple AI agents, needing a local monitoring solution for elegant session tracking across complex multi-agent interactions. Existing solutions like Langfuse and Opik are insufficient due to their less-than-ideal user interfaces for visualizing these complex workflows.
Step-by-Step Guide:
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Instrument Your Agents with OpenTelemetry: This is the core step. OpenTelemetry is a vendor-neutral instrumentation library that allows you to collect and export telemetry data (metrics, traces, and logs) from your AI agents. You’ll need to integrate OpenTelemetry into your agent’s code. This typically involves adding libraries and making calls to record events and attributes, providing context about each agent interaction. The level of detail you instrument will directly impact your ability to trace the multi-agent interactions.
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Choose and Deploy Uptrace: Uptrace is a distributed tracing system that works well in Kubernetes environments. It’s highly scalable and efficient, designed to handle the volume of data produced by complex multi-agent interactions. Deploy Uptrace in your Kubernetes cluster using their Helm charts. The installation process should be straightforward, following the steps in their documentation.
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Configure Uptrace to Receive OpenTelemetry Data: Configure Uptrace to receive the telemetry data exported by your OpenTelemetry-instrumented agents. This usually involves setting up a receiver (e.g., OTLP) within your Uptrace deployment and configuring your agents to send data to this receiver. You may need to configure your Kubernetes cluster for network access to enable communication between your agents and Uptrace.
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Visualize and Analyze Multi-Agent Interactions: Once data flows into Uptrace, you can utilize their interface to visualize and analyze your multi-agent interactions. A key benefit of Uptrace in this context is its ability to display agent interactions as a connected trace tree, showing parent-child relationships between agent calls. This makes debugging handoffs and identifying bottlenecks much easier. Uptrace also provides a powerful query interface to filter and analyze your data, providing a detailed view of your system’s behavior.
Common Pitfalls & What to Check Next:
- Instrumentation Granularity: Insufficient instrumentation in your agents will hinder your ability to effectively trace multi-agent sessions. Add detailed information such as agent IDs, timestamps, messages exchanged, and task handoffs.
- Network Connectivity: Verify that your agents can communicate with the Uptrace instance within your Kubernetes cluster. Check for firewall rules or network policies that might be blocking communication.
- Uptrace Configuration: Thoroughly review the Uptrace documentation and ensure that your deployment and configuration are correctly set up for receiving OpenTelemetry data.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
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