Observability for AI Agents: LangGraph, OpenAI Agents, and CrewAI

TLDR

If you’re building with LangGraph, OpenAI Agents, or CrewAI, you need more than logs. You need real observability: tracing every call, every tool, every prompt, and every RAG hop. I’ll break down what to track, how to evaluate, and why agent debugging isn’t optional. You’ll also see exactly how Maxim AI’s platform, docs, and products make this simple, so your AI agents don’t break in production.

Introduction

Agent frameworks like LangGraph, OpenAI Agents, and CrewAI make it easy to wire up complex LLM workflows. But here’s the thing: with that flexibility comes chaos, unless you have proper observability. If you can’t see what your agents are doing, you can’t trust them. And if you can’t debug fast, you’re shipping bugs. This post is your playbook for real-world observability, tracing, and evals that catch issues before your users do.

What to Track: The Core of Agent Observability

Let’s keep it simple. Here’s what matters:

• Session and trace IDs: Every user session should have a root trace. Every LLM call, tool, or RAG step is a span. That’s your agent tracing backbone.
• Prompt versions: Track every prompt and every variable. If your output quality drops, prompt management and versioning will save you.
• Model metadata: Log the provider, model, parameters, and cost. This is the foundation for llm monitoring and model evaluation.
• Tool calls: Record the tool, input, output, errors, and timing. Debugging LLM applications starts here.
• RAG tracing: Log what you retrieved, where from, and why. If your chatbot goes off the rails, you’ll know if the context was broken.
• Policy and safety checks: Track every moderation or guardrail hit. Hallucination detection and trustworthy AI depend on it.
• Evals: Score everything, machine, human, or LLM-as-a-judge. If you aren’t measuring, you’re guessing.

You can do all this out of the box with Maxim AI’s observability suite. Want details? Head to the Maxim AI docs for SDKs, UI workflows, and best practices.

Framework Patterns: LangGraph, OpenAI Agents, CrewAI

LangGraph

LangGraph is all about nodes and edges. Treat each node as a span: log inputs, outputs, timing, and status. For every LLM call, capture the model, tokens, and cost. For tools, log name, params, and results. For RAG, track queries, chunk IDs, and citations. Want to see how your agent actually thinks? This is how.

• Use Playground++ for prompt experiments and versioning.
• Run agent simulation to stress-test flows before you hit prod.
• Pipe traces into Maxim Observability for live dashboards and debugging.

OpenAI Agents

OpenAI’s Assistants API chains tool calls and retrieval. The trick is to track every tool call, every file ID, and every moderation event. If your agent fails, you need to know which step broke.

• Score tool chains and retrieval precision with evals from Maxim AI.
• Route calls through Bifrost Gateway for unified metrics, budget controls, and failover.
• Monitor live traffic and run automated quality checks with agent observability.

CrewAI

CrewAI is multi-agent orchestration. You want to know what each agent did, when, and why. Track roles, task handoffs, shared memory, and conflicts.

• Simulate multi-agent scenarios with agent simulation.
• Slice traces by agent, role, and failure in custom dashboards.
• Benchmark prompt versions per agent with Playground++.

Why You Need a Gateway

A solid AI gateway like Bifrost isn’t just about routing. It gives you semantic caching, automatic failover, access control, and native observability. That means lower latency, fewer outages, and easier debugging. Want to enforce budgets? Bifrost governance has you covered.

Security and Governance: Non-negotiables

Don’t ignore safety. Track prompt injection attempts, enforce role-based access, and keep your logs clean. Read up on jailbreaking and prompt injection for a reality check. And if you’re running in production, make sure your observability stack is enterprise-grade-Maxim AI docs have the details.

Wrap Up

Here’s the deal: agent frameworks are powerful, but without real observability, you’re flying blind. Trace everything. Simulate before you ship. Score every run. Monitor live. And use a gateway that doesn’t let you down.

Ready to see it in action? Book a demo or sign up now.