Framework Observability

LangGraph Observability & Runtime Monitoring

Debug complex stateful agent graphs, trace model decisions and tool calls across nodes, attribute step-level costs, and enforce execution budgets on cyclic workflows.

Early Access · 90 Days Free · No credit card requiredBuilt by the Observyze engineering team for production AI systems.
Graph Execution Lineage

Tracking Multi-Node State Transitions

In LangGraph, state flows through branching routers, retrieval steps, model reasoning, and tool validations. Observyze provides the visibility needed to debug where a graph execution stalled, retried, or diverged.

Production Graph Architecture Example (Illustrative)Trace Timeline
STEP 01140ms

Router Node

Intent classification

$0.002
STEP 02380ms

Retrieval Node

Vector search & context

$0.000
STEP 031.2s

Reasoner LLM

Tool selection plan

$0.018
STEP 04620ms

Tool Execution

API query & validation

$0.000
Detecting Cyclic Retries: When a validation node rejects a tool response and loops back to the reasoner, Observyze tracks cumulative iterations and lets configured execution budgets stop runaway loops.
Production Debugging

Operational Questions Observyze Answers

When a customer support agent or autonomous coding graph behaves unexpectedly, Observyze gives engineers the exact execution context:

Which node became slow or timed out?

Percentile latency breakdowns highlight slow vector lookups, rate-limited model endpoints, or unoptimized tool calls.

Why did an agent enter a retry loop?

Inspect tool validation failure payloads and intermediate reasoning traces to see why the graph rejected the previous output.

How much did the entire graph run cost?

Attribute token and dollar costs across all underlying OpenAI, Anthropic, Gemini, or Groq calls within that execution graph.

Where did the agent path diverge from expected flow?

Compare branching decision paths across runs to identify prompt regressions or routing misclassifications.

Instrumentation Model

How Observyze Instruments LangGraph

Observyze instruments the underlying LLM provider calls automatically via SDK wrappers, while custom spans can be wrapped around graph nodes to trace custom boundaries.

Underlying model calls traced via OpenAI/Anthropic client wrapper
Custom spans for tool execution and retrieval nodes
Non-blocking async telemetry dispatch
langgraph-observyze.ts
import { ObservyzeClient } from "@observyze/sdk";
import OpenAI from "openai";

const observyze = new ObservyzeClient({
  apiKey: process.env.OBSERVYZE_API_KEY!,
  projectId: process.env.OBSERVYZE_PROJECT_ID!,
});

// Instrument model client used in LangGraph nodes
const openai = observyze.wrapOpenAI(new OpenAI());

// In a LangGraph node function:
async function reasonerNode(state: AgentState) {
  const trace = observyze.startTrace("langgraph.reasoner", {
    sessionId: state.sessionId,
  });

  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: state.messages,
  });

  await trace.end();
  return { messages: [...state.messages, response.choices[0].message] };
}
Developer FAQ

Frequently Asked Questions

Technical details, integration patterns, and operational controls.

Observyze traces each graph node invocation via custom spans, capturing input state payloads, router decisions, model token metrics, and tool execution outputs into a unified session trace hierarchy.