diff --git a/website/docs/observability/mlflow.md b/website/docs/observability/mlflow.md
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+---
+id: mlflow
+title: MLflow Integration
+sidebar_label: MLflow
+---
+
+VoltAgent ships with an OpenTelemetry-based observability layer. You can export spans directly to [MLflow](https://mlflow.org) using standard OpenTelemetry packages — no additional wrapper needed. MLflow's OTLP endpoint ingests VoltAgent traces and renders them with rich span-type icons, chat UI, and token usage summaries.
+
+## Prerequisites
+
+- An MLflow server running and accessible. You can [set one up locally](https://mlflow.org/docs/latest/genai/getting-started/connect-environment.html) (via pip, Docker Compose, etc.) or use a managed service like [Databricks](https://www.databricks.com/), [AWS SageMaker](https://aws.amazon.com/sagemaker/ai/experiments/), and more.
+- A basic VoltAgent application setup.
+
+## Installation
+
+Install the OpenTelemetry SDK and OTLP protobuf exporter:
+
+```bash npm2yarn
+npm install @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto
+```
+
+## Setup
+
+### Configure VoltAgent
+
+In your application (e.g., `src/index.ts`) import the observability class and the OpenTelemetry packages:
+
+```typescript
+import { Agent, VoltAgent, VoltAgentObservability } from "@voltagent/core";
+import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";
+import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
+```
+
+Create a `VoltAgentObservability` instance and attach the MLflow exporter:
+
+```typescript
+const mlflowExporter = new OTLPTraceExporter({
+  url: `${process.env.MLFLOW_TRACKING_URI}/v1/traces`,
+  headers: {
+    "x-mlflow-experiment-id": process.env.MLFLOW_EXPERIMENT_ID ?? "0",
+  },
+});
+
+const observability = new VoltAgentObservability({
+  spanProcessors: [new BatchSpanProcessor(mlflowExporter)],
+});
+
+const agent = new Agent({
+  name: "my-voltagent-app",
+  instructions: "A helpful assistant",
+  model: "openai/gpt-4o-mini",
+});
+
+new VoltAgent({
+  agents: { agent },
+  observability,
+});
+```
+
+Add the required environment variables to your `.env` file:
+
+```bash
+OPENAI_API_KEY=your-api-key
+MLFLOW_TRACKING_URI=http://localhost:5000
+MLFLOW_EXPERIMENT_ID=0
+```
+
+### Run and View Traces
+
+Start the VoltAgent development server:
+
+```bash
+npm run dev
+```
+
+Interact with your agent via the [VoltAgent Console](https://console.voltagent.dev), then open the MLflow UI at `http://localhost:5000` to see your traces.
+
+![VoltAgent Trace in MLflow](pathname:///img/mlflow-tracing.png)
+
+## How it Works
+
+- VoltAgent initializes a global OpenTelemetry tracer provider.
+- The standard `OTLPTraceExporter` sends spans to MLflow's `/v1/traces` endpoint via OTLP/HTTP (protobuf).
+- MLflow automatically translates VoltAgent's semantic conventions for optimal visualization — mapping agent, LLM, tool, and memory spans to dedicated span types, extracting token usage metrics, and rendering chat messages in a rich UI.
+- All agent and workflow spans flow through the observability pipeline, giving you end-to-end visibility into your agent's execution.
+
+## Learn More
+
+- [MLflow Tracing](https://mlflow.org/docs/latest/genai/tracing/index.html) — Full tracing guide including evaluation, prompt management, and more.
+- [MLflow OpenTelemetry Integration](https://mlflow.org/docs/latest/genai/tracing/opentelemetry.html) — Details on MLflow's OpenTelemetry support.
+- [MLflow VoltAgent Guide](https://mlflow.org/docs/latest/genai/tracing/integrations/listing/voltagent.html) — Step-by-step setup tutorial with screenshots.
diff --git a/website/sidebars.ts b/website/sidebars.ts
index 9e63c46f2..160d22981 100644
--- a/website/sidebars.ts
+++ b/website/sidebars.ts
@@ -311,6 +311,7 @@ const sidebars: SidebarsConfig = {
         "observability/developer-console",
         "observability/logging",
         "observability/langfuse",
+        "observability/mlflow",
       ],
     },
     {
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