---
title: "Deploy mlflow: Agent Tracking & Tracing | Real Observability for your AI Agents"
description: "Self-hosted MLflow tracing backend with an instrumented demo agent"
category: "AI/ML"
url: https://railway.com/deploy/mlflow-agent-o11y-template
---

# Deploy mlflow: Agent Tracking & Tracing | Real Observability for your AI Agents

Self-hosted MLflow tracing backend with an instrumented demo agent

**[Deploy mlflow: Agent Tracking & Tracing | Real Observability for your AI Agents on Railway](https://railway.com/template/mlflow-agent-o11y-template)**

- **Creator:** Gabriel Amazonas's Projects
- **Category:** AI/ML
- **Total deploys:** 1

## Template content

### mlflow-server

- **Source:** https://github.com/impacte-tech/mlflow-agent-observability
- **Health check:** /health
- **Public domain:** Yes

### agent-app

- **Source:** https://github.com/impacte-tech/mlflow-agent-observability
- **Health check:** /health
- **Public domain:** Yes

## Documentation

# Deploy and Host MLflow Agent Observability on Railway

## About Hosting

This template deploys two services on Railway: a self-hosted **MLflow tracking
& tracing server** (the trace-ingestion backend) and a **FastAPI demo agent**
instrumented with the MLflow Tracing SDK. The demo agent is wired to the MLflow
server automatically and validates the whole pipeline from the first deploy —
click deploy, wait for the build, then watch traces appear in the MLflow UI.

The MLflow server is open by design so any of your own agents can push traces
without an API key. Use the tracking REST API (via the `mlflow-tracing` SDK) or
the OpenTelemetry ingestion endpoint at `/v1/traces` (with `Content-Type:
application/x-protobuf` or `application/json` plus the
`X-MLflow-Experiment-Id` header) — LangChain.js, Vercel AI SDK, Claude Agent SDK
and any plain OTel exporter can report in. SQLite metadata and trace artifacts
persist on a `/mlruns` volume; swap in Railway Postgres and S3 once you outgrow
the bundled setup.

## Why Deploy

- **Zero-config observability**: the included demo agent captures LLM calls,
  tool calls and agent handoffs end-to-end on the very first deploy.
- **Keyless trace ingestion**: no auth to wire up before your own agents can
  report traces; the server's security middleware accepts any Railway host and
  any CORS origin by default.
- **Bundled reference implementation**: a working OpenAI Agents SDK app that
  shows exactly how to instrument a FastAPI service with `mlflow-tracing`.
- **Reliability-tuned**: trace logging fails fast and never blocks agent
  requests (HTTP timeouts shortened, retries capped) so a slow trace backend
  can't stall your agents.

## Common Use Cases

- Inspect every LLM call, token count and latency for agents you run on Railway.
- Centralize traces from multiple services (Python, JS, any OTel-speaking SDK)
  in one self-hosted MLflow UI.
- Use the demo agent as a starting point for instrumenting your own FastAPI or
  OpenAI Agents SDK services.

## Dependencies for

- **mlflow-server**: Python 3.12 with the full MLflow package, SQLite metadata
  store and an artifact store rooted at the `/mlruns` volume.
- **agent-app**: Python 3.12 with FastAPI, the OpenAI Agents SDK and the
  `mlflow-tracing` package, pre-configured to report to the MLflow server's
  Railway public domain (HTTPS).

### Deployment Dependencies

- **GitHub**: both services build from this repository's `mlflow-server/` and
  `agent-app/` directories using Railway's Dockerfile builder.
- **Railway volume**: attach a volume to `mlflow-server` mounted at `/mlruns`
  to persist experiments, runs and traces across redeploys.
- **OpenAI-compatible API (optional)**: to run the demo agent, add
  `OPENAI_API_KEY` (official OpenAI API) or `OPENAI_COMPATIBLE_ENDPOINT` (e.g.
  Groq, OpenRouter, vLLM, Ollama) to `agent-app` after deploying.
- **Scaling (optional)**: Railway Postgres for the metadata store and S3 for
  artifacts when you exceed the bundled SQLite/volume setup.

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