MLflow REST API
Official vendor OpenAPI document · agent-readyAI/MLMl Inferencebearer22 EndpointsREST
For Agents
Create experiments, log runs and their metrics, search runs, and register models and versions with the MLflow REST API. Covers 22 endpoints across experiment tracking and the model registry.
Use for: Create a new experiment for a training job, Log a metric to an active run, Search runs in an experiment by their metrics, Register a trained model in the model registry
Not supported: Does not train models or run inference itself. Use for tracking experiments and runs and managing the model registry only.
The MLflow REST API manages the machine learning lifecycle, exposing experiments, runs, metrics, and a model registry over a REST interface. It lets you create experiments, start runs and log their metrics and parameters, search runs for comparison, and register trained models and their versions. Requests carry a bearer token and return structured JSON your pipeline can act on.
Install Jentic One Beta
Connect the MLflow REST API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the MLflow REST API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmlflow.org%2Fmlflow" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmlflow.org%2Fmlflow" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with MLflow REST API.
Create experiments and search them by attributes
Create runs and log their metrics and parameters
Search runs across an experiment for comparison
Register models and create model versions
Read an experiment or run by its ID
List the artifacts logged for a run
Use Cases
Patterns agents use MLflow REST API for, with concrete tasks.
★ AI Training Tracker via Jentic
An AI agent that runs training jobs can record experiments and metrics in MLflow without a developer wiring the API by hand. Through Jentic the agent searches for the run and metric operations by intent, receives the endpoints and their input schemas, and calls them with the bearer token injected at execution time. This lets an automated training loop log its own results to a shared tracking server.
Search Jentic for 'create a run', call POST /runs/create, then POST /runs/log-metric for each recorded metric
Run Comparison and Reporting
Teams that run many training jobs need to compare their metrics to pick the best model. The MLflow API searches runs within an experiment and returns their logged metrics, so a reporting job can rank runs automatically. Because the data comes straight from the tracking server, the comparison reflects the latest logged results.
Call POST /experiments/search to find the experiment, then POST /runs/search to rank its runs by a target metric
Model Registry Automation
Once a run produces a good model, it needs to be promoted into the registry for downstream use. The MLflow API registers a model and creates new versions of it, so a promotion step can register the winning run's model automatically. This keeps the registry current without manual bookkeeping.
Call POST /registered-models/create for a new model, then POST /model-versions/create to add the version from the winning run
Key Endpoints
22 endpoints — the mlflow rest api manages the machine learning lifecycle, exposing experiments, runs, metrics, and a model registry over a rest interface.
METHOD
PATH
DESCRIPTION
POST
/experiments/create
Create an experiment
POST
/experiments/search
Search experiments
POST
/runs/create
Create a run
POST
/runs/log-metric
Log a metric to a run
POST
/runs/search
Search runs
POST
/registered-models/create
Register a model
POST
/model-versions/create
Create a model version
GET
/runs/get
Get a run and its logged data
POST
/experiments/create
Create an experiment
POST
/experiments/search
Search experiments
POST
/runs/create
Create a run
POST
/runs/log-metric
Log a metric to a run
POST
/runs/search
Search runs
POST
/registered-models/create
Register a model
POST
/model-versions/create
Create a model version
GET
/runs/get
Get a run and its logged data
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the MLflow REST API by hand means setting the bearer token on every request and pointing your code at the right tracking server yourself. Through Jentic you install once, import MLflow from the API Directory, store the token once, and your agent calls it.
Permission scoping
MLflow's operations are verbs on flat paths (/experiments/create, /runs/search), so scoping is by operation: a rule can allow read operations like POST /runs/search while withholding creates and deletes. You choose which operations the agent may call, so a reporting-only agent never modifies experiments.
Credential isolation
Your MLflow token is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'create a run' or 'search runs by metric', and Jentic returns the matching MLflow operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using MLflow REST API through Jentic.
What authentication does the MLflow REST API use?
The MLflow REST API authenticates with a bearer token in the Authorization header, as declared in its OpenAPI spec. Through Jentic the token is stored encrypted by your own Jentic One instance and injected at execution time, so it never enters the agent's prompt, logs, or context.
Can I log metrics to a run with the MLflow API?
Yes. Call POST /runs/create to start a run, then POST /runs/log-metric to record each metric against it. You can later call POST /runs/search to compare runs within an experiment by their logged metrics.
What are the rate limits for the MLflow REST API?
The OpenAPI spec does not specify rate limits, since MLflow is self-hosted and limits depend on your tracking server. Check the MLflow documentation at https://mlflow.org/docs/latest/api_reference/rest-api.html for details.
How do I track a training run through Jentic?
Search Jentic for 'create a run', which resolves to the POST /runs/create operation, and Jentic returns its input schema so your agent can start a run and log metrics. Credentials are injected at call time from your own instance. To run it on your own infrastructure, install Jentic One from its GitHub repo.
Can I restrict what my agent is allowed to do with the MLflow API?
Yes. Write a rule that allows only the operations your agent needs, such as POST /runs/search and POST /experiments/search, so it can read and compare runs but cannot delete experiments, and every call it makes is logged by your own instance. You add create or delete operations to the allowed set only when you decide to.
Is there an MLflow MCP server?
You don't need an MCP server to give your agent MLflow. Jentic connects it directly from the API Directory: import MLflow, store your token once, and your agent calls the experiment, run, and registry operations on demand without loading another server's tool definitions into its context.
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