Machine Learning for AI Agents.

Ask your agent to train a model, tune it, compare it to the last run, or find an algorithm that fits your data. It just does it. No code. No guesswork. No forgotten context.

Quickstart  •  Python API  •  MCP Tools  •  Benchmarks  •  Docs

Agents can call it — Every algorithm, dataset, and metric ships with a JSON schema. Agents read the schema, call the tool, get structured results. No hallucinated parameters, no wrapper glue.

Agents can discover it — A queryable registry tagged by task, data shape, and benchmarks. Agents browse and pick instead of memorising class names.

Agents can trust it — Deterministic, typed, reproducible outputs. Every call is a loggable, replayable tool invocation you can audit, diff, and trust in production.

1. Install — one command, installs uv and tuiml globally:

curl -fsSL https://tuiml.ai/install.sh | bash

Already have Python? pip install tuiml works too.

2. Connect your agent — auto-detects Claude Desktop, Cursor, Claude Code, and more:

tuiml setup

3. Ask your agent — in any connected client:

"Train a random forest on my sales data and report the accuracy."

Your agent discovers algorithms, sets parameters from the schema, trains, evaluates, and returns structured results. No glue code.

The same runtime agents call is a first-class Python library. Every component — the model, each preprocessing step, the feature selector — is described the same way: a spec of the form {"name": ..., **params}. The data is its own spec, {"source": ..., "target": ...}.

import tuiml

# One call trains, evaluates, and returns metrics.
result = tuiml.train(
    {"name": "RandomForestClassifier", "n_estimators": 100},   # model spec
    {"source": "sales.csv", "target": "label"},                # data spec
    preprocessing=[{"name": "MinMaxScaler"}],
    cv=10,
)
print(result.metrics)          # {'accuracy_score': 0.97, 'f1_score': 0.96}
preds = result.model.predict(X_new)

Benchmark many algorithms across many datasets with tuiml.experiment(...), and browse the same registry agents use with tuiml.list_algorithms() / tuiml.search_algorithms(...) / tuiml.describe_algorithm(...). See the tutorials for the full tour.

Everything TuiML can do, your agent can do — the MCP server exposes 200+ typed tools with JSON schemas the agent reads directly.

Train · Tune · Compare — fit a model, sweep hyperparameters, and rank runs in one conversation. No notebook, no glue code.

Algorithm Discovery — the agent searches the catalog by task, data shape, or constraint and gets ranked recommendations with rationale, not a flat list of names.

Persistent Experiments — every run is logged with lineage and metrics, so today's model can be compared against last week's without re-running anything.

One-Call Serving — deploy a trained model to a local HTTP endpoint with a single tool call. Stop it the same way.

100% Local & Private — your data, your machine. No cloud, no API keys, no telemetry.

Key workflow tools: tuiml_train, tuiml_predict, tuiml_evaluate, tuiml_experiment, tuiml_tune, tuiml_plot, tuiml_list, tuiml_describe, tuiml_search.

Works with anything that speaks MCP — tuiml setup auto-detects Claude Desktop, Claude Code, Cursor, ChatGPT Desktop, Codex CLI, Zed, Continue, Windsurf, VS Code Copilot, Perplexity, Goose, and OpenClaw / NemoClaw. For manual setup, add this to your client's MCP config:

{
    "mcpServers": {
        "tuiml": { "command": "tuiml-mcp" }
    }
}

Average across 3,318 matched runs — 13 algorithms × 51 real-world TabArena datasets, 10-fold cross-validation, same data and folds for every framework:

_{Weka memory includes its in-process JVM baseline. Full per-algorithm and per-dataset results: tuiml.ai/docs/benchmarks.}

Full documentation is available at tuiml.ai/docs, including getting started guides, API reference, and tutorials. Want to contribute? Pick something from the Build Board — algorithms, integrations, and good first issues.

BSD 3-Clause License. See LICENSE for details.

@software{tuiml2026,
    title={TuiML: Machine Learning that agents can actually call},
    author={Verma, Nilesh and Bifet, Albert and Pfahringer, Bernhard and Lim, Nick},
    year={2026},
    url={https://tuiml.ai}
}

Built by the TuiML team — tuiml.ai
_{If TuiML is useful to you, consider leaving a ⭐ — it helps others find the project.}