Oumi

What does this agent do, and when should you use it?

Oumi is a fully open-source platform that covers the full lifecycle of foundation models: data synthesis and curation, pre-training and fine-tuning, evaluation, inference, and deployment. It is driven by a single oumi CLI plus YAML recipes, so you rarely have to write your own training loop or data pipeline. Training supports SFT, LoRA, QLoRA, DPO, and GRPO, scaling through FSDP, DeepSpeed, and DDP; inference integrates vLLM and SGLang, and models can be pushed to dedicated endpoints with oumi deploy. Beyond text models it handles vision-language models and agentic, tool-using models: you can define executable tool environments (database, HTTP endpoint, lookup, simulated) and run verl GRPO reinforcement learning over them. Evaluation spans standard benchmarks plus multi-criteria RubricJudge and LLM-as-a-Judge. It runs from a laptop up to clusters and clouds via oumi launch (AWS, Azure, GCP, Lambda, Modal, Slurm), and oumi-mcp exposes an MCP server for Claude and Cursor.

Oumi centers on the oumi command-line interface. You author or reuse a YAML config from configs/recipes and run oumi train -c <config> to launch training (SFT, LoRA, QLoRA, DPO, GRPO on PyTorch, optionally with FSDP/DeepSpeed/DDP), oumi evaluate -c <config> to score a model on standard benchmarks or custom judges, and oumi infer -c <config> --interactive for interactive inference through engines such as vLLM, SGLang, Fireworks, or OpenRouter. On the data side it reads raw corpora and uses LLM judges, including the multi-criteria RubricJudge, to synthesize and filter training data, including multi-turn tool-use conversations. For agentic training it manages executable tool environments (database, HTTP endpoint, lookup, simulated) and applies verl GRPO over them. For delivery, oumi deploy publishes models to dedicated inference endpoints on Fireworks and Parasail, oumi launch up submits jobs to cloud platforms, and oumi-mcp runs an MCP server consumable by Claude and Cursor. oumi analyze covers analysis, and the configs/recipes tree ships ready-made configurations for Qwen, Gemma, Llama, DeepSeek, GLM, Phi, OLMo, gpt-oss, Falcon, and more.

1. A team that wants to fine-tune Qwen3 or a Gemma model for its own domain and would rather start from an existing YAML recipe than write a training script.
2. A research group running reproducible GRPO reinforcement-learning experiments across multiple GPUs or a cloud cluster, needing FSDP or DeepSpeed.
3. A developer training a tool-calling agent model who needs to construct database, HTTP, or lookup tool environments and do reinforcement learning over them.
4. A data team that must synthesize, filter, and curate training data at scale with LLM-as-a-Judge or RubricJudge, including vision-language datasets.
5. An engineering team deploying a fine-tuned model locally through vLLM/SGLang, or publishing it to a managed endpoint with oumi deploy.
6. Developers already working inside an AI-assisted IDE such as Claude or Cursor who want to drive Oumi workflows through the oumi-mcp MCP server.

How do you install or deploy this agent?

The README recommends pip (uv):

# Basic installation
uv pip install oumi

# With GPU support
uv pip install 'oumi[gpu]'

# Latest development version
uv pip install git+https://github.com/oumi-ai/oumi.git

Docker and an experimental one-line script are also documented:

docker pull ghcr.io/oumi-ai/oumi:latest
docker run --gpus all -it ghcr.io/oumi-ai/oumi:latest oumi --help

curl -LsSf https://oumi.ai/install.sh | bash

Oumi is in beta and under active development: core features are stable, but some advanced features may change. More installation options are in the official installation guide.

How do you use this agent?

Pick a YAML config under configs/recipes and drive it with the oumi CLI:

# Training
oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml

# Evaluation
oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml

# Inference
oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml --interactive

To run remotely, submit with oumi launch; to train inside the container, mount your working directory and pass --config:

oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml --resources.cloud aws

docker run --gpus all -v $(pwd):/workspace -it ghcr.io/oumi-ai/oumi:latest \
    oumi train --config /workspace/my_config.yaml

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

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