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You can define and/or port in your agent loop, tools, rewards, and training data. Osmosis handles the rest to deliver task-specific models that can outperform foundation models on performance, cost, and latency.

Get Started

Platform Overview

Explore evaluations, training, benchmarks, datasets, and models in the web dashboard.

SDK Overview

Learn the Python interfaces for workflows, graders, contexts, and execution backends.

CLI Overview

Submit runs and manage Platform resources from a workspace repository.

Platform Onboarding

Create your workspace, connect its repository, and authenticate the CLI.

Multiply Quickstart

Use the Platform path to run a known-good evaluation and submit training.

Changelog

Review SDK and CLI releases, migration notes, and behavior changes.

Use Cases

Data Extraction

Build domain-specific extraction models to capture the exact structure and content for any document at a fraction of the cost of a foundation model or managed product.

Tool Use

Teach AI agents to use the exact tools they’ll have in production. Osmosis powers AI agents that stay reliable, even in the most complex multi-step, multi-tool tasks.

Code Generation

Train specialized coding models for blazing fast generation of domain-specific languages, front-end components, and tests — without needing a large model.

Why Osmosis

AI Agent Post-Training

Osmosis ships primitives and tool modules into the platform so coding agents like Claude Code, Codex, and others can start, monitor, and iterate on training runs.

Reinforcement Fine-Tuning

Osmosis implements and handles the RL algorithms and infrastructure that enable performant, GPU-efficient training runs.

Continuous Improvement

Osmosis integrates with your evaluation solutions and coding agents to automatically start re-training runs without the need for an engineer in the loop.
Last modified on August 10, 2026