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.Documentation Index
Fetch the complete documentation index at: https://docs.osmosis.ai/llms.txt
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Get Started
Platform Overview
Understand core concepts — workspaces, training runs, metrics, and model management.
CLI
Install and use the Osmosis CLI to manage datasets, training runs, and evaluations.
Workspace
Set up your local project and sync code to the platform.
Rollout
Build AgentWorkflows and Graders for Osmosis RL training.
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
We’ve built primitives and tool modules into our 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.