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
Use this file to discover all available pages before exploring further.
Get Started
Onboarding
Set up your platform workspace, GitHub repository, local clone, and CLI session.
Run the Multiply Example
After onboarding, run a known-good example from local eval to training.
Create Your Own Rollout
Use your AI coding agent to turn a task or dataset into a validated rollout.
Platform Overview
Understand workspaces, training runs, metrics, deployments, and model management.
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.