> ## 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. # Introduction > Osmosis is a post-training platform for LLMs. The Osmosis CLI abstracts away the infrastructure challenges of distributed training & RL pipeline design. 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 Set up your platform workspace, GitHub repository, local clone, and CLI session. After onboarding, run a known-good example from evaluation run to training run. Use your AI coding agent to turn a task or dataset into a validated rollout. Understand workspaces, training runs, metrics, deployments, and model management. ## Use Cases 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. 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. Train specialized coding models for blazing fast generation of domain-specific languages, front-end components, and tests — without needing a large model. ## Why Osmosis 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. Osmosis implements and handles the RL algorithms and infrastructure that enable performant, GPU-efficient training runs. Osmosis integrates with your evaluation solutions and coding agents to automatically start re-training runs without the need for an engineer in the loop.