OpenUncensored API enables uncensored models for coding agents

Integrating Uncensored Models

OpenUncensored provides an API that allows users to access 82 different uncensored models. These models include 60 text-based options and 22 image-based options for various tasks. The service is designed to be compatible with the OpenAI API standard, making it easy to plug into existing software infrastructure.

Because it follows this standard, any application that allows a user to set a custom OpenAI base URL can utilize these models. Users do not need to create an account or sign up for a service to begin using the API. Instead, they simply need to obtain an API key from the provider's credit page and point their software to the specific base URL.

As openuncensored.com reported, the service is particularly useful for developers who want to bypass the restrictions of mainstream AI providers. By simply changing a few lines of configuration, users can swap out a standard model for an uncensored alternative while maintaining the same workflow.

Optimizing for Coding Agents

For users focused on coding and agentic workflows, the service highlights a specific model: deepseek-v4.1-flash-1017-uncensored. This model is currently ranked at the top of their list because it is both fast and affordable for long sessions. It is particularly efficient because most of each request is served from the prompt cache, which reduces the overall cost of complex tasks.

The platform even allows users to let their AI agents handle the setup process automatically. By providing an agent with a specific markdown guide, the agent can navigate the purchase process, provide a payment link to the user, and then collect the API key itself. This automation streamlines the integration for developers who want to move quickly from setup to execution.

Several specific tools have been updated or can be easily configured to work with this API. These include Cursor, Codex, OpenCode, and various extensions like Cline, Kilo Code, and Roo Code. Each tool has a specific configuration path, such as adding a provider to a config file or entering a custom base URL in the settings menu.

Technical Implementation Details

The technical setup for these tools involves defining the base URL as https://openuncensored.com/api/v1 and providing the unique API key. For the Codex CLI, this involves adding a new provider block to the configuration file to handle the OpenAI Responses API. This ensures that multi-step tasks, shell commands, and file edits function exactly as they would with a standard provider.

OpenCode users can add the provider to their configuration file using a specific JSON schema. This setup allows the tool to recognize the uncensored DeepSeek model specifically. If the environment variable is not detected by the background service, users can run a standalone server to ensure the key is read correctly from the current shell.

For those using the OpenAI SDK, LangChain, or LiteLLM, the integration is straightforward. Users only need to initialize the client with the correct base URL and key. The service supports standard features like streaming, tools, and the usual parameters that developers expect from the OpenAI ecosystem.

Billing and Fallback Systems

The pricing structure of the service is designed to be transparent for the end user. Requests are billed per token at the exact price of the original provider, meaning there is no markup added by the intermediary. This includes a specific pricing structure for cached prompt tokens, which helps keep costs down for repetitive agent tasks.

To ensure reliability, the service includes an automatic fallback mechanism. If a specific model's provider goes down, the system automatically routes the request to the next best ranked model built on the same original foundation. This helps maintain uptime for developers who rely on consistent availability for their applications.

Users can monitor which model is actually answering their prompts by checking the X-OU-Model response header. If a user prefers to disable this automatic routing and only receive responses from their chosen model, they can send a specific routing command to turn off the fallback feature. This gives users control over the behavior of their AI agents.

Getting Started with Claude Code

Claude Code presents a unique integration path because it typically runs on Anthropic's models. However, it can still interact with uncensored models through a Model Context Protocol (MCP) server. By adding the specific MCP server via the command line, users can give Claude Code the tools to list models, check balances, and send prompts to the uncensored library.

This setup allows for a hybrid workflow where a user can compare different models or call specific uncensored models for specialized tasks. The process involves using the 'claude mcp add' command with the correct transport and header information. This effectively bridges the gap between Anthropic's ecosystem and the open uncensored models.

For users who want the simplest path, the service provides a direct curl command to test the connection immediately. By sending a simple 'Hello' prompt to the DeepSeek model, users can verify that their API key is active and the connection is stable. This allows for rapid testing before committing to a full integration in a development environment.