Azad Coder (GPT 5 & Claude)

Enables the AI agent to read, write, and modify multiple files across a workspace in coordinated operations, with support for advanced refactoring patterns. The agent maintains context across file boundaries and can perform cross-file dependency analysis to execute coherent multi-file transformations. Integration occurs through VS Code's file system API, allowing the agent to stage edits with preview and rollback capabilities before committing changes.

Allows the AI agent to execute shell commands in the VS Code integrated terminal, capture output and error streams, and autonomously recover from failures by analyzing error messages and retrying with corrected commands. The agent has access to the full shell environment (bash, zsh, PowerShell) and can chain commands, manage processes, and interpret exit codes. Built-in error reporting surfaces failures to the user with suggested remediation steps.

Allows users to set hard limits on task execution parameters (maximum time, maximum conversation turns, maximum credit spend) before launching autonomous execution. The agent monitors resource consumption in real-time and stops execution when any budget is exceeded, preventing runaway costs or infinite loops. Budget constraints are enforced at the task level and sub-task level, enabling fine-grained resource allocation. Users can configure default budgets for different task types.

Enables the agent to maintain separate context and state for multiple VS Code workspaces, automatically switching between them based on the active editor window. The agent can track which files and tasks belong to which workspace, avoid cross-workspace contamination, and maintain independent execution histories per workspace. This allows developers working on multiple projects simultaneously to use Azad without manual context resets.

Enables the agent to invoke multiple tools (file editing, terminal execution, browser automation, web search) in parallel within a single reasoning turn, coordinating results and handling dependencies. The agent can execute independent operations concurrently (e.g., run tests while editing files) and wait for results before proceeding. Tool invocation is orchestrated through a schema-based function registry that defines tool signatures, parameters, and return types.

Offers a free tier with 2.5 one-time credits, allowing new users to try Azad without payment. Free tier users have access to basic capabilities (code editing, terminal execution) but cannot access premium features (cloud execution, BYOK, remote monitoring). Upgrade paths to Developer ($20/mo, 15 credits/month) and Pro ($200/mo, 160 credits/month) tiers provide increasing credit allowances and feature access. Credit consumption varies by operation type and model selection.

Integrates real-time web search and documentation lookup capabilities, allowing the agent to fetch current information from the internet and retrieve API documentation, library references, and technical articles. The agent can search for solutions to coding problems, retrieve framework documentation, and synthesize information from multiple sources to inform code generation. Search results are ranked and filtered to prioritize relevant, authoritative sources.

Enables the AI agent to control a headless or headed browser instance using Playwright, allowing it to automate complex web interactions, scrape data, test web applications, and validate UI behavior. The agent can navigate pages, fill forms, click elements, wait for dynamic content, and capture screenshots or DOM state. Playwright integration provides cross-browser support (Chromium, Firefox, WebKit) and handles browser lifecycle management.

Enables the agent to break down complex coding tasks into smaller, manageable sub-tasks with independent execution contexts and budgets. Each sub-task can have its own time limit, turn limit, and credit budget, allowing the agent to allocate resources strategically. The agent maintains state across sub-tasks, enabling sequential or parallel execution with explicit handoff points. Sub-tasks can be monitored, paused, or rolled back independently.

Maintains explicit checkpoints of workspace state (file contents, terminal state, browser state) at key points during execution, allowing users to preview changes before committing and rollback to any previous checkpoint. Checkpoints are created automatically after significant operations (multi-file edits, command execution) and can be manually created by the user. Rollback restores all modified files and state to the checkpoint without requiring git operations.

Supports multiple AI models (Claude Sonnet 4.5, GPT-5) with configurable execution modes (Savings, Standard, Turbo) that trade off cost vs. capability. Savings mode reduces inference cost by ~50% (likely through quantization or smaller model variants), while Turbo mode increases cost by ~2x for higher-capability reasoning. The agent can automatically select models and modes based on task complexity, or users can manually override selection. Credit consumption varies by model, mode, and operation type.

Allows users with Developer ($20/mo) or Pro ($200/mo) tier subscriptions to provide their own API keys for custom model endpoints, enabling integration with self-hosted models, alternative providers, or proprietary model deployments. BYOK configuration is managed through extension settings, and the agent can route requests to custom endpoints instead of Azad's hosted infrastructure. This enables organizations to maintain data privacy by keeping code and context local.

Enables users with Developer or Pro tier subscriptions to execute long-running tasks on Azad's cloud infrastructure while monitoring progress from anywhere, including mobile devices. Tasks are submitted to the cloud, executed asynchronously, and results are streamed back to the client. This allows users to start a complex refactoring or build task and monitor it from a mobile device or different machine without keeping VS Code open.

Implements a multi-turn conversation loop where the agent can reason across multiple turns, maintain context across long-running tasks, and decompose complex problems into intermediate steps. The agent can ask clarifying questions, propose solutions, receive feedback, and refine its approach iteratively. Long-context support allows the agent to maintain awareness of previous decisions and avoid repeating work. Task segmentation enables the agent to break work into logical phases with explicit handoff points.