Smol developer

Generates complete, functional code implementations from natural language requirements by decomposing tasks into subtasks, iteratively writing and testing code, and refining based on execution feedback. Uses an agentic loop that chains LLM calls with code execution validation, maintaining context across multiple file writes and architectural decisions.

Implements a feedback loop where generated code is executed in a sandboxed environment, errors and output are captured, and the LLM uses this feedback to refine and fix the code. The agent maintains execution context across iterations, learning from test failures, runtime errors, and output mismatches to progressively improve implementations.

Generates complete project structures with multiple interdependent files, managing imports, dependencies, and architectural relationships across the codebase. The agent understands file organization patterns, creates appropriate directory hierarchies, and ensures cross-file references are correctly resolved during generation.

Translates high-level natural language specifications into executable code while preserving semantic intent, handling ambiguities through clarifying questions or reasonable assumptions. The agent maps requirements to implementation patterns, selects appropriate libraries and frameworks, and produces idiomatic code in the target language.

Breaks down complex development tasks into smaller, manageable subtasks, executes each step sequentially, and maintains state across the execution chain. The agent uses planning and reasoning to determine task dependencies, optimal execution order, and success criteria for each step.

Executes generated code in isolated E2B sandbox environments, capturing output, errors, and side effects without affecting the host system. The sandbox provides a controlled runtime with configurable resource limits, environment variables, and dependency management, enabling safe testing of untrusted generated code.

Provides intelligent code completion suggestions based on the current codebase context, including file history, imports, function signatures, and architectural patterns. The agent understands the semantic context of the code being written and suggests completions that maintain consistency with existing code style and patterns.

Automatically identifies required dependencies, generates appropriate import statements, and manages package configuration files (package.json, requirements.txt, etc.). The agent understands language-specific package managers and resolves version constraints to ensure generated code has all necessary dependencies declared.

Analyzes runtime errors, compilation failures, and test failures to diagnose root causes and suggest fixes. The agent interprets error messages, stack traces, and logs to understand what went wrong and proposes corrective code changes or configuration adjustments.

Provides a web-based UI for interacting with the code generation agent, displaying generated code, execution results, and allowing users to provide feedback or request modifications. The UI integrates with E2B for sandbox deployment and execution, presenting results in real-time as the agent works.