Smol developer
RepositoryFreeYour own junior AI developer, deployed via E2B UI
Capabilities10 decomposed
multi-file codebase generation from natural language specifications
Medium confidenceTransforms natural language product descriptions into complete, multi-file codebases by executing a three-phase pipeline: planning (dependency analysis via shared_deps.md), file path specification (structural scaffolding), and code generation (per-file synthesis). Each phase uses LLM prompts to maintain coherence across files and ensure proper dependency implementation, rather than generating isolated code snippets.
Uses a three-phase sequential pipeline (plan → file paths → code) with explicit shared dependency tracking via shared_deps.md, ensuring cross-file coherence. This differs from single-pass code generators that produce isolated snippets; the planning phase forces the LLM to reason about the entire system architecture before generating any code.
Maintains coherence across multiple files and properly implements dependencies (unlike Copilot's line-by-line completion), while being more flexible than rigid project scaffolders like create-react-app that lock you into predefined structures.
dependency-aware planning and shared state extraction
Medium confidenceAnalyzes natural language prompts to extract a coherent architectural plan and identifies shared dependencies (libraries, utilities, data structures, APIs) that will be used across multiple files. The planning phase outputs a shared_deps.md document that serves as a contract for all subsequent code generation, preventing duplicate definitions and ensuring consistent imports/exports across the codebase.
Explicitly separates planning from code generation as a distinct phase, forcing the LLM to reason about system-wide dependencies before writing any code. This is encoded in smol_dev/prompts.py as a dedicated planning prompt that outputs structured shared_deps.md, not just inline comments.
Unlike Copilot or ChatGPT which generate code line-by-line without explicit dependency planning, this approach ensures all files reference the same shared utilities and prevents the 'multiple implementations of the same function' problem common in multi-file generation.
file path specification and project structure scaffolding
Medium confidenceDetermines the complete directory structure and file layout for the generated codebase based on the plan and shared dependencies. This phase generates a list of file paths (e.g., src/components/Button.tsx, utils/api.py) that will be created, ensuring the project structure matches the intended architecture before any code is written. Prevents orphaned files and ensures logical organization.
Treats file path specification as an explicit, separate phase (not implicit in code generation). The LLM generates a complete file list before writing any code, allowing for structural validation and preventing the common problem of discovering missing files mid-generation.
More explicit than tools like Cursor or Copilot that infer file structure implicitly; provides a clear contract of what will be generated, reducing surprises and enabling better error handling.
per-file code generation with dependency injection
Medium confidenceGenerates the actual code content for each file in the scaffolded structure, with each file's prompt including the shared dependencies and previously generated files as context. Uses a sequential generation approach where each file is aware of the shared_deps.md contract and can reference utilities/types defined in other files. Implements dependency injection by passing the full dependency graph to each code generation prompt.
Each file generation prompt includes the full shared_deps.md and optionally previous files as context, enabling the LLM to generate imports and references that actually exist. This is implemented in smol_dev/main.py as a loop over file paths, passing accumulated context to each iteration.
More context-aware than single-file generators; prevents the common issue of generated code importing from non-existent modules. Slower than parallel generation but more reliable for multi-file coherence.
command-line interface with human-in-the-loop iteration
Medium confidenceProvides a Git Repo Mode CLI (via main.py) where users invoke code generation with a natural language prompt, receive generated code, and can iteratively refine the prompt based on the output. The CLI captures the full generation pipeline (planning → file paths → code) and outputs results to a local directory, enabling rapid prototyping with human feedback loops.
Implements a simple but effective CLI that exposes the full three-phase pipeline as a single command, with output written to disk. Designed for rapid iteration where users can inspect generated code and re-run with refined prompts, embodying the 'engineering with prompts' philosophy.
Simpler and more transparent than web UIs (like E2B); enables local-first workflows without external dependencies. Slower feedback loop than interactive IDEs but more flexible than one-shot code generation APIs.
python library integration (smol_dev package)
Medium confidenceExposes Smol Developer as an importable Python package (smol_dev) that can be embedded into other applications. Developers can import core functions from smol_dev/__init__.py and smol_dev/main.py to programmatically invoke the three-phase pipeline, enabling integration into custom tools, web services, or automation workflows without shelling out to the CLI.
Exposes the core three-phase pipeline as importable Python functions, allowing developers to call Smol Developer from within their own code. This is implemented in smol_dev/__init__.py and smol_dev/main.py with a simple function-based API (not class-based OOP).
More flexible than CLI-only tools; enables custom workflows and integrations. Less feature-rich than full frameworks like LangChain but simpler and more focused on code generation specifically.
api mode deployment and http endpoint exposure
Medium confidenceEnables Smol Developer to run as a web service exposing HTTP endpoints for code generation. Users can POST natural language prompts to the API and receive generated code as JSON responses. This mode supports deployment on platforms like E2B (as mentioned in the artifact description) and enables integration with web frontends, mobile apps, or remote clients without requiring local Python installation.
Wraps the three-phase pipeline in an HTTP server, enabling remote code generation without local Python setup. Designed for deployment on E2B (a serverless code execution platform) but can run on any platform supporting Python web frameworks.
More accessible than CLI/library modes for non-technical users and web-based workflows. Less performant than local generation due to network latency and cloud platform overhead.
prompt template system with phase-specific engineering
Medium confidenceImplements a structured prompt engineering system (in smol_dev/prompts.py) with separate, optimized prompts for each phase of the pipeline: planning prompts that extract architecture, file path prompts that scaffold structure, and code generation prompts that synthesize individual files. Each prompt is carefully crafted to guide the LLM toward specific outputs (e.g., shared_deps.md format, file path lists, syntactically correct code).
Separates prompts by phase (planning, file paths, code generation) with each prompt optimized for its specific task. This is encoded in smol_dev/prompts.py with distinct functions for each phase, rather than a single monolithic prompt.
More modular than single-prompt approaches; enables phase-specific optimization. Less flexible than fully customizable prompt systems but more maintainable than ad-hoc prompt concatenation.
multi-technology stack support with language-agnostic generation
Medium confidenceGenerates code for virtually any technology stack (React, FastAPI, Django, Vue, etc.) based on natural language specification. The system doesn't hard-code language-specific logic; instead, it relies on the LLM's knowledge to generate appropriate code for the specified stack. Supports multiple programming languages (Python, JavaScript/TypeScript, etc.) and frameworks within a single generation run.
Doesn't hard-code language or framework logic; instead, relies entirely on the LLM's training data to generate appropriate code for any specified stack. This makes it flexible but also dependent on LLM knowledge cutoff and training data quality.
More flexible than language-specific generators (e.g., Copilot for Python); supports full-stack generation in a single run. Less reliable than specialized tools for specific languages/frameworks due to lack of validation.
iterative refinement workflow with prompt-based engineering
Medium confidenceSupports a human-in-the-loop development model where users generate code, inspect the output, and refine the natural language prompt to improve results. The system is designed for iteration rather than one-shot generation, with each run producing a complete codebase that can be evaluated and used to inform the next prompt. This embodies the 'engineering with prompts' philosophy rather than traditional 'prompt engineering.'
Designed for iterative refinement where users inspect generated code and adjust prompts, rather than one-shot generation. This is enabled by the simple CLI and library interfaces that make re-running with new prompts easy.
More suitable for exploratory development than tools like GitHub Copilot (line-by-line completion). Less suitable for incremental updates than version-control-aware tools.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓solo developers prototyping MVPs quickly
- ✓teams bootstrapping new projects across varied tech stacks
- ✓non-technical founders validating product ideas with generated code
- ✓developers who want visibility into the AI's architectural decisions before code generation
- ✓teams building systems where dependency consistency is critical
- ✓projects requiring explicit documentation of shared interfaces
- ✓developers bootstrapping projects who care about clean structure
- ✓teams with strict project organization standards
Known Limitations
- ⚠Coherence degrades with very large codebases (100+ files) due to context window constraints
- ⚠Requires explicit tech stack specification in prompt; ambiguous specs produce inconsistent output
- ⚠No built-in version control or diff tracking — each generation is independent
- ⚠Generated code quality depends heavily on LLM model capability; may require manual refinement for production use
- ⚠Planning phase adds ~2-5 seconds latency per generation (additional LLM call)
- ⚠Shared dependency extraction is heuristic-based; complex circular dependencies may not be detected
Requirements
Input / Output
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Your own junior AI developer, deployed via E2B UI
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