AI-Assisted Development Toolkit

Problem

Developers spend significant time on repetitive tasks: boilerplate code, test generation, documentation, code reviews, and architectural decisions. Existing AI tools are either too generic or require significant setup.

Architecture

Modular Python package with plugin architecture. Core engine handles LLM interactions, prompt management, and context injection. Plugins provide specific capabilities (codegen, review, docs, arch). CLI and VS Code extension for integration. Uses local models (Ollama) with cloud fallback.

Technologies

PythonFastAPILangChainOllamaTree-sitterClickRichPydantic

Key Highlights

Multi-model support: local (Ollama) + cloud (OpenAI, Anthropic) with automatic fallback
Context-aware code generation using AST parsing and project structure analysis
Automated code review with customizable rules and team conventions
Architecture decision records (ADR) generation from design discussions
Test generation with coverage analysis and edge case detection
Documentation generation from code and commit history
VS Code extension with inline suggestions and chat interface

Challenges

Balancing context window limits with comprehensive project understanding
Handling different code styles and conventions across teams
Ensuring generated code follows security best practices
Managing prompt engineering for consistent outputs across models
Privacy concerns with cloud-based models

Solutions

Implemented RAG with codebase embeddings for relevant context retrieval
Created configurable style guides with team-specific rule sets
Built security scanning pipeline for generated code validation
Developed prompt templates with few-shot examples for each task type
Added local-first architecture with opt-in cloud features

Lessons Learned

Context quality matters more than model size for code tasks
Developers trust tools that explain their reasoning, not just output
Incremental adoption (one feature at a time) works better than all-in
Local models are sufficient for most code assistance tasks
Team buy-in requires measurable productivity metrics

Key Highlights

Multi-model support: local (Ollama) + cloud (OpenAI, Anthropic) with automatic fallback

Context-aware code generation using AST parsing and project structure analysis

Automated code review with customizable rules and team conventions

Architecture decision records (ADR) generation from design discussions

Test generation with coverage analysis and edge case detection

Documentation generation from code and commit history

VS Code extension with inline suggestions and chat interface

Solutions

Implemented RAG with codebase embeddings for relevant context retrieval

Created configurable style guides with team-specific rule sets

Built security scanning pipeline for generated code validation

Developed prompt templates with few-shot examples for each task type

Added local-first architecture with opt-in cloud features

Lessons Learned

Context quality matters more than model size for code tasks

Developers trust tools that explain their reasoning, not just output

Incremental adoption (one feature at a time) works better than all-in

Local models are sufficient for most code assistance tasks

Team buy-in requires measurable productivity metrics