# repo-sync-kitty - Alternatives Evaluation

## Decision Summary

| Component | Chosen Solution | Rationale |
|-----------|-----------------|-----------|
| Git Interaction | GitPython | Mature OO API, full git feature support, well-documented |
| Async/Parallelism | concurrent.futures ThreadPoolExecutor | Balance of simplicity and performance, git operations are I/O bound |
| CLI Framework | Typer | Modern, type-hint based, excellent DX |
| Config Parsing | tomllib + Pydantic Settings | Built-in TOML support (Python 3.11+), strong validation |
| Output Formatting | Rich | Beautiful terminal output, tables, progress bars |
| Forge API Client | httpx | Modern async-capable HTTP client |

---

## 1. Git Interaction Library

### Option A: GitPython ✅ SELECTED

**Pros**:
- Mature, battle-tested (most popular Python git library)
- Object-oriented interface: `Repo`, `Remote`, `Branch`, `Commit` objects
- Excellent for reading repository state (`repo.is_dirty()`, `repo.active_branch`)
- Well-documented with extensive examples
- Shells out to git for complex ops - gets full git power
- Built-in methods for exactly what we need: clone, fetch, pull, status
- Active maintenance

**Cons**:
- Requires git to be installed (shells out for many operations)
- Some memory overhead for large repositories
- Occasional edge cases with git version compatibility

**Maintenance**: Active, well-maintained

### Option B: Dulwich

**Pros**:
- Pure Python implementation of git protocol
- No dependency on git binary
- Good for programmatic access to git internals
- Useful for server-side git operations
- Easy to test (no subprocess)

**Cons**:
- Not a complete git implementation
- Some high-level commands have limitations
- Steeper learning curve
- Less intuitive API for common operations
- Would need wrapper code for some operations

**Maintenance**: Active, focused on protocol-level operations

### Option C: subprocess (git CLI)

**Pros**:
- Full access to all git features
- Always up-to-date with latest git
- Predictable behavior (same as manual git commands)
- Users can replicate commands manually

**Cons**:
- Need to parse text output
- Error handling requires parsing stderr
- No object model - just strings
- More boilerplate code

**Maintenance**: N/A - relies on system git

### Option D: pygit2 (libgit2 bindings)

**Pros**:
- Very fast (C library)
- Full-featured
- No git binary dependency

**Cons**:
- Native extension - harder to install
- Compilation required
- Less portable

### Decision: GitPython

**Rationale**:
- Mature OO API maps directly to our needs: `Repo.clone_from()`, `remote.fetch()`, `repo.is_dirty()`
- `repo.active_branch`, `repo.head.is_detached`, `repo.is_dirty()` - exactly what safety checks need
- Most popular Python git library - well-tested, good community support
- When it shells out to git, we get full git functionality
- Easier to write clean, readable code than subprocess text parsing
- Good balance of abstraction and power

---

## 2. Async/Parallelism Strategy

### Option A: asyncio with asyncio.subprocess

**Pros**:
- True async I/O
- Efficient for many concurrent operations
- Modern Python pattern
- Good for mixing with async HTTP calls (forge APIs)

**Cons**:
- Complexity of async/await throughout codebase
- Debugging async code is harder
- Need to manage event loops carefully
- Git operations are already I/O-bound, async overhead may not help

**Complexity**: High

### Option B: multiprocessing

**Pros**:
- True parallelism (bypasses GIL)
- Good for CPU-bound work
- Process isolation

**Cons**:
- Heavy overhead per process
- Complex data sharing between processes
- Overkill for I/O-bound git operations
- Memory multiplication

**Complexity**: Medium-High

### Option C: concurrent.futures ThreadPoolExecutor ✅ SELECTED

**Pros**:
- Simple API (`executor.map`, `executor.submit`)
- Good for I/O-bound operations (git, network)
- Easy to control parallelism (max_workers)
- Straightforward error handling
- Can be used with context managers
- Familiar to most Python developers

**Cons**:
- Limited by GIL for CPU-bound work (not our case)
- Less efficient than true async for very high concurrency

**Complexity**: Low

### Decision: concurrent.futures ThreadPoolExecutor

**Rationale**:
- Git operations are I/O-bound (disk, network) - threads work well
- Simple to implement and maintain
- Easy to set parallelism from config (max_workers = config.parallelism)
- Timeout handling is straightforward
- Error handling with futures is intuitive
- No need to restructure entire codebase for async

---

## 3. CLI Framework

### Option A: argparse (stdlib)

**Pros**:
- No external dependencies
- Stable, well-documented
- Familiar to Python developers

**Cons**:
- Verbose, boilerplate-heavy
- No automatic type conversion from hints
- Subcommands require manual setup
- No built-in help formatting improvements

### Option B: Click

**Pros**:
- Mature, widely used
- Good subcommand support
- Decorators reduce boilerplate
- Extensible

**Cons**:
- Decorator-based API less intuitive than type hints
- Separate from function signatures

### Option C: Typer ✅ SELECTED

**Pros**:
- Built on Click (inherits stability)
- Type hints define CLI interface
- Automatic help generation
- Excellent developer experience
- Modern Python patterns
- Rich integration built-in

**Cons**:
- Additional dependency
- Slightly newer (less battle-tested than Click alone)

### Decision: Typer

**Rationale**:
- User specified Typer in requirements
- Type hints align with Pydantic Settings usage
- Rich integration provides beautiful output
- Reduces boilerplate significantly
- Modern, maintainable code style

---

## 4. Config Parsing & Validation

### Option A: tomli/tomllib + dataclasses

**Pros**:
- Simple, lightweight
- tomllib is built into Python 3.11+
- dataclasses are stdlib

**Cons**:
- Manual validation required
- No automatic type coercion
- No environment variable support

### Option B: tomllib + Pydantic Settings ✅ SELECTED

**Pros**:
- Strong validation with clear error messages
- Automatic type coercion
- Environment variable override support
- Nested model support (remotes, projects)
- Field defaults and validators
- JSON Schema generation possible

**Cons**:
- Additional dependency (pydantic)
- Slightly heavier than pure dataclasses

### Decision: tomllib + Pydantic Settings

**Rationale**:
- User specified Pydantic Settings
- Config structure has nested models (remotes, projects) - Pydantic handles this well
- Validation is critical (invalid config should fail early with clear errors)
- Inheritance/defaults between common → remote → project needs custom validators
- Environment variable overrides useful for CI/CD scenarios

---

## 5. Forge API Clients (GitHub, GitLab, Bitbucket)

### Option A: PyGithub / python-gitlab / specific libraries

**Pros**:
- Full API coverage
- Typed responses
- Pagination handled

**Cons**:
- Multiple dependencies for multiple forges
- Different APIs to learn
- Heavier dependencies

### Option B: httpx ✅ SELECTED

**Pros**:
- Single dependency for all forges
- Modern, async-capable (can use sync mode)
- Similar API to requests
- Good timeout handling
- HTTP/2 support

**Cons**:
- Need to implement forge-specific logic ourselves
- Manual pagination handling

### Decision: httpx

**Rationale**:
- Scan functionality is relatively simple (list repos in org)
- Don't need full API coverage, just repo listing
- Single dependency vs multiple forge-specific libraries
- Can add forge-specific libraries later if needed
- httpx is modern and well-maintained

---

## 6. Output Formatting

### Option A: Plain print statements

**Pros**:
- No dependencies
- Simple

**Cons**:
- No colors, tables, progress bars
- Poor UX

### Option B: Rich ✅ SELECTED

**Pros**:
- Beautiful terminal output
- Tables, progress bars, syntax highlighting
- Markdown rendering
- Auto-detects terminal capabilities
- Integrates with Typer

**Cons**:
- Additional dependency

### Decision: Rich

**Rationale**:
- User specified Rich in requirements
- Progress bars essential for sync operation
- Tables perfect for status output
- Typer has built-in Rich integration
- --no-color flag support built-in

---

## Trade-offs Summary

| Trade-off | Decision | Reasoning |
|-----------|----------|-----------|
| Library vs CLI for git | CLI (subprocess) | Full feature access, debuggability |
| Async vs Threads | Threads | Simpler code, sufficient for I/O-bound work |
| Type safety | Pydantic | Strong validation, clear errors |
| Multiple forge libs vs single HTTP | Single (httpx) | Simpler dependency tree, sufficient for needs |

## Exit Strategies

If decisions need to be reversed:

1. **GitPython → Dulwich/subprocess**: Wrap git operations in abstraction layer from start; swap implementation later
2. **ThreadPoolExecutor → asyncio**: Would require significant refactor; design with this possibility (keep operations isolated)
3. **httpx → forge-specific libs**: HTTP calls isolated in forge modules; easy to swap per-forge
