Examples

Real-world examples of using voria to fix open source issues.

Example 1: Simple Bug Fix

Scenario

A simple bug in a Python library where a function returns wrong type.

Command

bash
voria issue 42

What Happens

1. ▶

   Fetch Issue

   terminal
   Issue #42: TypeError in calculate_total()
   Description: When given empty list, returns None instead of 0
   

2. ▶

   Plan

   terminal
   Detected: Simple return value bug
   Plan: Add condition to return 0 for empty input
   Estimated effort: 5 minutes
   

3. ▶

   Generate Patch

   diff
   -def calculate_total(items):
   +def calculate_total(items):
   +    if not items:
   +        return 0
        return sum(items)
   

4. ▶

   Test

   terminal
   Running pytest...
   ✓ All 12 tests pass
   ✓ New test cases pass
   

5. ▶

   Result

   terminal
   ✅ Issue fixed!
   Pull Request created: #1234
   

Example 2: Feature Implementation with Iteration

Scenario

Adding a new feature that requires multiple iterations to get tests passing.

Command

bash
voria issue 100 --verbose --max-iterations 5

Iteration Details

Iteration 1:

• ▶Plan: Add new
  terminal

  filter()

  method
• ▶Patch: Adds basic filter implementation
• ▶Test: FAILED - Missing parameter validation

Iteration 2:

• ▶Analysis: Tests expect validation error
• ▶Patch: Add parameter type checking
• ▶Test: FAILED - Missing docstring

Iteration 3:

• ▶Analysis: Linting checks require docs
• ▶Patch: Add docstring and examples
• ▶Test: PASSED ✓

Result: Feature ready after 3 iterations

Example 3: Complex Multi-File Change

Scenario

API migration requiring changes across 5+ files.

Command

bash
voria issue 200 --dry-run --verbose

Dry Run Output

terminal
Would modify:
  ✓ src/api.py (3 changes)
  ✓ src/models.py (2 changes)
  ✓ tests/test_api.py (5 changes)
  ✓ docs/api.md (1 change)
  ✓ setup.py (1 change)

Would run tests:
  ✓ pytest tests/ (estimated 2m)
  ✓ Coverage report (estimated 30s)

Estimated impact:
  - Lines added: 120
  - Lines removed: 85
  - Files modified: 5

After verification:

bash
voria issue 200 --create-pr

🧪 Example 4: Using Different LLM Providers

Compare LLMs

Modal (Fastest, Free)

bash
voria issue 50 --llm modal --verbose
Time: 2 minutes
Cost: $0
Result: Works but basic

OpenAI GPT-5.4 (Best Quality)

bash
voria issue 50 --llm openai --verbose
Time: 5 minutes
Cost: $0.15
Result: Excellent, well-documented

Google Gemini (Best Balance)

bash
voria issue 50 --llm gemini --verbose
Time: 3 minutes
Cost: $0.05
Result: Good quality, fast

Example 5: Custom Test Command

Scenario

Repository uses custom test framework (not auto-detected).

Setup

bash
voria issue 75 --test-cmd "npm run test:coverage" --test-pattern "*.test.js"

What Happens

1. ▶Generates patch
2. ▶Applies patch
3. ▶Runs:
   terminal

   npm run test:coverage

4. ▶Parses custom coverage output
5. ▶Determines success/failure
6. ▶Iterates if needed

Example 6: Code Quality Issues

Scenario

Issue about code formatting/linting violations.

Command

bash
voria issue 15 --hook "pre-test: black src/ && pylint src/"

Workflow

1. ▶Plan: Identify formatting needed
2. ▶Patch: Generate fixes
3. ▶Apply patch
4. ▶Pre-test: Run black formatter + pylint
5. ▶Test: Run test suite
6. ▶Success: Clean code + passing tests

Example 7: Documentation Update

Scenario

GitHub issue requesting documentation improvements.

Command

bash
voria issue 88 --save-iterations --output markdown

Result: Markdown Report

markdown
# Issue #88: Fix Documentation

## Plan
Add examples and API reference for new module

## Changes Made
- Added quickstart guide
- Added 5 practical examples
- Updated API documentation
- Added troubleshooting section

## Test Results
- ✅ All documentation builds successfully
- ✅ Links are valid
- ✅ Code examples run without errors

Example 8: Security-Related Fix

Scenario

Security vulnerability that needs careful handling.

Command

bash
voria issue 300 \
  --require-approval \
  --skip-auto-pr \
  --save-analysis \
  --verbose

Output

terminal
[SECURITY] Analyzing vulnerability...
Identified: SQL injection in user input handling
Severity: HIGH

Generated fix:
✓ Adds parameterized queries
✓ Adds input validation
✓ Adds security tests

Awaiting approval before:
- Applying patch
- Running tests
- Creating PR

Review: ./analysis/issue-300-security-review.md

Example 9: Performance Optimization

Scenario

Issue about slow function that needs optimization.

Command

bash
voria issue 42 \
  --test-cmd "pytest benchmarks/" \
  --performance-baseline 2.5s \
  --verbose

Workflow

1. ▶Plan: Identify optimization opportunity
2. ▶Generate: Optimize algorithm/caching
3. ▶Benchmark:
   terminal

   pytest benchmarks/

   shows 0.8s (3x faster!)
4. ▶Test: All tests pass
5. ▶Success: "30% performance improvement"

Example 10: Batch Processing Multiple Issues

Scenario

Fix multiple related issues at once.

Script

bash
#!/bin/bash
ISSUES=(42 43 44 45 46)
for issue in "${ISSUES[@]}"; do
  echo "Processing issue #$issue..."
  voria issue $issue --create-pr --llm gemini
  sleep 2  # Rate limiting
done

Output

terminal
Processing issue #42...
✅ Created PR #1234

Processing issue #43...
✅ Created PR #1235

Processing issue #44...
✅ Created PR #1236

...

All issues processed! 5 PRs created.

Example 11: Investigating Before Fixing

Scenario

Complex issue - want to analyze before fixing.

Commands

bash
# Step 1: Just plan
voria plan 999 --verbose

# Step 2: Dry run
voria issue 999 --dry-run

# Step 3: Dry run with specific LLM
voria issue 999 --dry-run --llm claude

# Step 4: Dry run with custom tests
voria issue 999 --dry-run --test-cmd "npm run test:all"

# Step 5: Actually run it
voria issue 999 --create-pr

Example 12: Handling Failures

Scenario

Fix attempt fails - how to debug and retry.

Commands

bash
# First attempt
voria issue 50 --verbose

# If it fails, review logs
voria logs issue 50 --level DEBUG

# Try with different LLM
voria issue 50 --llm claude

# Or with higher iterations
voria issue 50 --max-iterations 8

# Or manually fix and apply
voria apply manual-plan

Example 13: Progress Monitoring

Scenario

Monitor voria's progress on an issue.

Commands

bash
# Terminal 1: Start voria
voria issue 42 --verbose --follow-logs

# Terminal 2: Monitor status
while true; do
  voria status issue 42
  sleep 5
done

# Terminal 3: Check token usage
watch voria token info

Example 14: GitHub Enterprise Integration

Scenario

Using voria with GitHub Enterprise (on-premise).

Setup

bash
# Configure GitHub Enterprise
python3 -m voria.core.setup

# Select: github-enterprise
# Enter: https://github.enterprise.com
# Token: ghp_...

Usage

bash
voria issue 42 --github https://github.enterprise.com

Best Practice: The Complete Workflow

Full Workflow Example

bash
# 1. Investigate
voria plan 42 --verbose

# 2. Dry run (no modifications)
voria issue 42 --dry-run --verbose

# 3. Check what changed (before applying)
voria logs issue 42 --dry-run

# 4. Run with minimal risk
voria issue 42 \
  --max-iterations 3 \
  --require-approval \
  --save-analysis

# 5. If successful, create PR
voria issue 42 --create-pr

# 6. Monitor the PR
voria status pr-id

# 7. Review results
voria logs issue 42 --output markdown > review.md

Learning from Examples

Run the Examples:

bash
# Try all examples in sequence
for ex in {1..5}; do
  echo "Running example $ex..."
  voria example $ex --verbose
done

Understand the Output:

bash
# Generate detailed analysis
voria issue 42 --output json | jq . | less

# Export to markdown
voria issue 42 --output markdown > report.md

# Compare different approaches
diff <(voria issue 42 --llm openai --output json) \
     <(voria issue 42 --llm modal --output json)

Try these examples! Start with simplest (Example 1) and work up to more complex ones.

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