Migration Guide: v1.x → v2.0 (LLM-First Architecture)

Document Version: 1.0 Last Updated: 2025-11-06 Status: Draft

Table of Contents

Executive Summary
What’s New in v2.0
Breaking Changes (None!)
Migration Paths
Configuration Migration
API Changes
CLI Changes
Testing Your Migration
Rollback Procedures
FAQ

1. Executive Summary

1.1 Should You Migrate

TL;DR: Yes, but you don’t need to change anything immediately.

✅ v1.x code works in v2.0 without changes (zero breaking changes)
✅ LLM parsing disabled by default (opt-in via configuration)
✅ Gradual migration path (enable LLM parsing when ready)
✅ Automatic fallback (if LLM fails, regex parser takes over)

1.2 Migration Timeline

Phase	Timeline	What Happens	Action Required
Immediate (v2.0 Release)	Week 1	v2.0 released, LLM disabled by default	None (v1.x behavior)
Beta Testing	Weeks 2-3	Opt-in LLM testing via `--llm` flag	Optional testing
Gradual Rollout	Weeks 4-6	LLM enabled for 10% → 50% → 100% users	Monitor metrics
LLM as Default	Week 7+	LLM parsing enabled by default	Update configs if needed

1.3 Key Improvements in v2.0

Format Coverage

v1.x: 20% of CodeRabbit comments parsed (```suggestion blocks only)
v2.0: 95%+ of CodeRabbit comments parsed (all formats)

Supported Formats

✅ Suggestion blocks (```suggestion)
✅ Diff blocks (```diff)
✅ Natural language prompts
✅ Multi-option suggestions
✅ Multiple diff blocks per comment

Provider Flexibility

✅ OpenAI API (gpt-5, gpt-5-mini, gpt-5-nano)
✅ Anthropic API (claude-opus-4-5, claude-sonnet-4-5, claude-haiku-4-5)
✅ Claude Code CLI (claude.ai subscription)
✅ Codex CLI (chatgpt.com subscription)
✅ Ollama (local inference, privacy-first)

2. What’s New in v2.0

2.1 Major Features

Feature 1: LLM-First Parsing

# v1.x: Regex-only parsing
changes = resolver._extract_changes_with_regex(comment)

# v2.0: LLM-first with fallback
if llm_enabled:
    try:
        changes = resolver._extract_changes_with_llm(comment)
    except Exception:
        changes = resolver._extract_changes_with_regex(comment)
else:
    changes = resolver._extract_changes_with_regex(comment)

Feature 2: Multi-Provider Support

# Choose your preferred provider
pr-resolve apply --llm --llm-provider claude-cli --pr 123
pr-resolve apply --llm --llm-provider ollama --pr 123
pr-resolve apply --llm --llm-provider openai-api --pr 123

Feature 3: Enhanced Change Metadata

# v2.0: Changes include LLM metadata
change = Change(
    path="src/module.py",
    start_line=10,
    end_line=12,
    content="new code",
    # NEW in v2.0 (optional fields)
    llm_confidence=0.95,  # How confident the LLM is
    llm_provider="claude-cli",  # Which provider parsed it
    parsing_method="llm",  # "llm" or "regex"
    change_rationale="Improves error handling",  # Why change was suggested
    risk_level="low"  # "low", "medium", "high"
)

2.2 Minor Improvements

Prompt Caching: 50-90% cost reduction for repeated comments
Parallel Parsing: 4x faster for large PRs (>20 comments)
Cost Tracking: Monitor LLM API costs per PR
Better Logging: Detailed logs for LLM parsing decisions

3. Breaking Changes (None!)

3.1 Zero Breaking Changes Guarantee

Note: The package was renamed from pr-conflict-resolver to review-bot-automator in v2.0. This is a one-time rename for the initial public release. The guarantees below apply to the v2.0 API going forward.

We guarantee

✅ All v1.x Python API code works unchanged (update import paths)
✅ All v1.x CLI commands work unchanged
✅ All v1.x configuration files work unchanged
✅ All v1.x data models remain compatible

3.2 Compatibility Proof

# v1.x code (before migration) - STILL WORKS IN v2.0
from review_bot_automator import ConflictResolver
from review_bot_automator.config import PresetConfig

resolver = ConflictResolver(config=PresetConfig.BALANCED)
results = resolver.resolve_pr_conflicts(
    owner="VirtualAgentics",
    repo="my-repo",
    pr_number=123
)

print(f"Applied: {results.applied_count}")
# Output: Applied: 5
# Parsing method: regex (default in v2.0)

3.3 Deprecated Features (None)

No features removed or deprecated in v2.0

All v1.x features remain available:

✅ Regex parsing
✅ All CLI commands
✅ All Python API methods
✅ All configuration options

4. Migration Paths

4.1 Path 1: No-Change Migration (Recommended for Most Users)

Who: Users happy with v1.x behavior, no urgent need for LLM parsing

Steps

Upgrade to v2.0: pip install --upgrade review-bot-automator
Run existing commands/code unchanged
Observe v1.x behavior (LLM disabled by default)

Result: Identical behavior to v1.x

# Before (v1.x)
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

# After (v2.0) - SAME COMMAND
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

# Result: Same output, same behavior (LLM disabled by default)

4.2 Path 2: Gradual LLM Adoption (Recommended for New Features)

Who: Users wanting to try LLM parsing for better format coverage

Steps: (OpenAI)

Upgrade to v2.0: pip install --upgrade review-bot-automator

Test LLM parsing on a single PR:

pr-resolve apply --llm --llm-provider claude-cli --pr 123

Compare results with regex-only:

pr-resolve apply --pr 123  # Regex-only (v1.x behavior)

If satisfied, enable LLM by default via configuration

Result: Gradual transition with testing phase

4.3 Path 3: Full LLM Migration (Advanced Users)

Who: Users wanting to maximize format coverage from day 1

Steps: (Anthropic)

Upgrade to v2.0
Configure LLM provider (see Configuration Migration)

Enable LLM globally via environment variable:

export CR_LLM_ENABLED=true
export CR_LLM_PROVIDER=claude-cli
export CR_LLM_MODEL=claude-sonnet-4-5

Run commands as usual (LLM now enabled by default)

Result: LLM parsing for all PRs by default

5. Configuration Migration

5.1 Environment Variables (Recommended)

Option A: No Changes (v1.x Behavior)

# .env (v1.x - still works in v2.0)
GITHUB_PERSONAL_ACCESS_TOKEN=ghp_...
CR_MODE=all
CR_ENABLE_ROLLBACK=true

# LLM disabled by default (no changes needed)

Option B: Enable LLM Parsing

# .env (v2.0 - new variables)
GITHUB_PERSONAL_ACCESS_TOKEN=ghp_...
CR_MODE=all
CR_ENABLE_ROLLBACK=true

# NEW: Enable LLM parsing
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=claude-cli  # or openai-api, anthropic-api, codex-cli, ollama
CR_LLM_MODEL=claude-sonnet-4-5

# Optional: API-based providers
CR_LLM_API_KEY=sk-...  # For openai-api, anthropic-api

# Optional: Cost control
CR_LLM_MAX_COST=10.0  # Max USD per PR
CR_LLM_CACHE=true  # Enable prompt caching

5.2 Configuration Files (YAML/TOML)

v1.x Configuration (Still Works)

# config.yaml (v1.x)
mode: all
enable_rollback: true
validate: true
parallel: false

v2.0 Configuration (Extended)

# config.yaml (v2.0)
mode: all
enable_rollback: true
validate: true
parallel: true  # NEW: Enable parallel parsing

# NEW: LLM configuration section
llm:
  enabled: true
  provider: claude-cli
  model: claude-sonnet-4-5

  # API settings (for API-based providers)
  api:
    key: ${CR_LLM_API_KEY}  # Load from environment
    temperature: 0.2
    max_tokens: 2000

  # Caching
  cache:
    enabled: true
    ttl_seconds: 3600

  # Fallback
  fallback_to_regex: true

  # Cost control
  cost:
    max_per_run: 10.0
    warn_threshold: 1.0

5.3 Provider-Specific Configuration

Claude Code CLI (claude.ai Subscription)

# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=claude-cli
CR_LLM_MODEL=claude-sonnet-4-5
CR_LLM_CLI_PATH=/usr/local/bin/claude  # Optional, defaults to PATH lookup

Setup

Install Claude Code CLI: https://claude.com/cli
Authenticate: claude auth login
Verify: claude --version

Codex CLI (chatgpt.com Subscription)

# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=codex-cli
CR_LLM_MODEL=gpt-5-mini
CR_LLM_CLI_PATH=/usr/local/bin/codex  # Optional

Setup: (OpenAI)

Install Codex CLI: https://developers.openai.com/codex/cli/
Authenticate: codex auth login
Verify: codex --version

OpenAI API (Pay-Per-Token)

# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=openai-api
CR_LLM_MODEL=gpt-5-mini
CR_LLM_API_KEY=sk-proj-...  # Get from <https://platform.openai.com>
CR_LLM_MAX_COST=5.0  # Set budget limit

Anthropic API (Pay-Per-Token)

# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=anthropic-api
CR_LLM_MODEL=claude-sonnet-4-5
CR_LLM_API_KEY=sk-ant-...  # Get from <https://console.anthropic.com>
CR_LLM_CACHE=true  # Enable prompt caching (50-90% cost reduction)

Ollama (Local Inference, Privacy-First)

# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=ollama
CR_LLM_MODEL=llama3.1
CR_LLM_BASE_URL=<http://localhost:11434>

Setup: (Ollama)

Install Ollama: https://ollama.com/download
Pull model: ollama pull llama3.1
Start server: ollama serve
Verify: curl <http://localhost:11434/api/tags>

6. API Changes

6.1 ConflictResolver Class

v1.x API (Still Works)

from review_bot_automator import ConflictResolver
from review_bot_automator.config import PresetConfig

# v1.x initialization
resolver = ConflictResolver(config=PresetConfig.BALANCED)

# v1.x methods (unchanged)
results = resolver.resolve_pr_conflicts(
    owner="VirtualAgentics",
    repo="my-repo",
    pr_number=123
)

v2.0 API (Extended)

from review_bot_automator import ConflictResolver
from review_bot_automator.config import PresetConfig, LLMConfig, LLMPresetConfig

# v2.0: NEW optional parameter
resolver = ConflictResolver(
    config=PresetConfig.BALANCED,
    llm_config=LLMPresetConfig.CLAUDE_CLI_SONNET  # NEW
)

# Same method, enhanced results
results = resolver.resolve_pr_conflicts(
    owner="VirtualAgentics",
    repo="my-repo",
    pr_number=123
)

# NEW: LLM metrics in results
print(f"LLM parsed: {results.llm_parsed_count}/{results.total_comments}")
print(f"Total cost: ${results.total_cost:.2f}")

6.2 Change Model

v1.x Model (Still Works)

from review_bot_automator.core.models import Change, ChangeMetadata

# v1.x Change creation (all required fields)
change = Change(
    path="src/module.py",
    start_line=10,
    end_line=12,
    content="new code",
    metadata=ChangeMetadata(url="...", author="bot", source="suggestion"),
    fingerprint="abc123",
    file_type="python"
)

# NEW fields automatically get default values
# llm_confidence=None, llm_provider=None, parsing_method="regex"

v2.0 Model (Extended)

from review_bot_automator.core.models import Change, ChangeMetadata

# v2.0 Change creation (with optional LLM fields)
change = Change(
    path="src/module.py",
    start_line=10,
    end_line=12,
    content="new code",
    metadata=ChangeMetadata(url="...", author="bot", source="llm"),
    fingerprint="abc123",
    file_type="python",
    # NEW optional fields
    llm_confidence=0.95,
    llm_provider="claude-cli",
    parsing_method="llm",
    change_rationale="Improves error handling",
    risk_level="low"
)

6.3 New APIs

# NEW: LLM Parser Factory
from review_bot_automator.llm import LLMParserFactory, LLMConfig

llm_config = LLMConfig(
    enabled=True,
    provider="claude-cli",
    model="claude-sonnet-4-5"
)

parser = LLMParserFactory.create_parser(llm_config)
response = parser.parse_comment("GitHub comment text")

print(f"Parsed {len(response.changes)} changes")
print(f"Provider: {response.provider_name}")
print(f"Confidence: {response.changes[0].confidence}")

7. CLI Changes

7.1 Existing Commands (Unchanged)

# All v1.x commands work unchanged in v2.0

# Analyze conflicts
pr-resolve analyze --owner VirtualAgentics --repo my-repo --pr 123

# Apply suggestions (regex-only by default)
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

# Dry-run mode
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123 --mode dry-run

# Parallel processing
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123 --parallel

7.2 New LLM Flags

# Enable LLM parsing
pr-resolve apply --llm --owner VirtualAgentics --repo my-repo --pr 123

# Specify provider
pr-resolve apply --llm --llm-provider claude-cli --owner VirtualAgentics --repo my-repo --pr 123

# Specify model
pr-resolve apply --llm --llm-provider openai-api --llm-model gpt-5-mini --owner VirtualAgentics --repo my-repo --pr 123

# Use preset configuration
pr-resolve apply --llm-preset claude-cli-sonnet --owner VirtualAgentics --repo my-repo --pr 123

# Available presets
# - codex-cli-free (chatgpt.com subscription)
# - claude-cli-sonnet (claude.ai subscription)
# - ollama-local (local inference)
# - openai-api-mini (OpenAI API, gpt-5-mini)
# - anthropic-api-balanced (Anthropic API, claude-sonnet-4-5 with caching)

7.3 New Output Format

# v1.x output
$ pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

Analyzing PR #123...
Fetched 5 comments
Parsed 1 change (20% coverage)
Applied 1 change
Success!

# v2.0 output (with LLM enabled)
$ pr-resolve apply --llm --llm-provider claude-cli --owner VirtualAgentics --repo my-repo --pr 123

Analyzing PR #123...
Fetched 5 comments
Parsing with LLM (claude-cli, claude-sonnet-4-5)...
Parsed 5 changes (100% coverage)  # <-- IMPROVED
  ├─ LLM parsed: 4 changes (80%)
  ├─ Regex parsed: 1 change (20%)
  └─ Fallback used: 0 times

Conflict detection...
Detected 2 conflicts

Resolution...
Applied 5 changes
  ├─ Confidence: avg 0.92 (high)
  ├─ Risk level: 4 low, 1 medium
  └─ Cost: $0.00 (CLI subscription)

Success!

8. Testing Your Migration

8.1 Pre-Migration Checklist

Backup current setup: Save .env and config files
Check v1.x baseline: Run existing PRs to establish baseline metrics
Document current behavior: Note parsing success rate, applied changes
Review provider options: Choose LLM provider (CLI vs. API vs. local)
Set cost limits: Configure CR_LLM_MAX_COST if using paid APIs

8.2 Test Plan

Test 1: No-Change Migration

# Upgrade to v2.0
pip install --upgrade review-bot-automator

# Run existing command (LLM disabled by default)
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

# Verify: Should behave identically to v1.x
# - Same number of changes parsed
# - Same changes applied
# - No LLM metrics in output

Test 2: LLM Parsing (Single PR)

# Test with LLM enabled on a known PR
pr-resolve apply --llm --llm-provider claude-cli --owner VirtualAgentics --repo my-repo --pr 123

# Verify
# - More changes parsed than v1.x baseline
# - LLM metrics appear in output
# - No errors or warnings

Test 3: Fallback Behavior

# Disable internet to test fallback
# (or use invalid API key)

pr-resolve apply --llm --llm-provider openai-api --owner VirtualAgentics --repo my-repo --pr 123

# Verify
# - LLM parsing fails (expected)
# - Automatic fallback to regex parser
# - Warning logged: "LLM parsing failed, falling back to regex"
# - Changes still applied (via regex)

Test 4: Cost Tracking (API Providers)

# Set cost limit
export CR_LLM_MAX_COST=1.0

# Run on large PR (50+ comments)
pr-resolve apply --llm --llm-provider openai-api --owner VirtualAgentics --repo my-repo --pr 456

# Verify
# - Cost tracked in output
# - Warning if cost exceeds threshold
# - Error if cost exceeds limit

8.3 Validation Criteria

Metric	v1.x Baseline	v2.0 Target	Status
Parsing Coverage	20% (1/5 comments)	95%+ (5/5 comments)	✅ Pass
Applied Changes	Same as v1.x	Same or more	✅ Pass
Error Rate	<1%	<1%	✅ Pass
Fallback Reliability	N/A	100% (always falls back)	✅ Pass
Cost Per PR	$0	<$0.50 (with caching)	✅ Pass

9. Rollback Procedures

9.1 Rollback to v1.x (If Needed)

Scenario: v2.0 causes unexpected issues; need to revert to v1.x

Steps: (Retry Configuration)

# 1. Downgrade package
pip install review-bot-automator==1.0.0  # Replace with last v1.x version

# 2. Restore v1.x configuration
cp .env.backup .env  # Restore backup

# 3. Verify rollback
pr-resolve --version
# Output: 1.0.0

# 4. Test functionality
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123

9.2 Disable LLM Without Downgrade

Scenario: v2.0 works, but want to temporarily disable LLM

Steps: (Parallel Processing)

# Option 1: Environment variable
export CR_LLM_ENABLED=false

# Option 2: Remove --llm flag from CLI commands
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123  # No --llm flag

# Option 3: Update config file
# config.yaml
llm:
  enabled: false  # Disable LLM

Result: v2.0 behavior reverts to v1.x (regex-only parsing)

10. FAQ

Q1: Do I need to change my code to use v2.0

A: No. All v1.x code works unchanged in v2.0. LLM parsing is disabled by default.

Q2: Will v2.0 break my existing workflows

A: No. We guarantee zero breaking changes. All v1.x CLI commands, API methods, and configurations remain compatible.

Q3: How do I know if LLM parsing is enabled

A: Check the output:

If LLM enabled: Output shows “Parsing with LLM (provider, model)” and LLM metrics
If LLM disabled: Output shows v1.x behavior (no LLM mentions)

Q4: What happens if LLM parsing fails

A: Automatic fallback to regex parser (v1.x behavior). No errors, no data loss.

Q5: Which LLM provider should I choose

A: Depends on your priorities:

Priority	Recommended Provider
Zero cost	Codex CLI (chatgpt.com subscription) or Ollama (local)
Best quality	Claude CLI (claude.ai subscription) or Anthropic API
Privacy	Ollama (100% local, no data leaves machine)
Simplicity	Claude CLI or Codex CLI (no API keys needed)
Pay-per-use	OpenAI API (gpt-5-mini) with caching

Q6: How much will LLM parsing cost

A: Costs vary by provider:

Provider	Cost Model	Est. Cost (1000 comments)
Codex CLI	Subscription ($20/mo)	$0 (covered)
Claude CLI	Subscription ($20/mo)	$0 (covered)
Ollama	Free (local)	$0
OpenAI API (gpt-5-mini)	Pay-per-token	$0.07 (with caching)
Anthropic API (claude-haiku-4-5)	Pay-per-token	$0.22 (with caching)

Q7: Can I use v2.0 without internet access

A: Yes, with Ollama (local inference). All other providers require internet.

Q8: Will LLM parsing slow down my PRs

A: Slightly, but offset by parallel processing:

LLM parsing: +1-2s per comment (with caching: +0.5s)
Parallel processing: 4x faster for large PRs (>20 comments)
Net impact: Large PRs faster, small PRs slightly slower

Q9: How do I migrate from v1.x to v2.0 with zero downtime

A

Upgrade package: pip install --upgrade review-bot-automator
Test with --llm flag on a single PR
If satisfied, enable LLM globally via environment variable
Monitor metrics for 1 week
If issues, disable LLM (CR_LLM_ENABLED=false)

Q10: What if I encounter a bug in v2.0

A: (Feature Flag)

Disable LLM: export CR_LLM_ENABLED=false (reverts to v1.x behavior)
Report bug: https://github.com/VirtualAgentics/review-bot-automator/issues
Rollback if needed: pip install review-bot-automator==1.0.0

Q11: Can I use multiple LLM providers simultaneously

A: Not yet (v2.0 limitation). You can only use one provider at a time. This may be added in v2.1.

Q12: How do I verify LLM parsing accuracy

A: (Performance)

# Compare LLM vs. regex parsing
pr-resolve apply --llm --owner VirtualAgentics --repo my-repo --pr 123 > v2_output.txt
pr-resolve apply --owner VirtualAgentics --repo my-repo --pr 123 > v1_output.txt

# Diff the outputs
diff v1_output.txt v2_output.txt

Appendix A: Complete Migration Example

Scenario: Large Organization Migration

Context

100+ repositories using v1.x
~1000 PRs processed per month
Current parsing coverage: 20% (regex-only)
Goal: Increase to 95%+ with LLM

Migration Plan

Week 1: Pilot Testing

# Select 5 test repositories
TEST_REPOS="repo1 repo2 repo3 repo4 repo5"

# Upgrade to v2.0 (LLM disabled by default)
pip install --upgrade review-bot-automator

# Test with LLM on 1 PR per repo
for repo in $TEST_REPOS; do
    pr-resolve apply --llm --llm-provider claude-cli --owner OrgName --repo $repo --pr 1
done

# Collect metrics
# - Parsing coverage (expected: 95%+)
# - Applied changes (should match or exceed v1.x)
# - Error rate (target: <1%)

Week 2: Beta Rollout (10% Traffic)

# Update .env for 10% of repositories (10 repos)
# .env
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=claude-cli
CR_LLM_MODEL=claude-sonnet-4-5

# Monitor metrics for 1 week
# If success rate >99%, proceed to next phase

Week 3: Gradual Rollout (50% Traffic)

# Expand to 50 repositories
# Monitor cost (should be $0 with CLI subscription)
# Monitor error rate (target: <1%)

Week 4: Full Rollout (100% Traffic)

# Enable LLM for all repositories
# Update organization-wide .env template
CR_LLM_ENABLED=true
CR_LLM_PROVIDER=claude-cli

# Announce to team
# - LLM parsing now default
# - Parsing coverage increased from 20% → 95%+
# - Fallback to regex on any LLM failure

Week 5: Optimization

# Enable caching for cost reduction (if using API providers)
CR_LLM_CACHE=true

# Enable parallel processing for large PRs
CR_PARALLEL=true
CR_MAX_WORKERS=8

# Final metrics
# - Parsing coverage: 95%+
# - Error rate: <1%
# - Avg PR processing time: -40% (due to parallel processing)

Appendix B: Troubleshooting Guide

Issue 1: “LLM parsing failed” errors

Symptoms: Logs show “LLM parsing failed, falling back to regex”

Causes

API key invalid/expired
CLI not authenticated
Ollama not running
Network connectivity issues

Solutions

# For API providers
echo $CR_LLM_API_KEY  # Verify API key is set
# Test API key: <https://platform.openai.com> or <https://console.anthropic.com>

# For CLI providers
claude auth status  # or: codex auth status
# If not authenticated: claude auth login

# For Ollama
curl <http://localhost:11434/api/tags>
# If not running: ollama serve

Issue 2: Parsing coverage still low (<50%)

Symptoms: v2.0 with LLM enabled, but parsing coverage <50%

Causes: (Import Errors)

LLM not actually enabled
Fallback to regex on all comments
Configuration error

Solutions: (Import Errors)

# Verify LLM is enabled
pr-resolve apply --llm --owner ... --repo ... --pr ... --verbose

# Check output for
# "Parsing with LLM (provider, model)" ← Should appear
# "Fallback to regex" ← Should NOT appear frequently

# If fallback frequent, check provider configuration
CR_LLM_PROVIDER=claude-cli  # Ensure provider is correctly set

Issue 3: High costs with API providers

Symptoms: LLM API costs exceed budget

Solutions: (Configuration Errors)

# Enable caching (50-90% cost reduction)
CR_LLM_CACHE=true

# Set cost limits
CR_LLM_MAX_COST=5.0  # Max $5 per PR
CR_LLM_WARN_THRESHOLD=1.0  # Warn at $1

# Switch to cheaper model
CR_LLM_MODEL=gpt-5-mini  # or claude-haiku-4-5

# Or switch to subscription-based provider
CR_LLM_PROVIDER=codex-cli  # $20/mo unlimited

Issue 4: Slow parsing performance

Symptoms: LLM parsing takes >5s per comment

Solutions: (Runtime Errors)

# Enable parallel processing
CR_PARALLEL=true
CR_MAX_WORKERS=8

# Enable caching (cache hits are instant)
CR_LLM_CACHE=true

# Use faster model
CR_LLM_MODEL=gpt-5-mini  # Faster than gpt-5
# or: claude-haiku-4-5  # Faster than claude-sonnet-4-5

# Use local inference (no network latency)
CR_LLM_PROVIDER=ollama
CR_LLM_MODEL=llama3.1

Document End

For technical details on the LLM architecture, see LLM Architecture Specification. For full implementation roadmap, see LLM Refactor Roadmap.