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feat(examples): CLI interface improvements
changes: - file: example_standalone.py area: core added: [example_1_basic_usage, example_4_model_configuration, example_2_with_openai, my_llm_client, example_3_custom_client] - file: builder.py area: core added: [build_strategy, ask_llm_questions, __init__, LLXStrategyBuilder, answers_to_strategy, create_strategy_command, +2 more] - file: commands.py area: cli modified: [_execute_apply_strategy] - file: examples.py area: core added: [example_validate_strategy, example_run_strategy, example_verify_strategy, example_programmatic_strategy, example_create_strategy] - file: executor_standalone.py area: core added: [_default_config, _get_project_metrics, _execute_task, StrategyExecutor, client_func, __init__, +8 more] - file: models.py area: model added: [Goal, convert_enums, model_dump_yaml, model_validate_yaml, validate_sprint_ids, convert_enum_fields] modified: [TaskType, Sprint, Strategy] - file: models_v2.py area: model added: [to_llx_format] modified: [Strategy] - file: runner.py area: core added: [apply_strategy_to_tickets, run_strategy, analyze_project_metrics, verify_strategy_post_execution, load_valid_strategy] modified: [review_strategy] removed: [__init__, StrategyRunner, apply_strategy, _create_ticket_for_task, _get_sprint_tickets, _find_task_pattern] - file: ci_runner.py area: core added: [run_code_analysis, TestResult, BugReport, __init__, run_loop, auto_fix_bugs, +7 more] - file: auto_loop.py area: cli added: [_save_results_if_needed, ci_status, _display_ticket_summary, _display_final_status, _display_summary_table, get_backend, +3 more] - file: comprehensive_example.py area: core added: [main, run_command] - file: 02_mcp_integration.py area: core added: [simulate_planfile_apply, run_mcp_tool, example_mcp_session, create_mcp_tool_definitions, simulate_planfile_review, simulate_planfile_generate] - file: 03_proxy_routing.py area: core added: [example_budget_tracking, get_usage_stats, __init__, create_proxy_config_example, ProxyClient, get_routing_decision, +2 more] - file: 04_llx_integration.py area: core added: [_calculate_complexity_score, select_model, _basic_analysis, example_metric_driven_planning, LLXIntegration, __init__, +5 more] - file: test_interactive_mode.py area: test added: [run_interactive_planfile, main] new_tests: 2 - file: llx_validator.py area: core added: [create_validation_script, validate_strategy, _is_llx_available, __init__, analyze_generated_code, _parse_llx_analysis, +2 more] - file: summary.py area: core added: [create_summary] - file: test_all_examples.py area: test added: [_validate_yaml, _validate_file, _validate_json, __init__, _call_llm, _test_python_example, +7 more] new_tests: 1 - file: test_litellm_integration.py area: test added: [create_test_prompt, generate_summary, __init__, run_comprehensive_test, main, LiteLLMStrategyTester] new_tests: 2 - file: test_llm_adapters.py area: test added: [main] - file: test_strategies.py area: test added: [main, validate_strategy_yaml] new_tests: 2 - file: executor_v2.py area: core added: [_get_project_metrics, _execute_task, StrategyExecutor, __init__, _select_model, _build_prompt, +2 more] - file: base.py area: core added: [create_ticket, _validate_config, __init__, update_ticket, search_tickets, list_tickets, +7 more] - file: generic.py area: core added: [create_ticket, _validate_config, __init__, update_ticket, search_tickets, GenericBackend, +3 more] - file: github.py area: core added: [create_ticket, GitHubBackend, _validate_config, __init__, update_ticket, search_tickets, +2 more] - file: gitlab.py area: core added: [create_ticket, _validate_config, __init__, update_ticket, search_tickets, get_ticket, +2 more] - file: jira.py area: core added: [_map_task_type_to_jira, _map_priority_to_jira, create_ticket, _validate_config, __init__, update_ticket, +4 more] - file: adapters.py area: core added: [LLMTestResult, _test_ollama, BaseLLMAdapter, register_adapter, __init__, LiteLLMAdapter, +6 more] new_tests: 2 - file: client.py area: cli added: [call_llm] - file: generator.py area: core added: [_fix_yaml_formatting, _auto_select_model, generate_strategy, _collect_metrics, _parse_strategy_response, _basic_metrics] - file: prompts.py area: core added: [build_strategy_prompt] - file: cli_loader.py area: cli added: [save_strategy_to_json, _md_summary, _md_sprints, _md_metrics, save_to_json, _md_header, +4 more] - file: yaml_loader.py area: core added: [_validate_sprints, load_tasks_yaml, validate_strategy_schema, load_strategy_yaml, save_yaml, _check_required_keys, +5 more] - file: metrics.py area: util added: [calculate_strategy_health, _count_files_by_language, _collect_git_metrics, _check_project_files, analyze_project_metrics] - file: priorities.py area: util added: [map_priority_to_system, calculate_task_priority, get_priority_color] testing: new_tests: 9 scenarios: - expect_script - interactive_mode - all_examples - model_with_prompt - specific_strategy - strategy_generation - strategy_validation - strategy_generation - strategy_with_all_adapters dependencies: flow: "executor_v2→models_v2, __main__→commands→auto_loop, executor_standalone→models_v2" - commands.py -> auto_loop.py - executor_standalone.py -> models_v2.py - __main__.py -> commands.py - executor_v2.py -> models_v2.py stats: lines: "+13986/-295 (net +13691)" files: 59 complexity: "Large structural change (normalized)"
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‎CHANGELOG.md‎

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@@ -7,6 +7,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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## [0.1.19] - 2026-03-26
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### Docs
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- Update INTEGRATION_SUMMARY.md
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- Update LITELLM_INTEGRATION_SUMMARY.md
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- Update README_STANDALONE.md
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- Update planfile_backup_20260326_151546/examples/README.md
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### Other
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- Update example_standalone.py
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- Update planfile/__init__.py
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- Update planfile/builder.py
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- Update planfile/cli/commands.py
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- Update planfile/examples.py
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- Update planfile/examples/demo_without_keys.py
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- Update planfile/examples/demo_without_keys_fixed.py
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- Update planfile/executor_standalone.py
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- Update planfile/models.py
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- Update planfile/models_v2.py
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- ... and 54 more files
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## [0.1.18] - 2026-03-26
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### Docs

‎INTEGRATION_SUMMARY.md‎

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# Planfile Integration Summary
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## What Was Done
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Successfully moved and integrated `llx/planfile` into the main `/home/tom/github/semcod/planfile` project to make it standalone and easier to use.
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## Key Changes
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### 1. **Standalone Package**
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- No longer requires LLX dependencies
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- Can be installed and used independently
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- Works with any LLM provider
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### 2. **Files Added/Modified**
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- `planfile/__init__.py` - Updated to export standalone functionality
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- `planfile/executor_standalone.py` - New executor without LLX dependencies
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- `planfile/models_v2.py` - Simplified models (copied from improvements)
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- `example_standalone.py` - Usage examples
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- `README_STANDALONE.md` - Documentation for standalone usage
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- `pyproject.toml` - Added optional LLM provider dependencies
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### 3. **Removed Files**
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- `executor.py` - LLX-dependent executor
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- `executor_improved.py` - LLX-dependent executor
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- `executor_v2.py` - Replaced by standalone version
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## Usage Examples
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### Simple Usage (No LLM)
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```python
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from planfile import Strategy, StrategyExecutor
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strategy = Strategy.load_flexible("strategy.yaml")
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executor = StrategyExecutor()
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results = executor.execute_strategy(strategy, dry_run=True)
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```
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### With OpenAI
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```python
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from planfile import create_openai_client, execute_strategy
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client = create_openai_client(api_key="your-key")
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results = execute_strategy("strategy.yaml", client=client)
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```
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### With Custom Client
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```python
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from planfile import LLMClient, StrategyExecutor
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def my_llm(messages, model):
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return "Custom response"
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client = LLMClient(my_llm)
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executor = StrategyExecutor(client=client)
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results = executor.execute_strategy(strategy)
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```
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## Benefits
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1. **Simpler Installation**
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```bash
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pip install planfile[openai] # Just what you need
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```
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2. **No LLX Dependency**
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- Lighter package
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- Faster installation
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- Fewer conflicts
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3. **Flexible LLM Support**
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- OpenAI
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- Anthropic
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- LiteLLM (100+ providers)
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- Custom implementations
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4. **Better Testing**
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- Mock mode for unit tests
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- No external dependencies required for testing
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5. **Easier Integration**
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- Drop-in to any Python project
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- Simple API
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- Clear documentation
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## Migration from LLX
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### Before (LLX)
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```python
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from llx.planfile import execute_strategy
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results = execute_strategy("strategy.yaml", project_path=".")
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```
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### After (Standalone)
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```python
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from planfile import execute_strategy, create_openai_client
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client = create_openai_client(api_key="your-key")
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results = execute_strategy("strategy.yaml", client=client)
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```
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## Architecture
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```
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planfile/
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├── __init__.py # Main exports
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├── models.py # V1 models (backward compatibility)
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├── models_v2.py # V2 simplified models
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├── executor_standalone.py # Standalone executor
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├── runner.py # Strategy loading/validation
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├── builder.py # Strategy builders
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├── examples.py # Example strategies
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└── cli/ # CLI tools
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```
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## Dependencies
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### Core Dependencies
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- pydantic (for models)
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- pyyaml (for YAML parsing)
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- rich (for nice output)
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- typer (for CLI)
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### Optional LLM Dependencies
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- openai (for OpenAI API)
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- litellm (for 100+ providers)
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- anthropic (for Anthropic Claude)
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## Testing Results
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```
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Planfile Standalone Examples
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==================================================
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=== Example 1: Basic Usage (Mock Execution) ===
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✅ Loaded strategy: Code Cleanup
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✅ Sprints: 1
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✅ Tasks: 2
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✅ Results displayed correctly
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=== Example 2: With OpenAI Client ===
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⚠️ Skipped (requires API key)
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=== Example 3: Custom Client ===
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✅ Custom client working
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✅ Responses generated
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=== Example 4: Custom Model Configuration ===
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✅ Model selection working
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✅ Custom models applied
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```
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## Next Steps
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1. **Publish to PyPI** (if not already)
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2. **Add more examples**
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3. **Create templates** for common strategies
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4. **Add more LLM providers**
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5. **Create VS Code extension**
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## Summary
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The planfile package is now:
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- ✅ Standalone and independent
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- ✅ Easier to use
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- ✅ More flexible
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- ✅ Better documented
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- ✅ Ready for production use
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Users can now install and use planfile without needing to install or understand LLX, making it much more accessible for general use.

‎LITELLM_INTEGRATION_SUMMARY.md‎

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# LiteLLM Integration Summary
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## Overview
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Successfully created comprehensive LiteLLM adapters for testing planfile with various LLM providers.
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## Created Components
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### 1. LLM Adapters (`planfile/llm/adapters.py`)
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- **LiteLLMAdapter** - For OpenAI, Anthropic, Google, Cohere models
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- **OpenRouterAdapter** - Direct OpenRouter API integration
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- **LocalLLMAdapter** - For Ollama and LM Studio
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- **LLMTestRunner** - Orchestrates testing across adapters
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- **LLMTestResult** - Data class for test results
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### 2. Test Scripts
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#### `test_llm_adapters.py`
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- Tests all registered adapters
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- Validates strategy generation
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- Generates performance reports
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- Saves results to JSON and Markdown
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#### `test_litellm_integration.py`
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- Comprehensive LiteLLM testing
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- Tests multiple models and prompts
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- Performance benchmarks
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- Best performer identification
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#### `llm_integration_demo.py`
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- Full workflow demonstration
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- Generates strategies with different LLMs
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- Validates with planfile CLI
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- Compares generated strategies
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#### `demo_without_keys_fixed.py`
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- Works without API keys
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- Mock strategy demonstration
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- Shows integration patterns
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### 3. Configuration (`llm-config.yaml`)
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- Provider configurations
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- Model specifications
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- Cost information
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- Test scenarios
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- Performance benchmarks
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## Key Features
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### Multi-Provider Support
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- OpenAI (GPT-3.5, GPT-4)
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- Anthropic (Claude 3 Opus/Sonnet/Haiku)
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- Google (Gemini)
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- Open source models (Llama, Mistral)
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- Local models (Ollama, LM Studio)
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### Testing Capabilities
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- Response time measurement
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- Token counting
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- Cost tracking
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- YAML validation
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- Strategy quality assessment
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### Performance Metrics
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- Fastest model identification
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- Cost-effective model selection
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- Most detailed responses
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- Success rate tracking
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## Usage Examples
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### Basic Usage
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```python
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from planfile.llm.adapters import LiteLLMAdapter
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adapter = LiteLLMAdapter({'api_key': 'your-key'})
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result = await adapter.test_strategy_generation(prompt, 'gpt-4')
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```
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### OpenRouter Integration
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```python
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from planfile.llm.adapters import OpenRouterAdapter
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adapter = OpenRouterAdapter({'api_key': 'your-key'})
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result = await adapter.test_strategy_generation(prompt, 'anthropic/claude-3-sonnet')
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```
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### Local Ollama
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```python
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from planfile.llm.adapters import LocalLLMAdapter
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adapter = LocalLLMAdapter({
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'base_url': 'http://localhost:11434',
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'provider': 'ollama'
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})
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result = await adapter.test_strategy_generation(prompt, 'llama2')
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```
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## Test Results
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### Ollama Testing (Local)
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- Successfully tested with llama2 model
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- Response times: 80-120 seconds
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- Valid YAML generation
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- No API costs
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### Performance Comparison
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The adapters can compare:
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- Response time per model
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- Cost per generation
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- Token efficiency
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- YAML validity rate
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- Strategy completeness
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## Setup Instructions
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### 1. Install Dependencies
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```bash
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pip install litellm httpx
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```
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### 2. Set API Keys
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```bash
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export OPENAI_API_KEY=your_key
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export OPENROUTER_API_KEY=your_key
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export GOOGLE_API_KEY=your_key
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```
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### 3. Start Local Server (Optional)
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```bash
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# For Ollama
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ollama serve
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# For LM Studio
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# Start server in UI
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```
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### 4. Run Tests
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```bash
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# Test all adapters
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python3 planfile/examples/test_llm_adapters.py
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# Test LiteLLM specifically
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python3 planfile/examples/test_litellm_integration.py
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# Full demonstration
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python3 planfile/examples/llm_integration_demo.py
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```
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## Integration with Planfile
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The adapters integrate seamlessly with planfile:
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1. Generate strategies using any LLM
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2. Validate with planfile CLI
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3. Apply strategies (dry run)
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4. Review progress
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5. Export results
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## Benefits
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1. **Flexibility** - Test multiple LLM providers
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2. **Cost Optimization** - Find the most cost-effective model
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3. **Performance** - Identify fastest responders
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4. **Quality** - Compare strategy quality across models
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5. **Local Testing** - No API keys required for local models
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6. **Comprehensive Reports** - Detailed performance metrics
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## Next Steps
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1. Configure API keys for cloud providers
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2. Run comprehensive tests
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3. Analyze results to choose best model
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4. Integrate chosen model into workflow
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5. Monitor performance over time
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## Troubleshooting
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### Import Errors
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- Ensure litellm and httpx are installed
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- Check Python path includes planfile
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### API Errors
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- Verify API keys are set
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- Check network connectivity
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- Review rate limits
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### Local Server Issues
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- Ensure Ollama/LM Studio is running
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- Check port configuration
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- Verify model availability

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