# Feature Specification: Python Library with Universal LLM Support

**Feature Branch**: `001-python-litellm-library`
**Created**: 2025-12-19
**Status**: Draft
**Input**: Transform spec-kit into a standalone Python library with LiteLLM integration for 100+ LLM providers, MCP Server support, CLI interface, and structured outputs.

## User Scenarios & Testing *(mandatory)*

### User Story 1 - Library Installation and Basic Usage (Priority: P1)

As a developer, I want to install spec-kit as a Python package and use it programmatically in my projects to automate specification-driven development workflows without requiring manual CLI interaction.

**Why this priority**: This is the core value proposition - transforming from a CLI-only tool to a reusable library enables automation, integration with other tools, and programmatic access to all spec-kit capabilities.

**Independent Test**: Can be fully tested by installing the package via pip and calling basic API methods to create a specification, demonstrating that the library provides programmatic access.

**Acceptance Scenarios**:

1. **Given** a Python 3.11+ environment, **When** a developer runs the package installation command, **Then** the package installs successfully with all required dependencies.
2. **Given** an installed spec-kit library, **When** a developer imports and instantiates the main class with a project path, **Then** the library initializes without errors and is ready for use.
3. **Given** an initialized spec-kit instance, **When** a developer calls the specification creation method with a feature description, **Then** the library returns a structured specification object.

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### User Story 2 - Universal LLM Provider Support (Priority: P1)

As a developer, I want to use spec-kit with any LLM provider (OpenAI, Anthropic, Google, local Ollama, Groq, DeepSeek, etc.) so that I can choose the best model for my needs without being locked into a single vendor.

**Why this priority**: Universal LLM support removes vendor lock-in and enables cost optimization, privacy (local models), and flexibility. This is essential for enterprise adoption and developer choice.

**Independent Test**: Can be tested by configuring the library with different LLM provider credentials and generating a specification with each, verifying consistent output format regardless of provider.

**Acceptance Scenarios**:

1. **Given** spec-kit configured with OpenAI credentials, **When** a developer generates a specification, **Then** the system uses OpenAI models and returns a valid specification.
2. **Given** spec-kit configured for a local Ollama instance, **When** a developer generates a specification, **Then** the system uses the local model without external API calls.
3. **Given** spec-kit configured with an invalid or unavailable primary model, **When** a developer attempts an operation, **Then** the system automatically falls back to configured alternative models.
4. **Given** spec-kit with no explicit configuration, **When** a developer uses the library, **Then** sensible defaults are applied with clear error messages if required credentials are missing.

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### User Story 3 - Complete Specification Workflow (Priority: P1)

As a developer, I want to execute the full spec-driven development workflow (Constitution, Specify, Clarify, Plan, Tasks, Implement) through the library API to maintain the proven methodology while gaining automation capabilities.

**Why this priority**: Preserving the original workflow ensures that the transformation adds value (automation) without losing the methodology that makes spec-kit effective.

**Independent Test**: Can be tested by programmatically executing each workflow phase in sequence and verifying that outputs from each phase correctly feed into the next.

**Acceptance Scenarios**:

1. **Given** an initialized project, **When** a developer creates a constitution, **Then** the system generates project principles and guidelines in a consistent format.
2. **Given** a project constitution, **When** a developer creates a feature specification, **Then** the system generates structured requirements based on the constitution.
3. **Given** a feature specification, **When** a developer runs the clarify phase, **Then** the system identifies ambiguities and suggests clarification questions.
4. **Given** a clarified specification, **When** a developer generates a technical plan, **Then** the system produces an architecture overview compatible with the specification.
5. **Given** a technical plan, **When** a developer generates tasks, **Then** the system creates ordered, dependency-aware implementation tasks.
6. **Given** generated tasks, **When** a developer executes implementation, **Then** the system tracks task progress and coordinates execution.

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### User Story 4 - Command-Line Interface (Priority: P2)

As a developer who prefers terminal workflows, I want a modern CLI with rich output formatting so that I can use spec-kit interactively with clear visual feedback.

**Why this priority**: While the library API is primary, many developers prefer CLI tools for quick interactions. A well-designed CLI makes the tool accessible to developers who don't want to write integration code.

**Independent Test**: Can be tested by running CLI commands for each workflow phase and verifying that output is formatted correctly and all options work as documented.

**Acceptance Scenarios**:

1. **Given** spec-kit installed, **When** a developer runs the help command, **Then** all available commands and options are displayed with clear descriptions.
2. **Given** a project directory, **When** a developer runs initialization via CLI, **Then** the project is set up with proper directory structure and configuration files.
3. **Given** an initialized project, **When** a developer runs specification generation via CLI, **Then** progress is displayed with rich formatting and the result is saved to the appropriate file.
4. **Given** any CLI command, **When** an error occurs, **Then** a clear, actionable error message is displayed with suggested remediation.

---

### User Story 5 - MCP Server Integration (Priority: P2)

As a developer using Claude Desktop, Cursor, or other MCP-compatible tools, I want to access spec-kit functionality through the Model Context Protocol so that I can use it within my preferred AI assistant environment.

**Why this priority**: MCP integration extends spec-kit's reach to popular AI coding assistants, making it accessible without requiring developers to leave their workflow.

**Independent Test**: Can be tested by starting the MCP server and connecting from an MCP client, verifying that all spec-kit tools are available and functional.

**Acceptance Scenarios**:

1. **Given** spec-kit installed with MCP dependencies, **When** a developer starts the MCP server, **Then** the server starts successfully and listens for connections.
2. **Given** a running MCP server, **When** an MCP client connects, **Then** all spec-kit tools are exposed with proper descriptions and parameter schemas.
3. **Given** an MCP client connected to spec-kit, **When** a user invokes a specification tool, **Then** the tool executes and returns results in the expected MCP format.

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### User Story 6 - Structured Data Outputs (Priority: P2)

As a developer integrating spec-kit into automated pipelines, I want all outputs to be validated structured data so that I can reliably parse and process results programmatically.

**Why this priority**: Structured outputs enable reliable automation, reduce parsing errors, and provide clear data contracts for integrations.

**Independent Test**: Can be tested by generating specifications and verifying that all outputs conform to defined schemas and can be serialized/deserialized without data loss.

**Acceptance Scenarios**:

1. **Given** any spec-kit operation, **When** the operation completes, **Then** the result is a validated data structure conforming to a defined schema.
2. **Given** a structured output, **When** a developer serializes it to JSON, **Then** the JSON is valid and all fields are present.
3. **Given** a structured specification, **When** a developer requests Markdown export, **Then** the system generates human-readable Markdown preserving all information.

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### User Story 7 - Configuration Management (Priority: P3)

As a developer working on multiple projects, I want flexible configuration options (environment variables, config files, programmatic settings) so that I can manage different setups without code changes.

**Why this priority**: Configuration flexibility is a quality-of-life feature that reduces friction when working across different projects or environments.

**Independent Test**: Can be tested by applying configurations via each method (env vars, files, code) and verifying that settings are correctly applied with proper precedence.

**Acceptance Scenarios**:

1. **Given** environment variables set for LLM configuration, **When** spec-kit initializes, **Then** those values are used without requiring explicit configuration.
2. **Given** a project with a configuration file, **When** spec-kit initializes in that project, **Then** project-specific settings override defaults.
3. **Given** programmatic configuration passed to the library, **When** spec-kit initializes, **Then** programmatic settings take highest precedence.

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### User Story 8 - File-Based Artifact Storage (Priority: P3)

As a developer, I want all generated artifacts (specifications, plans, tasks) stored as human-readable files so that I can version control them, review diffs, and edit manually if needed.

**Why this priority**: File-based storage maintains compatibility with the existing spec-kit approach and integrates naturally with Git workflows.

**Independent Test**: Can be tested by generating artifacts and verifying they are saved as readable files in expected locations with correct formatting.

**Acceptance Scenarios**:

1. **Given** a specification generation request, **When** generation completes, **Then** the specification is saved as a Markdown file in the designated specs directory.
2. **Given** multiple workflow phases executed, **When** checking the project directory, **Then** each artifact is stored in its appropriate subdirectory with consistent naming.
3. **Given** a previously generated artifact, **When** regenerating for the same feature, **Then** versioning or backup is applied to preserve history.

---

### Edge Cases

- What happens when the configured LLM provider is unavailable or rate-limited?
  - System should retry with exponential backoff, then fall back to alternative models if configured.
- How does the system handle interrupted operations (network failure, process kill)?
  - Partial results should be preserved where possible, with clear status indicators.
- What happens when a user provides an empty or extremely vague feature description?
  - System should return a validation error with guidance on minimum requirements.
- How does the system behave when project configuration is corrupted?
  - System should detect corruption, warn the user, and offer to reset to defaults.
- What happens when LLM returns malformed or unexpected output?
  - System should validate outputs against schemas and retry with adjusted prompts if validation fails.

## Requirements *(mandatory)*

### Functional Requirements

- **FR-001**: System MUST provide a Python API that can be imported and used programmatically in other projects.
- **FR-002**: System MUST support multiple LLM providers through a unified interface (OpenAI, Anthropic, Google, Ollama, Groq, DeepSeek, and others via compatible adapters).
- **FR-003**: System MUST implement all phases of the spec-driven workflow: Constitution, Specification, Clarification, Planning, Task Generation, and Implementation tracking.
- **FR-004**: System MUST provide a command-line interface with rich formatting for interactive use.
- **FR-005**: System MUST expose functionality via MCP (Model Context Protocol) for integration with compatible AI assistants.
- **FR-006**: System MUST validate all LLM outputs against defined schemas to ensure data integrity.
- **FR-007**: System MUST support configuration via environment variables, configuration files, and programmatic settings with clear precedence rules.
- **FR-008**: System MUST persist all generated artifacts as human-readable files (Markdown format preferred for documentation artifacts).
- **FR-009**: System MUST handle LLM provider failures gracefully with retry logic and fallback options.
- **FR-010**: System MUST provide clear error messages with actionable guidance for all failure scenarios.
- **FR-011**: System MUST support both synchronous and asynchronous operation modes for the Python API.
- **FR-012**: System MUST allow custom prompt templates for organizations with specific documentation standards.
- **FR-013**: System MUST preserve backward compatibility with existing spec-kit Markdown artifacts (specifications, plans, tasks).
- **FR-014**: System MUST support streaming responses for long-running LLM operations to provide real-time feedback.

### Key Entities

- **Project**: Represents a software project being developed with spec-kit. Contains configuration, constitution, and feature specifications. Related to Features and Constitution.
- **Constitution**: Project-level principles, quality standards, and governance rules. Guides all feature specifications. One per Project.
- **Feature**: A discrete unit of functionality being specified and implemented. Contains specification, plan, and tasks. Belongs to a Project.
- **Specification**: Functional requirements, user stories, and success criteria for a Feature. Input for planning phase.
- **TechnicalPlan**: Architecture decisions, component design, and file structure for implementing a Feature. Derived from Specification.
- **Task**: An atomic unit of implementation work with dependencies, file paths, and validation criteria. Organized in phases (setup, tests, core, integration, polish).
- **LLMProvider**: Abstraction over different LLM services. Configured per Project or globally. Supports fallback chains.
- **Artifact**: Any file generated by the system (specs, plans, tasks, constitution). Versioned and stored in project directory.

## Success Criteria *(mandatory)*

### Measurable Outcomes

- **SC-001**: Developers can install and start using the library within 5 minutes, including first successful API call.
- **SC-002**: Library supports at least 10 different LLM providers without code changes (configuration only).
- **SC-003**: 100% of workflow phases (Constitution, Specify, Clarify, Plan, Tasks, Implement) are accessible via both Python API and CLI.
- **SC-004**: All generated outputs conform to documented schemas, with 0% parsing failures in normal operation.
- **SC-005**: System successfully recovers from transient LLM failures (timeouts, rate limits) in 95% of cases via retry/fallback.
- **SC-006**: CLI commands complete with user feedback (progress indicators, success/error messages) for all operations.
- **SC-007**: MCP server exposes all core tools and handles 100% of valid tool invocations successfully.
- **SC-008**: Generated Markdown artifacts are human-readable and render correctly in standard Markdown viewers.
- **SC-009**: Configuration changes take effect without code modifications for LLM provider, model selection, and output preferences.
- **SC-010**: Existing spec-kit users can migrate projects with zero data loss and minimal reconfiguration.

## Assumptions

- Python 3.11 or higher is available in target environments (required for modern type hints and performance).
- Developers have access to at least one LLM provider (cloud or local) for AI-powered features.
- Projects using spec-kit will follow Git-based version control workflows.
- MCP integration requires users to have MCP-compatible clients (Claude Desktop, Cursor, etc.) installed separately.
- Internet connectivity is available for cloud LLM providers; local providers (Ollama) require appropriate setup.
- The target audience has basic familiarity with Python package management (pip, virtual environments).

## Constraints

- Must maintain backward compatibility with existing spec-kit Markdown file formats.
- Must not require modifications to existing specs directories when upgrading from previous versions.
- Python package must work offline when configured with local LLM providers.
- No sensitive data (API keys, credentials) may be stored in generated artifacts or logs.
- Package size should remain reasonable for quick installations (minimize heavy dependencies).

## Out of Scope

- GUI or web interface (CLI and programmatic API only for this version).
- Direct code generation or execution beyond task tracking (implementation phase is for coordination, not automated coding).
- Built-in Git operations (users handle version control separately).
- Multi-user collaboration features (single-developer workflow focus).
- Billing or usage tracking for LLM API calls (users manage their own API usage).
