sunwaee 1.1.4

SUNWÆE CLI - The Almost Everything CLI.

Quick Commands

pip install -e .
sunwaee gen                    # Interactive AI chat
sunwaee notes create ...       # CRUD on notes
sunwaee tasks create ...       # CRUD on tasks

Testing

pytest                         # All unit/integration tests (mocked, no API keys needed)
pytest -m live                 # Live tests against real provider APIs (API keys required)
pytest -m "not live"           # Explicit unit-only run (same as plain pytest)

Unit tests (tests/) use mocked HTTP clients — no API keys needed, safe for CI.

Live tests (tests/gen/engine/live/) call real provider APIs with the cheapest model per provider and assert that every Response field is correctly populated — including cost, usage, and performance. Results are saved to tests/gen/engine/live/run/ as JSON for inspection.

API keys are read from environment variables: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, XAI_API_KEY. A test is skipped (not failed) when its key is absent.

Directory Structure

cli/sunwaee/
├── cli.py                        # Entry point; auto-discovers modules via Typer
├── config.py                     # TOML config management + env var overrides
├── core/
│   ├── audit.py                  # Append-only JSONL audit trail
│   ├── editor.py                 # $EDITOR integration
│   ├── fs.py                     # Markdown + YAML frontmatter I/O
│   ├── git.py                    # Workspace git init + auto-commit on mutations
│   ├── logger.py                 # Structured logging
│   ├── output.py                 # JSON/rich output formatter (human vs api/sun mode)
│   └── tools.py                  # @tool() decorator + ok()/err() result helpers
└── modules/
    ├── workspaces/               # Workspace CRUD
    ├── projects/                 # Project grouping within workspaces
    ├── notes/                    # Note CRUD + full-text search
    ├── tasks/                    # Task CRUD + subtasks + filtering
    ├── logs/                     # Audit log browsing
    ├── shell/                    # Shell command execution tool (for AI agent)
    └── gen/                      # AI agent (Sun)
        ├── agent.py              # ReAct loop implementation
        ├── session.py            # Session persistence (JSONL)
        ├── tools.py              # Aggregates all module tools for the agent
        ├── commands/             # CLI subcommands (gen, models, sessions, set-model)
        ├── engine/               # ← LLM abstraction layer (also used by api/)
        │   ├── base.py
        │   ├── factory.py
        │   ├── types.py
        │   ├── model.py
        │   ├── models/           # Model definitions per provider
        │   └── providers/        # AnthropicEngine, OpenAIEngine, GoogleEngine
        └── repl/                 # Interactive REPL (Rich + prompt_toolkit)
            ├── loop.py
            ├── input.py          # Tab completion
            └── display.py        # Formatted output

Commands

sunwaee init
sunwaee workspaces {create,list,read,update,delete}
sunwaee projects {create,list,read,update,delete}
sunwaee notes {create,list,read,update,delete}
sunwaee tasks {create,list,read,update,delete}
sunwaee logs {list,read}
sunwaee gen [--session ID] [--model MODEL]
sunwaee gen models list
sunwaee gen sessions list
sunwaee gen set-model

Module auto-discovery: cli.py scans modules/ for directories with app = typer.Typer() and registers them automatically — no changes needed to the core to add a module.

Data Storage

All data is file-based (no HTTP calls to api/ for CRUD):

• Content: ~/sunwaee/workspaces/{workspace}/{module}/{slug}.md (Markdown + YAML frontmatter)
• Sessions: ~/sunwaee/workspaces/{workspace}/gen/sessions/{id}.jsonl (append-only)
• Audit logs: ~/sunwaee/workspaces/{workspace}/logs/{year}/{month}/{day}/logs.jsonl
• Config: ~/.sunwaee/config.toml
• Each workspace is a git repository; every mutation is auto-committed

Output Modes

Controlled by SUNWAEE_CALLER env var:

• human: Rich colored terminal output (default)
• api or sun: JSON responses

JSON format:

{"ok": true, "data": {...}}
{"ok": false, "error": "message", "code": "NOT_FOUND"}

Error codes: NOT_FOUND, ALREADY_EXISTS, VALIDATION_ERROR, IO_ERROR, CONFIRMATION_REQUIRED

Module Pattern

Each module follows this structure:

# model.py
@dataclass
class Note:
    id: str = field(default_factory=_uuid)
    title: str = ""
    body: str = ""
    created_at: str = field(default_factory=_now)
    updated_at: str = field(default_factory=_now)

# tools.py — exposed to AI agent
@tool("Create, list, read, update or delete notes")
def notes(action: Literal["create","list","read","update","delete"], title: str = "", ...) -> str:
    # Uses core/fs, core/git, core/audit
    return ok(data) or err(message)

AI Agent ReAct Loop (modules/gen/agent.py)

async def stream_run(messages, tools, engine) -> AsyncIterator[Response]:
    for _ in range(10):
        async for response in engine.stream(messages, tools):
            yield response

        if response.stop_reason != StopReason.TOOL_USE or not response.tool_calls:
            break

        # Execute all tool calls concurrently
        results = await asyncio.gather(*[tc.fn(**tc.arguments) for tc in response.tool_calls])
        # Append tool results to messages, continue loop

Tools available to the agent: notes, tasks, workspaces, projects, shell.

---

LLM Engine Layer (cli/sunwaee/modules/gen/engine/)

This code is installed as the sunwaee PyPI package. Both api/ and cli/ import it via:

from sunwaee.modules.gen.engine import get_engine
from sunwaee.modules.gen.engine.types import Message, Tool, Response, ...

Types (engine/types.py)

class Role(Enum): SYSTEM, USER, ASSISTANT, TOOL
class StopReason(Enum): END_TURN, TOOL_USE, MAX_TOKENS

@dataclass
class Message:
    role: Role
    content: str | None = None
    reasoning_content: str | None = None     # Extended thinking / chain of thought
    reasoning_signature: str | None = None   # Anthropic: signature; Google: thoughtSignature
    tool_call_id: str | None = None          # For TOOL role messages
    tool_calls: list[ToolCall] | None = None # For ASSISTANT messages

@dataclass
class Tool:
    name: str
    description: str
    parameters: dict         # JSON Schema
    fn: Callable | None = None

@dataclass
class ToolCall:
    id: str
    name: str
    arguments: dict
    error: str | None = None
    duration: float = 0.0
    results: list[dict] | None = None

@dataclass
class Response:
    provider: str
    model: str
    streaming: bool = False
    synthetic: bool = False      # Sentinel messages (e.g., "reasoning in progress" for OpenAI)
    content: str | None = None
    reasoning_content: str | None = None
    reasoning_signature: str | None = None
    tool_calls: list[ToolCall] | None = None
    stop_reason: StopReason | None = None
    error: Error | None = None
    cost: Cost | None = None
    performance: Performance | None = None
    usage: Usage | None = None

@dataclass
class Usage:
    input_tokens: int = 0
    output_tokens: int = 0
    total_tokens: int = 0
    cache_read_tokens: int = 0
    cache_write_tokens: int = 0

@dataclass
class Cost:
    input: float = 0.0; output: float = 0.0
    cache_read: float = 0.0; cache_write: float = 0.0
    total: float = 0.0

@dataclass
class Performance:
    latency: float = 0.0            # Time to first chunk
    reasoning_duration: float = 0.0
    content_duration: float = 0.0
    total_duration: float = 0.0
    throughput: int = 0             # tokens/second

Factory (engine/factory.py)

def get_engine(
    provider: str,
    model: str,
    api_key: str | None = None,        # Falls back to {PROVIDER}_API_KEY env var
    max_tokens: int = 8192,
    thinking_budget: int | None = None, # For Anthropic/Google extended thinking
    reasoning_effort: str | None = None # For OpenAI: "low" | "medium" | "high"
) -> BaseEngine:

Provider routing:

• "anthropic" → AnthropicEngine (api.anthropic.com)
• "openai" → OpenAIEngine (api.openai.com/v1)
• "deepseek" → OpenAIEngine (api.deepseek.com/v1)
• "moonshot" → OpenAIEngine (api.moonshot.ai/v1)
• "xai" → OpenAIEngine (api.x.ai/v1)
• "google" → GoogleEngine

Engine Interface

class BaseEngine(ABC):
    @abstractmethod
    async def chat(self, messages: list[Message], tools: list[Tool] | None = None) -> Response: ...

    @abstractmethod
    async def stream(self, messages: list[Message], tools: list[Tool] | None = None) -> AsyncIterator[Response]: ...

Each chunk yielded during stream() carries incremental content or reasoning_content. The final chunk carries accumulated usage, cost, performance, and stop_reason.

Cost is computed automatically inside each engine (via compute_cost from engine/model.py) whenever usage is available. Both chat() and the final streaming chunk always set response.cost when the model is listed in the local registry.

Provider-specific notes:

• Anthropic: system prompt passed separately; supports extended thinking (thinking_budget); reasoning_signature in response
• OpenAI: system prompt included in messages; supports reasoning_effort; emits synthetic "reasoning in progress" Response for reasoning models
• Google: system prompt in systemInstruction; thinking via thinkingConfig; no tool call IDs (uses function name as ID)
• DeepSeek / Moonshot / xAI: use OpenAI-compatible endpoint via OpenAIEngine with custom base URL