Index

core

Core module — registries, types, configuration, and event bus.

Classes

AgentRegistry

Bases: RegistryBase[Type['BaseAgent']]

Registry for agent implementations.

EngineRegistry

Bases: RegistryBase[Type['InferenceEngine']]

Registry for inference engine backends.

MemoryRegistry

Bases: RegistryBase[Type['MemoryBackend']]

Registry for memory / retrieval backends.

ModelRegistry

Bases: RegistryBase[Any]

Registry for ModelSpec objects.

ToolRegistry

Bases: RegistryBase[Any]

Registry for tool specifications.

Conversation dataclass

Conversation(messages: List[Message] = list(), max_messages: Optional[int] = None)

Ordered list of messages with an optional sliding-window cap.

Functions

add

add(message: Message) -> None

Append a message, trimming oldest if max_messages is set.

Source code in src/openjarvis/core/types.py

def add(self, message: Message) -> None:
    """Append a message, trimming oldest if *max_messages* is set."""
    self.messages.append(message)
    if self.max_messages is not None and len(self.messages) > self.max_messages:
        self.messages = self.messages[-self.max_messages :]

window

window(n: int) -> List[Message]

Return the last n messages.

Source code in src/openjarvis/core/types.py

def window(self, n: int) -> List[Message]:
    """Return the last *n* messages."""
    if n <= 0:
        return []
    return self.messages[-n:]

Message dataclass

Message(role: Role, content: str = '', name: Optional[str] = None, tool_calls: Optional[List[ToolCall]] = None, tool_call_id: Optional[str] = None, metadata: Dict[str, Any] = dict())

A single chat message (OpenAI-compatible structure).

ModelSpec dataclass

ModelSpec(model_id: str, name: str, parameter_count_b: float, context_length: int, active_parameter_count_b: Optional[float] = None, quantization: Quantization = NONE, min_vram_gb: float = 0.0, supported_engines: Sequence[str] = (), provider: str = '', requires_api_key: bool = False, metadata: Dict[str, Any] = dict())

Metadata describing a language model.

Quantization

Bases: str, Enum

Model quantization formats.

Role

Bases: str, Enum

Chat message roles (OpenAI-compatible).

TelemetryRecord dataclass

TelemetryRecord(timestamp: float, model_id: str, prompt_tokens: int = 0, completion_tokens: int = 0, total_tokens: int = 0, latency_seconds: float = 0.0, ttft: float = 0.0, cost_usd: float = 0.0, energy_joules: float = 0.0, power_watts: float = 0.0, gpu_utilization_pct: float = 0.0, gpu_memory_used_gb: float = 0.0, gpu_temperature_c: float = 0.0, throughput_tok_per_sec: float = 0.0, energy_per_output_token_joules: float = 0.0, throughput_per_watt: float = 0.0, prefill_latency_seconds: float = 0.0, decode_latency_seconds: float = 0.0, prefill_energy_joules: float = 0.0, decode_energy_joules: float = 0.0, mean_itl_ms: float = 0.0, median_itl_ms: float = 0.0, p90_itl_ms: float = 0.0, p95_itl_ms: float = 0.0, p99_itl_ms: float = 0.0, std_itl_ms: float = 0.0, is_streaming: bool = False, engine: str = '', agent: str = '', energy_method: str = '', energy_vendor: str = '', batch_id: str = '', is_warmup: bool = False, cpu_energy_joules: float = 0.0, gpu_energy_joules: float = 0.0, dram_energy_joules: float = 0.0, tokens_per_joule: float = 0.0, metadata: Dict[str, Any] = dict())

Single telemetry observation recorded after an inference call.

ToolCall dataclass

ToolCall(id: str, name: str, arguments: str)

A single tool invocation request embedded in an assistant message.

ToolResult dataclass

ToolResult(tool_name: str, content: str, success: bool = True, usage: Dict[str, Any] = dict(), cost_usd: float = 0.0, latency_seconds: float = 0.0, metadata: Dict[str, Any] = dict())

Result returned by a tool invocation.