stats

Class info

Classes

Name	Children	Inherits
AggregatedMessageStats llmling_agent.talk.stats Statistics aggregated from multiple connections.	AggregatedTalkStats	object
AggregatedTalkStats llmling_agent.talk.stats Statistics aggregated from multiple connections.		AggregatedMessageStats
MessageStats llmling_agent.talk.stats Statistics for a single connection.	TalkStats	object
TalkStats llmling_agent.talk.stats Statistics for a single connection.		MessageStats

🛈 DocStrings

Manages message flow between agents/groups.

AggregatedMessageStats dataclass

Statistics aggregated from multiple connections.

Source code in src/llmling_agent/talk/stats.py

@dataclass(kw_only=True)
class AggregatedMessageStats:
    """Statistics aggregated from multiple connections."""

    stats: Sequence[MessageStats | AggregatedMessageStats] = field(default_factory=list)

    # def __init__(self, stats: list[TalkStats | AggregatedTalkStats]):
    #     self.stats = stats

    @property
    def messages(self) -> list[ChatMessage[Any]]:
        """All messages across all connections, flattened."""
        return [msg for stat in self.stats for msg in stat.messages]

    @property
    def message_count(self) -> int:
        """Total messages across all connections."""
        return len(self.messages)

    @property
    def tool_calls(self) -> list[ToolCallInfo]:
        """Accumulated tool calls going through this connection."""
        return [call for msg in self.messages for call in msg.tool_calls]

    @property
    def start_time(self) -> datetime:
        """Total messages across all connections."""
        return self.stats[0].start_time

    @property
    def num_connections(self) -> int:
        """Number of active connections."""
        return len(self.stats)

    @property
    def token_count(self) -> int:
        """Total tokens across all connections."""
        return sum(m.cost_info.token_usage["total"] for m in self.messages if m.cost_info)

    @property
    def total_cost(self) -> float:
        """Total cost in USD."""
        return sum(float(m.cost_info.total_cost) for m in self.messages if m.cost_info)

    @property
    def byte_count(self) -> int:
        """Total bytes transmitted."""
        return sum(len(str(msg.content).encode()) for msg in self.messages)

    @property
    def last_message_time(self) -> datetime | None:
        """Most recent message time."""
        if not self.messages:
            return None
        return max(msg.timestamp for msg in self.messages)

    def format(self, style: FormatStyle = "simple", **kwargs: Any) -> str:
        """Format all conversations in the team."""
        return "\n".join(msg.format(style, **kwargs) for msg in self.messages)

byte_count property

byte_count: int

Total bytes transmitted.

last_message_time property

last_message_time: datetime | None

Most recent message time.

message_count property

message_count: int

Total messages across all connections.

messages property

messages: list[ChatMessage[Any]]

All messages across all connections, flattened.

num_connections property

num_connections: int

Number of active connections.

start_time property

start_time: datetime

Total messages across all connections.

token_count property

token_count: int

Total tokens across all connections.

tool_calls property

tool_calls: list[ToolCallInfo]

Accumulated tool calls going through this connection.

total_cost property

total_cost: float

Total cost in USD.

format

format(style: FormatStyle = 'simple', **kwargs: Any) -> str

Format all conversations in the team.

Source code in src/llmling_agent/talk/stats.py

def format(self, style: FormatStyle = "simple", **kwargs: Any) -> str:
    """Format all conversations in the team."""
    return "\n".join(msg.format(style, **kwargs) for msg in self.messages)

AggregatedTalkStats dataclass

Bases: AggregatedMessageStats

Statistics aggregated from multiple connections.

Source code in src/llmling_agent/talk/stats.py

@dataclass(kw_only=True)
class AggregatedTalkStats(AggregatedMessageStats):
    """Statistics aggregated from multiple connections."""

    stats: Sequence[TalkStats | AggregatedTalkStats] = field(default_factory=list)

    @cached_property
    def source_names(self) -> set[str]:
        """Set of unique source names recursively."""

        def _collect_source_names(stat: TalkStats | AggregatedTalkStats) -> set[str]:
            """Recursively collect source names."""
            if isinstance(stat, TalkStats):
                return {stat.source_name} if stat.source_name else set()
            # It's a AggregatedTalkStats, recurse
            names = set()
            for s in stat.stats:
                names.update(_collect_source_names(s))
            return names

        names = set()
        for stat in self.stats:
            names.update(_collect_source_names(stat))
        return names

    @cached_property
    def target_names(self) -> set[str]:
        """Set of all target names across connections."""
        return {name for s in self.stats for name in s.target_names}

source_names cached property

source_names: set[str]

Set of unique source names recursively.

target_names cached property

target_names: set[str]

Set of all target names across connections.

MessageStats dataclass

Statistics for a single connection.

Source code in src/llmling_agent/talk/stats.py

@dataclass(frozen=True, kw_only=True)
class MessageStats:
    """Statistics for a single connection."""

    messages: MutableSequence[ChatMessage[Any]] = field(default_factory=list)
    start_time: datetime = field(default_factory=datetime.now)

    @property
    def message_count(self) -> int:
        """Number of messages transmitted."""
        return len(self.messages)

    @property
    def last_message_time(self) -> datetime | None:
        """When the last message was sent."""
        return self.messages[-1].timestamp if self.messages else None

    @property
    def token_count(self) -> int:
        """Total tokens used."""
        return sum(m.cost_info.token_usage["total"] for m in self.messages if m.cost_info)

    @property
    def tool_calls(self) -> list[ToolCallInfo]:
        """Accumulated tool calls going through this connection."""
        return [call for msg in self.messages for call in msg.tool_calls]

    @property
    def byte_count(self) -> int:
        """Total bytes transmitted."""
        return sum(len(str(msg.content).encode()) for msg in self.messages)

    def format(self, style: FormatStyle = "simple", **kwargs: Any) -> str:
        """Format the conversation that happened on this connection."""
        return "\n".join(msg.format(style, **kwargs) for msg in self.messages)

    @property
    def total_cost(self) -> float:
        """Total cost in USD."""
        return sum(float(m.cost_info.total_cost) for m in self.messages if m.cost_info)

byte_count property

byte_count: int

Total bytes transmitted.

last_message_time property

last_message_time: datetime | None

When the last message was sent.

message_count property

message_count: int

Number of messages transmitted.

token_count property

token_count: int

Total tokens used.

tool_calls property

tool_calls: list[ToolCallInfo]

Accumulated tool calls going through this connection.

total_cost property

total_cost: float

Total cost in USD.

format

format(style: FormatStyle = 'simple', **kwargs: Any) -> str

Format the conversation that happened on this connection.

Source code in src/llmling_agent/talk/stats.py

def format(self, style: FormatStyle = "simple", **kwargs: Any) -> str:
    """Format the conversation that happened on this connection."""
    return "\n".join(msg.format(style, **kwargs) for msg in self.messages)

TalkStats dataclass

Bases: MessageStats

Statistics for a single connection.

Source code in src/llmling_agent/talk/stats.py

@dataclass(frozen=True, kw_only=True)
class TalkStats(MessageStats):
    """Statistics for a single connection."""

    source_name: str | None
    target_names: set[str]