conversation

Class info

Classes

Name	Children	Inherits
ChatMessage llmling_agent.messaging.messages Common message format for all UI types.		Generic
ChatMessageContainer llmling_agent.messaging.message_container Container for tracking and managing chat messages.		EventedList
ConversationManager llmling_agent.agent.conversation Manages conversation state and system prompts.		object
SessionQuery llmling_agent_config.session Query configuration for session recovery.		Schema

🛈 DocStrings

Conversation management for LLMling agent.

ConversationManager

Manages conversation state and system prompts.

Source code in src/llmling_agent/agent/conversation.py

class ConversationManager:
    """Manages conversation state and system prompts."""

    @dataclass(frozen=True)
    class HistoryCleared:
        """Emitted when chat history is cleared."""

        session_id: str
        timestamp: datetime = field(default_factory=get_now)

    history_cleared = Signal(HistoryCleared)

    def __init__(
        self,
        agent: Agent[Any],
        session_config: MemoryConfig | None = None,
        *,
        resources: Sequence[Resource | PromptType | str] = (),
    ):
        """Initialize conversation manager.

        Args:
            agent: instance to manage
            session_config: Optional MemoryConfig
            resources: Optional paths to load as context
        """
        self._agent = agent
        self.chat_messages = ChatMessageContainer()
        self._last_messages: list[ChatMessage] = []
        self._pending_messages: deque[ChatMessage] = deque()
        self._config = session_config
        self._resources = list(resources)  # Store for async loading
        # Generate new ID if none provided
        self.id = str(uuid4())

        if session_config is not None and session_config.session is not None:
            storage = self._agent.context.storage
            self._current_history = storage.filter_messages_sync(session_config.session)
            if session_config.session.name:
                self.id = session_config.session.name

        # Note: max_messages and max_tokens will be handled in add_message/get_history
        # to maintain the rolling window during conversation

    def get_initialization_tasks(self) -> list[Coroutine[Any, Any, Any]]:
        """Get all initialization coroutines."""
        self._resources = []  # Clear so we dont load again on async init
        return [self.load_context_source(source) for source in self._resources]

    async def __aenter__(self) -> Self:
        """Initialize when used standalone."""
        if tasks := self.get_initialization_tasks():
            await asyncio.gather(*tasks)
        return self

    async def __aexit__(
        self,
        exc_type: type[BaseException] | None,
        exc_val: BaseException | None,
        exc_tb: TracebackType | None,
    ):
        """Clean up any pending messages."""
        self._pending_messages.clear()

    def __bool__(self) -> bool:
        return bool(self._pending_messages) or bool(self.chat_messages)

    def __repr__(self) -> str:
        return f"ConversationManager(id={self.id!r})"

    def __prompt__(self) -> str:
        if not self.chat_messages:
            return "No conversation history"

        last_msgs = self.chat_messages[-2:]
        parts = ["Recent conversation:"]
        parts.extend(msg.format() for msg in last_msgs)
        return "\n".join(parts)

    @overload
    def __getitem__(self, key: int) -> ChatMessage[Any]: ...

    @overload
    def __getitem__(self, key: slice | str) -> list[ChatMessage[Any]]: ...

    def __getitem__(
        self, key: int | slice | str
    ) -> ChatMessage[Any] | list[ChatMessage[Any]]:
        """Access conversation history.

        Args:
            key: Either:
                - Integer index for single message
                - Slice for message range
                - Agent name for conversation history with that agent
        """
        match key:
            case int():
                return self.chat_messages[key]
            case slice():
                return list(self.chat_messages[key])
            case str():
                query = SessionQuery(name=key)
                return self._agent.context.storage.filter_messages_sync(query=query)
            case _:
                msg = f"Invalid key type: {type(key)}"
                raise TypeError(msg)

    def __contains__(self, item: Any) -> bool:
        """Check if item is in history."""
        return item in self.chat_messages

    def __len__(self) -> int:
        """Get length of history."""
        return len(self.chat_messages)

    def get_message_tokens(self, message: ChatMessage) -> int:
        """Get token count for a single message."""
        content = "\n".join(message.format())
        return count_tokens(content, self._agent.model_name)

    async def format_history(
        self,
        *,
        max_tokens: int | None = None,
        include_system: bool = False,
        format_template: str | None = None,
        num_messages: int | None = None,  # Add this parameter
    ) -> str:
        """Format conversation history as a single context message.

        Args:
            max_tokens: Optional limit to include only last N tokens
            include_system: Whether to include system messages
            format_template: Optional custom format (defaults to agent/message pairs)
            num_messages: Optional limit to include only last N messages
        """
        template = format_template or "Agent {agent}: {content}\n"
        messages: list[str] = []
        token_count = 0

        # Get messages, optionally limited
        history: Sequence[ChatMessage[Any]] = self.chat_messages
        if num_messages:
            history = history[-num_messages:]

        if max_tokens:
            history = list(reversed(history))  # Start from newest when token limited

        for msg in history:
            # Check role directly from ChatMessage
            if not include_system and msg.role == "system":
                continue
            name = msg.name or msg.role.title()
            formatted = template.format(agent=name, content=str(msg.content))

            if max_tokens:
                # Count tokens in this message
                if msg.cost_info:
                    msg_tokens = msg.cost_info.token_usage["total"]
                else:
                    # Fallback to tiktoken if no cost info
                    msg_tokens = self.get_message_tokens(msg)

                if token_count + msg_tokens > max_tokens:
                    break
                token_count += msg_tokens
                # Add to front since we're going backwards
                messages.insert(0, formatted)
            else:
                messages.append(formatted)

        return "\n".join(messages)

    async def load_context_source(self, source: Resource | PromptType | str):
        """Load context from a single source."""
        try:
            match source:
                case str():
                    await self.add_context_from_path(source)
                case BaseResource():
                    await self.add_context_from_resource(source)
                case BasePrompt():
                    await self.add_context_from_prompt(source)
        except Exception:
            msg = "Failed to load context from %s"
            logger.exception(msg, "file" if isinstance(source, str) else source.type)

    def load_history_from_database(
        self,
        session: SessionIdType | SessionQuery = None,
        *,
        since: datetime | None = None,
        until: datetime | None = None,
        roles: set[MessageRole] | None = None,
        limit: int | None = None,
    ):
        """Load conversation history from database.

        Args:
            session: Session ID or query config
            since: Only include messages after this time (override)
            until: Only include messages before this time (override)
            roles: Only include messages with these roles (override)
            limit: Maximum number of messages to return (override)
        """
        storage = self._agent.context.storage
        match session:
            case SessionQuery() as query:
                # Override query params if provided
                if since is not None or until is not None or roles or limit:
                    update = {
                        "since": since.isoformat() if since else None,
                        "until": until.isoformat() if until else None,
                        "roles": roles,
                        "limit": limit,
                    }
                    query = query.model_copy(update=update)
                if query.name:
                    self.id = query.name
            case str() | UUID():
                self.id = str(session)
                query = SessionQuery(
                    name=self.id,
                    since=since.isoformat() if since else None,
                    until=until.isoformat() if until else None,
                    roles=roles,
                    limit=limit,
                )
            case None:
                # Use current session ID
                query = SessionQuery(
                    name=self.id,
                    since=since.isoformat() if since else None,
                    until=until.isoformat() if until else None,
                    roles=roles,
                    limit=limit,
                )
            case _:
                msg = f"Invalid type for session: {type(session)}"
                raise ValueError(msg)
        self.chat_messages.clear()
        self.chat_messages.extend(storage.filter_messages_sync(query))

    def get_history(
        self,
        include_pending: bool = True,
        do_filter: bool = True,
    ) -> list[ChatMessage]:
        """Get conversation history.

        Args:
            include_pending: Whether to include pending messages
            do_filter: Whether to apply memory config limits (max_tokens, max_messages)

        Returns:
            Filtered list of messages in chronological order
        """
        if include_pending and self._pending_messages:
            self.chat_messages.extend(self._pending_messages)
            self._pending_messages.clear()

        # 2. Start with original history
        history: Sequence[ChatMessage[Any]] = self.chat_messages

        # 3. Only filter if needed
        if do_filter and self._config:
            # First filter by message count (simple slice)
            if self._config.max_messages:
                history = history[-self._config.max_messages :]

            # Then filter by tokens if needed
            if self._config.max_tokens:
                token_count = 0
                filtered = []
                # Collect messages from newest to oldest until we hit the limit
                for msg in reversed(history):
                    msg_tokens = self.get_message_tokens(msg)
                    if token_count + msg_tokens > self._config.max_tokens:
                        break
                    token_count += msg_tokens
                    filtered.append(msg)
                history = list(reversed(filtered))

        return list(history)

    def get_pending_messages(self) -> list[ChatMessage]:
        """Get messages that will be included in next interaction."""
        return list(self._pending_messages)

    def clear_pending(self):
        """Clear pending messages without adding them to history."""
        self._pending_messages.clear()

    def set_history(self, history: list[ChatMessage]):
        """Update conversation history after run."""
        self.chat_messages.clear()
        self.chat_messages.extend(history)

    def clear(self):
        """Clear conversation history and prompts."""
        self.chat_messages = ChatMessageContainer()
        self._last_messages = []
        event = self.HistoryCleared(session_id=str(self.id))
        self.history_cleared.emit(event)

    @asynccontextmanager
    async def temporary_state(
        self,
        history: list[AnyPromptType] | SessionQuery | None = None,
        *,
        replace_history: bool = False,
    ) -> AsyncIterator[Self]:
        """Temporarily set conversation history.

        Args:
            history: Optional list of prompts to use as temporary history.
                    Can be strings, BasePrompts, or other prompt types.
            replace_history: If True, only use provided history. If False, append
                    to existing history.
        """
        from toprompt import to_prompt

        old_history = self.chat_messages.copy()

        try:
            messages: Sequence[ChatMessage[Any]] = ChatMessageContainer()
            if history is not None:
                if isinstance(history, SessionQuery):
                    messages = await self._agent.context.storage.filter_messages(history)
                else:
                    messages = [
                        ChatMessage(content=await to_prompt(p), role="user")
                        for p in history
                    ]

            if replace_history:
                self.chat_messages = ChatMessageContainer(messages)
            else:
                self.chat_messages.extend(messages)

            yield self

        finally:
            self.chat_messages = old_history

    def add_chat_messages(self, messages: Sequence[ChatMessage]):
        """Add new messages to history and update last_messages."""
        self._last_messages = list(messages)
        self.chat_messages.extend(messages)

    @property
    def last_run_messages(self) -> list[ChatMessage]:
        """Get messages from the last run converted to our format."""
        return self._last_messages

    def add_context_message(
        self,
        content: str,
        source: str | None = None,
        **metadata: Any,
    ):
        """Add a context message.

        Args:
            content: Text content to add
            source: Description of content source
            **metadata: Additional metadata to include with the message
        """
        meta_str = ""
        if metadata:
            meta_str = "\n".join(f"{k}: {v}" for k, v in metadata.items())
            meta_str = f"\nMetadata:\n{meta_str}\n"

        header = f"Content from {source}:" if source else "Additional context:"
        formatted = f"{header}{meta_str}\n{content}\n"

        chat_message = ChatMessage[str](
            content=formatted,
            role="user",
            name="user",
            model=self._agent.model_name,
            metadata=metadata,
            conversation_id="context",  # TODO: should probably allow DB field to be NULL
        )
        self._pending_messages.append(chat_message)
        # Emit as user message - will trigger logging through existing flow
        self._agent.message_received.emit(chat_message)

    async def add_context_from_path(
        self,
        path: StrPath,
        *,
        convert_to_md: bool = False,
        **metadata: Any,
    ):
        """Add file or URL content as context message.

        Args:
            path: Any UPath-supported path
            convert_to_md: Whether to convert content to markdown
            **metadata: Additional metadata to include with the message

        Raises:
            ValueError: If content cannot be loaded or converted
        """
        from upathtools import to_upath

        path_obj = to_upath(path)
        if convert_to_md:
            content = await self._agent.context.converter.convert_file(path)
            source = f"markdown:{path_obj.name}"
        else:
            content = await read_path(path)
            source = f"{path_obj.protocol}:{path_obj.name}"
        self.add_context_message(content, source=source, **metadata)

    async def add_context_from_resource(self, resource: Resource | str):
        """Add content from a LLMling resource."""
        if not self._agent.runtime:
            msg = "No runtime available"
            raise RuntimeError(msg)

        if isinstance(resource, str):
            content = await self._agent.runtime.load_resource(resource)
            self.add_context_message(
                str(content.content),
                source=f"Resource {resource}",
                mime_type=content.metadata.mime_type,
                **content.metadata.extra,
            )
        else:
            loader = self._agent.runtime._loader_registry.get_loader(resource)
            async for content in loader.load(resource):
                self.add_context_message(
                    str(content.content),
                    source=f"{resource.type}:{resource.uri}",
                    mime_type=content.metadata.mime_type,
                    **content.metadata.extra,
                )

    async def add_context_from_prompt(
        self,
        prompt: PromptType,
        metadata: dict[str, Any] | None = None,
        **kwargs: Any,
    ):
        """Add rendered prompt content as context message.

        Args:
            prompt: LLMling prompt (static, dynamic, or file-based)
            metadata: Additional metadata to include with the message
            kwargs: Optional kwargs for prompt formatting
        """
        try:
            # Format the prompt using LLMling's prompt system
            messages = await prompt.format(kwargs)
            # Extract text content from all messages
            content = "\n\n".join(msg.get_text_content() for msg in messages)

            self.add_context_message(
                content,
                source=f"prompt:{prompt.name or prompt.type}",
                prompt_args=kwargs,
                **(metadata or {}),
            )
        except Exception as e:
            msg = f"Failed to format prompt: {e}"
            raise ValueError(msg) from e

    def get_history_tokens(self) -> int:
        """Get token count for current history."""
        # Use cost_info if available
        return self.chat_messages.get_history_tokens(self._agent.model_name)

    def get_pending_tokens(self) -> int:
        """Get token count for pending messages."""
        text = "\n".join(msg.format() for msg in self._pending_messages)
        return count_tokens(text, self._agent.model_name)

last_run_messages property

last_run_messages: list[ChatMessage]

Get messages from the last run converted to our format.

HistoryCleared dataclass

Emitted when chat history is cleared.

Source code in src/llmling_agent/agent/conversation.py

@dataclass(frozen=True)
class HistoryCleared:
    """Emitted when chat history is cleared."""

    session_id: str
    timestamp: datetime = field(default_factory=get_now)

__aenter__ async

__aenter__() -> Self

Initialize when used standalone.

Source code in src/llmling_agent/agent/conversation.py

async def __aenter__(self) -> Self:
    """Initialize when used standalone."""
    if tasks := self.get_initialization_tasks():
        await asyncio.gather(*tasks)
    return self

__aexit__ async

__aexit__(
    exc_type: type[BaseException] | None,
    exc_val: BaseException | None,
    exc_tb: TracebackType | None,
)

Clean up any pending messages.

Source code in src/llmling_agent/agent/conversation.py

async def __aexit__(
    self,
    exc_type: type[BaseException] | None,
    exc_val: BaseException | None,
    exc_tb: TracebackType | None,
):
    """Clean up any pending messages."""
    self._pending_messages.clear()

__contains__

__contains__(item: Any) -> bool

Check if item is in history.

Source code in src/llmling_agent/agent/conversation.py

def __contains__(self, item: Any) -> bool:
    """Check if item is in history."""
    return item in self.chat_messages

__getitem__

__getitem__(key: int) -> ChatMessage[Any]

__getitem__(key: slice | str) -> list[ChatMessage[Any]]

__getitem__(key: int | slice | str) -> ChatMessage[Any] | list[ChatMessage[Any]]

Access conversation history.

Parameters:

Name	Type	Description	Default
key	int | slice | str	Either: - Integer index for single message - Slice for message range - Agent name for conversation history with that agent	required

Source code in src/llmling_agent/agent/conversation.py

def __getitem__(
    self, key: int | slice | str
) -> ChatMessage[Any] | list[ChatMessage[Any]]:
    """Access conversation history.

    Args:
        key: Either:
            - Integer index for single message
            - Slice for message range
            - Agent name for conversation history with that agent
    """
    match key:
        case int():
            return self.chat_messages[key]
        case slice():
            return list(self.chat_messages[key])
        case str():
            query = SessionQuery(name=key)
            return self._agent.context.storage.filter_messages_sync(query=query)
        case _:
            msg = f"Invalid key type: {type(key)}"
            raise TypeError(msg)

__init__

__init__(
    agent: Agent[Any],
    session_config: MemoryConfig | None = None,
    *,
    resources: Sequence[Resource | PromptType | str] = (),
)

Initialize conversation manager.

Parameters:

Name	Type	Description	Default
agent	Agent[Any]	instance to manage	required
session_config	MemoryConfig | None	Optional MemoryConfig	None
resources	Sequence[Resource | PromptType | str]	Optional paths to load as context	()

Source code in src/llmling_agent/agent/conversation.py

def __init__(
    self,
    agent: Agent[Any],
    session_config: MemoryConfig | None = None,
    *,
    resources: Sequence[Resource | PromptType | str] = (),
):
    """Initialize conversation manager.

    Args:
        agent: instance to manage
        session_config: Optional MemoryConfig
        resources: Optional paths to load as context
    """
    self._agent = agent
    self.chat_messages = ChatMessageContainer()
    self._last_messages: list[ChatMessage] = []
    self._pending_messages: deque[ChatMessage] = deque()
    self._config = session_config
    self._resources = list(resources)  # Store for async loading
    # Generate new ID if none provided
    self.id = str(uuid4())

    if session_config is not None and session_config.session is not None:
        storage = self._agent.context.storage
        self._current_history = storage.filter_messages_sync(session_config.session)
        if session_config.session.name:
            self.id = session_config.session.name

__len__

__len__() -> int

Get length of history.

Source code in src/llmling_agent/agent/conversation.py

def __len__(self) -> int:
    """Get length of history."""
    return len(self.chat_messages)

add_chat_messages

add_chat_messages(messages: Sequence[ChatMessage])

Add new messages to history and update last_messages.

Source code in src/llmling_agent/agent/conversation.py

def add_chat_messages(self, messages: Sequence[ChatMessage]):
    """Add new messages to history and update last_messages."""
    self._last_messages = list(messages)
    self.chat_messages.extend(messages)

add_context_from_path async

add_context_from_path(path: StrPath, *, convert_to_md: bool = False, **metadata: Any)

Add file or URL content as context message.

Parameters:

Name	Type	Description	Default
path	StrPath	Any UPath-supported path	required
convert_to_md	bool	Whether to convert content to markdown	False
**metadata	Any	Additional metadata to include with the message	{}

Raises:

Type	Description
ValueError	If content cannot be loaded or converted

Source code in src/llmling_agent/agent/conversation.py

async def add_context_from_path(
    self,
    path: StrPath,
    *,
    convert_to_md: bool = False,
    **metadata: Any,
):
    """Add file or URL content as context message.

    Args:
        path: Any UPath-supported path
        convert_to_md: Whether to convert content to markdown
        **metadata: Additional metadata to include with the message

    Raises:
        ValueError: If content cannot be loaded or converted
    """
    from upathtools import to_upath

    path_obj = to_upath(path)
    if convert_to_md:
        content = await self._agent.context.converter.convert_file(path)
        source = f"markdown:{path_obj.name}"
    else:
        content = await read_path(path)
        source = f"{path_obj.protocol}:{path_obj.name}"
    self.add_context_message(content, source=source, **metadata)

add_context_from_prompt async

add_context_from_prompt(
    prompt: PromptType, metadata: dict[str, Any] | None = None, **kwargs: Any
)

Add rendered prompt content as context message.

Parameters:

Name	Type	Description	Default
prompt	PromptType	LLMling prompt (static, dynamic, or file-based)	required
metadata	dict[str, Any] | None	Additional metadata to include with the message	None
kwargs	Any	Optional kwargs for prompt formatting	{}

Source code in src/llmling_agent/agent/conversation.py

async def add_context_from_prompt(
    self,
    prompt: PromptType,
    metadata: dict[str, Any] | None = None,
    **kwargs: Any,
):
    """Add rendered prompt content as context message.

    Args:
        prompt: LLMling prompt (static, dynamic, or file-based)
        metadata: Additional metadata to include with the message
        kwargs: Optional kwargs for prompt formatting
    """
    try:
        # Format the prompt using LLMling's prompt system
        messages = await prompt.format(kwargs)
        # Extract text content from all messages
        content = "\n\n".join(msg.get_text_content() for msg in messages)

        self.add_context_message(
            content,
            source=f"prompt:{prompt.name or prompt.type}",
            prompt_args=kwargs,
            **(metadata or {}),
        )
    except Exception as e:
        msg = f"Failed to format prompt: {e}"
        raise ValueError(msg) from e

add_context_from_resource async

add_context_from_resource(resource: Resource | str)

Add content from a LLMling resource.

Source code in src/llmling_agent/agent/conversation.py

async def add_context_from_resource(self, resource: Resource | str):
    """Add content from a LLMling resource."""
    if not self._agent.runtime:
        msg = "No runtime available"
        raise RuntimeError(msg)

    if isinstance(resource, str):
        content = await self._agent.runtime.load_resource(resource)
        self.add_context_message(
            str(content.content),
            source=f"Resource {resource}",
            mime_type=content.metadata.mime_type,
            **content.metadata.extra,
        )
    else:
        loader = self._agent.runtime._loader_registry.get_loader(resource)
        async for content in loader.load(resource):
            self.add_context_message(
                str(content.content),
                source=f"{resource.type}:{resource.uri}",
                mime_type=content.metadata.mime_type,
                **content.metadata.extra,
            )

add_context_message

add_context_message(content: str, source: str | None = None, **metadata: Any)

Add a context message.

Parameters:

Name	Type	Description	Default
content	str	Text content to add	required
source	str | None	Description of content source	None
**metadata	Any	Additional metadata to include with the message	{}

Source code in src/llmling_agent/agent/conversation.py

def add_context_message(
    self,
    content: str,
    source: str | None = None,
    **metadata: Any,
):
    """Add a context message.

    Args:
        content: Text content to add
        source: Description of content source
        **metadata: Additional metadata to include with the message
    """
    meta_str = ""
    if metadata:
        meta_str = "\n".join(f"{k}: {v}" for k, v in metadata.items())
        meta_str = f"\nMetadata:\n{meta_str}\n"

    header = f"Content from {source}:" if source else "Additional context:"
    formatted = f"{header}{meta_str}\n{content}\n"

    chat_message = ChatMessage[str](
        content=formatted,
        role="user",
        name="user",
        model=self._agent.model_name,
        metadata=metadata,
        conversation_id="context",  # TODO: should probably allow DB field to be NULL
    )
    self._pending_messages.append(chat_message)
    # Emit as user message - will trigger logging through existing flow
    self._agent.message_received.emit(chat_message)

clear

clear()

Clear conversation history and prompts.

Source code in src/llmling_agent/agent/conversation.py

def clear(self):
    """Clear conversation history and prompts."""
    self.chat_messages = ChatMessageContainer()
    self._last_messages = []
    event = self.HistoryCleared(session_id=str(self.id))
    self.history_cleared.emit(event)

clear_pending

clear_pending()

Clear pending messages without adding them to history.

Source code in src/llmling_agent/agent/conversation.py

def clear_pending(self):
    """Clear pending messages without adding them to history."""
    self._pending_messages.clear()

format_history async

format_history(
    *,
    max_tokens: int | None = None,
    include_system: bool = False,
    format_template: str | None = None,
    num_messages: int | None = None,
) -> str

Format conversation history as a single context message.

Parameters:

Name	Type	Description	Default
max_tokens	int | None	Optional limit to include only last N tokens	None
include_system	bool	Whether to include system messages	False
format_template	str | None	Optional custom format (defaults to agent/message pairs)	None
num_messages	int | None	Optional limit to include only last N messages	None

Source code in src/llmling_agent/agent/conversation.py

async def format_history(
    self,
    *,
    max_tokens: int | None = None,
    include_system: bool = False,
    format_template: str | None = None,
    num_messages: int | None = None,  # Add this parameter
) -> str:
    """Format conversation history as a single context message.

    Args:
        max_tokens: Optional limit to include only last N tokens
        include_system: Whether to include system messages
        format_template: Optional custom format (defaults to agent/message pairs)
        num_messages: Optional limit to include only last N messages
    """
    template = format_template or "Agent {agent}: {content}\n"
    messages: list[str] = []
    token_count = 0

    # Get messages, optionally limited
    history: Sequence[ChatMessage[Any]] = self.chat_messages
    if num_messages:
        history = history[-num_messages:]

    if max_tokens:
        history = list(reversed(history))  # Start from newest when token limited

    for msg in history:
        # Check role directly from ChatMessage
        if not include_system and msg.role == "system":
            continue
        name = msg.name or msg.role.title()
        formatted = template.format(agent=name, content=str(msg.content))

        if max_tokens:
            # Count tokens in this message
            if msg.cost_info:
                msg_tokens = msg.cost_info.token_usage["total"]
            else:
                # Fallback to tiktoken if no cost info
                msg_tokens = self.get_message_tokens(msg)

            if token_count + msg_tokens > max_tokens:
                break
            token_count += msg_tokens
            # Add to front since we're going backwards
            messages.insert(0, formatted)
        else:
            messages.append(formatted)

    return "\n".join(messages)

get_history

get_history(include_pending: bool = True, do_filter: bool = True) -> list[ChatMessage]

Get conversation history.

Parameters:

Name	Type	Description	Default
include_pending	bool	Whether to include pending messages	True
do_filter	bool	Whether to apply memory config limits (max_tokens, max_messages)	True

Returns:

Type	Description
list[ChatMessage]	Filtered list of messages in chronological order

Source code in src/llmling_agent/agent/conversation.py

def get_history(
    self,
    include_pending: bool = True,
    do_filter: bool = True,
) -> list[ChatMessage]:
    """Get conversation history.

    Args:
        include_pending: Whether to include pending messages
        do_filter: Whether to apply memory config limits (max_tokens, max_messages)

    Returns:
        Filtered list of messages in chronological order
    """
    if include_pending and self._pending_messages:
        self.chat_messages.extend(self._pending_messages)
        self._pending_messages.clear()

    # 2. Start with original history
    history: Sequence[ChatMessage[Any]] = self.chat_messages

    # 3. Only filter if needed
    if do_filter and self._config:
        # First filter by message count (simple slice)
        if self._config.max_messages:
            history = history[-self._config.max_messages :]

        # Then filter by tokens if needed
        if self._config.max_tokens:
            token_count = 0
            filtered = []
            # Collect messages from newest to oldest until we hit the limit
            for msg in reversed(history):
                msg_tokens = self.get_message_tokens(msg)
                if token_count + msg_tokens > self._config.max_tokens:
                    break
                token_count += msg_tokens
                filtered.append(msg)
            history = list(reversed(filtered))

    return list(history)

get_history_tokens

get_history_tokens() -> int

Get token count for current history.

Source code in src/llmling_agent/agent/conversation.py

def get_history_tokens(self) -> int:
    """Get token count for current history."""
    # Use cost_info if available
    return self.chat_messages.get_history_tokens(self._agent.model_name)

get_initialization_tasks

get_initialization_tasks() -> list[Coroutine[Any, Any, Any]]

Get all initialization coroutines.

Source code in src/llmling_agent/agent/conversation.py

def get_initialization_tasks(self) -> list[Coroutine[Any, Any, Any]]:
    """Get all initialization coroutines."""
    self._resources = []  # Clear so we dont load again on async init
    return [self.load_context_source(source) for source in self._resources]

get_message_tokens

get_message_tokens(message: ChatMessage) -> int

Get token count for a single message.

Source code in src/llmling_agent/agent/conversation.py

def get_message_tokens(self, message: ChatMessage) -> int:
    """Get token count for a single message."""
    content = "\n".join(message.format())
    return count_tokens(content, self._agent.model_name)

get_pending_messages

get_pending_messages() -> list[ChatMessage]

Get messages that will be included in next interaction.

Source code in src/llmling_agent/agent/conversation.py

def get_pending_messages(self) -> list[ChatMessage]:
    """Get messages that will be included in next interaction."""
    return list(self._pending_messages)

get_pending_tokens

get_pending_tokens() -> int

Get token count for pending messages.

Source code in src/llmling_agent/agent/conversation.py

def get_pending_tokens(self) -> int:
    """Get token count for pending messages."""
    text = "\n".join(msg.format() for msg in self._pending_messages)
    return count_tokens(text, self._agent.model_name)

load_context_source async

load_context_source(source: Resource | PromptType | str)

Load context from a single source.

Source code in src/llmling_agent/agent/conversation.py

async def load_context_source(self, source: Resource | PromptType | str):
    """Load context from a single source."""
    try:
        match source:
            case str():
                await self.add_context_from_path(source)
            case BaseResource():
                await self.add_context_from_resource(source)
            case BasePrompt():
                await self.add_context_from_prompt(source)
    except Exception:
        msg = "Failed to load context from %s"
        logger.exception(msg, "file" if isinstance(source, str) else source.type)

load_history_from_database

load_history_from_database(
    session: SessionIdType | SessionQuery = None,
    *,
    since: datetime | None = None,
    until: datetime | None = None,
    roles: set[MessageRole] | None = None,
    limit: int | None = None,
)

Load conversation history from database.

Parameters:

Name	Type	Description	Default
session	SessionIdType | SessionQuery	Session ID or query config	None
since	datetime | None	Only include messages after this time (override)	None
until	datetime | None	Only include messages before this time (override)	None
roles	set[MessageRole] | None	Only include messages with these roles (override)	None
limit	int | None	Maximum number of messages to return (override)	None

Source code in src/llmling_agent/agent/conversation.py

def load_history_from_database(
    self,
    session: SessionIdType | SessionQuery = None,
    *,
    since: datetime | None = None,
    until: datetime | None = None,
    roles: set[MessageRole] | None = None,
    limit: int | None = None,
):
    """Load conversation history from database.

    Args:
        session: Session ID or query config
        since: Only include messages after this time (override)
        until: Only include messages before this time (override)
        roles: Only include messages with these roles (override)
        limit: Maximum number of messages to return (override)
    """
    storage = self._agent.context.storage
    match session:
        case SessionQuery() as query:
            # Override query params if provided
            if since is not None or until is not None or roles or limit:
                update = {
                    "since": since.isoformat() if since else None,
                    "until": until.isoformat() if until else None,
                    "roles": roles,
                    "limit": limit,
                }
                query = query.model_copy(update=update)
            if query.name:
                self.id = query.name
        case str() | UUID():
            self.id = str(session)
            query = SessionQuery(
                name=self.id,
                since=since.isoformat() if since else None,
                until=until.isoformat() if until else None,
                roles=roles,
                limit=limit,
            )
        case None:
            # Use current session ID
            query = SessionQuery(
                name=self.id,
                since=since.isoformat() if since else None,
                until=until.isoformat() if until else None,
                roles=roles,
                limit=limit,
            )
        case _:
            msg = f"Invalid type for session: {type(session)}"
            raise ValueError(msg)
    self.chat_messages.clear()
    self.chat_messages.extend(storage.filter_messages_sync(query))

set_history

set_history(history: list[ChatMessage])

Update conversation history after run.

Source code in src/llmling_agent/agent/conversation.py

def set_history(self, history: list[ChatMessage]):
    """Update conversation history after run."""
    self.chat_messages.clear()
    self.chat_messages.extend(history)

temporary_state async

temporary_state(
    history: list[AnyPromptType] | SessionQuery | None = None,
    *,
    replace_history: bool = False,
) -> AsyncIterator[Self]

Temporarily set conversation history.

Parameters:

Name	Type	Description	Default
history	list[AnyPromptType] | SessionQuery | None	Optional list of prompts to use as temporary history. Can be strings, BasePrompts, or other prompt types.	None
replace_history	bool	If True, only use provided history. If False, append to existing history.	False

Source code in src/llmling_agent/agent/conversation.py

@asynccontextmanager
async def temporary_state(
    self,
    history: list[AnyPromptType] | SessionQuery | None = None,
    *,
    replace_history: bool = False,
) -> AsyncIterator[Self]:
    """Temporarily set conversation history.

    Args:
        history: Optional list of prompts to use as temporary history.
                Can be strings, BasePrompts, or other prompt types.
        replace_history: If True, only use provided history. If False, append
                to existing history.
    """
    from toprompt import to_prompt

    old_history = self.chat_messages.copy()

    try:
        messages: Sequence[ChatMessage[Any]] = ChatMessageContainer()
        if history is not None:
            if isinstance(history, SessionQuery):
                messages = await self._agent.context.storage.filter_messages(history)
            else:
                messages = [
                    ChatMessage(content=await to_prompt(p), role="user")
                    for p in history
                ]

        if replace_history:
            self.chat_messages = ChatMessageContainer(messages)
        else:
            self.chat_messages.extend(messages)

        yield self

    finally:
        self.chat_messages = old_history