Base

Agent

Bases: Node

Base class for an AI Agent that interacts with a Language Model and tools.

Source code in dynamiq/nodes/agents/base.py

class Agent(Node):
    """Base class for an AI Agent that interacts with a Language Model and tools."""

    AGENT_PROMPT_TEMPLATE: ClassVar[str] = AGENT_PROMPT_TEMPLATE

    llm: BaseLLM = Field(..., description="LLM used by the agent.")
    group: NodeGroup = NodeGroup.AGENTS
    error_handling: ErrorHandling = Field(default_factory=lambda: ErrorHandling(timeout_seconds=600))
    tools: list[Node] = []
    files: list[io.BytesIO | bytes] | None = None
    images: list[str | bytes | io.BytesIO] = None
    name: str = "Agent"
    max_loops: int = 1
    tool_output_max_length: int = TOOL_MAX_TOKENS
    tool_output_truncate_enabled: bool = True
    memory: Memory | None = Field(None, description="Memory node for the agent.")
    memory_limit: int = Field(100, description="Maximum number of messages to retrieve from memory")
    memory_retrieval_strategy: MemoryRetrievalStrategy | None = MemoryRetrievalStrategy.ALL
    verbose: bool = Field(False, description="Whether to print verbose logs.")

    input_message: Message | VisionMessage | None = None
    role: str | None = ""
    _prompt_blocks: dict[str, str] = PrivateAttr(default_factory=dict)
    _prompt_variables: dict[str, Any] = PrivateAttr(default_factory=dict)
    _mcp_servers: list[MCPServer] = PrivateAttr(default_factory=list)
    _mcp_server_tool_ids: list[str] = PrivateAttr(default_factory=list)

    model_config = ConfigDict(arbitrary_types_allowed=True)
    input_schema: ClassVar[type[AgentInputSchema]] = AgentInputSchema
    _json_schema_fields: ClassVar[list[str]] = ["role"]

    @classmethod
    def _generate_json_schema(
        cls, llms: dict[type[BaseLLM], list[str]] = {}, tools=list[type[Node]], **kwargs
    ) -> dict[str, Any]:
        """
        Generates full json schema for Agent with provided llms and tools.
        This schema is designed for compatibility with the WorkflowYamlParser,
        containing enough partial information to instantiate an Agent.
        Parameters name to be included in the schema are either defined in the _json_schema_fields class variable or
        passed via the fields parameter.

        It generates a schema using the provided LLMs and tools.

        Args:
            llms (dict[type[BaseLLM], list[str]]): Available llm providers and models.
            tools (list[type[Node]]): List of tools.

        Returns:
            dict[str, Any]: Generated json schema.
        """
        schema = super()._generate_json_schema(**kwargs)
        schema["properties"]["llm"] = {
            "anyOf": [
                {
                    "type": "object",
                    **llm._generate_json_schema(models=models, fields=["model", "temperature", "max_tokens"]),
                }
                for llm, models in llms.items()
            ],
            "additionalProperties": False,
        }

        schema["properties"]["tools"] = {
            "type": "array",
            "items": {"anyOf": [{"type": "object", **tool._generate_json_schema()} for tool in tools]},
        }

        schema["required"] += ["tools", "llm"]
        return schema

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self._intermediate_steps: dict[int, dict] = {}
        self._run_depends: list[dict] = []
        self._prompt = Prompt(messages=[])

        expanded_tools = []
        for tool in self.tools:
            if isinstance(tool, MCPServer):
                self._mcp_servers.append(tool)
                subtools = tool.get_mcp_tools()
                expanded_tools.extend(subtools)
                self._mcp_server_tool_ids.extend([subtool.id for subtool in subtools])
            else:
                expanded_tools.append(tool)
        self.tools = expanded_tools

        self._init_prompt_blocks()

    @model_validator(mode="after")
    def validate_input_fields(self):
        if self.input_message:
            self.input_message.role = MessageRole.USER

        return self

    def get_context_for_input_schema(self) -> dict:
        """Provides context for input schema that is required for proper validation."""
        return {"input_message": self.input_message, "role": self.role}

    @property
    def to_dict_exclude_params(self):
        return super().to_dict_exclude_params | {
            "llm": True,
            "tools": True,
            "memory": True,
            "files": True,
            "images": True,
        }

    def to_dict(self, **kwargs) -> dict:
        """Converts the instance to a dictionary."""
        data = super().to_dict(**kwargs)
        data["llm"] = self.llm.to_dict(**kwargs)

        data["tools"] = [tool.to_dict(**kwargs) for tool in self.tools if tool.id not in self._mcp_server_tool_ids]
        data["tools"] = data["tools"] + [mcp_server.to_dict(**kwargs) for mcp_server in self._mcp_servers]

        data["memory"] = self.memory.to_dict(**kwargs) if self.memory else None
        if self.files:
            data["files"] = [{"name": getattr(f, "name", f"file_{i}")} for i, f in enumerate(self.files)]
        if self.images:
            data["images"] = [{"name": getattr(f, "name", f"image_{i}")} for i, f in enumerate(self.images)]
        return data

    def init_components(self, connection_manager: ConnectionManager | None = None):
        """
        Initialize components for the manager and agents.

        Args:
            connection_manager (ConnectionManager, optional): The connection manager. Defaults to ConnectionManager.
        """
        connection_manager = connection_manager or ConnectionManager()
        super().init_components(connection_manager)
        if self.llm.is_postponed_component_init:
            self.llm.init_components(connection_manager)

        for tool in self.tools:
            if tool.is_postponed_component_init:
                tool.init_components(connection_manager)
            tool.is_optimized_for_agents = True

    def sanitize_tool_name(self, s: str):
        """Sanitize tool name to follow [^a-zA-Z0-9_-]."""
        s = s.replace(" ", "-")
        sanitized = re.sub(r"[^a-zA-Z0-9_-]", "", s)
        return sanitized

    def _init_prompt_blocks(self):
        """Initializes default prompt blocks and variables."""
        self._prompt_blocks = {
            "date": "{date}",
            "tools": "{tool_description}",
            "files": "{file_description}",
            "instructions": "",
        }
        self._prompt_variables = {
            "tool_description": self.tool_description,
            "file_description": self.file_description,
            "date": datetime.now().strftime("%d %B %Y"),
        }

    def set_block(self, block_name: str, content: str):
        """Adds or updates a prompt block."""
        self._prompt_blocks[block_name] = content

    def set_prompt_variable(self, variable_name: str, value: Any):
        """Sets or updates a prompt variable."""
        self._prompt_variables[variable_name] = value

    def _prepare_metadata(self, input_data: dict) -> dict:
        """
        Prepare metadata from input data.

        Args:
            input_data (dict): Input data containing user information

        Returns:
            dict: Processed metadata
        """
        EXCLUDED_KEYS = {"user_id", "session_id", "input", "metadata", "files", "images", "tool_params"}
        custom_metadata = input_data.get("metadata", {}).copy()
        custom_metadata.update({k: v for k, v in input_data.items() if k not in EXCLUDED_KEYS})

        if "files" in custom_metadata:
            del custom_metadata["files"]
        if "images" in custom_metadata:
            del custom_metadata["images"]
        if "tool_params" in custom_metadata:
            del custom_metadata["tool_params"]

        user_id = input_data.get("user_id")
        session_id = input_data.get("session_id")

        if user_id:
            custom_metadata["user_id"] = user_id
        if session_id:
            custom_metadata["session_id"] = session_id

        return custom_metadata

    def execute(
        self,
        input_data: AgentInputSchema,
        input_message: Message | VisionMessage | None = None,
        config: RunnableConfig | None = None,
        **kwargs,
    ) -> dict[str, Any]:
        """
        Executes the agent with the given input data.
        """
        log_data = dict(input_data).copy()

        if log_data.get("images"):
            log_data["images"] = [f"image_{i}" for i in range(len(log_data["images"]))]

        if log_data.get("files"):
            log_data["files"] = [f"file_{i}" for i in range(len(log_data["files"]))]

        logger.info(f"Agent {self.name} - {self.id}: started with input {log_data}")
        self.reset_run_state()
        config = ensure_config(config)
        self.run_on_node_execute_run(config.callbacks, **kwargs)

        custom_metadata = self._prepare_metadata(dict(input_data))

        input_message = input_message or self.input_message or create_message_from_input(dict(input_data))
        input_message = input_message.format_message(**dict(input_data))

        use_memory = self.memory and (dict(input_data).get("user_id") or dict(input_data).get("session_id"))

        if use_memory:
            history_messages = self._retrieve_memory(dict(input_data))
            if len(history_messages) > 0:
                history_messages.insert(
                    0,
                    Message(
                        role=MessageRole.SYSTEM,
                        content="Below is the previous conversation history. "
                        "Use this context to inform your response.",
                    ),
                )
            if isinstance(input_message, Message):
                memory_content = input_message.content
            else:
                text_parts = [
                    content.text for content in input_message.content if isinstance(content, VisionMessageTextContent)
                ]
                memory_content = " ".join(text_parts) if text_parts else "Image input"
            self.memory.add(role=MessageRole.USER, content=memory_content, metadata=custom_metadata)
        else:
            history_messages = None

        if self.role:
            self._prompt_blocks["role"] = Template(self.role).render(**dict(input_data))

        files = input_data.files
        if files:
            self.files = files
            self._prompt_variables["file_description"] = self.file_description

        if input_data.tool_params:
            kwargs["tool_params"] = input_data.tool_params

        self._prompt_variables.update(dict(input_data))
        kwargs = kwargs | {"parent_run_id": kwargs.get("run_id")}
        kwargs.pop("run_depends", None)

        result = self._run_agent(input_message, history_messages, config=config, **kwargs)

        if use_memory:
            self.memory.add(role=MessageRole.ASSISTANT, content=result, metadata=custom_metadata)

        execution_result = {
            "content": result,
            "intermediate_steps": self._intermediate_steps,
        }
        logger.info(f"Node {self.name} - {self.id}: finished with RESULT:\n{str(result)[:200]}...")

        return execution_result

    def retrieve_conversation_history(
        self,
        user_query: str = None,
        user_id: str = None,
        session_id: str = None,
        limit: int = None,
        strategy: MemoryRetrievalStrategy = MemoryRetrievalStrategy.ALL,
    ) -> list[Message]:
        """
        Retrieves conversation history for the agent using the specified strategy.

        Args:
            user_query: Current user input to find relevant context (for RELEVANT/HYBRID strategies)
            user_id: Optional user identifier
            session_id: Optional session identifier
            limit: Maximum number of messages to return (defaults to memory_limit)
            strategy: Which retrieval strategy to use (ALL, RELEVANT, or HYBRID)

        Returns:
            List of messages forming a valid conversation context
        """
        if not self.memory or not (user_id or session_id):
            return []

        filters = {}
        if user_id:
            filters["user_id"] = user_id
        if session_id:
            filters["session_id"] = session_id

        limit = limit or self.memory_limit

        if strategy == MemoryRetrievalStrategy.RELEVANT and not user_query:
            logger.warning("RELEVANT strategy selected but no user_query provided - falling back to ALL")
            strategy = MemoryRetrievalStrategy.ALL

        conversation = self.memory.get_agent_conversation(
            query=user_query,
            limit=limit,
            filters=filters,
            strategy=strategy,
        )
        return conversation

    def _retrieve_memory(self, input_data: dict) -> list[Message]:
        """
        Retrieves memory messages when user_id and/or session_id are provided.
        """
        user_id = input_data.get("user_id")
        session_id = input_data.get("session_id")

        user_query = input_data.get("input", "")
        history_messages = self.retrieve_conversation_history(
            user_query=user_query,
            user_id=user_id,
            session_id=session_id,
            strategy=self.memory_retrieval_strategy,
        )
        logger.info("Agent %s - %s: retrieved %d messages from memory", self.name, self.id, len(history_messages))
        return history_messages

    def _run_llm(
        self, messages: list[Message | VisionMessage], config: RunnableConfig | None = None, **kwargs
    ) -> RunnableResult:
        """Runs the LLM with a given prompt and handles streaming or full responses.

        Args:
            messages (list[Message | VisionMessage]): Input messages for llm.
            config (Optional[RunnableConfig]): Configuration for the runnable.
            kwargs: Additional keyword arguments.

        Returns:
            RunnableResult: Generated response.
        """
        try:
            llm_result = self.llm.run(
                input_data={},
                config=config,
                prompt=Prompt(messages=messages),
                run_depends=self._run_depends,
                **kwargs,
            )
            self._run_depends = [NodeDependency(node=self.llm).to_dict()]
            if llm_result.status != RunnableStatus.SUCCESS:
                error_message = f"LLM '{self.llm.name}' failed: {llm_result.error.message}"
                raise ValueError({error_message})

            return llm_result

        except Exception as e:
            raise e

    def stream_content(
        self,
        content: str | dict,
        source: str,
        step: str,
        config: RunnableConfig | None = None,
        by_tokens: bool | None = None,
        **kwargs,
    ) -> str | dict:
        """
        Streams data.

        Args:
            content (str | dict): Data that will be streamed.
            source (str): Source of the content.
            step (str): Description of the step.
            by_tokens (Optional[bool]): Determines whether to stream content by tokens or not.
                If None it is determined based on StreamingConfig. Defaults to None.
            config (Optional[RunnableConfig]): Configuration for the runnable.
            **kwargs: Additional keyword arguments.

        Returns:
            str | dict: Streamed data.
        """
        if not isinstance(source, str):
            raise ValueError(
                f"stream_content source parameter must be a string, got {type(source).__name__}: {source}. "
                f"This likely indicates incorrect parameter passing from the calling code."
            )

        if (by_tokens is None and self.streaming.by_tokens) or by_tokens:
            return self.stream_by_tokens(content=content, source=source, step=step, config=config, **kwargs)
        return self.stream_response(content=content, source=source, step=step, config=config, **kwargs)

    def stream_by_tokens(self, content: str, source: str, step: str, config: RunnableConfig | None = None, **kwargs):
        """Streams the input content to the callbacks."""
        if isinstance(content, dict):
            return self.stream_response(content, source, step, config, **kwargs)
        tokens = content.split(" ")
        final_response = []
        for token in tokens:
            final_response.append(token)
            token_with_prefix = " " + token
            token_for_stream = StreamChunk(
                choices=[
                    StreamChunkChoice(delta=StreamChunkChoiceDelta(content=token_with_prefix, source=source, step=step))
                ]
            )
            self.run_on_node_execute_stream(
                callbacks=config.callbacks,
                chunk=token_for_stream.model_dump(),
                **kwargs,
            )
        return " ".join(final_response)

    def stream_response(
        self, content: str | dict, source: str, step: str, config: RunnableConfig | None = None, **kwargs
    ):
        if not isinstance(source, str):
            raise ValueError(
                f"stream_response source parameter must be a string, got {type(source).__name__}: {source}. "
                f"This likely indicates a parameter ordering issue in the calling code."
            )

        response_for_stream = StreamChunk(
            choices=[StreamChunkChoice(delta=StreamChunkChoiceDelta(content=content, source=source, step=step))]
        )

        self.run_on_node_execute_stream(
            callbacks=config.callbacks,
            chunk=response_for_stream.model_dump(),
            **kwargs,
        )
        return content

    def _run_agent(
        self,
        input_message: Message | VisionMessage,
        history_messages: list[Message] | None = None,
        config: RunnableConfig | None = None,
        **kwargs,
    ) -> str:
        """Runs the agent with the generated prompt and handles exceptions."""
        formatted_prompt = self.generate_prompt()
        system_message = Message(role=MessageRole.SYSTEM, content=formatted_prompt)
        if history_messages:
            self._prompt.messages = [system_message, *history_messages, input_message]
        else:
            self._prompt.messages = [system_message, input_message]

        try:
            llm_result = self._run_llm(self._prompt.messages, config=config, **kwargs).output["content"]
            self._prompt.messages.append(Message(role=MessageRole.ASSISTANT, content=llm_result))

            if self.streaming.enabled:
                return self.stream_content(
                    content=llm_result,
                    source=self.name,
                    step="answer",
                    config=config,
                    **kwargs,
                )
            return llm_result

        except Exception as e:
            raise e

    def _get_tool(self, action: str) -> Node:
        """Retrieves the tool corresponding to the given action."""
        tool = self.tool_by_names.get(self.sanitize_tool_name(action))
        if not tool:
            raise AgentUnknownToolException(
                f"Unknown tool: {action}."
                "Use only available tools and provide only the tool's name in the action field. "
                "Do not include any additional reasoning. "
                "Please correct the action field or state that you cannot answer the question."
            )
        return tool

    def _apply_parameters(self, merged_input: dict, params: dict, source: str, debug_info: list = None):
        """Apply parameters from the specified source to the merged input."""
        if debug_info is None:
            debug_info = []
        for key, value in params.items():
            if key in merged_input and isinstance(value, dict) and isinstance(merged_input[key], dict):
                merged_nested = merged_input[key].copy()
                merged_input[key] = deep_merge(value, merged_nested)
                debug_info.append(f"  - From {source}: Merged nested {key}")
            else:
                merged_input[key] = value
                debug_info.append(f"  - From {source}: Set {key}={value}")

    def _run_tool(self, tool: Node, tool_input: dict, config, **kwargs) -> Any:
        """Runs a specific tool with the given input."""
        if self.files:
            if tool.is_files_allowed is True:
                tool_input["files"] = self.files

        merged_input = tool_input.copy() if isinstance(tool_input, dict) else {"input": tool_input}
        raw_tool_params = kwargs.get("tool_params", ToolParams())
        tool_params = (
            ToolParams.model_validate(raw_tool_params) if isinstance(raw_tool_params, dict) else raw_tool_params
        )

        if tool_params:
            debug_info = []
            if self.verbose:
                debug_info.append(f"Tool parameter merging for {tool.name} (ID: {tool.id}):")
                debug_info.append(f"Starting with input: {merged_input}")

            # 1. Apply global parameters (lowest priority)
            global_params = tool_params.global_params
            if global_params:
                self._apply_parameters(merged_input, global_params, "global", debug_info)

            # 2. Apply parameters by tool name (medium priority)
            name_params = tool_params.by_name_params.get(tool.name, {}) or tool_params.by_name_params.get(
                self.sanitize_tool_name(tool.name), {}
            )
            if name_params:
                self._apply_parameters(merged_input, name_params, f"name:{tool.name}", debug_info)

            # 3. Apply parameters by tool ID (highest priority)
            id_params = tool_params.by_id_params.get(tool.id, {})
            if id_params:
                self._apply_parameters(merged_input, id_params, f"id:{tool.id}", debug_info)

            if self.verbose and debug_info:
                logger.debug("\n".join(debug_info))

        tool_result = tool.run(
            input_data=merged_input,
            config=config,
            run_depends=self._run_depends,
            **(kwargs | {"recoverable_error": True}),
        )
        self._run_depends = [NodeDependency(node=tool).to_dict()]
        if tool_result.status != RunnableStatus.SUCCESS:
            error_message = f"Tool '{tool.name}' failed: {tool_result.error.to_dict()}"
            if tool_result.error.recoverable:
                raise ToolExecutionException({error_message})
            else:
                raise ValueError({error_message})
        tool_result_content = tool_result.output.get("content")
        tool_result_content_processed = process_tool_output_for_agent(
            content=tool_result_content,
            max_tokens=self.tool_output_max_length,
            truncate=self.tool_output_truncate_enabled,
        )
        return tool_result_content_processed

    @property
    def tool_description(self) -> str:
        """Returns a description of the tools available to the agent."""
        return (
            "\n".join(
                [
                    f"{tool.name}:\n <{tool.name}_description>\n{tool.description.strip()}\n<\\{tool.name}_description>"
                    for tool in self.tools
                ]
            )
            if self.tools
            else ""
        )

    @property
    def file_description(self) -> str:
        """Returns a description of the files available to the agent."""
        if self.files:
            file_description = "You can work with the following files:\n"
            for file in self.files:
                name = getattr(file, "name", "Unnamed file")
                description = getattr(file, "description", "No description")
                file_description += f"<file>: {name} - {description} <\\file>\n"
            return file_description
        return ""

    @property
    def tool_names(self) -> str:
        """Returns a comma-separated list of tool names available to the agent."""
        return ",".join([self.sanitize_tool_name(tool.name) for tool in self.tools])

    @property
    def tool_by_names(self) -> dict[str, Node]:
        """Returns a dictionary mapping tool names to their corresponding Node objects."""
        return {self.sanitize_tool_name(tool.name): tool for tool in self.tools}

    def reset_run_state(self):
        """Resets the agent's run state."""
        self._intermediate_steps = {}
        self._run_depends = []

    def generate_prompt(self, block_names: list[str] | None = None, **kwargs) -> str:
        """Generates the prompt using specified blocks and variables."""
        temp_variables = self._prompt_variables.copy()
        temp_variables.update(kwargs)

        formatted_prompt_blocks = {}
        for block, content in self._prompt_blocks.items():
            if block_names is None or block in block_names:

                formatted_content = content.format(**temp_variables)
                if content:
                    formatted_prompt_blocks[block] = formatted_content

        prompt = Template(self.AGENT_PROMPT_TEMPLATE).render(formatted_prompt_blocks).strip()
        prompt = self._clean_prompt(prompt)
        return textwrap.dedent(prompt)

    def _clean_prompt(self, prompt_text):
        cleaned = re.sub(r"\n{3,}", "\n\n", prompt_text)
        return cleaned.strip()

    def get_clone_attr_initializers(self) -> dict[str, Callable[[Node], Any]]:
        base = super().get_clone_attr_initializers()
        from dynamiq.prompts import Prompt

        base.update(
            {
                "_prompt": (lambda _self: Prompt(messages=[]) if Prompt else None),
            }
        )
        return base

file_description: str property

Returns a description of the files available to the agent.

tool_by_names: dict[str, Node] property

Returns a dictionary mapping tool names to their corresponding Node objects.

tool_description: str property

Returns a description of the tools available to the agent.

tool_names: str property

Returns a comma-separated list of tool names available to the agent.

execute(input_data, input_message=None, config=None, **kwargs)

Executes the agent with the given input data.

Source code in dynamiq/nodes/agents/base.py

def execute(
    self,
    input_data: AgentInputSchema,
    input_message: Message | VisionMessage | None = None,
    config: RunnableConfig | None = None,
    **kwargs,
) -> dict[str, Any]:
    """
    Executes the agent with the given input data.
    """
    log_data = dict(input_data).copy()

    if log_data.get("images"):
        log_data["images"] = [f"image_{i}" for i in range(len(log_data["images"]))]

    if log_data.get("files"):
        log_data["files"] = [f"file_{i}" for i in range(len(log_data["files"]))]

    logger.info(f"Agent {self.name} - {self.id}: started with input {log_data}")
    self.reset_run_state()
    config = ensure_config(config)
    self.run_on_node_execute_run(config.callbacks, **kwargs)

    custom_metadata = self._prepare_metadata(dict(input_data))

    input_message = input_message or self.input_message or create_message_from_input(dict(input_data))
    input_message = input_message.format_message(**dict(input_data))

    use_memory = self.memory and (dict(input_data).get("user_id") or dict(input_data).get("session_id"))

    if use_memory:
        history_messages = self._retrieve_memory(dict(input_data))
        if len(history_messages) > 0:
            history_messages.insert(
                0,
                Message(
                    role=MessageRole.SYSTEM,
                    content="Below is the previous conversation history. "
                    "Use this context to inform your response.",
                ),
            )
        if isinstance(input_message, Message):
            memory_content = input_message.content
        else:
            text_parts = [
                content.text for content in input_message.content if isinstance(content, VisionMessageTextContent)
            ]
            memory_content = " ".join(text_parts) if text_parts else "Image input"
        self.memory.add(role=MessageRole.USER, content=memory_content, metadata=custom_metadata)
    else:
        history_messages = None

    if self.role:
        self._prompt_blocks["role"] = Template(self.role).render(**dict(input_data))

    files = input_data.files
    if files:
        self.files = files
        self._prompt_variables["file_description"] = self.file_description

    if input_data.tool_params:
        kwargs["tool_params"] = input_data.tool_params

    self._prompt_variables.update(dict(input_data))
    kwargs = kwargs | {"parent_run_id": kwargs.get("run_id")}
    kwargs.pop("run_depends", None)

    result = self._run_agent(input_message, history_messages, config=config, **kwargs)

    if use_memory:
        self.memory.add(role=MessageRole.ASSISTANT, content=result, metadata=custom_metadata)

    execution_result = {
        "content": result,
        "intermediate_steps": self._intermediate_steps,
    }
    logger.info(f"Node {self.name} - {self.id}: finished with RESULT:\n{str(result)[:200]}...")

    return execution_result

generate_prompt(block_names=None, **kwargs)

Generates the prompt using specified blocks and variables.

Source code in dynamiq/nodes/agents/base.py

def generate_prompt(self, block_names: list[str] | None = None, **kwargs) -> str:
    """Generates the prompt using specified blocks and variables."""
    temp_variables = self._prompt_variables.copy()
    temp_variables.update(kwargs)

    formatted_prompt_blocks = {}
    for block, content in self._prompt_blocks.items():
        if block_names is None or block in block_names:

            formatted_content = content.format(**temp_variables)
            if content:
                formatted_prompt_blocks[block] = formatted_content

    prompt = Template(self.AGENT_PROMPT_TEMPLATE).render(formatted_prompt_blocks).strip()
    prompt = self._clean_prompt(prompt)
    return textwrap.dedent(prompt)

get_context_for_input_schema()

Provides context for input schema that is required for proper validation.

Source code in dynamiq/nodes/agents/base.py

def get_context_for_input_schema(self) -> dict:
    """Provides context for input schema that is required for proper validation."""
    return {"input_message": self.input_message, "role": self.role}

init_components(connection_manager=None)

Initialize components for the manager and agents.

Parameters:

Name	Type	Description	Default
connection_manager	ConnectionManager	The connection manager. Defaults to ConnectionManager.	None

Source code in dynamiq/nodes/agents/base.py

def init_components(self, connection_manager: ConnectionManager | None = None):
    """
    Initialize components for the manager and agents.

    Args:
        connection_manager (ConnectionManager, optional): The connection manager. Defaults to ConnectionManager.
    """
    connection_manager = connection_manager or ConnectionManager()
    super().init_components(connection_manager)
    if self.llm.is_postponed_component_init:
        self.llm.init_components(connection_manager)

    for tool in self.tools:
        if tool.is_postponed_component_init:
            tool.init_components(connection_manager)
        tool.is_optimized_for_agents = True

reset_run_state()

Resets the agent's run state.

Source code in dynamiq/nodes/agents/base.py

def reset_run_state(self):
    """Resets the agent's run state."""
    self._intermediate_steps = {}
    self._run_depends = []

retrieve_conversation_history(user_query=None, user_id=None, session_id=None, limit=None, strategy=MemoryRetrievalStrategy.ALL)

Retrieves conversation history for the agent using the specified strategy.

Parameters:

Name	Type	Description	Default
user_query	str	Current user input to find relevant context (for RELEVANT/HYBRID strategies)	None
user_id	str	Optional user identifier	None
session_id	str	Optional session identifier	None
limit	int	Maximum number of messages to return (defaults to memory_limit)	None
strategy	MemoryRetrievalStrategy	Which retrieval strategy to use (ALL, RELEVANT, or HYBRID)	ALL

Returns:

Type	Description
list[Message]	List of messages forming a valid conversation context

Source code in dynamiq/nodes/agents/base.py

def retrieve_conversation_history(
    self,
    user_query: str = None,
    user_id: str = None,
    session_id: str = None,
    limit: int = None,
    strategy: MemoryRetrievalStrategy = MemoryRetrievalStrategy.ALL,
) -> list[Message]:
    """
    Retrieves conversation history for the agent using the specified strategy.

    Args:
        user_query: Current user input to find relevant context (for RELEVANT/HYBRID strategies)
        user_id: Optional user identifier
        session_id: Optional session identifier
        limit: Maximum number of messages to return (defaults to memory_limit)
        strategy: Which retrieval strategy to use (ALL, RELEVANT, or HYBRID)

    Returns:
        List of messages forming a valid conversation context
    """
    if not self.memory or not (user_id or session_id):
        return []

    filters = {}
    if user_id:
        filters["user_id"] = user_id
    if session_id:
        filters["session_id"] = session_id

    limit = limit or self.memory_limit

    if strategy == MemoryRetrievalStrategy.RELEVANT and not user_query:
        logger.warning("RELEVANT strategy selected but no user_query provided - falling back to ALL")
        strategy = MemoryRetrievalStrategy.ALL

    conversation = self.memory.get_agent_conversation(
        query=user_query,
        limit=limit,
        filters=filters,
        strategy=strategy,
    )
    return conversation

sanitize_tool_name(s)

Sanitize tool name to follow [^a-zA-Z0-9_-].

Source code in dynamiq/nodes/agents/base.py

def sanitize_tool_name(self, s: str):
    """Sanitize tool name to follow [^a-zA-Z0-9_-]."""
    s = s.replace(" ", "-")
    sanitized = re.sub(r"[^a-zA-Z0-9_-]", "", s)
    return sanitized

set_block(block_name, content)

Adds or updates a prompt block.

Source code in dynamiq/nodes/agents/base.py

def set_block(self, block_name: str, content: str):
    """Adds or updates a prompt block."""
    self._prompt_blocks[block_name] = content

set_prompt_variable(variable_name, value)

Sets or updates a prompt variable.

Source code in dynamiq/nodes/agents/base.py

def set_prompt_variable(self, variable_name: str, value: Any):
    """Sets or updates a prompt variable."""
    self._prompt_variables[variable_name] = value

stream_by_tokens(content, source, step, config=None, **kwargs)

Streams the input content to the callbacks.

Source code in dynamiq/nodes/agents/base.py

def stream_by_tokens(self, content: str, source: str, step: str, config: RunnableConfig | None = None, **kwargs):
    """Streams the input content to the callbacks."""
    if isinstance(content, dict):
        return self.stream_response(content, source, step, config, **kwargs)
    tokens = content.split(" ")
    final_response = []
    for token in tokens:
        final_response.append(token)
        token_with_prefix = " " + token
        token_for_stream = StreamChunk(
            choices=[
                StreamChunkChoice(delta=StreamChunkChoiceDelta(content=token_with_prefix, source=source, step=step))
            ]
        )
        self.run_on_node_execute_stream(
            callbacks=config.callbacks,
            chunk=token_for_stream.model_dump(),
            **kwargs,
        )
    return " ".join(final_response)

stream_content(content, source, step, config=None, by_tokens=None, **kwargs)

Streams data.

Parameters:

Name	Type	Description	Default
content	str | dict	Data that will be streamed.	required
source	str	Source of the content.	required
step	str	Description of the step.	required
by_tokens	Optional[bool]	Determines whether to stream content by tokens or not. If None it is determined based on StreamingConfig. Defaults to None.	None
config	Optional[RunnableConfig]	Configuration for the runnable.	None
**kwargs		Additional keyword arguments.	{}

Returns:

Type	Description
str | dict	str | dict: Streamed data.

Source code in dynamiq/nodes/agents/base.py

def stream_content(
    self,
    content: str | dict,
    source: str,
    step: str,
    config: RunnableConfig | None = None,
    by_tokens: bool | None = None,
    **kwargs,
) -> str | dict:
    """
    Streams data.

    Args:
        content (str | dict): Data that will be streamed.
        source (str): Source of the content.
        step (str): Description of the step.
        by_tokens (Optional[bool]): Determines whether to stream content by tokens or not.
            If None it is determined based on StreamingConfig. Defaults to None.
        config (Optional[RunnableConfig]): Configuration for the runnable.
        **kwargs: Additional keyword arguments.

    Returns:
        str | dict: Streamed data.
    """
    if not isinstance(source, str):
        raise ValueError(
            f"stream_content source parameter must be a string, got {type(source).__name__}: {source}. "
            f"This likely indicates incorrect parameter passing from the calling code."
        )

    if (by_tokens is None and self.streaming.by_tokens) or by_tokens:
        return self.stream_by_tokens(content=content, source=source, step=step, config=config, **kwargs)
    return self.stream_response(content=content, source=source, step=step, config=config, **kwargs)

to_dict(**kwargs)

Converts the instance to a dictionary.

Source code in dynamiq/nodes/agents/base.py

def to_dict(self, **kwargs) -> dict:
    """Converts the instance to a dictionary."""
    data = super().to_dict(**kwargs)
    data["llm"] = self.llm.to_dict(**kwargs)

    data["tools"] = [tool.to_dict(**kwargs) for tool in self.tools if tool.id not in self._mcp_server_tool_ids]
    data["tools"] = data["tools"] + [mcp_server.to_dict(**kwargs) for mcp_server in self._mcp_servers]

    data["memory"] = self.memory.to_dict(**kwargs) if self.memory else None
    if self.files:
        data["files"] = [{"name": getattr(f, "name", f"file_{i}")} for i, f in enumerate(self.files)]
    if self.images:
        data["images"] = [{"name": getattr(f, "name", f"image_{i}")} for i, f in enumerate(self.images)]
    return data

AgentManager

Bases: Agent

Manager class that extends the Agent class to include specific actions.

Source code in dynamiq/nodes/agents/base.py

class AgentManager(Agent):
    """Manager class that extends the Agent class to include specific actions."""

    _actions: dict[str, Callable] = PrivateAttr(default_factory=dict)
    name: str = "Agent Manager"
    input_schema: ClassVar[type[AgentManagerInputSchema]] = AgentManagerInputSchema

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self._init_actions()

    def to_dict(self, **kwargs) -> dict:
        """Converts the instance to a dictionary."""
        data = super().to_dict(**kwargs)
        data["_actions"] = {
            k: getattr(action, "__name__", str(action))
            for k, action in self._actions.items()
        }
        return data

    def _init_actions(self):
        """Initializes the default actions for the manager."""
        self._actions = {
            "plan": self._plan,
            "assign": self._assign,
            "final": self._final,
            "handle_input": self._handle_input,
        }

    def add_action(self, name: str, action: Callable):
        """Adds a custom action to the manager."""
        self._actions[name] = action

    def get_context_for_input_schema(self) -> dict:
        """Provides context for input schema that is required for proper validation."""
        return {"actions": list(self._actions.keys())}

    def execute(
        self, input_data: AgentManagerInputSchema, config: RunnableConfig | None = None, **kwargs
    ) -> dict[str, Any]:
        """Executes the manager agent with the given input data and action."""
        log_data = dict(input_data).copy()

        if log_data.get("images"):
            log_data["images"] = [f"image_{i}" for i in range(len(log_data["images"]))]

        if log_data.get("files"):
            log_data["files"] = [f"file_{i}" for i in range(len(log_data["files"]))]

        logger.info(f"Agent {self.name} - {self.id}: started with input {log_data}")
        self.reset_run_state()
        config = config or RunnableConfig()
        self.run_on_node_execute_run(config.callbacks, **kwargs)

        action = input_data.action

        self._prompt_variables.update(dict(input_data))

        kwargs = kwargs | {"parent_run_id": kwargs.get("run_id")}
        kwargs.pop("run_depends", None)
        _result_llm = self._actions[action](config=config, **kwargs)
        result = {"action": action, "result": _result_llm}

        execution_result = {
            "content": result,
            "intermediate_steps": self._intermediate_steps,
        }
        logger.info(f"Agent {self.name} - {self.id}: finished with RESULT:\n{str(result)[:200]}...")

        return execution_result

    def _plan(self, config: RunnableConfig, **kwargs) -> str:
        """Executes the 'plan' action."""
        prompt = self._prompt_blocks.get("plan").format(**self._prompt_variables, **kwargs)
        llm_result = self._run_llm([Message(role=MessageRole.USER, content=prompt)], config, **kwargs).output["content"]

        return llm_result

    def _assign(self, config: RunnableConfig, **kwargs) -> str:
        """Executes the 'assign' action."""
        prompt = self._prompt_blocks.get("assign").format(**self._prompt_variables, **kwargs)
        llm_result = self._run_llm([Message(role=MessageRole.USER, content=prompt)], config, **kwargs).output["content"]

        return llm_result

    def _final(self, config: RunnableConfig, **kwargs) -> str:
        """Executes the 'final' action."""
        prompt = self._prompt_blocks.get("final").format(**self._prompt_variables, **kwargs)
        llm_result = self._run_llm(
            [Message(role=MessageRole.USER, content=prompt)], config, by_tokens=False, **kwargs
        ).output["content"]
        if self.streaming.enabled:
            return self.stream_content(
                content=llm_result,
                step="manager_final_output",
                source=self.name,
                config=config,
                by_tokens=False,
                **kwargs,
            )
        return llm_result

    def _handle_input(self, config: RunnableConfig, **kwargs) -> str:
        """
        Executes the single 'handle_input' action to either respond or plan
        based on user request complexity.
        """
        prompt = self._prompt_blocks.get("handle_input").format(**self._prompt_variables, **kwargs)
        llm_result = self._run_llm([Message(role=MessageRole.USER, content=prompt)], config, **kwargs).output["content"]
        return llm_result

add_action(name, action)

Adds a custom action to the manager.

Source code in dynamiq/nodes/agents/base.py

def add_action(self, name: str, action: Callable):
    """Adds a custom action to the manager."""
    self._actions[name] = action

execute(input_data, config=None, **kwargs)

Executes the manager agent with the given input data and action.

Source code in dynamiq/nodes/agents/base.py

def execute(
    self, input_data: AgentManagerInputSchema, config: RunnableConfig | None = None, **kwargs
) -> dict[str, Any]:
    """Executes the manager agent with the given input data and action."""
    log_data = dict(input_data).copy()

    if log_data.get("images"):
        log_data["images"] = [f"image_{i}" for i in range(len(log_data["images"]))]

    if log_data.get("files"):
        log_data["files"] = [f"file_{i}" for i in range(len(log_data["files"]))]

    logger.info(f"Agent {self.name} - {self.id}: started with input {log_data}")
    self.reset_run_state()
    config = config or RunnableConfig()
    self.run_on_node_execute_run(config.callbacks, **kwargs)

    action = input_data.action

    self._prompt_variables.update(dict(input_data))

    kwargs = kwargs | {"parent_run_id": kwargs.get("run_id")}
    kwargs.pop("run_depends", None)
    _result_llm = self._actions[action](config=config, **kwargs)
    result = {"action": action, "result": _result_llm}

    execution_result = {
        "content": result,
        "intermediate_steps": self._intermediate_steps,
    }
    logger.info(f"Agent {self.name} - {self.id}: finished with RESULT:\n{str(result)[:200]}...")

    return execution_result

get_context_for_input_schema()

Provides context for input schema that is required for proper validation.

Source code in dynamiq/nodes/agents/base.py

def get_context_for_input_schema(self) -> dict:
    """Provides context for input schema that is required for proper validation."""
    return {"actions": list(self._actions.keys())}

to_dict(**kwargs)

Converts the instance to a dictionary.

Source code in dynamiq/nodes/agents/base.py

def to_dict(self, **kwargs) -> dict:
    """Converts the instance to a dictionary."""
    data = super().to_dict(**kwargs)
    data["_actions"] = {
        k: getattr(action, "__name__", str(action))
        for k, action in self._actions.items()
    }
    return data

AgentStatus

Bases: str, Enum

Represents the status of an agent's execution.

Source code in dynamiq/nodes/agents/base.py

class AgentStatus(str, Enum):
    """Represents the status of an agent's execution."""

    SUCCESS = "success"
    FAIL = "fail"

StreamChunk

Bases: BaseModel

Model for streaming chunks with choices containing delta updates.

Source code in dynamiq/nodes/agents/base.py

class StreamChunk(BaseModel):
    """Model for streaming chunks with choices containing delta updates."""

    choices: list[StreamChunkChoice]

StreamChunkChoice

Bases: BaseModel

Stream chunk choice model.

Source code in dynamiq/nodes/agents/base.py

class StreamChunkChoice(BaseModel):
    """Stream chunk choice model."""

    delta: StreamChunkChoiceDelta

StreamChunkChoiceDelta

Bases: BaseModel

Delta model for content chunks.

Source code in dynamiq/nodes/agents/base.py

class StreamChunkChoiceDelta(BaseModel):
    """Delta model for content chunks."""
    content: str | dict
    source: str
    step: str

    @field_validator('source')
    @classmethod
    def validate_source(cls, v):
        """Ensure source is always a string."""
        if not isinstance(v, str):
            raise ValueError(f"source must be a string, got {type(v).__name__}: {v}")
        return v

validate_source(v) classmethod

Ensure source is always a string.

Source code in dynamiq/nodes/agents/base.py

@field_validator('source')
@classmethod
def validate_source(cls, v):
    """Ensure source is always a string."""
    if not isinstance(v, str):
        raise ValueError(f"source must be a string, got {type(v).__name__}: {v}")
    return v