310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088 | 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.")
file_store: FileStoreConfig = Field(
default_factory=lambda: FileStoreConfig(enabled=True, backend=InMemoryFileStore()),
description="Configuration for file storage used by the agent.",
)
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)
_tool_cache: dict[ToolCacheEntry, Any] = {}
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
if self.file_store_backend:
if self.file_store.agent_file_write_enabled:
self.tools.append(FileWriteTool(file_store=self.file_store_backend))
self.tools.append(FileReadTool(file_store=self.file_store_backend))
self.tools.append(FileListTool(file_store=self.file_store_backend))
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."""
role_for_validation = self.role or ""
if role_for_validation and (
"{% raw %}" not in role_for_validation and "{% endraw %}" not in role_for_validation
):
role_for_validation = f"{{% raw %}}{role_for_validation}{{% endraw %}}"
return {"input_message": self.input_message, "role": role_for_validation}
@property
def to_dict_exclude_params(self):
return super().to_dict_exclude_params | {
"llm": True,
"tools": True,
"memory": True,
"files": True,
"images": True,
"file_store": 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 }}",
"instructions": "",
"context": "{{ context }}",
}
self._prompt_variables = {
"tool_description": self.tool_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:
# Only auto-wrap the entire role in a raw block if the user did not
# provide explicit raw/endraw markers. This allows roles to mix
# literal sections (via raw) with Jinja variables like {{ input }}
# without creating nested raw blocks.
if ("{% raw %}" in self.role) or ("{% endraw %}" in self.role):
self._prompt_blocks["role"] = self.role
else:
self._prompt_blocks["role"] = f"{{% raw %}}{self.role}{{% endraw %}}"
files = input_data.files
if files:
self._ensure_named_files(files)
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,
}
if self.file_store_backend and not self.file_store_backend.is_empty():
execution_result["files"] = self.file_store_backend.list_files_bytes()
logger.info(
f"Agent {self.name} - {self.id}: returning {len(execution_result['files'])}"
" accumulated file(s) in FileStore"
)
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,
**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.
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."
)
return self.stream_response(content=content, source=source, step=step, config=config, **kwargs)
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."""
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 self.file_store_backend and tool.is_files_allowed:
for field_name, field in tool.input_schema.model_fields.items():
if field.json_schema_extra and field.json_schema_extra.get("map_from_storage", False):
if field_name in merged_input:
merged_input[field_name] = FileMappedInput(
input=merged_input[field_name], files=self.file_store_backend.list_files_bytes()
)
else:
merged_input[field_name] = self.file_store_backend.list_files_bytes()
if isinstance(tool, Python):
merged_input["files"] = self.file_store_backend.list_files_bytes()
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_any = tool_params.by_name_params.get(tool.name) or tool_params.by_name_params.get(
self.sanitize_tool_name(tool.name)
)
if name_params_any:
if isinstance(name_params_any, ToolParams):
if self.verbose:
debug_info.append(
f" - From name:{tool.name}: encountered nested ToolParams (ignored for non-agent tool)"
)
elif isinstance(name_params_any, dict):
self._apply_parameters(merged_input, name_params_any, f"name:{tool.name}", debug_info)
# 3. Apply parameters by tool ID (highest priority)
id_params_any = tool_params.by_id_params.get(tool.id)
if id_params_any:
if isinstance(id_params_any, ToolParams):
if self.verbose:
debug_info.append(
f" - From id:{tool.id}: encountered nested ToolParams (ignored for non-agent tool)"
)
elif isinstance(id_params_any, dict):
self._apply_parameters(merged_input, id_params_any, f"id:{tool.id}", debug_info)
if self.verbose and debug_info:
logger.debug("\n".join(debug_info))
child_kwargs = kwargs | {"recoverable_error": True}
is_child_agent = isinstance(tool, Agent)
if is_child_agent and tool_params:
nested_any = (
tool_params.by_id_params.get(getattr(tool, "id", ""))
or tool_params.by_name_params.get(getattr(tool, "name", ""))
or tool_params.by_name_params.get(self.sanitize_tool_name(getattr(tool, "name", "")))
)
if nested_any:
if isinstance(nested_any, ToolParams):
nested_tp = nested_any
elif isinstance(nested_any, dict):
nested_tp = ToolParams.model_validate(nested_any)
else:
nested_tp = None
if nested_tp:
child_kwargs = child_kwargs | {"tool_params": nested_tp}
tool_result = tool.run(
input_data=merged_input,
config=config,
run_depends=self._run_depends,
**child_kwargs,
)
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_output_content = tool_result.output.get("content")
self._handle_tool_generated_files(tool, tool_result)
tool_result_content_processed = process_tool_output_for_agent(
content=tool_result_output_content,
max_tokens=self.tool_output_max_length,
truncate=self.tool_output_truncate_enabled,
)
self._tool_cache[ToolCacheEntry(action=tool.name, action_input=tool_input)] = tool_result_content_processed
return tool_result_content_processed
def _ensure_named_files(self, files: list[io.BytesIO | bytes]) -> None:
"""Ensure all uploaded files have name and description attributes and store them in file_store if available."""
named = []
for i, f in enumerate(files):
if isinstance(f, bytes):
bio = io.BytesIO(f)
bio.name = f"file_{i}.bin"
bio.description = "User-provided file"
if self.file_store_backend:
try:
self.file_store_backend.store(
file_path=bio.name,
content=f,
content_type="application/octet-stream",
metadata={"description": bio.description, "source": "user_upload"},
overwrite=True,
)
except Exception as e:
logger.warning(f"Failed to store file {bio.name} in file_store: {e}")
named.append(bio)
elif isinstance(f, io.BytesIO):
if not hasattr(f, "name"):
f.name = f"file_{i}"
if not hasattr(f, "description"):
f.description = "User-provided file"
if self.file_store_backend:
try:
content = f.read()
f.seek(0)
self.file_store_backend.store(
file_path=f.name,
content=content,
content_type="application/octet-stream",
metadata={"description": f.description, "source": "user_upload"},
overwrite=True,
)
except Exception as e:
logger.warning(f"Failed to store file {f.name} in file_store: {e}")
named.append(f)
else:
named.append(f)
return named
def _handle_tool_generated_files(self, tool: Node, tool_result: RunnableResult) -> None:
"""
Handle files generated by tools and store them in the file store.
Args:
tool: The tool that generated the files
tool_result: The result from the tool execution
"""
if not self.file_store_backend:
return
if isinstance(tool_result.output, dict) and "files" in tool_result.output:
tool_files = tool_result.output.get("files", [])
if tool_files:
stored_files = []
for file in tool_files:
if isinstance(file, io.BytesIO):
file_name = getattr(file, "name", f"file_{id(file)}.bin")
file_description = getattr(file, "description", "Tool-generated file")
content_type = getattr(file, "content_type", "application/octet-stream")
content = file.read()
file.seek(0)
self.file_store_backend.store(
file_path=file_name,
content=content,
content_type=content_type,
metadata={"description": file_description, "source": "tool_generated"},
overwrite=True,
)
stored_files.append(file_name)
elif isinstance(file, bytes):
file_name = f"file_{id(file)}.bin"
file_description = f"Tool-{tool.name}-generated file"
content_type = "application/octet-stream"
self.file_store_backend.store(
file_path=file_name,
content=file,
content_type=content_type,
metadata={"description": file_description, "source": "tool_generated"},
overwrite=True,
)
stored_files.append(file_name)
else:
logger.warning(f"Unsupported file type from tool '{tool.name}': {type(file)}")
logger.info(f"Tool '{tool.name}' generated {len(stored_files)} file(s): {stored_files}")
@property
def file_store_backend(self) -> FileStore | None:
"""Get the file store backend from the configuration if enabled."""
return self.file_store.backend if self.file_store.enabled else None
@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 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 = []
self._tool_cache: dict[ToolCacheEntry, Any] = {}
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 = Template(content).render(**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
|