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""" WalkthroughThe changes introduce a new asynchronous method Changes
Sequence Diagram(s)sequenceDiagram
participant AgentComponent
participant Toolkit
participant Callbacks
AgentComponent->>Toolkit: _get_component_toolkit()
AgentComponent->>Toolkit: get_tools(tool_name, description, callbacks)
Toolkit-->>AgentComponent: List[Tool]
AgentComponent->>Toolkit: update_tools_metadata(tools_metadata) (if present)
Toolkit-->>AgentComponent: Updated List[Tool]
AgentComponent-->>Caller: List[Tool]
sequenceDiagram
participant APIRequestComponent
participant httpx.AsyncClient
APIRequestComponent->>httpx.AsyncClient: make_api_request(url, params, headers, body)
httpx.AsyncClient-->>APIRequestComponent: Response
APIRequestComponent-->>Caller: Data (API Response)
sequenceDiagram
participant AgentComponent
participant BuildConfig
AgentComponent->>BuildConfig: update_build_config(field_value, field_name)
alt provider == "Custom"
AgentComponent->>BuildConfig: Delete provider-specific fields
AgentComponent->>BuildConfig: Update agent_llm field for custom models
end
BuildConfig-->>AgentComponent: Updated build config
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Actionable comments posted: 3
🔭 Outside diff range comments (2)
src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json (1)
1464-1472: 💡 Verification agent🧩 Analysis chain
Confirm override of tool‐retrieval hook
Ensure thatToolCallingAgentComponent(the parent class) actually invokes this_get_toolshook. To verify, you can search the codebase for references to_get_toolsin the base class:
🏁 Script executed:
#!/bin/bash # Verify ToolCallingAgentComponent defines or calls _get_tools ast-grep --pattern $'class ToolCallingAgentComponent { $$$ async _get_tools($_) { $$$ } }'Length of output: 91
I couldn’t find any definition or call of _get_tools with the previous pattern. Let’s broaden the search across the repo:
🏁 Script executed:
#!/bin/bash # Search for any definitions of `_get_tools` rg -n "async _get_tools" -C 2 # Search for any references or invocations of `_get_tools` rg -n "_get_tools" -C 2Length of output: 69299
Let’s inspect the
ToolCallingAgentComponentimplementation and see whether it invokes_get_tools:
🏁 Script executed:
#!/bin/bash # Locate the ToolCallingAgentComponent definition FILE=$(rg -l "class ToolCallingAgentComponent") echo "Found in: $FILE" # Show the start of the class sed -n '1,200p' "$FILE" # Check for any calls to _get_tools inside it grep -R "_get_tools" -n "$FILE"Length of output: 1537
Let’s pinpoint the Python file and inspect the class body directly:
🏁 Script executed:
#!/bin/bash # Locate the .py file defining ToolCallingAgentComponent PYFILE=$(rg -l "^class ToolCallingAgentComponent" -g "*.py") echo "Class file: $PYFILE" # Show the first 200 lines of that file sed -n '1,200p' "$PYFILE" # Check within the class for any reference or invocation of _get_tools grep -R "_get_tools" -n "$PYFILE"Length of output: 2670
Let’s inspect
LCToolsAgentComponentto see if it invokes the_get_toolshook:
🏁 Script executed:
#!/bin/bash # Locate the LCToolsAgentComponent definition grep -R "class LCToolsAgentComponent" -n src/backend/base/langflow/base/agents/agent.py # Show the first 200 lines of that file (including class body) sed -n '1,200p' src/backend/base/langflow/base/agents/agent.py # Search within that class for any calls to _get_tools grep -R "self._get_tools" -n src/backend/base/langflow/base/agents/agent.pyLength of output: 8229
Let’s inspect the
LCToolsAgentComponentclass body—particularly itsbuild_agentmethod—to see if it ever calls_get_tools:
🏁 Script executed:
#!/bin/bash # Print LCToolsAgentComponent definition and its build_agent implementation sed -n '190,300p' src/backend/base/langflow/base/agents/agent.py # Look for any use of `_get_tools` or assignment to `tools` in that class grep -nE "_get_tools|tools\s*=" -n src/backend/base/langflow/base/agents/agent.py | sed -n '1,20p'Length of output: 2966
Override of
_get_toolsis never invoked
TheToolCallingAgentComponent(and its baseLCToolsAgentComponent) never callself._get_tools(), so any subclass override (e.g. in your JSON‐defined AgentComponent) is effectively dead code.Locations requiring changes:
- src/backend/base/langflow/base/agents/agent.py
Inclass LCToolsAgentComponent.build_agent, before passingself.toolsintoAgentExecutor.from_agent_and_tools, call_get_tools()when no tools are provided.Suggested snippet:
def build_agent(self) -> AgentExecutor: - self.validate_tool_names() - agent = self.create_agent_runnable() - return AgentExecutor.from_agent_and_tools( - agent=RunnableAgent(...), - tools=self.tools, - **self.get_agent_kwargs(flatten=True), - ) + self.validate_tool_names() + agent = self.create_agent_runnable() + # Populate tools via hook if none supplied + if not self.tools: + self.tools = await self._get_tools() # <— invoke override + return AgentExecutor.from_agent_and_tools( + agent=RunnableAgent(...), + tools=self.tools, + **self.get_agent_kwargs(flatten=True), + )Without this, any custom
_get_toolsimplementation in Starter Project components will never run.src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json (1)
2180-2187: 🛠️ Refactor suggestionValidate and integrate the new
_get_toolsmethod.The new asynchronous helper
async def _get_tools(self) -> list[Tool]: component_toolkit = _get_component_toolkit() tools_names = self._build_tools_names() agent_description = self.get_tool_description() description = f"{agent_description}{tools_names}" tools = component_toolkit(component=self).get_tools( tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks() ) if hasattr(self, "tools_metadata"): tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) return tools
- Ensure this method is actually invoked by the agent’s setup or message‐response flow.
- Consider renaming to
get_tools(no leading underscore) if it’s part of the public API.- Add error handling around the toolkit calls to surface failures cleanly.
- Add unit tests or integration tests to verify tool retrieval and metadata updating.
♻️ Duplicate comments (6)
src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json (2)
1630-1880: Add toolkit integration snippet (duplicate of above)
This code block mirrors the same import additions and_get_toolsmethod in another starter project. Please refer to the feedback on the previous segment for validation and refactoring suggestions.
2556-2840: Add toolkit integration snippet (duplicate of above)
This is the third occurrence of the import and_get_toolsmethod changes. All recommendations from the first review comment apply here as well.src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json (4)
2489-2492: ****
Duplicate of import feedback for the Local Expert Agent code block—please review the same guidelines above.
2801-2808: ****
Duplicate of_get_toolsfeedback for the Local Expert Agent code block—apply the same validation and integration checks.
3231-3234: ****
Duplicate of import feedback for the Travel Concierge Agent code block—confirm namespace, usage, and consistency.
3610-3616: ****
Duplicate of_get_toolsfeedback for the Travel Concierge Agent code block—ensure invocation, naming, and error handling are correctly implemented.
🧹 Nitpick comments (20)
src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json (1)
488-630: Enhance tool integration: imports and_get_toolsmethod added
The updated code block now imports_get_component_toolkitandTool, and defines the new asynchronous_get_toolsmethod to dynamically construct and update tools via the custom component toolkit.
Action items:
- Verify that
_get_component_toolkit()returns the correct toolkit factory and that invoking it with(component=self)yields a valid toolkit instance.- Ensure the
Tooltype aligns with whatget_toolsreturns.- Consider adding a docstring and error handling around the toolkit calls (e.g., catching toolkit initialization failures).
- Remove the deprecated
# TODO: Agent Description Depreciated Feature to be removedcomment onceagent_descriptionis fully deprecated.src/backend/base/langflow/initial_setup/starter_projects/Search agent.json (2)
1211-1220: Group and Order Imports Per PEP8
The three new imports (_get_component_toolkit,update_component_build_config,Tool) are correct, but they should be organized with the existing Langflow imports—split into standard library, third-party, and first-party sections. Consider running isort or manually reordering to keep the import block clean and consistent.
1260-1270: Enhance_get_toolswith Docstring and Instance Reuse
The newly added_get_toolsmethod works as intended, but you could improve clarity and efficiency by:
- Adding a docstring to explain its purpose.
- Creating the toolkit instance once instead of invoking
_get_component_toolkittwice.- Optionally wrapping calls in a
try/exceptto log and surface any errors.Example refactor:
- async def _get_tools(self) -> list[Tool]: - component_toolkit = _get_component_toolkit() - tools_names = self._build_tools_names() - agent_description = self.get_tool_description() - description = f"{agent_description}{tools_names}" - tools = component_toolkit(component=self).get_tools( - tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks() - ) - if hasattr(self, "tools_metadata"): - tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) - return tools + async def _get_tools(self) -> list[Tool]: + """Retrieve agent tools via the component toolkit and apply metadata updates if provided.""" + toolkit = _get_component_toolkit()(component=self) + description = f"{self.get_tool_description()}{self._build_tools_names()}" + try: + tools = toolkit.get_tools( + tool_name="Call_Agent", + tool_description=description, + callbacks=self.get_langchain_callbacks(), + ) + if hasattr(self, "tools_metadata"): + tools = toolkit.update_tools_metadata( + tools=tools, + metadata=self.tools_metadata, + ) + return tools + except Exception as e: + logger.error(f"Error retrieving tools: {e}") + raisesrc/backend/base/langflow/initial_setup/starter_projects/Research Agent.json (1)
1962-1968: Typo in TODO comment: “Depreciated” → “Deprecated”
The comment# TODO: Agent Description Depreciated Feature to be removedshould read “Deprecated” rather than “Depreciated.” Consider updating the wording or removing the TODO once the agent description feature is fully retired.
src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json (2)
1769-1774: Avoid importing private functions directly
Importing_get_component_toolkit(note the leading underscore) ties this component to a private API surface that may change. Consider exposing and consuming a public factory or helper method (e.g.get_component_toolkit) inlangflow.customto decouple from internal implementation details.
1847-1854: Add docstring and error handling to_get_tools
The newasync def _get_toolsmethod lacks documentation and does not guard against toolkit failures. For maintainability, add a short docstring explaining its behavior and wrap calls toget_tools/update_tools_metadatain atry/exceptthat logs or surfaces errors clearly.src/backend/base/langflow/initial_setup/starter_projects/Financial Agent.json (2)
2460-2465: Group and document new imports for clarity
The two added imports for_get_component_toolkitandToolare correct, but to improve readability they should be adjacent and follow the existing import ordering conventions (standard → third-party → local). Also consider adding a brief comment explaining why this toolkit is required.Example grouping:
-from langflow.custom.custom_component.component import _get_component_toolkit -from langflow.custom.utils import update_component_build_config -from langflow.field_typing import Tool +from langflow.custom.custom_component.component import _get_component_toolkit # dynamic tool builder +from langflow.field_typing import Tool # type for toolkit outputs +from langflow.custom.utils import update_component_build_config # helper for live config updates
3160-3190: Enhance_get_toolswith docstring and error handling
The newasync def _get_toolsmethod correctly leverages the component toolkit to assemble the agent’s toolset and applies metadata overrides. To make maintenance easier and avoid unhandled errors at runtime, please:
- Add a docstring summarizing its purpose.
- Wrap the toolkit invocation in a try/except to surface meaningful errors if the toolkit fails.
async def _get_tools(self) -> list[Tool]: + \"\"\"Retrieve and prepare the agent’s toolset via the component toolkit.\"\"\" + try: component_toolkit = _get_component_toolkit() tools_names = self._build_tools_names() agent_description = self.get_tool_description() # TODO: Agent Description Depreciated Feature to be removed description = f"{agent_description}{tools_names}" tools = component_toolkit(component=self).get_tools( tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks() ) if hasattr(self, "tools_metadata"): tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) - return tools + return tools + except Exception as err: + logger.error(f"Failed to retrieve tools: {err}") + raise RuntimeError("Error initializing agent tools") from errsrc/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json (1)
1356-1429: Enhance_get_toolsintegration and imports
- The new imports for
_get_component_toolkitandToolshould be grouped and alphabetized with the other internal imports to maintain readability.- Add a concise docstring to
_get_toolsdescribing its behavior and the expected return type (list[Tool]).- Wrap the calls to the component toolkit (
get_toolsandupdate_tools_metadata) in atry/exceptblock so failures are logged vialogger.errorand raised cleanly.- If this method is invoked frequently, consider caching the toolkit instance or the generated tools list to avoid repeated initialization overhead.
src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json (2)
1495-1505: Ensure_get_toolsasync workflow and error handling.The new method centralizes dynamic tool retrieval:
async def _get_tools(self) -> list[Tool]: component_toolkit = _get_component_toolkit() tools = component_toolkit(component=self).get_tools(...) if hasattr(self, "tools_metadata"): tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) return tools– Verify
get_toolsis synchronous; if it’s async, it needsawait.
– Add try/except around toolkit invocations to catch and log failures early.
– Optionally log the number of tools retrieved for observability.
1500-1500: Fix typo in TODO comment.The inline comment reads:
# TODO: Agent Description Depreciated Feature to be removedPlease correct “Depreciated” → “Deprecated” to avoid confusion.
src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json (2)
1575-1583: Integrate_get_toolsinto tool-loading workflow
You've introduced an async_get_toolsoverride to fetch and update tools metadata via the component toolkit. Confirm thatToolCallingAgentComponentwill invoke this override automatically. If not, consider explicitly calling:self.tools = await self._get_tools()at the start of
message_responsebefore validatingself.tools.
1578-1579: Clean up deprecated TODO comment
The# TODO: Agent Description Depreciated Feature to be removedcomment appears outdated. Remove it or replace it with a tracked issue reference if you plan to address this deprecation later.src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json (1)
844-844: Validate the new_get_toolsasynchronous method.
The_get_toolsmethod centralizes tool creation and metadata updates, which is a solid design. A few considerations:
- Add error handling around
get_toolsandupdate_tools_metadatato catch and log failures.- Ensure you have unit tests covering both the happy path and exceptional cases.
- Address the
# TODO: Agent Description Depreciated Feature to be removedby cleaning up the deprecatedagent_descriptiononce the migration is complete.src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json (2)
1455-1463: Verify import paths and ordering
The new imports for_get_component_toolkitandToolare essential to support_get_tools, but please confirm:
- The module path
langflow.custom.custom_component.componentis correct and available across all environments.- Imports are grouped (standard library, third-party, internal) and alphabetized within each group to maintain consistency.
1464-1472: Review_get_toolsimplementation and integration
The asynchronous_get_toolsmethod dynamically builds tools via the component toolkit. Consider:
- Reusing a single
component_toolkitinstance instead of calling_get_component_toolkit()twice.- Removing the deprecated TODO comment once agent description handling is fully deprecated.
- Adding debug-level logging around tool creation to simplify future troubleshooting.
src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json (1)
270-270: Ensure proper integration of_get_toolsand tidy deprecation TODOThe new imports (
_get_component_toolkit,Tool) and theasync def _get_tools(...)method provide dynamic tool retrieval, which is great. A few follow-ups:
- Correct the typo in the TODO comment: change “Depreciated” to “Deprecated” for accuracy.
- Confirm that
ToolCallingAgentComponentwill automatically invoke_get_tools(). If not, you may need to call it explicitly inmessage_response.- Consider adding a concise docstring to
_get_toolsto explain its purpose and parameters.- # TODO: Agent Description Depreciated Feature to be removed + # TODO: Remove deprecated agent_description featuresrc/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json (1)
1363-1363: Fix typo in TODO comment
In the new method you have:# TODO: Agent Description Depreciated Feature to be removed
Depreciatedshould be spelled Deprecated.src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json (1)
1843-1846: Ensure new imports are necessary and correctly namespaced.The added imports
from langflow.custom.custom_component.component import _get_component_toolkit from langflow.custom.utils import update_component_build_config from langflow.field_typing import Toolshould be verified:
- Confirm that
_get_component_toolkitandupdate_component_build_configare available in the installed package version.- Group and sort imports consistently with project conventions.
- Remove any unused imports if the functionality isn’t invoked immediately.
src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json (1)
1468-1477: Add a docstring and refine the TODO
Consider adding a concise docstring to_get_toolsdescribing its inputs, outputs, and behavior. Also, replace or remove the deprecated# TODO: Agent Description Depreciated Feature to be removedcomment to avoid confusion in future maintenance.
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📒 Files selected for processing (24)
src/backend/base/langflow/components/agents/agent.py(2 hunks)src/backend/base/langflow/components/data/api_request.py(2 hunks)src/backend/base/langflow/components/data/url.py(1 hunks)src/backend/base/langflow/components/helpers/structured_output.py(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Financial Agent.json(2 hunks)src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json(2 hunks)src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json(2 hunks)src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json(2 hunks)src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Market Research.json(3 hunks)src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json(2 hunks)src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json(62 hunks)src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Search agent.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json(3 hunks)src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json(1 hunks)src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json(3 hunks)src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json(1 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (2)
src/backend/base/langflow/components/data/url.py (1)
src/backend/base/langflow/template/field/base.py (1)
Output(181-255)
src/backend/base/langflow/components/data/api_request.py (2)
src/backend/base/langflow/template/field/base.py (1)
Output(181-255)src/backend/base/langflow/schema/data.py (1)
Data(17-249)
🪛 Pylint (3.3.7)
src/backend/base/langflow/components/agents/agent.py
[convention] 281-281: Line too long (106/100)
(C0301)
[convention] 284-284: Line too long (118/100)
(C0301)
[warning] 278-278: TODO: Agent Description Depreciated Feature to be removed
(W0511)
🔇 Additional comments (19)
src/backend/base/langflow/components/helpers/structured_output.py (1)
128-128: LGTM! Consistent tool mode configuration.The addition of
tool_mode=Falseexplicitly disables tool mode for the DataFrame output, aligning with the broader pattern of output configuration refinements mentioned in the PR objectives.src/backend/base/langflow/components/data/url.py (1)
113-114: LGTM! Consistent tool mode configuration across outputs.The addition of
tool_mode=Falseto both the Message and DataFrame outputs maintains consistency with similar changes across other components in this PR, ensuring uniform handling of outputs in tool mode environments.src/backend/base/langflow/components/data/api_request.py (1)
406-406: Method implementation renamed to match output declaration.The method implementation has been correctly renamed to match the updated output declaration, ensuring consistency between the interface and implementation.
src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json (2)
12-14: Formatting-only updates
These changes expand single-line arrays into multi-line arrays for improved readability and consistency. No semantic or behavioral changes have been introduced.Also applies to: 19-21, 40-42, 47-49, 68-70, 75-77, 96-98, 103-105, 125-127, 132-136, 155-157, 162-165, 185-187, 192-193, 210-212, 221-223, 2485-2487
1492-1497: Explicitly disable tool mode for DataFrame output
Thestructured_output_dataframeoutput now includes"tool_mode": false, ensuring this output remains unaffected by tool-based operations. This aligns with the PR objective of refining output configurations for tool modes.src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json (1)
595-598: Aligntool_modeflag with other starter projectsThe
tool_modefor thestructured_output_dataframeoutput has been toggled fromtruetofalseto match the standardized configuration in other JSON templates. This is a metadata-only change and doesn’t impact any logic.src/backend/base/langflow/components/agents/agent.py (1)
15-15: LGTM: Clean import additions.The new imports for
_get_component_toolkitandToolare appropriately added to support the new_get_toolsmethod functionality.Also applies to: 17-17
src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json (1)
1236-1242: Consistent disabling of tool_mode for DataFrame output
The"tool_mode"flag for thestructured_output_dataframeoutput has been correctly toggled fromtruetofalse, aligning this starter project with the other templates in this PR. No further adjustments are needed here.src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json (1)
1389-1393: Disable tool mode for DataFrame output
This change aligns thestructured_output_dataframeoutput with other starter projects by standardizing on"tool_mode": false. No functional behavior is altered, and consistency across components is maintained.src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json (1)
1122-1128: Imports for toolkit integration look correct
The additions offrom langflow.custom.custom_component.component import _get_component_toolkit from langflow.custom.utils import update_component_build_config from langflow.field_typing import Toolalign with the new
_get_toolsimplementation. Ensure that these module paths remain valid and thatToolmatches the expected runtime type.src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json (1)
1490-1492: Verify new imports for tool integration.You’ve added:
from langflow.custom.custom_component.component import _get_component_toolkit from langflow.field_typing import Tool– Confirm that
_get_component_toolkit()returns a factory supporting both
component=selfandcomponent=self, metadata=….
– EnsureToolis the correct typing alias for the toolkit’s output.
Run a quick import check or small snippet in your environment to validate resolution.src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json (1)
1455-1465: Added dynamic toolkit and Tool imports
The new imports of_get_component_toolkitandToolare required by the_get_toolsmethod. Please verify that these modules exist at the specified paths and that they’re consistently imported across other starter-project JSON templates.src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json (1)
844-844: Review new imports for component toolkit integration.
The added imports for_get_component_toolkitandToolcorrectly support dynamic tool retrieval via the custom component toolkit. Please verify:
- The import paths (
langflow.custom.custom_component.componentandlangflow.field_typing) are accurate and resolvable in your runtime.- No circular dependencies are introduced by these additions.
src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json (2)
1296-1301: Confirm dynamic toolkit imports
The newly added importsfrom langflow.custom.custom_component.component import _get_component_toolkit from langflow.field_typing import Toolare essential for the
_get_toolsimplementation. Ensure these are consistently applied across allAgentComponentvariants (in other starter projects and core components) so that dynamic tool retrieval works everywhere.
1360-1367: Review addition of async_get_toolsmethod
Theasync def _get_tools(self) -> list[Tool]:centralizes tool creation and optional metadata updates. Verify that wherever tools are consumed (e.g., inmessage_responsevia the baseToolCallingAgentComponent), this override is invoked correctly.src/backend/base/langflow/initial_setup/starter_projects/Market Research.json (2)
859-863: Consistenttool_modeFlag Adjustment for DataFrame Output
Thestructured_output_dataframeoutput’stool_modeflag has been changed fromtruetofalse, aligning this starter project with the core component behavior and other starter flows. This ensures that DataFrame outputs aren’t treated as tools by default.
1338-1342: New_get_toolsMethod Enables Dynamic Tool Retrieval
A new asynchronous_get_toolsmethod (with accompanying imports of_get_component_toolkitandTool) has been added to theAgentComponenttemplate, matching core agent enhancements. This allows dynamic creation and metadata updates of tools via the component toolkit.src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json (2)
922-924: Rename API request output method for consistency
Theoutputsdeclaration has been updated frommake_api_requeststomake_api_request, which correctly matches the renamedasync def make_api_requestimplementation below. This ensures the component wiring remains consistent.
1468-1470: Implement dynamic tool retrieval in the agent
The new_get_toolsmethod correctly leverages_get_component_toolkitto assemble tools and applies optional metadata updates whentools_metadatais present. This matches the pattern introduced in the coreAgentComponentand enhances dynamic tool integration.
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|
||
| outputs = [ | ||
| Output(display_name="API Response", name="data", method="make_api_requests"), | ||
| Output(display_name="API Response", name="data", method="make_api_request"), |
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💡 Verification agent
🧩 Analysis chain
LGTM! Method name consistency fix.
The change from make_api_requests to make_api_request aligns the output declaration with the actual method name, maintaining proper consistency.
Let me verify that this method name change doesn't break any external references:
🏁 Script executed:
#!/bin/bash
# Description: Search for any external references to the old method name
# Expect: No references to the old method name should exist
# Search for references to the old method name
rg -i "make_api_requests" --type py -A 2 -B 2Length of output: 1670
Update Tests to Reflect Renamed Method
The implementation was renamed from make_api_requests → make_api_request, but the unit tests still call the old method. Please update the following in
src/backend/tests/unit/components/data/test_api_request_component.py:
- Rename the test function and calls:
--- a/src/backend/tests/unit/components/data/test_api_request_component.py +++ b/src/backend/tests/unit/components/data/test_api_request_component.py @@ - async def test_make_api_requests(self, component): + async def test_make_api_request(self, component): # Test making API requests url = "https://example.com/api/test" respx.get(url).mock(return_value=Response(200, json=response_data)) - result = await component.make_api_requests() + result = await component.make_api_request() assert isinstance(result, Data) component.url_input = "not_a_valid_url" with pytest.raises(ValueError, match="Invalid URL provided"): - await component.make_api_requests() + await component.make_api_request()
After updating, rerun the tests to confirm everything passes.
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| Output(display_name="API Response", name="data", method="make_api_request"), | |
| async def test_make_api_request(self, component): | |
| # Test making API requests | |
| url = "https://example.com/api/test" | |
| respx.get(url).mock(return_value=Response(200, json=response_data)) | |
| result = await component.make_api_request() | |
| assert isinstance(result, Data) | |
| component.url_input = "not_a_valid_url" | |
| with pytest.raises(ValueError, match="Invalid URL provided"): | |
| await component.make_api_request() |
🤖 Prompt for AI Agents
In src/backend/tests/unit/components/data/test_api_request_component.py, update
the test function names and any calls that still use the old method name
make_api_requests to the new method name make_api_request to match the
implementation change in
src/backend/base/langflow/components/data/api_request.py at line 172. After
renaming, rerun the tests to ensure they pass successfully.
| async def _get_tools(self) -> list[Tool]: | ||
| component_toolkit = _get_component_toolkit() | ||
| tools_names = self._build_tools_names() | ||
| agent_description = self.get_tool_description() | ||
| # TODO: Agent Description Depreciated Feature to be removed | ||
| description = f"{agent_description}{tools_names}" | ||
| tools = component_toolkit(component=self).get_tools( | ||
| tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks() | ||
| ) | ||
| if hasattr(self, "tools_metadata"): | ||
| tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) | ||
| return tools |
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🧩 Analysis chain
Improve code formatting and address deprecation warning.
The new _get_tools method implements dynamic tool creation correctly, but has several areas for improvement:
- Line length violations: Lines 281 and 284 exceed 100 characters
- Deprecated functionality: The TODO comment indicates agent_description is deprecated
- Inefficient instantiation: Creating
component_toolkittwice seems unnecessary
Apply this diff to address the formatting and efficiency issues:
async def _get_tools(self) -> list[Tool]:
- component_toolkit = _get_component_toolkit()
+ component_toolkit_class = _get_component_toolkit()
tools_names = self._build_tools_names()
agent_description = self.get_tool_description()
# TODO: Agent Description Depreciated Feature to be removed
description = f"{agent_description}{tools_names}"
- tools = component_toolkit(component=self).get_tools(
- tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks()
+ tools = component_toolkit_class(component=self).get_tools(
+ tool_name="Call_Agent",
+ tool_description=description,
+ callbacks=self.get_langchain_callbacks()
)
if hasattr(self, "tools_metadata"):
- tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)
+ toolkit_with_metadata = component_toolkit_class(
+ component=self, metadata=self.tools_metadata
+ )
+ tools = toolkit_with_metadata.update_tools_metadata(tools=tools)
return toolsPlease verify that the inherited methods _build_tools_names(), get_tool_description(), and get_langchain_callbacks() exist in the parent classes:
🏁 Script executed:
#!/bin/bash
# Description: Verify inherited method dependencies exist in parent classes
# Search for method definitions in parent classes
echo "Searching for _build_tools_names method:"
ast-grep --pattern 'def _build_tools_names($_) {
$$$
}'
echo "Searching for get_tool_description method:"
ast-grep --pattern 'def get_tool_description($_) {
$$$
}'
echo "Searching for get_langchain_callbacks method:"
ast-grep --pattern 'def get_langchain_callbacks($_) {
$$$
}'
# Also search in base classes
echo "Searching in base agent classes:"
rg -A 3 "def (_build_tools_names|get_tool_description|get_langchain_callbacks)" src/backend/base/langflow/base/agents/Length of output: 1263
Refactor _get_tools: fix formatting, remove deprecated agent_description, and implement missing callback method
– Rename component_toolkit → component_toolkit_cls and invoke _get_component_toolkit() only once.
– Break out each parameter onto its own line (100 char limit).
– Remove/deprecate agent_description concatenation—rely on tools_names or update the base get_tool_description().
– Implement or import get_langchain_callbacks() (no definition found in src/backend/base/langflow/base/agents/agent.py).
– Verified that _build_tools_names() and get_tool_description() live in the base Agent class.
Proposed diff:
async def _get_tools(self) -> list[Tool]:
- component_toolkit = _get_component_toolkit()
+ component_toolkit_cls = _get_component_toolkit()
tools_names = self._build_tools_names()
- agent_description = self.get_tool_description()
- # TODO: Agent Description Depreciated Feature to be removed
- description = f"{agent_description}{tools_names}"
+ # agent_description is deprecated; derive description purely from tool names
+ description = tools_names
tools = component_toolkit_cls(component=self).get_tools(
- tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks()
+ tool_name="Call_Agent",
+ tool_description=description,
+ callbacks=self.get_langchain_callbacks(),
)
if hasattr(self, "tools_metadata"):
- tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)
+ metadata_toolkit = component_toolkit_cls(
+ component=self, metadata=self.tools_metadata
+ )
+ tools = metadata_toolkit.update_tools_metadata(tools=tools)
return toolsPlease add or import a concrete get_langchain_callbacks() implementation and remove the now-redundant TODO around agent_description.
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| async def _get_tools(self) -> list[Tool]: | |
| component_toolkit = _get_component_toolkit() | |
| tools_names = self._build_tools_names() | |
| agent_description = self.get_tool_description() | |
| # TODO: Agent Description Depreciated Feature to be removed | |
| description = f"{agent_description}{tools_names}" | |
| tools = component_toolkit(component=self).get_tools( | |
| tool_name="Call_Agent", tool_description=description, callbacks=self.get_langchain_callbacks() | |
| ) | |
| if hasattr(self, "tools_metadata"): | |
| tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools) | |
| return tools | |
| async def _get_tools(self) -> list[Tool]: | |
| component_toolkit_cls = _get_component_toolkit() | |
| tools_names = self._build_tools_names() | |
| # agent_description is deprecated; derive description purely from tool names | |
| description = tools_names | |
| tools = component_toolkit_cls(component=self).get_tools( | |
| tool_name="Call_Agent", | |
| tool_description=description, | |
| callbacks=self.get_langchain_callbacks(), | |
| ) | |
| if hasattr(self, "tools_metadata"): | |
| metadata_toolkit = component_toolkit_cls( | |
| component=self, metadata=self.tools_metadata | |
| ) | |
| tools = metadata_toolkit.update_tools_metadata(tools=tools) | |
| return tools |
🧰 Tools
🪛 Pylint (3.3.7)
[convention] 281-281: Line too long (106/100)
(C0301)
[convention] 284-284: Line too long (118/100)
(C0301)
[warning] 278-278: TODO: Agent Description Depreciated Feature to be removed
(W0511)
🤖 Prompt for AI Agents
In src/backend/base/langflow/components/agents/agent.py lines 274 to 285,
refactor the _get_tools method by renaming component_toolkit to
component_toolkit_cls and calling _get_component_toolkit() only once to avoid
redundant instantiation. Remove the deprecated agent_description and its
concatenation with tools_names, relying solely on tools_names or updating
get_tool_description() if needed. Break the arguments in the get_tools call into
separate lines to respect the 100 character limit. Finally, implement or import
the missing get_langchain_callbacks() method to ensure callbacks are properly
provided, and remove the TODO comment about agent_description.
| "title_case": false, | ||
| "type": "code", | ||
| "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.logging import logger\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())] + [{\"icon\": \"brain\"}],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n try:\n # Get LLM model and validate\n llm_model, display_name = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Validate tools\n if not self.tools:\n msg = \"Tools are required to run the agent. Please add at least one tool.\"\n raise ValueError(msg)\n\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools,\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n return await self.run_agent(agent)\n\n except (ValueError, TypeError, KeyError) as e:\n logger.error(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n logger.error(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n except Exception as e:\n logger.error(f\"Unexpected error: {e!s}\")\n raise\n\n async def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n # filter out empty values\n memory_kwargs = {k: v for k, v in memory_kwargs.items() if v is not None}\n\n return await MemoryComponent(**self.get_base_args()).set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except Exception as e:\n logger.error(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n" | ||
| "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.custom.custom_component.component import _get_component_toolkit\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.field_typing import Tool\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.logging import logger\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"OpenAI\",\n real_time_refresh=True,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())] + [{\"icon\": \"brain\"}],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n try:\n # Get LLM model and validate\n llm_model, display_name = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Validate tools\n if not self.tools:\n msg = \"Tools are required to run the agent. Please add at least one tool.\"\n raise ValueError(msg)\n\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools,\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n return await self.run_agent(agent)\n\n except (ValueError, TypeError, KeyError) as e:\n logger.error(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n logger.error(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n except Exception as e:\n logger.error(f\"Unexpected error: {e!s}\")\n raise\n\n async def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n # filter out empty values\n memory_kwargs = {k: v for k, v in memory_kwargs.items() if v is not None}\n\n return await MemoryComponent(**self.get_base_args()).set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except Exception as e:\n logger.error(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n elif field_value == \"Custom\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n value=\"Custom\",\n real_time_refresh=True,\n input_types=[\"LanguageModel\"],\n options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\", tool_description=description, callbacks=self.get_langchain_callbacks()\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n return tools\n" |
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Review dynamic tool integration additions
This change replaces the code block of AgentComponent to introduce:
- New imports for
_get_component_toolkit,update_component_build_config, andTool. - An
async def _get_tools(self) -> list[Tool]method that leverages the component toolkit to dynamically fetch and optionally update tool metadata.
Suggestions:
- Organize imports into standard, third-party, and local groups (PEP8), and remove any unused imports.
- Confirm that
_get_component_toolkit()returns a callable factory accepting bothcomponentandmetadata. - Wrap the toolkit calls in try/except to emit clear errors if tool retrieval or metadata updates fail.
- Remove the deprecated “Agent Description” TODO ahead of the next major version.
To verify the toolkit factory signature across the codebase, you can run:
🏁 Script executed:
#!/bin/bash
# Search for the definition of _get_component_toolkit
rg --ignore-case "def _get_component_toolkit" -nLength of output: 146
🏁 Script executed:
#!/bin/bash
rg -n -B3 -A3 "def _get_component_toolkit" src/backend/base/langflow/custom/custom_component/component.pyLength of output: 337
🏁 Script executed:
#!/bin/bash
# Show full _get_component_toolkit function
rg -n -A50 "def _get_component_toolkit" src/backend/base/langflow/custom/custom_component/component.py
# Inspect the ComponentToolkit class definition and its callable interface
rg -n -B3 -A20 "class ComponentToolkit" src/backend/base/langflow/base/tools/component_tool.pyLength of output: 3404
Refactor dynamic tool integration for maintainability
Please address the following actionable cleanup and error-handling improvements in AgentComponent._get_tools:
- Reorganize imports at the top of
Simple Agent.jsoninto three sections—standard library, third-party, then local—and remove any that aren’t actually used (e.g. verify whetherStructuredTool,dotdict, etc. are all needed). - Surround the calls to
_get_component_toolkit(...), its.get_tools(...), and the optional.update_tools_metadata(...)in a try/except block so that any failures during toolkit resolution or metadata application emit a clear, descriptive error before re-raising. - Eliminate the obsolete “TODO: Agent Description Depreciated Feature to be removed” comment.
- (No change needed for
_get_component_toolkit()’s signature—its returnedComponentToolkit(component, metadata=None)factory is correctly defined.)
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json at
line 1393, reorganize the imports into three groups: standard library,
third-party, and local imports, removing any unused ones such as StructuredTool
or dotdict if they are not used elsewhere. Wrap the calls to
_get_component_toolkit(), its get_tools() method, and the optional
update_tools_metadata() method inside a try/except block to catch and log errors
clearly before re-raising them. Remove the obsolete TODO comment about the
deprecated Agent Description feature from the _get_tools method. No changes are
needed to the _get_component_toolkit() signature as it is correctly defined.
…/langflow into fix-tool-name-change
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🧹 Nitpick comments (2)
src/backend/base/langflow/initial_setup/starter_projects/Market Research.json (2)
1341-1342: Imports for component toolkit and Tool type look appropriate
The new imports_get_component_toolkitandToolsupport the added_get_toolsmethod.
1345-1353: Refine the new_get_toolsmethod
The implementation integrates tools dynamically, but consider the following enhancements:
- Add a docstring explaining the purpose and return value.
- Fix typo in comment: change "Depreciated" to "Deprecated".
- Ensure spacing when concatenating descriptions, e.g.
f"{agent_description} {tools_names}".- Optionally wrap toolkit calls in
try/exceptto surface or log failures cleanly.
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src/backend/tests/unit/components/data/test_api_request_component.py (1)
241-241: LGTM! Consider adding a docstring for better documentation.The method renaming from
test_make_api_requeststotest_make_api_requestimproves naming consistency with the component method being tested.Consider adding a docstring to address the static analysis hint:
+ """Test the make_api_request method functionality.""" async def test_make_api_request(self, component):🧰 Tools
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[convention] 241-241: Missing function or method docstring
(C0116)
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src/backend/tests/unit/components/data/test_api_request_component.py
[convention] 241-241: Missing function or method docstring
(C0116)
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🔇 Additional comments (2)
src/backend/tests/unit/components/data/test_api_request_component.py (2)
249-249: LGTM! Method call correctly updated.The method call is properly updated to match the renamed component method.
259-259: LGTM! Error test method call correctly updated.The method call in the error handling test is properly updated to use the renamed method.
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* tool name changes * tool name changes * revert URL * reset structured output * [autofix.ci] apply automated fixes * fix test make_api_request name * change tool name test --------- Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
2 participants
This pull request introduces enhancements and fixes across several components in the backend of the Langflow project. The key changes focus on adding functionality for tool integration, correcting method names, and refining output configurations to include the
tool_modeparameter.Tool Integration Enhancements:
src/backend/base/langflow/components/agents/agent.py: Added_get_toolsmethod to enable dynamic tool creation and metadata updates using a component toolkit. This method integrates tools with descriptions and callbacks, enhancing the agent's functionality.src/backend/base/langflow/components/agents/agent.py: Imported_get_component_toolkitandToolto support the new_get_toolsmethod.Method Name Corrections:
src/backend/base/langflow/components/data/api_request.py: Renamedmake_api_requeststomake_api_requestin both theOutputdefinition and the method implementation to ensure consistency. [1] [2]Output Configuration Refinements:
src/backend/base/langflow/components/data/url.py: Addedtool_mode=Falseto theOutputdefinitions forfetch_content_textandas_dataframemethods, improving configurability for tool-based operations.src/backend/base/langflow/components/helpers/structured_output.py: Addedtool_mode=Falseto theOutputdefinition foras_dataframe, aligning it with similar changes in other components.Summary by CodeRabbit