feat(assistant): QA fixes + model portability, budget, and error visibility#14038
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WalkthroughThe change adds bounded assistant iteration limits, model remediation and fallback notices, curated provider defaults, regenerated starter Agent templates, frontend notice rendering, sidebar minimization after flow application, and compatibility fixes for legacy content blocks. ChangesAssistant runtime and model selection
LFX model and schema behavior
Starter Agent templates
Assistant frontend
Estimated code review effort: 5 (Critical) | ~120 minutes Sequence Diagram(s)sequenceDiagram
participant Client
participant assist_stream
participant execute_flow_with_validation_streaming
participant AgentComponent
participant model_remediation
participant AssistantPanel
Client->>assist_stream: send iterations_limit
assist_stream->>execute_flow_with_validation_streaming: start streaming with limit
execute_flow_with_validation_streaming->>AgentComponent: execute prepared flow
AgentComponent->>model_remediation: find remediation for model error
model_remediation-->>AgentComponent: return override
AgentComponent->>AgentComponent: retry with model override
execute_flow_with_validation_streaming-->>AssistantPanel: complete event with notices
AssistantPanel->>AssistantPanel: render model notice
Possibly related PRs
Suggested labels: Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 5❌ Failed checks (1 warning, 4 inconclusive)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 2
🧹 Nitpick comments (7)
src/frontend/src/components/core/assistantPanel/components/__tests__/assistant-model-notice.test.tsx (1)
40-87: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winAdd edge case tests for multiple notices and missing optional fields.
The four tests cover the main rendering paths well, but the coding guidelines call for edge and negative case coverage. Two untested branches are worth covering:
- Multiple notices — the component maps over
noticesand renders one<span>per item; no test verifies this.- Fallback without
used_model— the component'sdescribe()checksn.type === "model_fallback" && n.used_model; whenused_modelis absent it silently falls through to the remediation string. This behavior should be asserted to prevent regressions if the condition changes.- Undefined
failed_model— the?? "?"fallback is never exercised.♻️ Suggested additional test cases
it("uses the remediation string when the model was retried, not swapped", () => { const notices: NoticeType[] = [ { type: "model_remediation", reason: "tools unsupported", failed_model: "gpt-5.6", }, ]; render(<AssistantModelNotice notices={notices} />); const content = screen.getByTestId("tooltip-content").textContent ?? ""; expect(content).toContain("assistant.modelNotice.remediation"); }); + + it("renders multiple notices as separate entries", () => { + const notices: NoticeType[] = [ + { + type: "model_fallback", + reason: "requires a subscription", + failed_model: "glm-5:cloud", + used_model: "llama3.2:latest", + }, + { + type: "model_remediation", + reason: "tools unsupported", + failed_model: "gpt-5.6", + }, + ]; + render(<AssistantModelNotice notices={notices} />); + const content = screen.getByTestId("tooltip-content").textContent ?? ""; + expect(content).toContain("assistant.modelNotice.fallback"); + expect(content).toContain("assistant.modelNotice.remediation"); + }); + + it("falls back to remediation text when a fallback notice lacks used_model", () => { + const notices: NoticeType[] = [ + { + type: "model_fallback", + reason: "requires a subscription", + failed_model: "glm-5:cloud", + }, + ]; + render(<AssistantModelNotice notices={notices} />); + const content = screen.getByTestId("tooltip-content").textContent ?? ""; + expect(content).toContain("assistant.modelNotice.remediation"); + expect(content).not.toContain("assistant.modelNotice.fallback"); + }); + + it("substitutes '?' when failed_model is undefined", () => { + const notices: NoticeType[] = [ + { + type: "model_remediation", + reason: "tools unsupported", + }, + ]; + render(<AssistantModelNotice notices={notices} />); + const content = screen.getByTestId("tooltip-content").textContent ?? ""; + expect(content).toContain('"failed":"?"'); + }); });🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/frontend/src/components/core/assistantPanel/components/__tests__/assistant-model-notice.test.tsx` around lines 40 - 87, Add edge-case tests to the AssistantModelNotice suite: verify multiple notices render one notice element per item, assert a model_fallback notice without used_model uses the remediation text, and cover a notice with undefined failed_model to confirm the "?" fallback is rendered. Use the existing AssistantModelNotice queries and notice shapes without changing component behavior.Source: Coding guidelines
src/lfx/src/lfx/base/models/unified_models/instantiation.py (1)
370-377: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick winOverrides are merged with no safety net, and a bad one can never be remediated.
get_llmblindly merges cached/explicit overrides intokwargswith no check against keys already set by provider-specific setup (WatsonXurl/project_id, Azureendpoint, OpenRouter headers, etc.), andAgentComponent.message_responsetreats any resultingTypeErroridentically to a genuine hard input error — re-raising immediately with no remediation attempt. This is harmless today (single, well-formed remediation), but the registry is explicitly meant to grow, so a future remediation with a colliding or invalid key would silently corrupt kwargs or permanently short-circuit retry with no diagnostic signal.
src/lfx/src/lfx/base/models/unified_models/instantiation.py#L370-L377: guard the merge (e.g. log/skip when an override key would clobber an already-set provider-specific kwarg) before applyingcached_overrides/overrides.src/lfx/src/lfx/components/models_and_agents/agent.py#L725-L727: consider distinguishing "override-induced"TypeErrors (e.g. by tagging or catching around the_get_llm()construction specifically) from hard input-validation errors so a bad override doesn't permanently block remediation without any log trail.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/lfx/src/lfx/base/models/unified_models/instantiation.py` around lines 370 - 377, Guard override merging in unified_models/instantiation.py around cached_overrides and overrides so remediation keys cannot clobber provider-specific kwargs; log and skip conflicting keys while preserving valid overrides. In agent.py around AgentComponent.message_response, distinguish TypeError raised during _get_llm() construction from hard input-validation TypeErrors, allowing override-related failures to be diagnosed and remediated instead of immediately re-raised.src/lfx/tests/unit/components/models_and_agents/test_agent_model_remediation.py (1)
21-21: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low valueRemove the redundant
@pytest.mark.asynciodecorators.asyncio_mode = "auto"already auto-detects async tests, so these markers can be dropped here and on the other async test.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/lfx/tests/unit/components/models_and_agents/test_agent_model_remediation.py` at line 21, Remove the redundant `@pytest.mark.asyncio` decorators from the async tests in this test module, including the decorated test at the referenced location and the other async test, while leaving their async definitions and test behavior unchanged.Source: Learnings
src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json (1)
1174-1174: 🚀 Performance & Scalability | 🔵 Trivial | ⚡ Quick winReuse the already-resolved LLM instance.
message_responseresolvesllm_modelinget_agent_requirements(), thencreate_agent_runnable()calls_get_llm()again. This can instantiate provider clients twice per request and may discard the resolved/connected instance; pass the resolved model into runnable construction instead.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/backend/base/langflow/initial_setup/starter_projects/Twitter` Thread Generator.json at line 1174, The resolved llm_model from get_agent_requirements is discarded because create_agent_runnable invokes _get_llm() again. Update create_agent_runnable and its callers, especially message_response and _run_agent_for_fallback, to accept and reuse the already-resolved LLM instance while preserving the existing tool binding and middleware construction.src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json (1)
589-589: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick winMake the mandatory-streaming contract explicit.
_get_llm()hardcodesstream=True, but the starter JSON still exposes astreaminput, includingfalsedefaults inSaaS Pricing.jsonandSequential Tasks Agents.json. Remove/hide the no-op control or regenerate consistent metadata.
src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json#L589-L589: align the embedded code andstreammetadata.src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json#L860-L860: remove or hide the no-op control.src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L414-L414: align the Researcher Agent metadata.src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L1153-L1153: align the Analysis & Editor Agent metadata.src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L2374-L2374: align the Writer Agent metadata.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/backend/base/langflow/initial_setup/starter_projects/SaaS` Pricing.json at line 589, The AgentComponent enforces streaming in _get_llm while starter-project metadata still exposes a no-op stream control. In SaaS Pricing.json at 589-589, remove or hide the stream input and align its metadata with the embedded AgentComponent code; apply the same no-op-control removal or hiding in SEO Keyword Generator.json at 860-860. In Sequential Tasks Agents.json at 414-414, 1153-1153, and 2374-2374, align the Researcher, Analysis & Editor, and Writer Agent metadata respectively so they no longer advertise a configurable stream value inconsistent with _get_llm.src/backend/tests/unit/agentic/services/helpers/test_event_consumer.py (1)
242-266: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low valueTest logic is correct and well-documented. The test properly verifies that
"[]"and its whitespace-padded variant are dropped while subsequent real content is yielded.The
@pytest.mark.asynciodecorator at line 243 is unnecessary — the project'spyproject.tomlconfiguresasyncio_mode = 'auto', which auto-detects async tests. All existing tests in this file use the decorator, so the new test follows convention, but new tests can omit it. Based on learnings, in the langflow-ai/langflow repository, pytest-asyncio is configured withasyncio_mode = 'auto'inpyproject.toml, meaning@pytest.mark.asynciodecorators are not needed for async tests under anytests/path.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/backend/tests/unit/agentic/services/helpers/test_event_consumer.py` around lines 242 - 266, Remove the unnecessary `@pytest.mark.asyncio` decorator from test_should_skip_empty_content_block_list_chunk, relying on the repository’s auto asyncio mode. Leave the test logic and documentation unchanged.Source: Learnings
src/backend/tests/unit/agentic/services/test_default_model_strength.py (1)
68-71: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low valueMinor inconsistency: test checks "Groq" absence but tests "Ollama" default.
The test asserts
"Groq" not in ASSISTANT_PREFERRED_MODELSbut then verifiesget_default_model("Ollama"). Using the same provider for both assertions would be clearer:♻️ Consistency fix
def test_provider_without_a_curated_preference_keeps_the_catalog_default(): """The preference map is an override, not a gate: unlisted providers still resolve.""" - assert "Groq" not in ASSISTANT_PREFERRED_MODELS + assert "Ollama" not in ASSISTANT_PREFERRED_MODELS assert get_default_model("Ollama") == "llama3.3"🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/backend/tests/unit/agentic/services/test_default_model_strength.py` around lines 68 - 71, Update test_provider_without_a_curated_preference_keeps_the_catalog_default so the provider used in the ASSISTANT_PREFERRED_MODELS absence assertion matches the provider passed to get_default_model; use “Ollama” consistently in both assertions.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@src/backend/base/langflow/initial_setup/starter_projects/SaaS` Pricing.json:
- Line 589: Update AgentComponent._build_middleware at all affected
sites—src/backend/base/langflow/initial_setup/starter_projects/SaaS
Pricing.json:589-589, SEO Keyword Generator.json:860-860, and Sequential Tasks
Agents.json:414-414, 1153-1153, 2374-2374—to avoid adding ToolRetryMiddleware
when the connected tools include an SQL-backed SQLComponent. Preserve retries
for non-SQL tools and existing middleware behavior otherwise.
In `@src/backend/base/langflow/initial_setup/starter_projects/Twitter` Thread
Generator.json:
- Line 1174: Update AgentComponent.json_response to use the same bounded
model-remediation retry flow as message_response, including provider/model
resolution, applied-remediation tracking, override updates, and persistence via
remember. Retry orchestrate_structured_output after applicable remediation,
while preserving the existing Data(error=...) fallback when no remediation
applies or retries are exhausted.
---
Nitpick comments:
In `@src/backend/base/langflow/initial_setup/starter_projects/SaaS` Pricing.json:
- Line 589: The AgentComponent enforces streaming in _get_llm while
starter-project metadata still exposes a no-op stream control. In SaaS
Pricing.json at 589-589, remove or hide the stream input and align its metadata
with the embedded AgentComponent code; apply the same no-op-control removal or
hiding in SEO Keyword Generator.json at 860-860. In Sequential Tasks Agents.json
at 414-414, 1153-1153, and 2374-2374, align the Researcher, Analysis & Editor,
and Writer Agent metadata respectively so they no longer advertise a
configurable stream value inconsistent with _get_llm.
In `@src/backend/base/langflow/initial_setup/starter_projects/Twitter` Thread
Generator.json:
- Line 1174: The resolved llm_model from get_agent_requirements is discarded
because create_agent_runnable invokes _get_llm() again. Update
create_agent_runnable and its callers, especially message_response and
_run_agent_for_fallback, to accept and reuse the already-resolved LLM instance
while preserving the existing tool binding and middleware construction.
In `@src/backend/tests/unit/agentic/services/helpers/test_event_consumer.py`:
- Around line 242-266: Remove the unnecessary `@pytest.mark.asyncio` decorator
from test_should_skip_empty_content_block_list_chunk, relying on the
repository’s auto asyncio mode. Leave the test logic and documentation
unchanged.
In `@src/backend/tests/unit/agentic/services/test_default_model_strength.py`:
- Around line 68-71: Update
test_provider_without_a_curated_preference_keeps_the_catalog_default so the
provider used in the ASSISTANT_PREFERRED_MODELS absence assertion matches the
provider passed to get_default_model; use “Ollama” consistently in both
assertions.
In
`@src/frontend/src/components/core/assistantPanel/components/__tests__/assistant-model-notice.test.tsx`:
- Around line 40-87: Add edge-case tests to the AssistantModelNotice suite:
verify multiple notices render one notice element per item, assert a
model_fallback notice without used_model uses the remediation text, and cover a
notice with undefined failed_model to confirm the "?" fallback is rendered. Use
the existing AssistantModelNotice queries and notice shapes without changing
component behavior.
In `@src/lfx/src/lfx/base/models/unified_models/instantiation.py`:
- Around line 370-377: Guard override merging in unified_models/instantiation.py
around cached_overrides and overrides so remediation keys cannot clobber
provider-specific kwargs; log and skip conflicting keys while preserving valid
overrides. In agent.py around AgentComponent.message_response, distinguish
TypeError raised during _get_llm() construction from hard input-validation
TypeErrors, allowing override-related failures to be diagnosed and remediated
instead of immediately re-raised.
In
`@src/lfx/tests/unit/components/models_and_agents/test_agent_model_remediation.py`:
- Line 21: Remove the redundant `@pytest.mark.asyncio` decorators from the async
tests in this test module, including the decorated test at the referenced
location and the other async test, while leaving their async definitions and
test behavior unchanged.
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📒 Files selected for processing (63)
docs/features/langflow-assistant-mcp.mddocs/features/langflow-assistant.mdsrc/backend/base/langflow/agentic/api/router.pysrc/backend/base/langflow/agentic/api/schemas.pysrc/backend/base/langflow/agentic/flows/LangflowAssistant.jsonsrc/backend/base/langflow/agentic/helpers/error_handling.pysrc/backend/base/langflow/agentic/services/assistant_service.pysrc/backend/base/langflow/agentic/services/flow_preparation.pysrc/backend/base/langflow/agentic/services/helpers/event_consumer.pysrc/backend/base/langflow/agentic/services/provider_service.pysrc/backend/base/langflow/initial_setup/starter_projects/Blog Writer.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Content Aggregator.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Deep Research Agent.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Document Q&A.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Market Research.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Multi Agent Flow.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.jsonsrc/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.jsonsrc/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Simple Agent.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.jsonsrc/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.jsonsrc/backend/tests/unit/agentic/flows/test_assistant_agent_iteration_budget.pysrc/backend/tests/unit/agentic/services/helpers/test_event_consumer.pysrc/backend/tests/unit/agentic/services/test_default_model_strength.pysrc/backend/tests/unit/agentic/services/test_iterations_budget.pysrc/frontend/src/components/core/assistantPanel/assistant-panel.types.tssrc/frontend/src/components/core/assistantPanel/components/__tests__/assistant-model-notice.test.tsxsrc/frontend/src/components/core/assistantPanel/components/assistant-flow-preview.tsxsrc/frontend/src/components/core/assistantPanel/components/assistant-message.tsxsrc/frontend/src/components/core/assistantPanel/components/assistant-model-notice.tsxsrc/frontend/src/components/core/assistantPanel/hooks/__tests__/use-assistant-chat-minimize-on-apply.test.tssrc/frontend/src/components/core/assistantPanel/hooks/use-assistant-chat.tssrc/frontend/src/controllers/API/queries/agentic/index.tssrc/frontend/src/controllers/API/queries/agentic/types.tssrc/frontend/src/locales/de.jsonsrc/frontend/src/locales/en.jsonsrc/frontend/src/locales/es.jsonsrc/frontend/src/locales/fr.jsonsrc/frontend/src/locales/ja.jsonsrc/frontend/src/locales/pt.jsonsrc/frontend/src/locales/zh-Hans.jsonsrc/lfx/src/lfx/base/models/model_remediation.pysrc/lfx/src/lfx/base/models/unified_models/instantiation.pysrc/lfx/src/lfx/components/models_and_agents/agent.pysrc/lfx/src/lfx/schema/content_types.pysrc/lfx/src/lfx/schema/message.pysrc/lfx/tests/unit/base/models/test_get_llm_streaming.pysrc/lfx/tests/unit/base/models/test_model_remediation.pysrc/lfx/tests/unit/components/models_and_agents/test_agent_model_remediation.pysrc/lfx/tests/unit/schema/test_message_content_blocks.py
| @@ -586,7 +586,7 @@ | |||
| "show": true, | |||
| "title_case": false, | |||
| "type": "code", | |||
| "value": "from __future__ import annotations\n\nimport uuid\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import ModelCallLimitMiddleware, ToolRetryMiddleware\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\n from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _agent_base_inputs():\n \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n inputs that are no-ops here and override info text on the inputs whose\n semantics shifted under create_agent.\n\n `verbose` is dropped because the create_agent event stream already surfaces\n every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n has nothing to toggle. Saved flows that still carry a `verbose` value just\n ignore it on load (the schema no longer declares it).\n \"\"\"\n drop = {\"verbose\"}\n overrides = {\n \"handle_parsing_errors\": BoolInput(\n name=\"handle_parsing_errors\",\n display_name=\"Handle Parse Errors\",\n value=True,\n advanced=True,\n info=(\n \"Adds tool-execution retry as a safety net. `create_agent` already \"\n \"feeds tool-call validation errors back to the LLM automatically; \"\n \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n \"runtime failures are retried (max 2 retries).\"\n ),\n ),\n \"max_iterations\": IntInput(\n name=\"max_iterations\",\n display_name=\"Max Iterations\",\n value=15,\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n info=(\n \"Maximum number of model calls the agent can make before stopping \"\n \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n ),\n ),\n }\n return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\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 documentation: str = \"https://docs.langflow.org/agents\"\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 ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *_agent_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=True,\n advanced=True,\n ),\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 BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n emits ``on_chat_model_stream`` chunks when the underlying chat model is\n instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n kept in the UI for backwards compatibility but is intentionally ignored here.\n Without ``stream=True``, the chat model accumulates the whole response and\n only emits ``on_chat_model_end``, silently disabling the Playground's live-\n typing view and breaking the streaming contract on the /events surface.\n \"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=True,\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = 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\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\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\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n `system_prompt` is a connectable MultilineInput, so the value can arrive\n as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n a raw Message has no `.replace` and used to crash the agent build.\n \"\"\"\n if prompt is None:\n return None\n prompt = _extract_text_content(prompt)\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n def create_agent_runnable(self):\n \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n Replaces the legacy `AgentExecutor` runnable inherited from\n `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n runs on the new graph API.\n\n `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n translated to LangGraph middleware. Without that translation those user inputs\n would silently become no-ops on the new API.\n\n Provider notes:\n - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n handles tool_choice correctly. The legacy `create_granite_agent` path was\n dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n API now rejects.\n - Ollama and other small/local models often emit malformed tool args. The\n ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n catches Pydantic ValidationErrors from bad args and feeds the error back\n to the LLM as a retry signal, so the agent recovers gracefully.\n \"\"\"\n llm = self._get_llm()\n tools = self.tools or []\n\n # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n # an LLM that doesn't support tool calling fails on the first user message\n # instead of when the user wires up the component, which is a much worse UX.\n # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n # (signature mismatch). Treat all three as the same UX failure.\n if tools:\n try:\n llm.bind_tools(tools)\n except (NotImplementedError, AttributeError, TypeError) as exc:\n # Include the underlying error so a broken tool schema or a\n # provider implementation bug is not silently disguised as a\n # \"model can't call tools\" UX error.\n msg = (\n f\"{self.display_name} does not support tool calling, \"\n \"or one of the connected tools failed to bind. \"\n \"Please connect a tool-calling capable language model and \"\n f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n )\n raise NotImplementedError(msg) from exc\n\n middleware = self._build_middleware(llm)\n return create_agent(\n model=llm,\n tools=tools,\n system_prompt=self.system_prompt or \"\",\n middleware=middleware or None,\n )\n\n def _compute_recursion_limit(self) -> int:\n \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n saved 0 or negative value cannot under-cap the graph below one full\n iteration. The +5 buffer covers start/end/router overhead.\n \"\"\"\n raw = getattr(self, \"max_iterations\", None)\n run_limit = max(1, int(raw)) if raw is not None else 15\n return run_limit * 2 + 5\n\n def _build_middleware(self, llm: Any) -> list:\n # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n # because some providers do credential resolution / client instantiation\n # lazily on each call. The caller — `create_agent_runnable` — already\n # resolved it once for `bind_tools`, so reuse that instance here.\n middleware: list = []\n max_iterations = getattr(self, \"max_iterations\", None)\n if max_iterations is not None:\n # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n # 0 or negative value (falsy) must NOT silently drop the limiter and\n # allow an unbounded model/tool loop — clamp it to a real minimum.\n run_limit = max(1, int(max_iterations))\n middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n # it on a no-tools agent inflates the compiled graph and adds per-invocation\n # middleware overhead for nothing, which is a measurable contributor to\n # trivial-prompt latency (QA UI-003).\n if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n middleware.append(ToolRetryMiddleware(max_retries=2))\n # WatsonX models have two known platform quirks; both still reproduce on\n # the current API, so we keep the protections from the legacy\n # `create_granite_agent` path.\n # 1. Multi-tool-call assistant turns are rejected (\"This model only\n # supports single tool-calls at once!\"). Clamp to one per turn.\n # 2. Tool args occasionally come back as literal placeholder strings\n # (e.g. `<result-from-search>`). Re-invoke once with a corrective\n # SystemMessage.\n # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n # applied to the final response, including any corrective re-invoke\n # produced by WatsonXPlaceholderMiddleware.\n if is_watsonx_model(llm):\n middleware.append(SingleToolCallMiddleware())\n middleware.append(WatsonXPlaceholderMiddleware())\n return middleware\n\n async def run_agent(self, agent) -> Message:\n \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n (in `lfx.base.agents.events`) can be reused unchanged.\n \"\"\"\n messages = build_initial_messages(\n input_value=self.input_value,\n chat_history=getattr(self, \"chat_history\", None),\n )\n input_dict = {\"messages\": messages}\n\n agent_message = self._build_initial_agent_message()\n token_usage_handler = TokenUsageCallbackHandler()\n\n # Stream tokens to the event manager when running inside the Langflow runtime.\n # This is what powers the live-typing view in the chat UI.\n on_token_callback: OnTokenFunctionType | None = None\n if getattr(self, \"_event_manager\", None):\n on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n # Align LangGraph's `recursion_limit` with `max_iterations` so the\n # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n # Each iteration is ~2 graph steps (model node + tools node); add 5\n # for start/end overhead.\n recursion_limit = self._compute_recursion_limit()\n\n stream = adapt_graph_events_to_executor_shape(\n agent.astream_events(\n input_dict,\n config={\n \"callbacks\": [\n AgentAsyncHandler(self.log),\n token_usage_handler,\n *self._get_shared_callbacks(),\n ],\n \"recursion_limit\": recursion_limit,\n },\n version=\"v2\",\n )\n )\n try:\n result = await process_agent_events(\n stream,\n agent_message,\n cast(\"SendMessageFunctionType\", self.send_message),\n on_token_callback,\n )\n except ExceptionWithMessageError as e:\n # Drop the half-stored partial message from the DB (only if it was\n # actually persisted) and tell the frontend to remove the stale bubble.\n if hasattr(e, \"agent_message\"):\n msg_id = e.agent_message.get_id()\n if msg_id:\n await delete_message(id_=msg_id)\n await self._send_message_event(e.agent_message, category=\"remove_message\")\n logger.error(f\"ExceptionWithMessageError: {e}\")\n raise\n\n usage_data = token_usage_handler.get_usage()\n if usage_data:\n self._token_usage = usage_data\n result.properties.usage = usage_data\n # Only round-trip the DB when the message was stored (Chat Output wired).\n # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n # then would create a phantom row.\n if result.get_id():\n stored_result = await self._update_stored_message(result)\n await self._send_message_event(stored_result)\n result = stored_result\n\n self.status = result\n return result\n\n def _build_initial_agent_message(self) -> Message:\n \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n if hasattr(self, \"graph\"):\n session_id = self.graph.session_id\n elif hasattr(self, \"_session_id\"):\n session_id = self._session_id\n else:\n session_id = None\n\n sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n return Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=sender_name,\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n # `text=\"\"` sentinel so MessageTable's no_content check accepts\n # an in-flight agent message whose content_blocks haven't been\n # populated yet. Mirrors ChatInput's convention.\n text=\"\",\n # Flat chronological event log; see lfx.base.agents.events.\n content_blocks=[],\n session_id=session_id or uuid.uuid4(),\n )\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\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,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys. `verbose` was dropped from the input set\n # (see `_agent_base_inputs` — the create_agent event stream already\n # surfaces every step), so it is intentionally NOT required here.\n # Saved flows that still carry a `verbose` value just ignore it on\n # load.\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"max_iterations\",\n \"handle_parsing_errors\",\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 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\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n 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\n return tools\n" | |||
| "value": "from __future__ import annotations\n\nimport uuid\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import ModelCallLimitMiddleware, ToolRetryMiddleware\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\n from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _agent_base_inputs():\n \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n inputs that are no-ops here and override info text on the inputs whose\n semantics shifted under create_agent.\n\n `verbose` is dropped because the create_agent event stream already surfaces\n every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n has nothing to toggle. Saved flows that still carry a `verbose` value just\n ignore it on load (the schema no longer declares it).\n \"\"\"\n drop = {\"verbose\"}\n overrides = {\n \"handle_parsing_errors\": BoolInput(\n name=\"handle_parsing_errors\",\n display_name=\"Handle Parse Errors\",\n value=True,\n advanced=True,\n info=(\n \"Adds tool-execution retry as a safety net. `create_agent` already \"\n \"feeds tool-call validation errors back to the LLM automatically; \"\n \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n \"runtime failures are retried (max 2 retries).\"\n ),\n ),\n \"max_iterations\": IntInput(\n name=\"max_iterations\",\n display_name=\"Max Iterations\",\n value=15,\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n info=(\n \"Maximum number of model calls the agent can make before stopping \"\n \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n ),\n ),\n }\n return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\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 documentation: str = \"https://docs.langflow.org/agents\"\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 ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *_agent_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=True,\n advanced=True,\n ),\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 BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n emits ``on_chat_model_stream`` chunks when the underlying chat model is\n instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n kept in the UI for backwards compatibility but is intentionally ignored here.\n Without ``stream=True``, the chat model accumulates the whole response and\n only emits ``on_chat_model_end``, silently disabling the Playground's live-\n typing view and breaking the streaming contract on the /events surface.\n \"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=True,\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n overrides=getattr(self, \"_model_overrides\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = 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\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\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\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n `system_prompt` is a connectable MultilineInput, so the value can arrive\n as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n a raw Message has no `.replace` and used to crash the agent build.\n \"\"\"\n if prompt is None:\n return None\n prompt = _extract_text_content(prompt)\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n def create_agent_runnable(self):\n \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n Replaces the legacy `AgentExecutor` runnable inherited from\n `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n runs on the new graph API.\n\n `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n translated to LangGraph middleware. Without that translation those user inputs\n would silently become no-ops on the new API.\n\n Provider notes:\n - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n handles tool_choice correctly. The legacy `create_granite_agent` path was\n dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n API now rejects.\n - Ollama and other small/local models often emit malformed tool args. The\n ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n catches Pydantic ValidationErrors from bad args and feeds the error back\n to the LLM as a retry signal, so the agent recovers gracefully.\n \"\"\"\n llm = self._get_llm()\n tools = self.tools or []\n\n # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n # an LLM that doesn't support tool calling fails on the first user message\n # instead of when the user wires up the component, which is a much worse UX.\n # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n # (signature mismatch). Treat all three as the same UX failure.\n if tools:\n try:\n llm.bind_tools(tools)\n except (NotImplementedError, AttributeError, TypeError) as exc:\n # Include the underlying error so a broken tool schema or a\n # provider implementation bug is not silently disguised as a\n # \"model can't call tools\" UX error.\n msg = (\n f\"{self.display_name} does not support tool calling, \"\n \"or one of the connected tools failed to bind. \"\n \"Please connect a tool-calling capable language model and \"\n f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n )\n raise NotImplementedError(msg) from exc\n\n middleware = self._build_middleware(llm)\n return create_agent(\n model=llm,\n tools=tools,\n system_prompt=self.system_prompt or \"\",\n middleware=middleware or None,\n )\n\n def _compute_recursion_limit(self) -> int:\n \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n saved 0 or negative value cannot under-cap the graph below one full\n iteration. The +5 buffer covers start/end/router overhead.\n \"\"\"\n raw = getattr(self, \"max_iterations\", None)\n run_limit = max(1, int(raw)) if raw is not None else 15\n return run_limit * 2 + 5\n\n def _build_middleware(self, llm: Any) -> list:\n # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n # because some providers do credential resolution / client instantiation\n # lazily on each call. The caller — `create_agent_runnable` — already\n # resolved it once for `bind_tools`, so reuse that instance here.\n middleware: list = []\n max_iterations = getattr(self, \"max_iterations\", None)\n if max_iterations is not None:\n # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n # 0 or negative value (falsy) must NOT silently drop the limiter and\n # allow an unbounded model/tool loop — clamp it to a real minimum.\n run_limit = max(1, int(max_iterations))\n middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n # it on a no-tools agent inflates the compiled graph and adds per-invocation\n # middleware overhead for nothing, which is a measurable contributor to\n # trivial-prompt latency (QA UI-003).\n if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n middleware.append(ToolRetryMiddleware(max_retries=2))\n # WatsonX models have two known platform quirks; both still reproduce on\n # the current API, so we keep the protections from the legacy\n # `create_granite_agent` path.\n # 1. Multi-tool-call assistant turns are rejected (\"This model only\n # supports single tool-calls at once!\"). Clamp to one per turn.\n # 2. Tool args occasionally come back as literal placeholder strings\n # (e.g. `<result-from-search>`). Re-invoke once with a corrective\n # SystemMessage.\n # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n # applied to the final response, including any corrective re-invoke\n # produced by WatsonXPlaceholderMiddleware.\n if is_watsonx_model(llm):\n middleware.append(SingleToolCallMiddleware())\n middleware.append(WatsonXPlaceholderMiddleware())\n return middleware\n\n async def run_agent(self, agent) -> Message:\n \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n (in `lfx.base.agents.events`) can be reused unchanged.\n \"\"\"\n messages = build_initial_messages(\n input_value=self.input_value,\n chat_history=getattr(self, \"chat_history\", None),\n )\n input_dict = {\"messages\": messages}\n\n agent_message = self._build_initial_agent_message()\n token_usage_handler = TokenUsageCallbackHandler()\n\n # Stream tokens to the event manager when running inside the Langflow runtime.\n # This is what powers the live-typing view in the chat UI.\n on_token_callback: OnTokenFunctionType | None = None\n if getattr(self, \"_event_manager\", None):\n on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n # Align LangGraph's `recursion_limit` with `max_iterations` so the\n # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n # Each iteration is ~2 graph steps (model node + tools node); add 5\n # for start/end overhead.\n recursion_limit = self._compute_recursion_limit()\n\n stream = adapt_graph_events_to_executor_shape(\n agent.astream_events(\n input_dict,\n config={\n \"callbacks\": [\n AgentAsyncHandler(self.log),\n token_usage_handler,\n *self._get_shared_callbacks(),\n ],\n \"recursion_limit\": recursion_limit,\n },\n version=\"v2\",\n )\n )\n try:\n result = await process_agent_events(\n stream,\n agent_message,\n cast(\"SendMessageFunctionType\", self.send_message),\n on_token_callback,\n )\n except ExceptionWithMessageError as e:\n # Drop the half-stored partial message from the DB (only if it was\n # actually persisted) and tell the frontend to remove the stale bubble.\n if hasattr(e, \"agent_message\"):\n msg_id = e.agent_message.get_id()\n if msg_id:\n await delete_message(id_=msg_id)\n await self._send_message_event(e.agent_message, category=\"remove_message\")\n logger.error(f\"ExceptionWithMessageError: {e}\")\n raise\n\n usage_data = token_usage_handler.get_usage()\n if usage_data:\n self._token_usage = usage_data\n result.properties.usage = usage_data\n # Only round-trip the DB when the message was stored (Chat Output wired).\n # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n # then would create a phantom row.\n if result.get_id():\n stored_result = await self._update_stored_message(result)\n await self._send_message_event(stored_result)\n result = stored_result\n\n self.status = result\n return result\n\n def _build_initial_agent_message(self) -> Message:\n \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n if hasattr(self, \"graph\"):\n session_id = self.graph.session_id\n elif hasattr(self, \"_session_id\"):\n session_id = self._session_id\n else:\n session_id = None\n\n sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n return Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=sender_name,\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n # `text=\"\"` sentinel so MessageTable's no_content check accepts\n # an in-flight agent message whose content_blocks haven't been\n # populated yet. Mirrors ChatInput's convention.\n text=\"\",\n # Flat chronological event log; see lfx.base.agents.events.\n content_blocks=[],\n session_id=session_id or uuid.uuid4(),\n )\n\n def _selected_provider_and_model(self) -> tuple[str | None, str | None]:\n \"\"\"Provider/name of the selected model, for error-driven remediation.\"\"\"\n try:\n selected = self._resolve_selected_model()\n if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n return selected[0].get(\"provider\"), selected[0].get(\"name\")\n except (AttributeError, TypeError, KeyError, ImportError):\n pass\n return None, None\n\n async def message_response(self) -> Message:\n from lfx.base.models.model_remediation import find_remediation, remember\n\n provider, model_name = self._selected_provider_and_model()\n applied: set[str] = set()\n while True:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n self._agent_result = result\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except Exception as e:\n # Error-driven remediation (see model_remediation): run_agent wraps\n # the provider error in ExceptionWithMessageError, so match the chain.\n error_text = f\"{e} {getattr(e, '__cause__', '') or ''}\"\n remediation = find_remediation(error_text, provider, already_applied=applied)\n if remediation is None:\n label = type(e).__name__\n await logger.aerror(f\"{label}: {e!s}\")\n raise\n applied.add(remediation.name)\n self._model_overrides = {**(getattr(self, \"_model_overrides\", None) or {}), **remediation.overrides}\n await logger.awarning(\n f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n )\n continue\n else:\n if getattr(self, \"_model_overrides\", None):\n remember(provider, model_name, self._model_overrides)\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\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,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys. `verbose` was dropped from the input set\n # (see `_agent_base_inputs` — the create_agent event stream already\n # surfaces every step), so it is intentionally NOT required here.\n # Saved flows that still carry a `verbose` value just ignore it on\n # load.\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"max_iterations\",\n \"handle_parsing_errors\",\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 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\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n 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\n return tools\n" | |||
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🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
uv run python - <<'PY'
import inspect
from langchain.agents.middleware import ToolRetryMiddleware
print(inspect.signature(ToolRetryMiddleware))
print(inspect.getsource(ToolRetryMiddleware))
PYRepository: langflow-ai/langflow
Length of output: 198
🏁 Script executed:
python3 - <<'PY'
import inspect
import sys
mods = [
("langchain.agents.middleware", "ToolRetryMiddleware"),
("langchain.agents.middleware", "ModelCallLimitMiddleware"),
]
for mod_name, attr in mods:
try:
mod = __import__(mod_name, fromlist=[attr])
obj = getattr(mod, attr)
print(f"## {mod_name}.{attr}")
print(inspect.signature(obj))
try:
print(inspect.getsource(obj))
except Exception as e:
print(f"<no source: {e}>")
except Exception as e:
print(f"## import failed for {mod_name}.{attr}: {e}")
PYRepository: langflow-ai/langflow
Length of output: 359
🏁 Script executed:
rg -n "ToolRetryMiddleware|max_retries|handle_parsing_errors|add_calculator_tool|add_current_date_tool|sql" src/backend/base/langflow/initial_setup/starter_projects/SaaS\ Pricing.json src/backend/base/langflow/initial_setup/starter_projects/SEO\ Keyword\ Generator.json src/backend/base/langflow/initial_setup/starter_projects/Sequential\ Tasks\ Agents.jsonRepository: langflow-ai/langflow
Length of output: 50377
🏁 Script executed:
python3 - <<'PY'
from pathlib import Path
import json
import re
paths = [
Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json"),
Path("src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json"),
Path("src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json"),
]
for path in paths:
text = path.read_text()
print(f"## {path}")
for pat in ["ToolRetryMiddleware(max_retries=2)", "handle_parsing_errors", "add_calculator_tool", "sql", "SQL"]:
print(f"{pat}: {text.count(pat)}")
print()
PYRepository: langflow-ai/langflow
Length of output: 719
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python3 - <<'PY'
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
text = path.read_text().splitlines()
for target in [560, 580, 600, 620]:
start = max(0, target - 15)
end = min(len(text), target + 15)
print(f"## lines {start+1}-{end}")
for i in range(start, end):
print(f"{i+1}: {text[i]}")
print()
PYRepository: langflow-ai/langflow
Length of output: 50376
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
print(type(data).__name__)
if isinstance(data, dict):
print(data.keys())
elif isinstance(data, list):
print("len", len(data))
print(type(data[0]).__name__, data[0].keys() if isinstance(data[0], dict) else None)
PYRepository: langflow-ai/langflow
Length of output: 286
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
def walk(obj, path=()):
if isinstance(obj, dict):
if "name" in obj or "display_name" in obj or "type" in obj:
if obj.get("name") in {"sql", "SQL", "Agent"} or obj.get("display_name") in {"SQL", "Agent"}:
print(path, {k: obj.get(k) for k in ("name","display_name","type","id","component") if k in obj})
for k, v in obj.items():
walk(v, path + (str(k),))
elif isinstance(obj, list):
for i, v in enumerate(obj):
walk(v, path + (str(i),))
walk(data)
PYRepository: langflow-ai/langflow
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
nodes = data["data"]["nodes"]
print("nodes:", len(nodes))
for idx, node in enumerate(nodes):
d = node.get("data", {})
print(idx, d.get("display_name"), d.get("name"), d.get("type"), d.get("id"))
PYRepository: langflow-ai/langflow
Length of output: 354
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
print("edges:", len(data["data"].get("edges", [])))
for e in data["data"].get("edges", []):
print({k: e.get(k) for k in ("source", "target", "sourceHandle", "targetHandle", "data") if k in e})
PYRepository: langflow-ai/langflow
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
if "sql" in str(d).lower() or "sql" in str(node).lower():
print("NODE", idx)
for k in ("display_name", "name", "type", "id", "node", "component", "template"):
if k in d:
v = d[k]
if isinstance(v, str) and len(v) > 500:
v = v[:500] + "..."
print(k, "=", v)
print("keys:", list(d.keys())[:50])
print()
PYRepository: langflow-ai/langflow
Length of output: 50376
🏁 Script executed:
python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
name = d.get("display_name") or d.get("name") or d.get("type")
print(idx, name)
PYRepository: langflow-ai/langflow
Length of output: 214
🏁 Script executed:
python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
if any("sql" in str(v).lower() for v in d.values() if isinstance(v, (str, list, dict))):
print(f"NODE {idx}")
for key in ("display_name", "name", "type", "template", "node"):
if key in d:
v = d[key]
if isinstance(v, (dict, list)):
print(f"{key} = <{type(v).__name__}>")
else:
print(f"{key} = {v}")
print("---")
PYRepository: langflow-ai/langflow
Length of output: 320
🏁 Script executed:
python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
nodes = data["data"]["nodes"]
print("node_count", len(nodes))
for idx, node in enumerate(nodes):
d = node.get("data", {})
label = d.get("display_name") or d.get("name") or d.get("type") or ""
print(idx, label)
PYRepository: langflow-ai/langflow
Length of output: 227
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
print("edges:")
for e in data["data"].get("edges", []):
print(e.get("source"), "->", e.get("target"), " | ", e.get("sourceHandle"), "->", e.get("targetHandle"))
PYRepository: langflow-ai/langflow
Length of output: 900
🏁 Script executed:
python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
text = json.dumps(d, ensure_ascii=False)
if "sql" in text.lower() or "SELECT" in text or "INSERT" in text or "UPDATE" in text or "DELETE" in text:
print(f"NODE {idx}")
for key in ("display_name", "name", "type", "node", "component"):
if key in d:
print(key, "=", d[key])
print()
PYRepository: langflow-ai/langflow
Length of output: 50376
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
label = d.get("display_name") or d.get("name") or d.get("type")
if label == "SQLComponent":
print("NODE", idx, label)
for k in sorted(d.keys()):
if k in {"template", "node", "code", "metadata"}:
continue
v = d[k]
if isinstance(v, (str, int, float, bool)) or v is None:
print(f"{k}: {v}")
print("template keys:", list(d.get("template", {}).keys())[:80])
PYRepository: langflow-ai/langflow
Length of output: 238
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
if d.get("display_name") == "SQLComponent" or d.get("name") == "SQLComponent":
tmpl = d.get("template", {})
for key in tmpl:
if any(tok in key.lower() for tok in ["query", "table", "db", "sql", "read", "write", "mode", "transaction"]):
print(key, "=", tmpl[key])
PYRepository: langflow-ai/langflow
Length of output: 158
🌐 Web query:
LangChain ToolRetryMiddleware retries tool execution on failure semantics retry_on on_failure documentation
💡 Result:
The ToolRetryMiddleware in LangChain is a prebuilt middleware component designed to automatically retry failed tool calls using configurable strategies, including exponential backoff [1][2][3]. Key semantics for retry behavior are managed through the following parameters: retry_on This parameter determines which exceptions trigger a retry [4][5]. It accepts either: - A tuple of exception types (e.g., (ConnectionError, TimeoutError)) [2][5]. - A callable that accepts an exception and returns a boolean (True to retry, False to fail immediately) [4][5]. By default, the middleware retries on all exceptions [4][5]. on_failure This parameter defines the behavior once the maximum number of retries (max_retries) is exhausted [4][5]. Supported options include: - 'continue': Returns a ToolMessage containing error details, allowing the agent to handle the failure and potentially recover [4][5]. - 'error': Re-raises the original exception, which halts agent execution [4][5]. - Custom callable: A function that accepts the exception and returns a string, which is then used as the content for the resulting ToolMessage [4][5]. For backward compatibility, 'return_message' is supported as an alias for 'continue', and 'raise' is supported as an alias for 'error' [4][5]. Additional Configuration - max_retries: The maximum number of retry attempts [2][4]. - backoff_factor: The multiplier for exponential backoff (delay = initial_delay * (backoff_factor retry_number)) [2][4][5]. - initial_delay: The delay in seconds before the first retry [2][4][5]. - max_delay: A cap on the delay growth [2][4][5]. - jitter: A boolean flag to add random variation (+/- 25%) to the delay to prevent thundering herd problems [2][4][5].
Citations:
- 1: https://reference.langchain.com/python/langchain/agents/middleware/tool_retry/ToolRetryMiddleware
- 2: https://docs.langchain.com/oss/python/langchain/middleware/built-in?featured_on=talkpython
- 3: feat(langchain_v1): tool retry middleware langchain-ai/langchain#33503
- 4: https://github.com/langchain-ai/langchain/blob/50febb79/libs/langchain_v1/langchain/agents/middleware/tool_retry.py
- 5: https://github.com/langchain-ai/langchain/blob/8b21400627672754a56350e552d39b23f77206a3/libs/langchain_v1/langchain/agents/middleware/tool_retry.py
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python3 - <<'PY'
import json
from pathlib import Path
path = Path("src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json")
data = json.loads(path.read_text())
for idx, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
if d.get("display_name") == "SQLComponent" or d.get("name") == "SQLComponent":
print("NODE", idx)
print("keys:", sorted(d.keys()))
tmpl = d.get("template", {})
print("template keys:", sorted(tmpl.keys()))
for k, v in tmpl.items():
if any(tok in k.lower() for tok in ["query", "table", "db", "sql", "read", "write", "mode", "transaction", "tool"]):
print(f"{k} = {v}")
PYRepository: langflow-ai/langflow
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python3 - <<'PY'
import json
from pathlib import Path
for rel in [
"src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json",
"src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json",
]:
data = json.loads(Path(rel).read_text())
print(f"## {rel}")
for i, node in enumerate(data["data"]["nodes"]):
d = node.get("data", {})
print(i, d.get("display_name") or d.get("name") or d.get("type"))
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Skip retries for the SQL-backed agent. ToolRetryMiddleware(max_retries=2) can re-run a failed SQL statement, which is unsafe here because the connected SQLComponent may execute non-idempotent queries.
📍 Affects 3 files
src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json#L589-L589(this comment)src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json#L860-L860src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L414-L414src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L1153-L1153src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json#L2374-L2374
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@src/backend/base/langflow/initial_setup/starter_projects/SaaS` Pricing.json
at line 589, Update AgentComponent._build_middleware at all affected
sites—src/backend/base/langflow/initial_setup/starter_projects/SaaS
Pricing.json:589-589, SEO Keyword Generator.json:860-860, and Sequential Tasks
Agents.json:414-414, 1153-1153, 2374-2374—to avoid adding ToolRetryMiddleware
when the connected tools include an SQL-backed SQLComponent. Preserve retries
for non-SQL tools and existing middleware behavior otherwise.
| @@ -1171,7 +1171,7 @@ | |||
| "show": true, | |||
| "title_case": false, | |||
| "type": "code", | |||
| "value": "from __future__ import annotations\n\nimport uuid\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import ModelCallLimitMiddleware, ToolRetryMiddleware\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\n from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _agent_base_inputs():\n \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n inputs that are no-ops here and override info text on the inputs whose\n semantics shifted under create_agent.\n\n `verbose` is dropped because the create_agent event stream already surfaces\n every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n has nothing to toggle. Saved flows that still carry a `verbose` value just\n ignore it on load (the schema no longer declares it).\n \"\"\"\n drop = {\"verbose\"}\n overrides = {\n \"handle_parsing_errors\": BoolInput(\n name=\"handle_parsing_errors\",\n display_name=\"Handle Parse Errors\",\n value=True,\n advanced=True,\n info=(\n \"Adds tool-execution retry as a safety net. `create_agent` already \"\n \"feeds tool-call validation errors back to the LLM automatically; \"\n \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n \"runtime failures are retried (max 2 retries).\"\n ),\n ),\n \"max_iterations\": IntInput(\n name=\"max_iterations\",\n display_name=\"Max Iterations\",\n value=15,\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n info=(\n \"Maximum number of model calls the agent can make before stopping \"\n \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n ),\n ),\n }\n return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\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 documentation: str = \"https://docs.langflow.org/agents\"\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 ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *_agent_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=True,\n advanced=True,\n ),\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 BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n emits ``on_chat_model_stream`` chunks when the underlying chat model is\n instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n kept in the UI for backwards compatibility but is intentionally ignored here.\n Without ``stream=True``, the chat model accumulates the whole response and\n only emits ``on_chat_model_end``, silently disabling the Playground's live-\n typing view and breaking the streaming contract on the /events surface.\n \"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=True,\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = 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\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\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\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n `system_prompt` is a connectable MultilineInput, so the value can arrive\n as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n a raw Message has no `.replace` and used to crash the agent build.\n \"\"\"\n if prompt is None:\n return None\n prompt = _extract_text_content(prompt)\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n def create_agent_runnable(self):\n \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n Replaces the legacy `AgentExecutor` runnable inherited from\n `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n runs on the new graph API.\n\n `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n translated to LangGraph middleware. Without that translation those user inputs\n would silently become no-ops on the new API.\n\n Provider notes:\n - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n handles tool_choice correctly. The legacy `create_granite_agent` path was\n dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n API now rejects.\n - Ollama and other small/local models often emit malformed tool args. The\n ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n catches Pydantic ValidationErrors from bad args and feeds the error back\n to the LLM as a retry signal, so the agent recovers gracefully.\n \"\"\"\n llm = self._get_llm()\n tools = self.tools or []\n\n # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n # an LLM that doesn't support tool calling fails on the first user message\n # instead of when the user wires up the component, which is a much worse UX.\n # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n # (signature mismatch). Treat all three as the same UX failure.\n if tools:\n try:\n llm.bind_tools(tools)\n except (NotImplementedError, AttributeError, TypeError) as exc:\n # Include the underlying error so a broken tool schema or a\n # provider implementation bug is not silently disguised as a\n # \"model can't call tools\" UX error.\n msg = (\n f\"{self.display_name} does not support tool calling, \"\n \"or one of the connected tools failed to bind. \"\n \"Please connect a tool-calling capable language model and \"\n f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n )\n raise NotImplementedError(msg) from exc\n\n middleware = self._build_middleware(llm)\n return create_agent(\n model=llm,\n tools=tools,\n system_prompt=self.system_prompt or \"\",\n middleware=middleware or None,\n )\n\n def _compute_recursion_limit(self) -> int:\n \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n saved 0 or negative value cannot under-cap the graph below one full\n iteration. The +5 buffer covers start/end/router overhead.\n \"\"\"\n raw = getattr(self, \"max_iterations\", None)\n run_limit = max(1, int(raw)) if raw is not None else 15\n return run_limit * 2 + 5\n\n def _build_middleware(self, llm: Any) -> list:\n # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n # because some providers do credential resolution / client instantiation\n # lazily on each call. The caller — `create_agent_runnable` — already\n # resolved it once for `bind_tools`, so reuse that instance here.\n middleware: list = []\n max_iterations = getattr(self, \"max_iterations\", None)\n if max_iterations is not None:\n # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n # 0 or negative value (falsy) must NOT silently drop the limiter and\n # allow an unbounded model/tool loop — clamp it to a real minimum.\n run_limit = max(1, int(max_iterations))\n middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n # it on a no-tools agent inflates the compiled graph and adds per-invocation\n # middleware overhead for nothing, which is a measurable contributor to\n # trivial-prompt latency (QA UI-003).\n if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n middleware.append(ToolRetryMiddleware(max_retries=2))\n # WatsonX models have two known platform quirks; both still reproduce on\n # the current API, so we keep the protections from the legacy\n # `create_granite_agent` path.\n # 1. Multi-tool-call assistant turns are rejected (\"This model only\n # supports single tool-calls at once!\"). Clamp to one per turn.\n # 2. Tool args occasionally come back as literal placeholder strings\n # (e.g. `<result-from-search>`). Re-invoke once with a corrective\n # SystemMessage.\n # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n # applied to the final response, including any corrective re-invoke\n # produced by WatsonXPlaceholderMiddleware.\n if is_watsonx_model(llm):\n middleware.append(SingleToolCallMiddleware())\n middleware.append(WatsonXPlaceholderMiddleware())\n return middleware\n\n async def run_agent(self, agent) -> Message:\n \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n (in `lfx.base.agents.events`) can be reused unchanged.\n \"\"\"\n messages = build_initial_messages(\n input_value=self.input_value,\n chat_history=getattr(self, \"chat_history\", None),\n )\n input_dict = {\"messages\": messages}\n\n agent_message = self._build_initial_agent_message()\n token_usage_handler = TokenUsageCallbackHandler()\n\n # Stream tokens to the event manager when running inside the Langflow runtime.\n # This is what powers the live-typing view in the chat UI.\n on_token_callback: OnTokenFunctionType | None = None\n if getattr(self, \"_event_manager\", None):\n on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n # Align LangGraph's `recursion_limit` with `max_iterations` so the\n # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n # Each iteration is ~2 graph steps (model node + tools node); add 5\n # for start/end overhead.\n recursion_limit = self._compute_recursion_limit()\n\n stream = adapt_graph_events_to_executor_shape(\n agent.astream_events(\n input_dict,\n config={\n \"callbacks\": [\n AgentAsyncHandler(self.log),\n token_usage_handler,\n *self._get_shared_callbacks(),\n ],\n \"recursion_limit\": recursion_limit,\n },\n version=\"v2\",\n )\n )\n try:\n result = await process_agent_events(\n stream,\n agent_message,\n cast(\"SendMessageFunctionType\", self.send_message),\n on_token_callback,\n )\n except ExceptionWithMessageError as e:\n # Drop the half-stored partial message from the DB (only if it was\n # actually persisted) and tell the frontend to remove the stale bubble.\n if hasattr(e, \"agent_message\"):\n msg_id = e.agent_message.get_id()\n if msg_id:\n await delete_message(id_=msg_id)\n await self._send_message_event(e.agent_message, category=\"remove_message\")\n logger.error(f\"ExceptionWithMessageError: {e}\")\n raise\n\n usage_data = token_usage_handler.get_usage()\n if usage_data:\n self._token_usage = usage_data\n result.properties.usage = usage_data\n # Only round-trip the DB when the message was stored (Chat Output wired).\n # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n # then would create a phantom row.\n if result.get_id():\n stored_result = await self._update_stored_message(result)\n await self._send_message_event(stored_result)\n result = stored_result\n\n self.status = result\n return result\n\n def _build_initial_agent_message(self) -> Message:\n \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n if hasattr(self, \"graph\"):\n session_id = self.graph.session_id\n elif hasattr(self, \"_session_id\"):\n session_id = self._session_id\n else:\n session_id = None\n\n sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n return Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=sender_name,\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n # `text=\"\"` sentinel so MessageTable's no_content check accepts\n # an in-flight agent message whose content_blocks haven't been\n # populated yet. Mirrors ChatInput's convention.\n text=\"\",\n # Flat chronological event log; see lfx.base.agents.events.\n content_blocks=[],\n session_id=session_id or uuid.uuid4(),\n )\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\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,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys. `verbose` was dropped from the input set\n # (see `_agent_base_inputs` — the create_agent event stream already\n # surfaces every step), so it is intentionally NOT required here.\n # Saved flows that still carry a `verbose` value just ignore it on\n # load.\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"max_iterations\",\n \"handle_parsing_errors\",\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 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\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n 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\n return tools\n" | |||
| "value": "from __future__ import annotations\n\nimport uuid\nfrom contextlib import contextmanager\nfrom datetime import datetime, timezone\nfrom typing import TYPE_CHECKING, Any, cast\n\nfrom langchain.agents import create_agent\nfrom langchain.agents.middleware import ModelCallLimitMiddleware, ToolRetryMiddleware\n\nfrom lfx.components.models_and_agents.agent_helpers.graph_event_adapter import (\n adapt_graph_events_to_executor_shape,\n)\nfrom lfx.components.models_and_agents.agent_helpers.messages_input_builder import (\n build_initial_messages,\n)\nfrom lfx.components.models_and_agents.agent_helpers.placeholder_corrective_middleware import (\n WatsonXPlaceholderMiddleware,\n)\nfrom lfx.components.models_and_agents.agent_helpers.single_tool_call_middleware import (\n SingleToolCallMiddleware,\n)\nfrom lfx.components.models_and_agents.memory import MemoryComponent, aget_agent_chat_history\n\nif TYPE_CHECKING:\n from langchain_core.tools import Tool\n\n from lfx.schema.log import OnTokenFunctionType, SendMessageFunctionType\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.callback import AgentAsyncHandler\nfrom lfx.base.agents.default_system_prompt import DEFAULT_SYSTEM_PROMPT_TEMPLATE\nfrom lfx.base.agents.events import ExceptionWithMessageError, process_agent_events\nfrom lfx.base.agents.token_callback import TokenUsageCallbackHandler\nfrom lfx.base.agents.utils import get_chat_output_sender_name\nfrom lfx.base.constants import STREAM_INFO_TEXT\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.components.agentics.helpers.model_config import validate_model_selection\nfrom lfx.components.helpers import CalculatorComponent, CurrentDateComponent\nfrom lfx.components.langchain_utilities.ibm_granite_handler import is_watsonx_model\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, ModelInput, StrInput\nfrom lfx.io import IntInput, MessageTextInput, MultilineInput, Output, SecretStrInput, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.memory import delete_message\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\nfrom lfx.utils.constants import MESSAGE_SENDER_AI\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\ndef _agent_base_inputs():\n \"\"\"Return base inputs tailored to AgentComponent's create_agent path.\n\n `get_base_inputs()` returns a shared list — replace, don't mutate. We drop\n inputs that are no-ops here and override info text on the inputs whose\n semantics shifted under create_agent.\n\n `verbose` is dropped because the create_agent event stream already surfaces\n every agent step via the \"Agent Steps\" content blocks; the legacy boolean\n has nothing to toggle. Saved flows that still carry a `verbose` value just\n ignore it on load (the schema no longer declares it).\n \"\"\"\n drop = {\"verbose\"}\n overrides = {\n \"handle_parsing_errors\": BoolInput(\n name=\"handle_parsing_errors\",\n display_name=\"Handle Parse Errors\",\n value=True,\n advanced=True,\n info=(\n \"Adds tool-execution retry as a safety net. `create_agent` already \"\n \"feeds tool-call validation errors back to the LLM automatically; \"\n \"this flag layers `ToolRetryMiddleware` on top so transient tool \"\n \"runtime failures are retried (max 2 retries).\"\n ),\n ),\n \"max_iterations\": IntInput(\n name=\"max_iterations\",\n display_name=\"Max Iterations\",\n value=15,\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n info=(\n \"Maximum number of model calls the agent can make before stopping \"\n \"(maps to `ModelCallLimitMiddleware.run_limit` on the create_agent \"\n \"path). Must be at least 1 — it is a safety cap, never 'unlimited'.\"\n ),\n ),\n }\n return [overrides.get(inp.name, inp) for inp in LCToolsAgentComponent.get_base_inputs() if inp.name not in drop]\n\n\ndef _extract_text_content(value) -> str:\n \"\"\"Pull a string payload from a Message-like, AIMessage-like, or string value.\"\"\"\n if isinstance(value, str):\n return value\n text = getattr(value, \"text\", None)\n if isinstance(text, str):\n return text\n content = getattr(value, \"content\", None)\n if isinstance(content, str):\n return content\n return str(value) if value is not None else \"\"\n\n\n@contextmanager\ndef _suppress_send_message(component: Any):\n \"\"\"Temporarily replace component.send_message with a no-op for the duration of the block.\n\n Used during the structured-output prompt fallback: run_agent streams the agent's\n final answer through self.send_message (correct for message_response), but in\n json_response the orchestrator parses that text into structured Data which the\n downstream Chat Output emits — leaving the original emission in place produces a\n duplicate message in the playground. The original method is always restored on exit,\n even when the wrapped call raises.\n \"\"\"\n original = component.send_message\n\n async def _noop(message, *_args, **_kwargs):\n return message\n\n component.send_message = _noop\n try:\n yield\n finally:\n component.send_message = original\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 documentation: str = \"https://docs.langflow.org/agents\"\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 ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n # Agents require tool calling — the filter is honored by\n # ``handle_model_input_update`` so models that can't run with\n # tools never reach the picker (and any saved selection that\n # no longer satisfies the constraint is auto-replaced).\n filters={\"tool_calling\": True},\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=(\n \"System Prompt: Initial instructions and context provided to guide the agent's behavior. \"\n \"Supports dynamic placeholders: {current_date}, {model_name}, {optional_user_context}.\"\n ),\n value=DEFAULT_SYSTEM_PROMPT_TEMPLATE,\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *_agent_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=STREAM_INFO_TEXT,\n value=True,\n advanced=True,\n ),\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 BoolInput(\n name=\"add_calculator_tool\",\n display_name=\"Calculator\",\n advanced=True,\n info=(\n \"If true, adds a zero-config arithmetic calculator tool to the agent \"\n \"(safe: only +, -, *, /, ** operators via AST).\"\n ),\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n Output(\n name=\"structured_response\",\n display_name=\"Structured Response\",\n method=\"json_response\",\n types=[\"Data\"],\n ),\n ]\n\n def _resolve_selected_model(self):\n \"\"\"Resolve the selected model, including legacy agent_llm/model_name inputs.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return self.model\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n return self.model\n\n legacy_provider = getattr(self, \"agent_llm\", None)\n legacy_model_name = getattr(self, \"model_name\", None)\n if not legacy_provider or not legacy_model_name:\n return self.model\n\n options = get_language_model_options(user_id=self.user_id)\n for option in options:\n if option.get(\"provider\") == legacy_provider and option.get(\"name\") == legacy_model_name:\n return [option]\n\n return [\n {\n \"name\": legacy_model_name,\n \"provider\": legacy_provider,\n \"metadata\": {},\n }\n ]\n\n def _get_max_tokens_value(self):\n \"\"\"Return the user-supplied max_tokens or None when unset/zero.\"\"\"\n val = getattr(self, \"max_tokens\", None)\n if val in {\"\", 0}:\n return None\n return val\n\n def _get_llm(self):\n \"\"\"Override parent to include max_tokens from the Agent's input field.\n\n Streaming is mandatory for AgentComponent: ``runnable.astream_events(v2)`` only\n emits ``on_chat_model_stream`` chunks when the underlying chat model is\n instantiated with ``streaming=True``. Unlike the LanguageModel component (where\n ``stream`` is a user-facing toggle), the Agent has no opt-out — the toggle is\n kept in the UI for backwards compatibility but is intentionally ignored here.\n Without ``stream=True``, the chat model accumulates the whole response and\n only emits ``on_chat_model_end``, silently disabling the Playground's live-\n typing view and breaking the streaming contract on the /events surface.\n \"\"\"\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=getattr(self, \"api_key\", None),\n stream=True,\n max_tokens=self._get_max_tokens_value(),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n overrides=getattr(self, \"_model_overrides\", None),\n )\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n from langchain_core.tools import StructuredTool\n\n selected_model = self._resolve_selected_model()\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n is_connected_model = isinstance(selected_model, BaseLanguageModel)\n except ImportError:\n is_connected_model = False\n\n if not is_connected_model:\n validate_model_selection(selected_model)\n\n # Ensure _get_llm() uses the resolved model (e.g. from legacy agent_llm/model_name)\n self.model = selected_model\n llm_model = 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\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\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\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == current_date_tool.name for t in self.tools):\n self.tools.append(current_date_tool)\n\n # Add calculator tool if enabled (zero-config arithmetic)\n if getattr(self, \"add_calculator_tool\", False):\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n calculator_tool = (await CalculatorComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(calculator_tool, StructuredTool):\n msg = \"CalculatorComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n # Skip if an externally-connected tool already provides the same name.\n # Duplicate tool names are rejected by Anthropic/Gemini with HTTP 400.\n if not any(getattr(t, \"name\", None) == calculator_tool.name for t in self.tools):\n self.tools.append(calculator_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n def _get_resolved_model_name(self) -> str:\n \"\"\"Best-effort human-readable model name for {model_name} injection.\"\"\"\n try:\n from langchain_core.language_models import BaseLanguageModel\n\n if isinstance(self.model, BaseLanguageModel):\n return type(self.model).__name__\n except ImportError:\n pass\n\n if isinstance(self.model, list) and self.model:\n first = self.model[0]\n if isinstance(first, dict):\n name = first.get(\"name\")\n if isinstance(name, str) and name:\n return name\n\n legacy_model_name = getattr(self, \"model_name\", None)\n if isinstance(legacy_model_name, str) and legacy_model_name:\n return legacy_model_name\n return \"\"\n\n def _inject_dynamic_prompt_values(self, prompt: Any | None) -> str | None:\n \"\"\"Replace known env placeholders in the system prompt.\n\n Handles {current_date}, {model_name}, and {optional_user_context} (the\n last one ships with the structured DEFAULT_SYSTEM_PROMPT_TEMPLATE and\n is currently unused at the AgentComponent layer, so it resolves to \"\").\n Uses str.replace (not str.format) so user prompts containing literal\n braces such as JSON examples ({\"key\": 1}) never break the agent.\n\n `system_prompt` is a connectable MultilineInput, so the value can arrive\n as a Message (e.g. a Prompt node wired in). Normalize it to text first —\n a raw Message has no `.replace` and used to crash the agent build.\n \"\"\"\n if prompt is None:\n return None\n prompt = _extract_text_content(prompt)\n if not prompt:\n return prompt\n replacements = {\n \"{current_date}\": datetime.now(tz=timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S UTC\"),\n \"{model_name}\": self._get_resolved_model_name(),\n \"{optional_user_context}\": \"\",\n }\n for placeholder, value in replacements.items():\n prompt = prompt.replace(placeholder, value)\n return prompt\n\n def create_agent_runnable(self):\n \"\"\"Build the LangGraph `CompiledStateGraph` via `langchain.agents.create_agent`.\n\n Replaces the legacy `AgentExecutor` runnable inherited from\n `ToolCallingAgentComponent`. Other agent components (tool_calling, csv, json,\n openapi, sql*, vector_store_router) keep the legacy path — only AgentComponent\n runs on the new graph API.\n\n `max_iterations` and `handle_parsing_errors` (legacy AgentExecutor knobs) are\n translated to LangGraph middleware. Without that translation those user inputs\n would silently become no-ops on the new API.\n\n Provider notes:\n - WatsonX/Granite work natively with create_agent — `ChatWatsonx.bind_tools`\n handles tool_choice correctly. The legacy `create_granite_agent` path was\n dropped because it hardcoded `tool_choice='required'`, which the WatsonX\n API now rejects.\n - Ollama and other small/local models often emit malformed tool args. The\n ToolRetryMiddleware (default `retry_on=(Exception,)`, `on_failure='continue'`)\n catches Pydantic ValidationErrors from bad args and feeds the error back\n to the LLM as a retry signal, so the agent recovers gracefully.\n \"\"\"\n llm = self._get_llm()\n tools = self.tools or []\n\n # Eager bind_tools validation. `create_agent(...)` is lazy — without this,\n # an LLM that doesn't support tool calling fails on the first user message\n # instead of when the user wires up the component, which is a much worse UX.\n # Gated on a non-empty tools list so a no-tool Agent on a plain chat model\n # (which legitimately has no `bind_tools`) isn't shut out at flow-build time.\n # Providers signal \"no tool calling\" inconsistently — `NotImplementedError`\n # (langchain default), `AttributeError` (no `bind_tools` attr), or `TypeError`\n # (signature mismatch). Treat all three as the same UX failure.\n if tools:\n try:\n llm.bind_tools(tools)\n except (NotImplementedError, AttributeError, TypeError) as exc:\n # Include the underlying error so a broken tool schema or a\n # provider implementation bug is not silently disguised as a\n # \"model can't call tools\" UX error.\n msg = (\n f\"{self.display_name} does not support tool calling, \"\n \"or one of the connected tools failed to bind. \"\n \"Please connect a tool-calling capable language model and \"\n f\"verify your tools are well-formed. Underlying error: {exc!s}\"\n )\n raise NotImplementedError(msg) from exc\n\n middleware = self._build_middleware(llm)\n return create_agent(\n model=llm,\n tools=tools,\n system_prompt=self.system_prompt or \"\",\n middleware=middleware or None,\n )\n\n def _compute_recursion_limit(self) -> int:\n \"\"\"Derive the LangGraph recursion_limit from the user-set max_iterations.\n\n Mirrors the clamp in `_build_middleware` (max(1, max_iterations)) so a\n saved 0 or negative value cannot under-cap the graph below one full\n iteration. The +5 buffer covers start/end/router overhead.\n \"\"\"\n raw = getattr(self, \"max_iterations\", None)\n run_limit = max(1, int(raw)) if raw is not None else 15\n return run_limit * 2 + 5\n\n def _build_middleware(self, llm: Any) -> list:\n # `llm` is passed in (rather than re-fetched via `self._get_llm()`)\n # because some providers do credential resolution / client instantiation\n # lazily on each call. The caller — `create_agent_runnable` — already\n # resolved it once for `bind_tools`, so reuse that instance here.\n middleware: list = []\n max_iterations = getattr(self, \"max_iterations\", None)\n if max_iterations is not None:\n # `max_iterations` is a safety cap, not an \"unlimited\" toggle. A saved\n # 0 or negative value (falsy) must NOT silently drop the limiter and\n # allow an unbounded model/tool loop — clamp it to a real minimum.\n run_limit = max(1, int(max_iterations))\n middleware.append(ModelCallLimitMiddleware(run_limit=run_limit))\n # ToolRetryMiddleware only matters when there ARE tools to retry. Attaching\n # it on a no-tools agent inflates the compiled graph and adds per-invocation\n # middleware overhead for nothing, which is a measurable contributor to\n # trivial-prompt latency (QA UI-003).\n if getattr(self, \"handle_parsing_errors\", False) and self.tools:\n middleware.append(ToolRetryMiddleware(max_retries=2))\n # WatsonX models have two known platform quirks; both still reproduce on\n # the current API, so we keep the protections from the legacy\n # `create_granite_agent` path.\n # 1. Multi-tool-call assistant turns are rejected (\"This model only\n # supports single tool-calls at once!\"). Clamp to one per turn.\n # 2. Tool args occasionally come back as literal placeholder strings\n # (e.g. `<result-from-search>`). Re-invoke once with a corrective\n # SystemMessage.\n # Order: SingleToolCallMiddleware first (outermost) so the clamp is\n # applied to the final response, including any corrective re-invoke\n # produced by WatsonXPlaceholderMiddleware.\n if is_watsonx_model(llm):\n middleware.append(SingleToolCallMiddleware())\n middleware.append(WatsonXPlaceholderMiddleware())\n return middleware\n\n async def run_agent(self, agent) -> Message:\n \"\"\"Run the LangGraph `CompiledStateGraph` and return the final agent Message.\n\n Overrides the legacy `LCAgentComponent.run_agent` (which builds an\n `{\"input\": str, \"chat_history\": [...]}` dict for `AgentExecutor`). The graph\n wants `{\"messages\": [BaseMessage, ...]}`. The event stream is wrapped with\n `adapt_graph_events_to_executor_shape` so the legacy `process_agent_events`\n (in `lfx.base.agents.events`) can be reused unchanged.\n \"\"\"\n messages = build_initial_messages(\n input_value=self.input_value,\n chat_history=getattr(self, \"chat_history\", None),\n )\n input_dict = {\"messages\": messages}\n\n agent_message = self._build_initial_agent_message()\n token_usage_handler = TokenUsageCallbackHandler()\n\n # Stream tokens to the event manager when running inside the Langflow runtime.\n # This is what powers the live-typing view in the chat UI.\n on_token_callback: OnTokenFunctionType | None = None\n if getattr(self, \"_event_manager\", None):\n on_token_callback = cast(\"OnTokenFunctionType\", self._event_manager.on_token)\n\n # Align LangGraph's `recursion_limit` with `max_iterations` so the\n # middleware cap (ModelCallLimitMiddleware) is what bounds the loop —\n # not LangGraph's default 25-step guard, which fires at ~12 model+tool\n # iterations and raises a raw GraphRecursionError (QA UI-009/UI-010).\n # Each iteration is ~2 graph steps (model node + tools node); add 5\n # for start/end overhead.\n recursion_limit = self._compute_recursion_limit()\n\n stream = adapt_graph_events_to_executor_shape(\n agent.astream_events(\n input_dict,\n config={\n \"callbacks\": [\n AgentAsyncHandler(self.log),\n token_usage_handler,\n *self._get_shared_callbacks(),\n ],\n \"recursion_limit\": recursion_limit,\n },\n version=\"v2\",\n )\n )\n try:\n result = await process_agent_events(\n stream,\n agent_message,\n cast(\"SendMessageFunctionType\", self.send_message),\n on_token_callback,\n )\n except ExceptionWithMessageError as e:\n # Drop the half-stored partial message from the DB (only if it was\n # actually persisted) and tell the frontend to remove the stale bubble.\n if hasattr(e, \"agent_message\"):\n msg_id = e.agent_message.get_id()\n if msg_id:\n await delete_message(id_=msg_id)\n await self._send_message_event(e.agent_message, category=\"remove_message\")\n logger.error(f\"ExceptionWithMessageError: {e}\")\n raise\n\n usage_data = token_usage_handler.get_usage()\n if usage_data:\n self._token_usage = usage_data\n result.properties.usage = usage_data\n # Only round-trip the DB when the message was stored (Chat Output wired).\n # `_should_skip_message=True` leaves `result.get_id()` empty; persisting\n # then would create a phantom row.\n if result.get_id():\n stored_result = await self._update_stored_message(result)\n await self._send_message_event(stored_result)\n result = stored_result\n\n self.status = result\n return result\n\n def _build_initial_agent_message(self) -> Message:\n \"\"\"Construct the placeholder agent Message that `process_agent_events` mutates.\"\"\"\n if hasattr(self, \"graph\"):\n session_id = self.graph.session_id\n elif hasattr(self, \"_session_id\"):\n session_id = self._session_id\n else:\n session_id = None\n\n sender_name = get_chat_output_sender_name(self) or self.display_name or \"AI\"\n return Message(\n sender=MESSAGE_SENDER_AI,\n sender_name=sender_name,\n properties={\"icon\": \"Bot\", \"state\": \"partial\"},\n # `text=\"\"` sentinel so MessageTable's no_content check accepts\n # an in-flight agent message whose content_blocks haven't been\n # populated yet. Mirrors ChatInput's convention.\n text=\"\",\n # Flat chronological event log; see lfx.base.agents.events.\n content_blocks=[],\n session_id=session_id or uuid.uuid4(),\n )\n\n def _selected_provider_and_model(self) -> tuple[str | None, str | None]:\n \"\"\"Provider/name of the selected model, for error-driven remediation.\"\"\"\n try:\n selected = self._resolve_selected_model()\n if isinstance(selected, list) and selected and isinstance(selected[0], dict):\n return selected[0].get(\"provider\"), selected[0].get(\"name\")\n except (AttributeError, TypeError, KeyError, ImportError):\n pass\n return None, None\n\n async def message_response(self) -> Message:\n from lfx.base.models.model_remediation import find_remediation, remember\n\n provider, model_name = self._selected_provider_and_model()\n applied: set[str] = set()\n while True:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self._inject_dynamic_prompt_values(self.system_prompt),\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n self._agent_result = result\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except Exception as e:\n # Error-driven remediation (see model_remediation): run_agent wraps\n # the provider error in ExceptionWithMessageError, so match the chain.\n error_text = f\"{e} {getattr(e, '__cause__', '') or ''}\"\n remediation = find_remediation(error_text, provider, already_applied=applied)\n if remediation is None:\n label = type(e).__name__\n await logger.aerror(f\"{label}: {e!s}\")\n raise\n applied.add(remediation.name)\n self._model_overrides = {**(getattr(self, \"_model_overrides\", None) or {}), **remediation.overrides}\n await logger.awarning(\n f\"model.remediation.applied name={remediation.name} provider={provider} model={model_name}\"\n )\n continue\n else:\n if getattr(self, \"_model_overrides\", None):\n remember(provider, model_name, self._model_overrides)\n return result\n\n async def json_response(self) -> Data:\n \"\"\"Produce structured Data via native LLM structured output, with prompt-based fallback.\n\n Native path (no tools, llm has with_structured_output) bypasses the agent loop and\n returns provider-validated JSON. When tools are attached, falls back to running the\n agent with a schema-augmented system prompt and parsing the final message content.\n \"\"\"\n from lfx.components.models_and_agents.structured_output.structured_output_orchestrator import (\n orchestrate_structured_output,\n )\n\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n except (ValueError, TypeError) as exc:\n await logger.aerror(f\"json_response.requirements_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n injected_system_prompt = self._inject_dynamic_prompt_values(getattr(self, \"system_prompt\", \"\") or \"\") or \"\"\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n output_schema = getattr(self, \"output_schema\", None) or []\n has_tools = bool(self.tools)\n\n async def _run_agent_for_fallback(augmented_prompt: str) -> str:\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=augmented_prompt,\n )\n agent_runnable = self.create_agent_runnable()\n with _suppress_send_message(self):\n result = await self.run_agent(agent_runnable)\n return _extract_text_content(result)\n\n try:\n return await orchestrate_structured_output(\n llm=llm_model,\n output_schema=output_schema,\n system_prompt=injected_system_prompt,\n format_instructions=format_instructions,\n input_value=_extract_text_content(self.input_value),\n run_prompt_fallback=_run_agent_for_fallback,\n prefer_native=not has_tools,\n )\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as exc:\n await logger.aerror(f\"json_response.orchestration_failed: {exc}\")\n return Data(data={\"content\": \"\", \"error\": str(exc)})\n\n async def get_memory_data(self):\n # Scope by flow_id so default playground session names (e.g. \"New Session 0\")\n # cannot leak chat history across unrelated flows. See issue #13059.\n # The helper also returns [] when n_messages == 0, preserving the\n # explicit \"memory disabled\" contract from MemoryComponent.retrieve_messages.\n messages = await aget_agent_chat_history(\n session_id=self.graph.session_id,\n flow_id=getattr(self.graph, \"flow_id\", None),\n context_id=self.context_id,\n n_messages=self.n_messages,\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\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,\n build_config: dotdict,\n field_value: list[dict],\n field_name: str | None = None,\n ) -> dotdict:\n # Update model options with caching (for all field changes).\n # The tool-calling constraint lives on the ModelInput's ``filters``\n # field (declared above); ``handle_model_input_update`` reads it\n # and applies the filter to both the dropdown options and the\n # sticky-default re-injection path.\n build_config = handle_model_input_update(\n component=self,\n build_config=dict(build_config),\n field_value=field_value,\n field_name=field_name,\n )\n build_config = dotdict(build_config)\n\n if field_name == \"model\":\n build_config = self.update_input_types(build_config)\n\n # Validate required keys. `verbose` was dropped from the input set\n # (see `_agent_base_inputs` — the create_agent event stream already\n # surfaces every step), so it is intentionally NOT required here.\n # Saved flows that still carry a `verbose` value just ignore it on\n # load.\n default_keys = [\n \"code\",\n \"_type\",\n \"model\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"add_calculator_tool\",\n \"system_prompt\",\n \"max_iterations\",\n \"handle_parsing_errors\",\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 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\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n 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\n return tools\n" | |||
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
printf '\n== locate structured output orchestrator ==\n'
fd -a 'structured_output_orchestrator.py' .
printf '\n== search for orchestrate_structured_output and remediation ==\n'
rg -n "def orchestrate_structured_output|orchestrate_structured_output\(|find_remediation\(|remember\(" -S .
printf '\n== outline orchestrator file if found ==\n'
orchestrator=$(fd -a 'structured_output_orchestrator.py' . | head -n 1)
if [ -n "${orchestrator:-}" ]; then
ast-grep outline "$orchestrator" --view expanded || true
printf '\n== first 260 lines of orchestrator ==\n'
wc -l "$orchestrator"
sed -n '1,260p' "$orchestrator" | cat -n
fi
printf '\n== locate AgentComponent source ==\n'
rg -n "async def json_response|async def message_response|def create_agent_runnable|def _build_middleware|def get_agent_requirements" -S src/backend/base/langflow/initial_setup/starter_projects/Twitter\ Thread\ Generator.jsonRepository: langflow-ai/langflow
Length of output: 50378
🏁 Script executed:
#!/bin/bash
set -euo pipefail
orchestrator="src/lfx/src/lfx/components/models_and_agents/structured_output/structured_output_orchestrator.py"
tests="src/lfx/tests/unit/components/models_and_agents/structured_output/test_structured_output_orchestrator.py"
printf '\n== orchestrator outline ==\n'
ast-grep outline "$orchestrator" --view expanded || true
printf '\n== orchestrator source (first 260 lines) ==\n'
wc -l "$orchestrator"
sed -n '1,260p' "$orchestrator" | cat -n
printf '\n== orchestrator tests around error/retry cases ==\n'
sed -n '1,430p' "$tests" | cat -n | sed -n '1,430p'
printf '\n== model remediation source ==\n'
sed -n '1,220p' src/lfx/src/lfx/base/models/model_remediation.py | cat -nRepository: langflow-ai/langflow
Length of output: 26657
🏁 Script executed:
#!/bin/bash
set -euo pipefail
orchestrator="src/lfx/src/lfx/components/models_and_agents/structured_output/structured_output_orchestrator.py"
tests="src/lfx/tests/unit/components/models_and_agents/structured_output/test_structured_output_orchestrator.py"
remediation="src/lfx/src/lfx/base/models/model_remediation.py"
printf '\n== orchestrator outline ==\n'
ast-grep outline "$orchestrator" --view expanded || true
printf '\n== orchestrator source (first 260 lines) ==\n'
wc -l "$orchestrator"
sed -n '1,260p' "$orchestrator" | cat -n
printf '\n== orchestrator tests around error/retry cases ==\n'
rg -n "error|retry|exception|fallback|structured_output" "$tests"
sed -n '1,430p' "$tests" | cat -n
printf '\n== model remediation source ==\n'
sed -n '1,220p' "$remediation" | cat -nRepository: langflow-ai/langflow
Length of output: 30916
Apply the same remediation retry in json_response. message_response already retries provider-specific failures and persists overrides, but the structured-output path makes one attempt and then returns Data(error=...). Reuse the bounded remediation loop here so recoverable model-constraint errors don’t bypass /structured_response.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@src/backend/base/langflow/initial_setup/starter_projects/Twitter` Thread
Generator.json at line 1174, Update AgentComponent.json_response to use the same
bounded model-remediation retry flow as message_response, including
provider/model resolution, applied-remediation tracking, override updates, and
persistence via remember. Retry orchestrate_structured_output after applicable
remediation, while preserving the existing Data(error=...) fallback when no
remediation applies or retries are exhausted.
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## release-1.11.0 #14038 +/- ##
==================================================
+ Coverage 60.16% 60.33% +0.16%
==================================================
Files 2333 2341 +8
Lines 228353 229901 +1548
Branches 32038 32356 +318
==================================================
+ Hits 137386 138708 +1322
- Misses 89351 89531 +180
- Partials 1616 1662 +46
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erichare
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LGTM @Cristhianzl ! two issues that were raised that arent blockers, could be fixed in this PR or a follow up:
-
Don’t persist remediation until the retry succeeds —
[assistant_service.py:999](https://github.com/langflow-ai/langflow/blob/6041a4dc94e6212927bc47d1b4c984c7e0669e71/src/backend/base/langflow/agentic/services/assistant_service.py#L988-L1014)remember()writes the override into a process-global cache before the retried flow succeeds. I reproduced a recognized/v1/responseserror followed by a failed retry: the request correctly ended with an error, butcached_overrides("OpenAI", "gpt-5.6")still containeduse_responses_api=True. Later requests automatically consume that cached value in[instantiation.py:370-376](https://github.com/langflow-ai/langflow/blob/6041a4dc94e6212927bc47d1b4c984c7e0669e71/src/lfx/src/lfx/base/models/unified_models/instantiation.py#L370-L376).Treat the override as provisional and roll it back after an unsuccessful retry, or pass it through the current execution directly and only promote it to the shared cache after success.
-
Backport the client half of
/iterations—[schemas.py:41](https://github.com/langflow-ai/langflow/blob/6041a4dc94e6212927bc47d1b4c984c7e0669e71/src/backend/base/langflow/agentic/api/schemas.py#L30-L41)The backend accepts
iterations_limit, but the frontend request body never sends it in[use-assistant-chat.ts:331-338](https://github.com/langflow-ai/langflow/blob/6041a4dc94e6212927bc47d1b4c984c7e0669e71/src/frontend/src/components/core/assistantPanel/hooks/use-assistant-chat.ts#L331-L338), and the request type omits it. The new backend test explicitly promises a per-session/iterations Ncommand, but typing that command currently sends it as an ordinary prompt. PR #14007 contains the omitted parser, storage, request wiring, and tests.
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Summary
Five independent fixes to the Langflow Assistant, all reported from QA or live usage:
two QA bugs, and three improvements backported from #14007 so they can ship in this release without waiting for that (much larger) PR to land.
Summary by CodeRabbit