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"timestamp": "2026-04-10T23:07:33.664192+00:00",
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"line": 93,
"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
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{
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"remediation": "Verify tool result status/validity before using",
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"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
"line": 563,
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"remediation": "Verify tool result status/validity before using",
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{
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent_toolkits\\vectorstore\\base.py",
"line": 45,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Construct a VectorStore agent from an LLM and tools.\n\n !!! note\n This class is deprecated. See below for a replacement that uses tool\n calling methods and LangGraph. Install LangGraph with:\n\n ```bash\n pip install -U langgraph\n ```\n\n ```python\n from langchain_core.tools import create_retriever_tool\n from langchain_core.vectorstores import InMemoryVectorStore\n from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n from langgraph.prebuilt import create_react_agent\n\n model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n\n vector_store = InMemoryVectorStore.from_texts(\n [\n \"Dogs are great companions, known for their loyalty and friendliness.\",\n \"Cats are independent pets that often enjoy their own space.\",\n ],\n OpenAIEmbeddings(),\n )\n\n tool = create_retriever_tool(\n vector_store.as_retriever(),\n \"pet_information_retriever\",\n \"Fetches information about pets.\",\n )\n\n agent = create_react_agent(model, [tool])\n\n for step in agent.stream(\n {\"messages\": [(\"human\", \"What are dogs known for?\")]},\n stream_mode=\"values\",\n ):\n step[\"messages\"][-1].pretty_print()\n ```\n\n Args:\n llm: LLM that will be used by the agent\n toolkit: Set of tools for the agent\n callback_manager: Object to handle the callback\n prefix: The prefix prompt for the agent.\n verbose: If you want to see the content of the scratchpad.\n agent_executor_kwargs: If there is any other parameter you want to send to the\n agent.\n kwargs: Additional named parameters to pass to the `ZeroShotAgent`.\n\n Returns:\n Returns a callable AgentExecutor object.\n Either you can call it or use run method with the query to get the response.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent_toolkits\\vectorstore\\base.py",
"line": 142,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Construct a VectorStore router agent from an LLM and tools.\n\n !!! note\n This class is deprecated. See below for a replacement that uses tool calling\n methods and LangGraph. Install LangGraph with:\n\n ```bash\n pip install -U langgraph\n ```\n\n ```python\n from langchain_core.tools import create_retriever_tool\n from langchain_core.vectorstores import InMemoryVectorStore\n from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n from langgraph.prebuilt import create_react_agent\n\n model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n\n pet_vector_store = InMemoryVectorStore.from_texts(\n [\n \"Dogs are great companions, known for their loyalty and friendliness.\",\n \"Cats are independent pets that often enjoy their own space.\",\n ],\n OpenAIEmbeddings(),\n )\n\n food_vector_store = InMemoryVectorStore.from_texts(\n [\n \"Carrots are orange and delicious.\",\n \"Apples are red and delicious.\",\n ],\n OpenAIEmbeddings(),\n )\n\n tools = [\n create_retriever_tool(\n pet_vector_store.as_retriever(),\n \"pet_information_retriever\",\n \"Fetches information about pets.\",\n ),\n create_retriever_tool(\n food_vector_store.as_retriever(),\n \"food_information_retriever\",\n \"Fetches information about food.\",\n ),\n ]\n\n agent = create_react_agent(model, tools)\n\n for step in agent.stream(\n {\"messages\": [(\"human\", \"Tell me about carrots.\")]},\n stream_mode=\"values\",\n ):\n step[\"messages\"][-1].pretty_print()\n ```\n\n Args:\n llm: LLM that will be used by the agent\n toolkit: Set of tools for the agent which have routing capability with multiple\n vector stores\n callback_manager: Object to handle the callback\n prefix: The prefix prompt for the router agent.\n If not provided uses default `ROUTER_PREFIX`.\n verbose: If you want to see the content of the scratchpad.\n agent_executor_kwargs: If there is any other parameter you want to send to the\n agent.\n kwargs: Additional named parameters to pass to the `ZeroShotAgent`.\n\n Returns:\n Returns a callable `AgentExecutor` object.\n Either you can call it or use run method with the query to get the response.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\openai_assistant\\base.py",
"line": 140,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Run an OpenAI Assistant.\n\n Example using OpenAI tools:\n ```python\n from langchain_experimental.openai_assistant import OpenAIAssistantRunnable\n\n interpreter_assistant = OpenAIAssistantRunnable.create_assistant(\n name=\"langchain assistant\",\n instructions=\"You are a personal math tutor. \"\n \"Write and run code to answer math questions.\",\n tools=[{\"type\": \"code_interpreter\"}],\n model=\"gpt-4-1106-preview\",\n )\n output = interpreter_assistant.invoke(\n {\"content\": \"What's 10 - 4 raised to the 2.7\"}\n )\n ```\n\n Example using custom tools and AgentExecutor:\n ```python\n from langchain_experimental.openai_assistant import OpenAIAssistantRunnable\n from langchain_classic.agents import AgentExecutor\n from langchain_classic.tools import E2BDataAnalysisTool\n\n\n tools = [E2BDataAnalysisTool(api_key=\"...\")]\n agent = OpenAIAssistantRunnable.create_assistant(\n name=\"langchain assistant e2b tool\",\n instructions=\"You are a personal math tutor. \"\n \"Write and run code to answer math questions.\",\n tools=tools,\n model=\"gpt-4-1106-preview\",\n as_agent=True,\n )\n\n agent_executor = AgentExecutor(agent=agent, tools=tools)\n agent_executor.invoke({\"content\": \"What's 10 - 4 raised to the 2.7\"})\n ```\n\n Example using custom tools and custom execution:\n ```python\n from langchain_experimental.openai_assistant import OpenAIAssistantRunnable\n from langchain_classic.agents import AgentExecutor\n from langchain_core.agents import AgentFinish\n from langchain_classic.tools import E2BDataAnalysisTool\n\n\n tools = [E2BDataAnalysisTool(api_key=\"...\")]\n agent = OpenAIAssistantRunnable.create_assistant(\n name=\"langchain assistant e2b tool\",\n instructions=\"You are a personal math tutor. \"\n \"Write and run code to answer math questions.\",\n tools=tools,\n model=\"gpt-4-1106-preview\",\n as_agent=True,\n )\n\n\n def execute_agent(agent, tools, input):\n tool_map = {tool.name: tool for tool in tools}\n response = agent.invoke(input)\n while not isinstance(response, AgentFinish):\n tool_outputs = []\n for action in response:\n tool_output = tool_map[action.tool].invoke(action.tool_input)\n tool_outputs.append(\n {\n \"output\": tool_output,\n \"tool_call_id\": action.tool_call_id,\n }\n )\n response = agent.invoke(\n {\n \"tool_outputs\": tool_outputs,\n \"run_id\": action.run_id,\n \"thread_id\": action.thread_id,\n }\n )\n\n return response\n\n\n response = execute_agent(\n agent, tools, {\"content\": \"What's 10 - 4 raised to the 2.7\"}\n )\n next_response = execute_agent(\n agent,\n tools,\n {\"content\": \"now add 17.241\", \"thread_id\": response.thread_id},\n )\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\openai_functions_agent\\base.py",
"line": 125,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\openai_functions_agent\\base.py",
"line": 131,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\openai_functions_multi_agent\\base.py",
"line": 228,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\self_ask_with_search\\base.py",
"line": 102,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create an agent that uses self-ask with search prompting.\n\n Args:\n llm: LLM to use as the agent.\n tools: List of tools. Should just be of length 1, with that tool having\n name `Intermediate Answer`\n prompt: The prompt to use, must have input key `agent_scratchpad` which will\n contain agent actions and tool outputs.\n\n Returns:\n A Runnable sequence representing an agent. It takes as input all the same input\n variables as the prompt passed in does. It returns as output either an\n AgentAction or AgentFinish.\n\n Examples:\n ```python\n from langchain_classic import hub\n from langchain_anthropic import ChatAnthropic\n from langchain_classic.agents import (\n AgentExecutor,\n create_self_ask_with_search_agent,\n )\n\n prompt = hub.pull(\"hwchase17/self-ask-with-search\")\n model = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n tools = [...] # Should just be one tool with name `Intermediate Answer`\n\n agent = create_self_ask_with_search_agent(model, tools, prompt)\n agent_executor = AgentExecutor(agent=agent, tools=tools)\n\n agent_executor.invoke({\"input\": \"hi\"})\n ```\n\n Prompt:\n\n The prompt must have input key `agent_scratchpad` which will\n contain agent actions and tool outputs as a string.\n\n Here's an example:\n\n ```python\n from langchain_core.prompts import PromptTemplate\n\n template = '''Question: Who lived longer, Muhammad Ali or Alan Turing?\n Are follow up questions needed here: Yes.\n Follow up: How old was Muhammad Ali when he died?\n Intermediate answer: Muhammad Ali was 74 years old when he died.\n Follow up: How old was Alan Turing when he died?\n Intermediate answer: Alan Turing was 41 years old when he died.\n So the final answer is: Muhammad Ali\n\n Question: When was the founder of craigslist born?\n Are follow up questions needed here: Yes.\n Follow up: Who was the founder of craigslist?\n Intermediate answer: Craigslist was founded by Craig Newmark.\n Follow up: When was Craig Newmark born?\n Intermediate answer: Craig Newmark was born on December 6, 1952.\n So the final answer is: December 6, 1952\n\n Question: Who was the maternal grandfather of George Washington?\n Are follow up questions needed here: Yes.\n Follow up: Who was the mother of George Washington?\n Intermediate answer: The mother of George Washington was Mary Ball Washington.\n Follow up: Who was the father of Mary Ball Washington?\n Intermediate answer: The father of Mary Ball Washington was Joseph Ball.\n So the final answer is: Joseph Ball\n\n Question: Are both the directors of Jaws and Casino Royale from the same country?\n Are follow up questions needed here: Yes.\n Follow up: Who is the director of Jaws?\n Intermediate answer: The director of Jaws is Steven Spielberg.\n Follow up: Where is Steven Spielberg from?\n Intermediate answer: The United States.\n Follow up: Who is the director of Casino Royale?\n Intermediate answer: The director of Casino Royale is Martin Campbell.\n Follow up: Where is Martin Campbell from?\n Intermediate answer: New Zealand.\n So the final answer is: No\n\n Question: {input}\n Are followup questions needed here:{agent_scratchpad}'''\n\n prompt = PromptTemplate.from_template(template)\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\xml\\base.py",
"line": 123,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create an agent that uses XML to format its logic.\n\n Args:\n llm: LLM to use as the agent.\n tools: Tools this agent has access to.\n prompt: The prompt to use, must have input keys\n `tools`: contains descriptions for each tool.\n `agent_scratchpad`: contains previous agent actions and tool outputs.\n tools_renderer: This controls how the tools are converted into a string and\n then passed into the LLM.\n stop_sequence: bool or list of str.\n If `True`, adds a stop token of \"\" to avoid hallucinates.\n If `False`, does not add a stop token.\n If a list of str, uses the provided list as the stop tokens.\n\n You may to set this to False if the LLM you are using\n does not support stop sequences.\n\n Returns:\n A Runnable sequence representing an agent. It takes as input all the same input\n variables as the prompt passed in does. It returns as output either an\n AgentAction or AgentFinish.\n\n Example:\n ```python\n from langchain_classic import hub\n from langchain_anthropic import ChatAnthropic\n from langchain_classic.agents import AgentExecutor, create_xml_agent\n\n prompt = hub.pull(\"hwchase17/xml-agent-convo\")\n model = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n tools = ...\n\n agent = create_xml_agent(model, tools, prompt)\n agent_executor = AgentExecutor(agent=agent, tools=tools)\n\n agent_executor.invoke({\"input\": \"hi\"})\n\n # Use with chat history\n from langchain_core.messages import AIMessage, HumanMessage\n\n agent_executor.invoke(\n {\n \"input\": \"what's my name?\",\n # Notice that chat_history is a string\n # since this prompt is aimed at LLMs, not chat models\n \"chat_history\": \"Human: My name is Bob\\nAI: Hello Bob!\",\n }\n )\n ```\n\n Prompt:\n\n The prompt must have input keys:\n * `tools`: contains descriptions for each tool.\n * `agent_scratchpad`: contains previous agent actions and tool outputs as\n an XML string.\n\n Here's an example:\n\n ```python\n from langchain_core.prompts import PromptTemplate\n\n template = '''You are a helpful assistant. Help the user answer any questions.\n\n You have access to the following tools:\n\n {tools}\n\n In order to use a tool, you can use and tags. You will then get back a response in the form \n For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:\n\n searchweather in SF\n 64 degrees\n\n When you are done, respond with a final answer between . For example:\n\n The weather in SF is 64 degrees\n\n Begin!\n\n Previous Conversation:\n {chat_history}\n\n Question: {input}\n {agent_scratchpad}'''\n prompt = PromptTemplate.from_template(template)\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\api\\base.py",
"line": 69,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Chain that makes API calls and summarizes the responses to answer a question.\n\n **Security Note**: This API chain uses the requests toolkit\n to make `GET`, `POST`, `PATCH`, `PUT`, and `DELETE` requests to an API.\n\n Exercise care in who is allowed to use this chain. If exposing\n to end users, consider that users will be able to make arbitrary\n requests on behalf of the server hosting the code. For example,\n users could ask the server to make a request to a private API\n that is only accessible from the server.\n\n Control access to who can submit issue requests using this toolkit and\n what network access it has.\n\n See https://docs.langchain.com/oss/python/security-policy for more\n information.\n\n !!! note\n This class is deprecated. See below for a replacement implementation using\n LangGraph. The benefits of this implementation are:\n\n - Uses LLM tool calling features to encourage properly-formatted API requests;\n - Support for both token-by-token and step-by-step streaming;\n - Support for checkpointing and memory of chat history;\n - Easier to modify or extend\n (e.g., with additional tools, structured responses, etc.)\n\n Install LangGraph with:\n\n ```bash\n pip install -U langgraph\n ```\n\n ```python\n from typing import Annotated, Sequence\n from typing_extensions import TypedDict\n\n from langchain_classic.chains.api.prompt import API_URL_PROMPT\n from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit\n from langchain_community.utilities.requests import TextRequestsWrapper\n from langchain_core.messages import BaseMessage\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_openai import ChatOpenAI\n from langchain_core.runnables import RunnableConfig\n from langgraph.graph import END, StateGraph\n from langgraph.graph.message import add_messages\n from langgraph.prebuilt.tool_node import ToolNode\n\n # NOTE: There are inherent risks in giving models discretion\n # to execute real-world actions. We must \"opt-in\" to these\n # risks by setting allow_dangerous_request=True to use these tools.\n # This can be dangerous for calling unwanted requests. Please make\n # sure your custom OpenAPI spec (yaml) is safe and that permissions\n # associated with the tools are narrowly-scoped.\n ALLOW_DANGEROUS_REQUESTS = True\n\n # Subset of spec for https://jsonplaceholder.typicode.com\n api_spec = \"\"\"\n openapi: 3.0.0\n info:\n title: JSONPlaceholder API\n version: 1.0.0\n servers:\n - url: https://jsonplaceholder.typicode.com\n paths:\n /posts:\n get:\n summary: Get posts\n parameters: &id001\n - name: _limit\n in: query\n required: false\n schema:\n type: integer\n example: 2\n description: Limit the number of results\n \"\"\"\n\n model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n toolkit = RequestsToolkit(\n requests_wrapper=TextRequestsWrapper(headers={}), # no auth required\n allow_dangerous_requests=ALLOW_DANGEROUS_REQUESTS,\n )\n tools = toolkit.get_tools()\n\n api_request_chain = (\n API_URL_PROMPT.partial(api_docs=api_spec)\n | model.bind_tools(tools, tool_choice=\"any\")\n )\n\n class ChainState(TypedDict):\n \"\"\"LangGraph state.\"\"\"\n\n messages: Annotated[Sequence[BaseMessage], add_messages]\n\n\n async def acall_request_chain(state: ChainState, config: RunnableConfig):\n last_message = state[\"messages\"][-1]\n response = await api_request_chain.ainvoke(\n {\"question\": last_message.content}, config\n )\n return {\"messages\": [response]}\n\n async def acall_model(state: ChainState, config: RunnableConfig):\n response = await model.ainvoke(state[\"messages\"], config)\n return {\"messages\": [response]}\n\n graph_builder = StateGraph(ChainState)\n graph_builder.add_node(\"call_tool\", acall_request_chain)\n graph_builder.add_node(\"execute_tool\", ToolNode(tools))\n graph_builder.add_node(\"call_model\", acall_model)\n graph_builder.set_entry_point(\"call_tool\")\n graph_builder.add_edge(\"call_tool\", \"execute_tool\")\n graph_builder.add_edge(\"execute_tool\", \"call_model\")\n graph_builder.add_edge(\"call_model\", END)\n chain = graph_builder.compile()\n ```\n\n ```python\n example_query = \"Fetch the top two posts. What are their titles?\"\n\n events = chain.astream(\n {\"messages\": [(\"user\", example_query)]},\n stream_mode=\"values\",\n )\n async for event in events:\n event[\"messages\"][-1].pretty_print()\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\combine_documents\\stuff.py",
"line": 34,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a chain for passing a list of Documents to a model.\n\n Args:\n llm: Language model.\n prompt: Prompt template. Must contain input variable `\"context\"` (override by\n setting document_variable), which will be used for passing in the formatted\n documents.\n output_parser: Output parser. Defaults to `StrOutputParser`.\n document_prompt: Prompt used for formatting each document into a string. Input\n variables can be \"page_content\" or any metadata keys that are in all\n documents. \"page_content\" will automatically retrieve the\n `Document.page_content`, and all other inputs variables will be\n automatically retrieved from the `Document.metadata` dictionary. Default to\n a prompt that only contains `Document.page_content`.\n document_separator: String separator to use between formatted document strings.\n document_variable_name: Variable name to use for the formatted documents in the\n prompt. Defaults to `\"context\"`.\n\n Returns:\n An LCEL Runnable. The input is a dictionary that must have a `\"context\"` key\n that maps to a `list[Document]`, and any other input variables expected in the\n prompt. The `Runnable` return type depends on `output_parser` used.\n\n Example:\n ```python\n # pip install -U langchain langchain-openai\n\n from langchain_openai import ChatOpenAI\n from langchain_core.documents import Document\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_classic.chains.combine_documents import (\n create_stuff_documents_chain,\n )\n\n prompt = ChatPromptTemplate.from_messages(\n [(\"system\", \"What are everyone's favorite colors:\\n\\n{context}\")]\n )\n model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n chain = create_stuff_documents_chain(model, prompt)\n\n docs = [\n Document(page_content=\"Jesse loves red but not yellow\"),\n Document(\n page_content=\"Jamal loves green but not as much as he loves orange\"\n ),\n ]\n\n chain.invoke({\"context\": docs})\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\constitutional_ai\\base.py",
"line": 30,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Chain for applying constitutional principles.\n\n !!! note\n This class is deprecated. See below for a replacement implementation using\n LangGraph. The benefits of this implementation are:\n\n - Uses LLM tool calling features instead of parsing string responses;\n - Support for both token-by-token and step-by-step streaming;\n - Support for checkpointing and memory of chat history;\n - Easier to modify or extend (e.g., with additional tools, structured responses, etc.)\n\n Install LangGraph with:\n\n ```bash\n pip install -U langgraph\n ```\n\n ```python\n from typing import List, Optional, Tuple\n\n from langchain_classic.chains.constitutional_ai.prompts import (\n CRITIQUE_PROMPT,\n REVISION_PROMPT,\n )\n from langchain_classic.chains.constitutional_ai.models import ConstitutionalPrinciple\n from langchain_core.output_parsers import StrOutputParser\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_openai import ChatOpenAI\n from langgraph.graph import END, START, StateGraph\n from typing_extensions import Annotated, TypedDict\n\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n class Critique(TypedDict):\n \"\"\"Generate a critique, if needed.\"\"\"\n critique_needed: Annotated[bool, ..., \"Whether or not a critique is needed.\"]\n critique: Annotated[str, ..., \"If needed, the critique.\"]\n\n critique_prompt = ChatPromptTemplate.from_template(\n \"Critique this response according to the critique request. \"\n \"If no critique is needed, specify that.\\n\\n\"\n \"Query: {query}\\n\\n\"\n \"Response: {response}\\n\\n\"\n \"Critique request: {critique_request}\"\n )\n\n revision_prompt = ChatPromptTemplate.from_template(\n \"Revise this response according to the critique and reivsion request.\\n\\n\"\n \"Query: {query}\\n\\n\"\n \"Response: {response}\\n\\n\"\n \"Critique request: {critique_request}\\n\\n\"\n \"Critique: {critique}\\n\\n\"\n \"If the critique does not identify anything worth changing, ignore the \"\n \"revision request and return 'No revisions needed'. If the critique \"\n \"does identify something worth changing, revise the response based on \"\n \"the revision request.\\n\\n\"\n \"Revision Request: {revision_request}\"\n )\n\n chain = model | StrOutputParser()\n critique_chain = critique_prompt | model.with_structured_output(Critique)\n revision_chain = revision_prompt | model | StrOutputParser()\n\n\n class State(TypedDict):\n query: str\n constitutional_principles: List[ConstitutionalPrinciple]\n initial_response: str\n critiques_and_revisions: List[Tuple[str, str]]\n response: str\n\n\n async def generate_response(state: State):\n \"\"\"Generate initial response.\"\"\"\n response = await chain.ainvoke(state[\"query\"])\n return {\"response\": response, \"initial_response\": response}\n\n async def critique_and_revise(state: State):\n \"\"\"Critique and revise response according to principles.\"\"\"\n critiques_and_revisions = []\n response = state[\"initial_response\"]\n for principle in state[\"constitutional_principles\"]:\n critique = await critique_chain.ainvoke(\n {\n \"query\": state[\"query\"],\n \"response\": response,\n \"critique_request\": principle.critique_request,\n }\n )\n if critique[\"critique_needed\"]:\n revision = await revision_chain.ainvoke(\n {\n \"query\": state[\"query\"],\n \"response\": response,\n \"critique_request\": principle.critique_request,\n \"critique\": critique[\"critique\"],\n \"revision_request\": principle.revision_request,\n }\n )\n response = revision\n critiques_and_revisions.append((critique[\"critique\"], revision))\n else:\n critiques_and_revisions.append((critique[\"critique\"], \"\"))\n return {\n \"critiques_and_revisions\": critiques_and_revisions,\n \"response\": response,\n }\n\n graph = StateGraph(State)\n graph.add_node(\"generate_response\", generate_response)\n graph.add_node(\"critique_and_revise\", critique_and_revise)\n\n graph.add_edge(START, \"generate_response\")\n graph.add_edge(\"generate_response\", \"critique_and_revise\")\n graph.add_edge(\"critique_and_revise\", END)\n app = graph.compile()\n ```\n\n ```python\n constitutional_principles=[\n ConstitutionalPrinciple(\n critique_request=\"Tell if this answer is good.\",\n revision_request=\"Give a better answer.\",\n )\n ]\n\n query = \"What is the meaning of life? Answer in 10 words or fewer.\"\n\n async for step in app.astream(\n {\"query\": query, \"constitutional_principles\": constitutional_principles},\n stream_mode=\"values\",\n ):\n subset = [\"initial_response\", \"critiques_and_revisions\", \"response\"]\n print({k: v for k, v in step.items() if k in subset})\n ```\n\n Example:\n ```python\n from langchain_openai import OpenAI\n from langchain_classic.chains import LLMChain, ConstitutionalChain\n from langchain_classic.chains.constitutional_ai.models \\\n import ConstitutionalPrinciple\n\n llmodelm = OpenAI()\n\n qa_prompt = PromptTemplate(\n template=\"Q: {question} A:\",\n input_variables=[\"question\"],\n )\n qa_chain = LLMChain(llm=model, prompt=qa_prompt)\n\n constitutional_chain = ConstitutionalChain.from_llm(\n llm=model,\n chain=qa_chain,\n constitutional_principles=[\n ConstitutionalPrinciple(\n critique_request=\"Tell if this answer is good.\",\n revision_request=\"Give a better answer.\",\n )\n ],\n )\n\n constitutional_chain.run(question=\"What is the meaning of life?\")\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\conversation\\base.py",
"line": 20,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Chain to have a conversation and load context from memory.\n\n This class is deprecated in favor of `RunnableWithMessageHistory`. Please refer\n to this tutorial for more detail: https://python.langchain.com/docs/tutorials/chatbot/\n\n `RunnableWithMessageHistory` offers several benefits, including:\n\n - Stream, batch, and async support;\n - More flexible memory handling, including the ability to manage memory\n outside the chain;\n - Support for multiple threads.\n\n Below is a minimal implementation, analogous to using `ConversationChain` with\n the default `ConversationBufferMemory`:\n\n ```python\n from langchain_core.chat_history import InMemoryChatMessageHistory\n from langchain_core.runnables.history import RunnableWithMessageHistory\n from langchain_openai import ChatOpenAI\n\n\n store = {} # memory is maintained outside the chain\n\n\n def get_session_history(session_id: str) -> InMemoryChatMessageHistory:\n if session_id not in store:\n store[session_id] = InMemoryChatMessageHistory()\n return store[session_id]\n\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n\n chain = RunnableWithMessageHistory(model, get_session_history)\n chain.invoke(\n \"Hi I'm Bob.\",\n config={\"configurable\": {\"session_id\": \"1\"}},\n ) # session_id determines thread\n ```\n\n Memory objects can also be incorporated into the `get_session_history` callable:\n\n ```python\n from langchain_classic.memory import ConversationBufferWindowMemory\n from langchain_core.chat_history import InMemoryChatMessageHistory\n from langchain_core.runnables.history import RunnableWithMessageHistory\n from langchain_openai import ChatOpenAI\n\n\n store = {} # memory is maintained outside the chain\n\n\n def get_session_history(session_id: str) -> InMemoryChatMessageHistory:\n if session_id not in store:\n store[session_id] = InMemoryChatMessageHistory()\n return store[session_id]\n\n memory = ConversationBufferWindowMemory(\n chat_memory=store[session_id],\n k=3,\n return_messages=True,\n )\n assert len(memory.memory_variables) == 1\n key = memory.memory_variables[0]\n messages = memory.load_memory_variables({})[key]\n store[session_id] = InMemoryChatMessageHistory(messages=messages)\n return store[session_id]\n\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n\n chain = RunnableWithMessageHistory(model, get_session_history)\n chain.invoke(\n \"Hi I'm Bob.\",\n config={\"configurable\": {\"session_id\": \"1\"}},\n ) # session_id determines thread\n ```\n\n Example:\n ```python\n from langchain_classic.chains import ConversationChain\n from langchain_openai import OpenAI\n\n conversation = ConversationChain(llm=OpenAI())\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\conversational_retrieval\\base.py",
"line": 416,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\elasticsearch_database\\base.py",
"line": 135,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\elasticsearch_database\\base.py",
"line": 154,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\flare\\base.py",
"line": 147,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\hyde\\base.py",
"line": 81,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\llm_math\\base.py",
"line": 34,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Chain that interprets a prompt and executes python code to do math.\n\n !!! note\n This class is deprecated. See below for a replacement implementation using\n LangGraph. The benefits of this implementation are:\n\n - Uses LLM tool calling features;\n - Support for both token-by-token and step-by-step streaming;\n - Support for checkpointing and memory of chat history;\n - Easier to modify or extend\n (e.g., with additional tools, structured responses, etc.)\n\n Install LangGraph with:\n\n ```bash\n pip install -U langgraph\n ```\n\n ```python\n import math\n from typing import Annotated, Sequence\n\n from langchain_core.messages import BaseMessage\n from langchain_core.runnables import RunnableConfig\n from langchain_core.tools import tool\n from langchain_openai import ChatOpenAI\n from langgraph.graph import END, StateGraph\n from langgraph.graph.message import add_messages\n from langgraph.prebuilt.tool_node import ToolNode\n import numexpr\n from typing_extensions import TypedDict\n\n @tool\n def calculator(expression: str) -> str:\n \"\"\"Calculate expression using Python's numexpr library.\n\n Expression should be a single line mathematical expression\n that solves the problem.\n ```\n\n Examples:\n \"37593 * 67\" for \"37593 times 67\"\n \"37593**(1/5)\" for \"37593^(1/5)\"\n \"\"\"\n local_dict = {\"pi\": math.pi, \"e\": math.e}\n return str(\n numexpr.evaluate(\n expression.strip(),\n global_dict={}, # restrict access to globals\n local_dict=local_dict, # add common mathematical functions\n )\n )\n\n model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n tools = [calculator]\n model_with_tools = model.bind_tools(tools, tool_choice=\"any\")\n\n class ChainState(TypedDict):\n \"\"\"LangGraph state.\"\"\"\n\n messages: Annotated[Sequence[BaseMessage], add_messages]\n\n async def acall_chain(state: ChainState, config: RunnableConfig):\n last_message = state[\"messages\"][-1]\n response = await model_with_tools.ainvoke(state[\"messages\"], config)\n return {\"messages\": [response]}\n\n async def acall_model(state: ChainState, config: RunnableConfig):\n response = await model.ainvoke(state[\"messages\"], config)\n return {\"messages\": [response]}\n\n graph_builder = StateGraph(ChainState)\n graph_builder.add_node(\"call_tool\", acall_chain)\n graph_builder.add_node(\"execute_tool\", ToolNode(tools))\n graph_builder.add_node(\"call_model\", acall_model)\n graph_builder.set_entry_point(\"call_tool\")\n graph_builder.add_edge(\"call_tool\", \"execute_tool\")\n graph_builder.add_edge(\"execute_tool\", \"call_model\")\n graph_builder.add_edge(\"call_model\", END)\n chain = graph_builder.compile()\n\n ```python\n example_query = \"What is 551368 divided by 82\"\n\n events = chain.astream(\n {\"messages\": [(\"user\", example_query)]},\n stream_mode=\"values\",\n )\n async for event in events:\n event[\"messages\"][-1].pretty_print()\n ```\n\n ```txt\n ================================ Human Message =================================\n\n What is 551368 divided by 82\n ================================== Ai Message ==================================\n Tool Calls:\n calculator (call_MEiGXuJjJ7wGU4aOT86QuGJS)\n Call ID: call_MEiGXuJjJ7wGU4aOT86QuGJS\n Args:\n expression: 551368 / 82\n ================================= Tool Message =================================\n Name: calculator\n\n 6724.0\n ================================== Ai Message ==================================\n\n 551368 divided by 82 equals 6724.\n ```\n\n Example:\n ```python\n from langchain_classic.chains import LLMMathChain\n from langchain_openai import OpenAI\n\n llm_math = LLMMathChain.from_llm(OpenAI())\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\natbot\\base.py",
"line": 121,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\natbot\\crawler.py",
"line": 134,
"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\natbot\\crawler.py",
"line": 478,
"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\openai_functions\\base.py",
"line": 52,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: '[Legacy] Create an LLM chain that uses OpenAI functions.\n\n Args:\n functions: A sequence of either dictionaries, pydantic.BaseModels classes, or\n Python functions. If dictionaries are passed in, they are assumed to\n already be a valid OpenAI functions. If only a single\n function is passed in, then it will be enforced that the model use that\n function. pydantic.BaseModels and Python functions should have docstrings\n describing what the function does. For best results, pydantic.BaseModels\n should have descriptions of the parameters and Python functions should have\n Google Python style args descriptions in the docstring. Additionally,\n Python functions should only use primitive types (str, int, float, bool) or\n pydantic.BaseModels for arguments.\n llm: Language model to use, assumed to support the OpenAI function-calling API.\n prompt: BasePromptTemplate to pass to the model.\n enforce_single_function_usage: only used if a single function is passed in. If\n True, then the model will be forced to use the given function. If `False`,\n then the model will be given the option to use the given function or not.\n output_key: The key to use when returning the output in LLMChain.__call__.\n output_parser: BaseLLMOutputParser to use for parsing model outputs. By default\n will be inferred from the function types. If pydantic.BaseModels are passed\n in, then the OutputParser will try to parse outputs using those. Otherwise\n model outputs will simply be parsed as JSON. If multiple functions are\n passed in and they are not pydantic.BaseModels, the chain output will\n include both the name of the function that was returned and the arguments\n to pass to the function.\n **kwargs: Additional keyword arguments to pass to LLMChain.\n\n Returns:\n An LLMChain that will pass in the given functions to the model when run.\n\n Example:\n ```python\n from typing import Optional\n\n from langchain_classic.chains.openai_functions import create_openai_fn_chain\n from langchain_openai import ChatOpenAI\n from langchain_core.prompts import ChatPromptTemplate\n\n from pydantic import BaseModel, Field\n\n\n class RecordPerson(BaseModel):\n \"\"\"Record some identifying information about a person.\"\"\"\n\n name: str = Field(..., description=\"The person's name\")\n age: int = Field(..., description=\"The person's age\")\n fav_food: str | None = Field(None, description=\"The person's favorite food\")\n\n\n class RecordDog(BaseModel):\n \"\"\"Record some identifying information about a dog.\"\"\"\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n\n model = ChatOpenAI(model=\"gpt-4\", temperature=0)\n prompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are a world class algorithm for recording entities.\"),\n (\"human\", \"Make calls to the relevant function to record the entities in the following input: {input}\"),\n (\"human\", \"Tip: Make sure to answer in the correct format\"),\n ]\n )\n chain = create_openai_fn_chain([RecordPerson, RecordDog], model, prompt)\n chain.run(\"Harry was a chubby brown beagle who loved chicken\")\n # -> RecordDog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\openai_functions\\base.py",
"line": 158,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: '[Legacy] Create an LLMChain that uses an OpenAI function to get a structured output.\n\n Args:\n output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary\n is passed in, it's assumed to already be a valid JsonSchema.\n For best results, pydantic.BaseModels should have docstrings describing what\n the schema represents and descriptions for the parameters.\n llm: Language model to use, assumed to support the OpenAI function-calling API.\n prompt: BasePromptTemplate to pass to the model.\n output_key: The key to use when returning the output in LLMChain.__call__.\n output_parser: BaseLLMOutputParser to use for parsing model outputs. By default\n will be inferred from the function types. If pydantic.BaseModels are passed\n in, then the OutputParser will try to parse outputs using those. Otherwise\n model outputs will simply be parsed as JSON.\n **kwargs: Additional keyword arguments to pass to LLMChain.\n\n Returns:\n An LLMChain that will pass the given function to the model.\n\n Example:\n ```python\n from typing import Optional\n\n from langchain_classic.chains.openai_functions import create_structured_output_chain\n from langchain_openai import ChatOpenAI\n from langchain_core.prompts import ChatPromptTemplate\n\n from pydantic import BaseModel, Field\n\n class Dog(BaseModel):\n \"\"\"Identifying information about a dog.\"\"\"\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\", temperature=0)\n prompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are a world class algorithm for extracting information in structured formats.\"),\n (\"human\", \"Use the given format to extract information from the following input: {input}\"),\n (\"human\", \"Tip: Make sure to answer in the correct format\"),\n ]\n )\n chain = create_structured_output_chain(Dog, model, prompt)\n chain.run(\"Harry was a chubby brown beagle who loved chicken\")\n # -> Dog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\openai_functions\\citation_fuzzy_match.py",
"line": 78,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a citation fuzzy match Runnable.\n\n Example usage:\n\n ```python\n from langchain_classic.chains import create_citation_fuzzy_match_runnable\n from langchain_openai import ChatOpenAI\n\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n context = \"Alice has blue eyes. Bob has brown eyes. Charlie has green eyes.\"\n question = \"What color are Bob's eyes?\"\n\n chain = create_citation_fuzzy_match_runnable(model)\n chain.invoke({\"question\": question, \"context\": context})\n ```\n\n Args:\n llm: Language model to use for the chain. Must implement bind_tools.\n\n Returns:\n Runnable that can be used to answer questions with citations.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\openai_functions\\openapi.py",
"line": 272,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a chain for querying an API from a OpenAPI spec.\n\n Note: this class is deprecated. See below for a replacement implementation.\n The benefits of this implementation are:\n\n - Uses LLM tool calling features to encourage properly-formatted API requests;\n - Includes async support.\n\n ```python\n from typing import Any\n\n from langchain_classic.chains.openai_functions.openapi import openapi_spec_to_openai_fn\n from langchain_community.utilities.openapi import OpenAPISpec\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_openai import ChatOpenAI\n\n # Define API spec. Can be JSON or YAML\n api_spec = \\\"\\\"\\\"\n {\n \"openapi\": \"3.1.0\",\n \"info\": {\n \"title\": \"JSONPlaceholder API\",\n \"version\": \"1.0.0\"\n },\n \"servers\": [\n {\n \"url\": \"https://jsonplaceholder.typicode.com\"\n }\n ],\n \"paths\": {\n \"/posts\": {\n \"get\": {\n \"summary\": \"Get posts\",\n \"parameters\": [\n {\n \"name\": \"_limit\",\n \"in\": \"query\",\n \"required\": false,\n \"schema\": {\n \"type\": \"integer\",\n \"example\": 2\n },\n \"description\": \"Limit the number of results\"\n }\n ]\n }\n }\n }\n }\n \\\"\\\"\\\"\n\n parsed_spec = OpenAPISpec.from_text(api_spec)\n openai_fns, call_api_fn = openapi_spec_to_openai_fn(parsed_spec)\n tools = [\n {\"type\": \"function\", \"function\": fn}\n for fn in openai_fns\n ]\n\n prompt = ChatPromptTemplate.from_template(\n \"Use the provided APIs to respond to this user query:\\\\n\\\\n{query}\"\n )\n model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0).bind_tools(tools)\n\n def _execute_tool(message) -> Any:\n if tool_calls := message.tool_calls:\n tool_call = message.tool_calls[0]\n response = call_api_fn(name=tool_call[\"name\"], fn_args=tool_call[\"args\"])\n response.raise_for_status()\n return response.json()\n else:\n return message.content\n\n chain = prompt | model | _execute_tool\n ```\n\n ```python\n response = chain.invoke({\"query\": \"Get me top two posts.\"})\n ```\n\n Args:\n spec: OpenAPISpec or url/file/text string corresponding to one.\n llm: language model, should be an OpenAI function-calling model, e.g.\n `ChatOpenAI(model=\"gpt-3.5-turbo-0613\")`.\n prompt: Main prompt template to use.\n request_chain: Chain for taking the functions output and executing the request.\n params: Request parameters.\n headers: Request headers.\n verbose: Whether to run the chain in verbose mode.\n llm_chain_kwargs: LLM chain additional keyword arguments.\n **kwargs: Additional keyword arguments to pass to the chain.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\openai_functions\\tagging.py",
"line": 57,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create tagging chain from schema.\n\n Create a chain that extracts information from a passage\n based on a schema.\n\n This function is deprecated. Please use `with_structured_output` instead.\n See example usage below:\n\n ```python\n from typing_extensions import Annotated, TypedDict\n from langchain_anthropic import ChatAnthropic\n\n class Joke(TypedDict):\n \"\"\"Tagged joke.\"\"\"\n\n setup: Annotated[str, ..., \"The setup of the joke\"]\n punchline: Annotated[str, ..., \"The punchline of the joke\"]\n\n # Or any other chat model that supports tools.\n # Please reference to the documentation of structured_output\n # to see an up to date list of which models support\n # with_structured_output.\n model = ChatAnthropic(model=\"claude-3-haiku-20240307\", temperature=0)\n structured_model = model.with_structured_output(Joke)\n structured_model.invoke(\n \"Why did the cat cross the road? To get to the other \"\n \"side... and then lay down in the middle of it!\"\n )\n ```\n\n Read more here: https://docs.langchain.com/oss/python/langchain/models#structured-outputs\n\n Args:\n schema: The schema of the entities to extract.\n llm: The language model to use.\n prompt: The prompt template to use for the chain.\n kwargs: Additional keyword arguments to pass to the chain.\n\n Returns:\n Chain (`LLMChain`) that can be used to extract information from a passage.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\qa_with_sources\\retrieval.py",
"line": 53,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\router\\llm_router.py",
"line": 34,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'A router chain that uses an LLM chain to perform routing.\n\n This class is deprecated. See below for a replacement, which offers several\n benefits, including streaming and batch support.\n\n Below is an example implementation:\n\n ```python\n from operator import itemgetter\n from typing import Literal\n from typing_extensions import TypedDict\n\n from langchain_core.output_parsers import StrOutputParser\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n from langchain_openai import ChatOpenAI\n\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n prompt_1 = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are an expert on animals.\"),\n (\"human\", \"{query}\"),\n ]\n )\n prompt_2 = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are an expert on vegetables.\"),\n (\"human\", \"{query}\"),\n ]\n )\n\n chain_1 = prompt_1 | model | StrOutputParser()\n chain_2 = prompt_2 | model | StrOutputParser()\n\n route_system = \"Route the user's query to either the animal \"\n \"or vegetable expert.\"\n route_prompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", route_system),\n (\"human\", \"{query}\"),\n ]\n )\n\n\n class RouteQuery(TypedDict):\n \"\"\"Route query to destination.\"\"\"\n destination: Literal[\"animal\", \"vegetable\"]\n\n\n route_chain = (\n route_prompt\n | model.with_structured_output(RouteQuery)\n | itemgetter(\"destination\")\n )\n\n chain = {\n \"destination\": route_chain, # \"animal\" or \"vegetable\"\n \"query\": lambda x: x[\"query\"], # pass through input query\n } | RunnableLambda(\n # if animal, chain_1. otherwise, chain_2.\n lambda x: chain_1 if x[\"destination\"] == \"animal\" else chain_2,\n )\n\n chain.invoke({\"query\": \"what color are carrots\"})\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\router\\multi_prompt.py",
"line": 34,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'A multi-route chain that uses an LLM router chain to choose amongst prompts.\n\n This class is deprecated. See below for a replacement, which offers several\n benefits, including streaming and batch support.\n\n Below is an example implementation:\n\n ```python\n from operator import itemgetter\n from typing import Literal\n\n from langchain_core.output_parsers import StrOutputParser\n from langchain_core.prompts import ChatPromptTemplate\n from langchain_core.runnables import RunnableConfig\n from langchain_openai import ChatOpenAI\n from langgraph.graph import END, START, StateGraph\n from typing_extensions import TypedDict\n\n model = ChatOpenAI(model=\"gpt-4o-mini\")\n\n # Define the prompts we will route to\n prompt_1 = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are an expert on animals.\"),\n (\"human\", \"{input}\"),\n ]\n )\n prompt_2 = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are an expert on vegetables.\"),\n (\"human\", \"{input}\"),\n ]\n )\n\n # Construct the chains we will route to. These format the input query\n # into the respective prompt, run it through a chat model, and cast\n # the result to a string.\n chain_1 = prompt_1 | model | StrOutputParser()\n chain_2 = prompt_2 | model | StrOutputParser()\n\n\n # Next: define the chain that selects which branch to route to.\n # Here we will take advantage of tool-calling features to force\n # the output to select one of two desired branches.\n route_system = \"Route the user's query to either the animal \"\n \"or vegetable expert.\"\n route_prompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", route_system),\n (\"human\", \"{input}\"),\n ]\n )\n\n\n # Define schema for output:\n class RouteQuery(TypedDict):\n \"\"\"Route query to destination expert.\"\"\"\n\n destination: Literal[\"animal\", \"vegetable\"]\n\n\n route_chain = route_prompt | model.with_structured_output(RouteQuery)\n\n\n # For LangGraph, we will define the state of the graph to hold the query,\n # destination, and final answer.\n class State(TypedDict):\n query: str\n destination: RouteQuery\n answer: str\n\n\n # We define functions for each node, including routing the query:\n async def route_query(state: State, config: RunnableConfig):\n destination = await route_chain.ainvoke(state[\"query\"], config)\n return {\"destination\": destination}\n\n\n # And one node for each prompt\n async def prompt_1(state: State, config: RunnableConfig):\n return {\"answer\": await chain_1.ainvoke(state[\"query\"], config)}\n\n\n async def prompt_2(state: State, config: RunnableConfig):\n return {\"answer\": await chain_2.ainvoke(state[\"query\"], config)}\n\n\n # We then define logic that selects the prompt based on the classification\n def select_node(state: State) -> Literal[\"prompt_1\", \"prompt_2\"]:\n if state[\"destination\"] == \"animal\":\n return \"prompt_1\"\n else:\n return \"prompt_2\"\n\n\n # Finally, assemble the multi-prompt chain. This is a sequence of two steps:\n # 1) Select \"animal\" or \"vegetable\" via the route_chain, and collect the\n # answer alongside the input query.\n # 2) Route the input query to chain_1 or chain_2, based on the\n # selection.\n graph = StateGraph(State)\n graph.add_node(\"route_query\", route_query)\n graph.add_node(\"prompt_1\", prompt_1)\n graph.add_node(\"prompt_2\", prompt_2)\n\n graph.add_edge(START, \"route_query\")\n graph.add_conditional_edges(\"route_query\", select_node)\n graph.add_edge(\"prompt_1\", END)\n graph.add_edge(\"prompt_2\", END)\n app = graph.compile()\n\n result = await app.ainvoke({\"query\": \"what color are carrots\"})\n print(result[\"destination\"])\n print(result[\"answer\"])\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\sql_database\\query.py",
"line": 41,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a chain that generates SQL queries.\n\n *Security Note*: This chain generates SQL queries for the given database.\n\n The SQLDatabase class provides a get_table_info method that can be used\n to get column information as well as sample data from the table.\n\n To mitigate risk of leaking sensitive data, limit permissions\n to read and scope to the tables that are needed.\n\n Optionally, use the SQLInputWithTables input type to specify which tables\n are allowed to be accessed.\n\n Control access to who can submit requests to this chain.\n\n See https://docs.langchain.com/oss/python/security-policy for more information.\n\n Args:\n llm: The language model to use.\n db: The SQLDatabase to generate the query for.\n prompt: The prompt to use. If none is provided, will choose one\n based on dialect. See Prompt section below for more.\n k: The number of results per select statement to return.\n get_col_comments: Whether to retrieve column comments along with table info.\n\n Returns:\n A chain that takes in a question and generates a SQL query that answers\n that question.\n\n Example:\n ```python\n # pip install -U langchain langchain-community langchain-openai\n from langchain_openai import ChatOpenAI\n from langchain_classic.chains import create_sql_query_chain\n from langchain_community.utilities import SQLDatabase\n\n db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n chain = create_sql_query_chain(model, db)\n response = chain.invoke({\"question\": \"How many employees are there\"})\n ```\n\n Prompt:\n If no prompt is provided, a default prompt is selected based on the SQLDatabase\n dialect. If one is provided, it must support input variables:\n\n * input: The user question plus suffix \"\\\\nSQLQuery: \" is passed here.\n * top_k: The number of results per select statement (the `k` argument to\n this function) is passed in here.\n * table_info: Table definitions and sample rows are passed in here. If the\n user specifies \"table_names_to_use\" when invoking chain, only those\n will be included. Otherwise, all tables are included.\n * dialect (optional): If dialect input variable is in prompt, the db\n dialect will be passed in here.\n\n Here's an example prompt:\n\n ```python\n from langchain_core.prompts import PromptTemplate\n\n template = '''Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\n Use the following format:\n\n Question: \"Question here\"\n SQLQuery: \"SQL Query to run\"\n SQLResult: \"Result of the SQLQuery\"\n Answer: \"Final answer here\"\n\n Only use the following tables:\n\n {table_info}.\n\n Question: {input}'''\n prompt = PromptTemplate.from_template(template)\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\structured_output\\base.py",
"line": 75,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a runnable sequence that uses OpenAI functions.\n\n Args:\n functions: A sequence of either dictionaries, pydantic.BaseModels classes, or\n Python functions. If dictionaries are passed in, they are assumed to\n already be a valid OpenAI functions. If only a single\n function is passed in, then it will be enforced that the model use that\n function. pydantic.BaseModels and Python functions should have docstrings\n describing what the function does. For best results, pydantic.BaseModels\n should have descriptions of the parameters and Python functions should have\n Google Python style args descriptions in the docstring. Additionally,\n Python functions should only use primitive types (str, int, float, bool) or\n pydantic.BaseModels for arguments.\n llm: Language model to use, assumed to support the OpenAI function-calling API.\n prompt: BasePromptTemplate to pass to the model.\n enforce_single_function_usage: only used if a single function is passed in. If\n True, then the model will be forced to use the given function. If `False`,\n then the model will be given the option to use the given function or not.\n output_parser: BaseLLMOutputParser to use for parsing model outputs. By default\n will be inferred from the function types. If pydantic.BaseModels are passed\n in, then the OutputParser will try to parse outputs using those. Otherwise\n model outputs will simply be parsed as JSON. If multiple functions are\n passed in and they are not pydantic.BaseModels, the chain output will\n include both the name of the function that was returned and the arguments\n to pass to the function.\n **llm_kwargs: Additional named arguments to pass to the language model.\n\n Returns:\n A runnable sequence that will pass in the given functions to the model when run.\n\n Example:\n ```python\n from typing import Optional\n\n from langchain_classic.chains.structured_output import create_openai_fn_runnable\n from langchain_openai import ChatOpenAI\n from pydantic import BaseModel, Field\n\n\n class RecordPerson(BaseModel):\n '''Record some identifying information about a person.'''\n\n name: str = Field(..., description=\"The person's name\")\n age: int = Field(..., description=\"The person's age\")\n fav_food: str | None = Field(None, description=\"The person's favorite food\")\n\n\n class RecordDog(BaseModel):\n '''Record some identifying information about a dog.'''\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n\n model = ChatOpenAI(model=\"gpt-4\", temperature=0)\n structured_model = create_openai_fn_runnable([RecordPerson, RecordDog], model)\n structured_model.invoke(\"Harry was a chubby brown beagle who loved chicken)\n # -> RecordDog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chains\\structured_output\\base.py",
"line": 200,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Create a runnable for extracting structured outputs.\n\n Args:\n output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary\n is passed in, it's assumed to already be a valid JsonSchema.\n For best results, pydantic.BaseModels should have docstrings describing what\n the schema represents and descriptions for the parameters.\n llm: Language model to use. Assumed to support the OpenAI function-calling API\n if mode is 'openai-function'. Assumed to support OpenAI response_format\n parameter if mode is 'openai-json'.\n prompt: BasePromptTemplate to pass to the model. If mode is 'openai-json' and\n prompt has input variable 'output_schema' then the given output_schema\n will be converted to a JsonSchema and inserted in the prompt.\n output_parser: Output parser to use for parsing model outputs. By default\n will be inferred from the function types. If pydantic.BaseModel is passed\n in, then the OutputParser will try to parse outputs using the pydantic\n class. Otherwise model outputs will be parsed as JSON.\n mode: How structured outputs are extracted from the model. If 'openai-functions'\n then OpenAI function calling is used with the deprecated 'functions',\n 'function_call' schema. If 'openai-tools' then OpenAI function\n calling with the latest 'tools', 'tool_choice' schema is used. This is\n recommended over 'openai-functions'. If 'openai-json' then OpenAI model\n with response_format set to JSON is used.\n enforce_function_usage: Only applies when mode is 'openai-tools' or\n 'openai-functions'. If `True`, then the model will be forced to use the given\n output schema. If `False`, then the model can elect whether to use the output\n schema.\n return_single: Only applies when mode is 'openai-tools'. Whether to a list of\n structured outputs or a single one. If `True` and model does not return any\n structured outputs then chain output is None. If `False` and model does not\n return any structured outputs then chain output is an empty list.\n kwargs: Additional named arguments.\n\n Returns:\n A runnable sequence that will return a structured output(s) matching the given\n output_schema.\n\n OpenAI tools example with Pydantic schema (mode='openai-tools'):\n ```python\n from typing import Optional\n\n from langchain_classic.chains import create_structured_output_runnable\n from langchain_openai import ChatOpenAI\n from pydantic import BaseModel, Field\n\n\n class RecordDog(BaseModel):\n '''Record some identifying information about a dog.'''\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n prompt = ChatPromptTemplate.from_messages(\n [\n (\"system\", \"You are an extraction algorithm. Please extract every possible instance\"),\n ('human', '{input}')\n ]\n )\n structured_model = create_structured_output_runnable(\n RecordDog,\n model,\n mode=\"openai-tools\",\n enforce_function_usage=True,\n return_single=True\n )\n structured_model.invoke({\"input\": \"Harry was a chubby brown beagle who loved chicken\"})\n # -> RecordDog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n ```\n\n OpenAI tools example with dict schema (mode=\"openai-tools\"):\n ```python\n from typing import Optional\n\n from langchain_classic.chains import create_structured_output_runnable\n from langchain_openai import ChatOpenAI\n\n\n dog_schema = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"record_dog\",\n \"description\": \"Record some identifying information about a dog.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"description\": \"The dog's name\",\n \"type\": \"string\"\n },\n \"color\": {\n \"description\": \"The dog's color\",\n \"type\": \"string\"\n },\n \"fav_food\": {\n \"description\": \"The dog's favorite food\",\n \"type\": \"string\"\n }\n },\n \"required\": [\"name\", \"color\"]\n }\n }\n }\n\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n structured_model = create_structured_output_runnable(\n dog_schema,\n model,\n mode=\"openai-tools\",\n enforce_function_usage=True,\n return_single=True\n )\n structured_model.invoke(\"Harry was a chubby brown beagle who loved chicken\")\n # -> {'name': 'Harry', 'color': 'brown', 'fav_food': 'chicken'}\n ```\n\n OpenAI functions example (mode=\"openai-functions\"):\n ```python\n from typing import Optional\n\n from langchain_classic.chains import create_structured_output_runnable\n from langchain_openai import ChatOpenAI\n from pydantic import BaseModel, Field\n\n class Dog(BaseModel):\n '''Identifying information about a dog.'''\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n structured_model = create_structured_output_runnable(Dog, model, mode=\"openai-functions\")\n structured_model.invoke(\"Harry was a chubby brown beagle who loved chicken\")\n # -> Dog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n ```\n\n OpenAI functions with prompt example:\n ```python\n from typing import Optional\n\n from langchain_classic.chains import create_structured_output_runnable\n from langchain_openai import ChatOpenAI\n from langchain_core.prompts import ChatPromptTemplate\n from pydantic import BaseModel, Field\n\n class Dog(BaseModel):\n '''Identifying information about a dog.'''\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n structured_model = create_structured_output_runnable(Dog, model, mode=\"openai-functions\")\n system = '''Extract information about any dogs mentioned in the user input.'''\n prompt = ChatPromptTemplate.from_messages(\n [(\"system\", system), (\"human\", \"{input}\"),]\n )\n chain = prompt | structured_model\n chain.invoke({\"input\": \"Harry was a chubby brown beagle who loved chicken\"})\n # -> Dog(name=\"Harry\", color=\"brown\", fav_food=\"chicken\")\n ```\n\n OpenAI json response format example (mode=\"openai-json\"):\n ```python\n from typing import Optional\n\n from langchain_classic.chains import create_structured_output_runnable\n from langchain_openai import ChatOpenAI\n from langchain_core.prompts import ChatPromptTemplate\n from pydantic import BaseModel, Field\n\n class Dog(BaseModel):\n '''Identifying information about a dog.'''\n\n name: str = Field(..., description=\"The dog's name\")\n color: str = Field(..., description=\"The dog's color\")\n fav_food: str | None = Field(None, description=\"The dog's favorite food\")\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n structured_model = create_structured_output_runnable(Dog, model, mode=\"openai-json\")\n system = '''You are a world class assistant for extracting information in structured JSON formats. \n Extract a valid JSON blob from the user input that matches the following JSON Schema:\n\n {output_schema}'''\n prompt = ChatPromptTemplate.from_messages(\n [(\"system\", system), (\"human\", \"{input}\"),]\n )\n chain = prompt | structured_model\n chain.invoke({\"input\": \"Harry was a chubby brown beagle who loved chicken\"})\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\chat_models\\base.py",
"line": 79,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Initialize a chat model from any supported provider using a unified interface.\n\n **Two main use cases:**\n\n 1. **Fixed model** – specify the model upfront and get back a ready-to-use chat\n model.\n 2. **Configurable model** – choose to specify parameters (including model name) at\n runtime via `config`. Makes it easy to switch between models/providers without\n changing your code\n\n !!! note\n Requires the integration package for the chosen model provider to be installed.\n\n See the `model_provider` parameter below for specific package names\n (e.g., `pip install langchain-openai`).\n\n Refer to the [provider integration's API reference](https://docs.langchain.com/oss/python/integrations/providers)\n for supported model parameters to use as `**kwargs`.\n\n Args:\n model: The name or ID of the model, e.g. `'o3-mini'`, `'claude-sonnet-4-5-20250929'`.\n\n You can also specify model and model provider in a single argument using\n `'{model_provider}:{model}'` format, e.g. `'openai:o1'`.\n\n Will attempt to infer `model_provider` from model if not specified.\n\n The following providers will be inferred based on these model prefixes:\n\n - `gpt-...` | `o1...` | `o3...` -> `openai`\n - `claude...` -> `anthropic`\n - `amazon...` -> `bedrock`\n - `gemini...` -> `google_vertexai`\n - `command...` -> `cohere`\n - `accounts/fireworks...` -> `fireworks`\n - `mistral...` -> `mistralai`\n - `deepseek...` -> `deepseek`\n - `grok...` -> `xai`\n - `sonar...` -> `perplexity`\n model_provider: The model provider if not specified as part of the model arg\n (see above).\n\n Supported `model_provider` values and the corresponding integration package\n are:\n\n - `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)\n - `anthropic` -> [`langchain-anthropic`](https://docs.langchain.com/oss/python/integrations/providers/anthropic)\n - `azure_openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)\n - `azure_ai` -> [`langchain-azure-ai`](https://docs.langchain.com/oss/python/integrations/providers/microsoft)\n - `google_vertexai` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)\n - `google_genai` -> [`langchain-google-genai`](https://docs.langchain.com/oss/python/integrations/providers/google)\n - `bedrock` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)\n - `bedrock_converse` -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)\n - `cohere` -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere)\n - `fireworks` -> [`langchain-fireworks`](https://docs.langchain.com/oss/python/integrations/providers/fireworks)\n - `together` -> [`langchain-together`](https://docs.langchain.com/oss/python/integrations/providers/together)\n - `mistralai` -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai)\n - `huggingface` -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface)\n - `groq` -> [`langchain-groq`](https://docs.langchain.com/oss/python/integrations/providers/groq)\n - `ollama` -> [`langchain-ollama`](https://docs.langchain.com/oss/python/integrations/providers/ollama)\n - `google_anthropic_vertex` -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)\n - `deepseek` -> [`langchain-deepseek`](https://docs.langchain.com/oss/python/integrations/providers/deepseek)\n - `ibm` -> [`langchain-ibm`](https://docs.langchain.com/oss/python/integrations/providers/ibm)\n - `nvidia` -> [`langchain-nvidia-ai-endpoints`](https://docs.langchain.com/oss/python/integrations/providers/nvidia)\n - `xai` -> [`langchain-xai`](https://docs.langchain.com/oss/python/integrations/providers/xai)\n - `perplexity` -> [`langchain-perplexity`](https://docs.langchain.com/oss/python/integrations/providers/perplexity)\n configurable_fields: Which model parameters are configurable at runtime:\n\n - `None`: No configurable fields (i.e., a fixed model).\n - `'any'`: All fields are configurable. **See security note below.**\n - `list[str] | Tuple[str, ...]`: Specified fields are configurable.\n\n Fields are assumed to have `config_prefix` stripped if a `config_prefix` is\n specified.\n\n If `model` is specified, then defaults to `None`.\n\n If `model` is not specified, then defaults to `(\"model\", \"model_provider\")`.\n\n !!! warning \"Security note\"\n\n Setting `configurable_fields=\"any\"` means fields like `api_key`,\n `base_url`, etc., can be altered at runtime, potentially redirecting\n model requests to a different service/user.\n\n Make sure that if you're accepting untrusted configurations that you\n enumerate the `configurable_fields=(...)` explicitly.\n\n config_prefix: Optional prefix for configuration keys.\n\n Useful when you have multiple configurable models in the same application.\n\n If `'config_prefix'` is a non-empty string then `model` will be configurable\n at runtime via the `config[\"configurable\"][\"{config_prefix}_{param}\"]` keys.\n See examples below.\n\n If `'config_prefix'` is an empty string then model will be configurable via\n `config[\"configurable\"][\"{param}\"]`.\n **kwargs: Additional model-specific keyword args to pass to the underlying\n chat model's `__init__` method. Common parameters include:\n\n - `temperature`: Model temperature for controlling randomness.\n - `max_tokens`: Maximum number of output tokens.\n - `timeout`: Maximum time (in seconds) to wait for a response.\n - `max_retries`: Maximum number of retry attempts for failed requests.\n - `base_url`: Custom API endpoint URL.\n - `rate_limiter`: A\n [`BaseRateLimiter`][langchain_core.rate_limiters.BaseRateLimiter]\n instance to control request rate.\n\n Refer to the specific model provider's\n [integration reference](https://reference.langchain.com/python/integrations/)\n for all available parameters.\n\n Returns:\n A [`BaseChatModel`][langchain_core.language_models.BaseChatModel] corresponding\n to the `model_name` and `model_provider` specified if configurability is\n inferred to be `False`. If configurable, a chat model emulator that\n initializes the underlying model at runtime once a config is passed in.\n\n Raises:\n ValueError: If `model_provider` cannot be inferred or isn't supported.\n ImportError: If the model provider integration package is not installed.\n\n ???+ example \"Initialize a non-configurable model\"\n\n ```python\n # pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai\n\n from langchain_classic.chat_models import init_chat_model\n\n o3_mini = init_chat_model(\"openai:o3-mini\", temperature=0)\n claude_sonnet = init_chat_model(\"anthropic:claude-sonnet-4-5-20250929\", temperature=0)\n gemini_2-5_flash = init_chat_model(\n \"google_vertexai:gemini-2.5-flash\", temperature=0\n )\n\n o3_mini.invoke(\"what's your name\")\n claude_sonnet.invoke(\"what's your name\")\n gemini_2-5_flash.invoke(\"what's your name\")\n ```\n\n ??? example \"Partially configurable model with no default\"\n\n ```python\n # pip install langchain langchain-openai langchain-anthropic\n\n from langchain_classic.chat_models import init_chat_model\n\n # (We don't need to specify configurable=True if a model isn't specified.)\n configurable_model = init_chat_model(temperature=0)\n\n configurable_model.invoke(\n \"what's your name\", config={\"configurable\": {\"model\": \"gpt-4o\"}}\n )\n # Use GPT-4o to generate the response\n\n configurable_model.invoke(\n \"what's your name\",\n config={\"configurable\": {\"model\": \"claude-sonnet-4-5-20250929\"}},\n )\n ```\n\n ??? example \"Fully configurable model with a default\"\n\n ```python\n # pip install langchain langchain-openai langchain-anthropic\n\n from langchain_classic.chat_models import init_chat_model\n\n configurable_model_with_default = init_chat_model(\n \"openai:gpt-4o\",\n configurable_fields=\"any\", # This allows us to configure other params like temperature, max_tokens, etc at runtime.\n config_prefix=\"foo\",\n temperature=0,\n )\n\n configurable_model_with_default.invoke(\"what's your name\")\n # GPT-4o response with temperature 0 (as set in default)\n\n configurable_model_with_default.invoke(\n \"what's your name\",\n config={\n \"configurable\": {\n \"foo_model\": \"anthropic:claude-sonnet-4-5-20250929\",\n \"foo_temperature\": 0.6,\n }\n },\n )\n # Override default to use Sonnet 4.5 with temperature 0.6 to generate response\n ```\n\n ??? example \"Bind tools to a configurable model\"\n\n You can call any chat model declarative methods on a configurable model in the\n same way that you would with a normal model:\n\n ```python\n # pip install langchain langchain-openai langchain-anthropic\n\n from langchain_classic.chat_models import init_chat_model\n from pydantic import BaseModel, Field\n\n\n class GetWeather(BaseModel):\n '''Get the current weather in a given location'''\n\n location: str = Field(\n ..., description=\"The city and state, e.g. San Francisco, CA\"\n )\n\n\n class GetPopulation(BaseModel):\n '''Get the current population in a given location'''\n\n location: str = Field(\n ..., description=\"The city and state, e.g. San Francisco, CA\"\n )\n\n\n configurable_model = init_chat_model(\n \"gpt-4o\", configurable_fields=(\"model\", \"model_provider\"), temperature=0\n )\n\n configurable_model_with_tools = configurable_model.bind_tools(\n [\n GetWeather,\n GetPopulation,\n ]\n )\n configurable_model_with_tools.invoke(\n \"Which city is hotter today and which is bigger: LA or NY?\"\n )\n # Use GPT-4o\n\n configurable_model_with_tools.invoke(\n \"Which city is hotter today and which is bigger: LA or NY?\",\n config={\"configurable\": {\"model\": \"claude-sonnet-4-5-20250929\"}},\n )\n # Use Sonnet 4.5\n ```\n\n !!! warning \"Behavior changed in `langchain` 0.2.8\"\n\n Support for `configurable_fields` and `config_prefix` added.\n\n !!! warning \"Behavior changed in `langchain` 0.2.12\"\n\n Support for Ollama via langchain-ollama package added\n (`langchain_ollama.ChatOllama`). Previously,\n the now-deprecated langchain-community version of Ollama was imported\n (`langchain_community.chat_models.ChatOllama`).\n\n Support for AWS Bedrock models via the Converse API added\n (`model_provider=\"bedrock_converse\"`).\n\n !!! warning \"Behavior changed in `langchain` 0.3.5\"\n\n Out of beta.\n\n !!! warning \"Behavior changed in `langchain` 0.3.19\"\n\n Support for Deepseek, IBM, Nvidia, and xAI models added.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
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"message": "Hardcoded model name: 'gpt-4' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
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"message": "Hardcoded model name: ' requires a language model to function. Failed to create the default 'gpt-4' model. Please manually provide an evaluation LLM or check your openai credentials.' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\loading.py",
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"message": "Hardcoded model name: 'Load evaluators specified by a list of evaluator types.\n\n Parameters\n ----------\n evaluators : Sequence[EvaluatorType]\n The list of evaluator types to load.\n llm : BaseLanguageModel, optional\n The language model to use for evaluation, if none is provided, a default\n ChatOpenAI gpt-4 model will be used.\n config : dict, optional\n A dictionary mapping evaluator types to additional keyword arguments,\n by default None\n **kwargs : Any\n Additional keyword arguments to pass to all evaluators.\n\n Returns:\n -------\n List[Chain]\n The loaded evaluators.\n\n Examples:\n --------\n >>> from langchain_classic.evaluation import load_evaluators, EvaluatorType\n >>> evaluators = [EvaluatorType.QA, EvaluatorType.CRITERIA]\n >>> loaded_evaluators = load_evaluators(evaluators, criteria=\"helpfulness\")\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\agents\\trajectory_eval_chain.py",
"line": 97,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'A chain for evaluating ReAct style agents.\n\n This chain is used to evaluate ReAct style agents by reasoning about\n the sequence of actions taken and their outcomes.\n Based on the paper \"ReAct: Synergizing Reasoning and Acting in Language Models\"\n (https://arxiv.org/abs/2210.03629)\n\n Example:\n ```python\n from langchain_classic.agents import AgentType, initialize_agent\n from langchain_openai import ChatOpenAI\n from langchain_classic.evaluation import TrajectoryEvalChain\n from langchain_classic.tools import tool\n\n @tool\n def geography_answers(country: str, question: str) -> str:\n \"\"\"Very helpful answers to geography questions.\"\"\"\n return f\"{country}? IDK - We may never know {question}.\"\n\n model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n agent = initialize_agent(\n tools=[geography_answers],\n llm=model,\n agent=AgentType.OPENAI_FUNCTIONS,\n return_intermediate_steps=True,\n )\n\n question = \"How many dwell in the largest minor region in Argentina?\"\n response = agent(question)\n\n eval_chain = TrajectoryEvalChain.from_llm(\n llm=model, agent_tools=[geography_answers], return_reasoning=True\n )\n\n result = eval_chain.evaluate_agent_trajectory(\n input=question,\n agent_trajectory=response[\"intermediate_steps\"],\n prediction=response[\"output\"],\n reference=\"Paris\",\n )\n print(result[\"score\"]) # noqa: T201\n # 0\n\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\comparison\\eval_chain.py",
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"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Pairwise String Evaluation Chain.\n\n A chain for comparing two outputs, such as the outputs\n of two models, prompts, or outputs of a single model on similar inputs.\n\n Attributes:\n output_parser (BaseOutputParser): The output parser for the chain.\n\n Example:\n >>> from langchain_openai import ChatOpenAI\n >>> from langchain_classic.evaluation.comparison import PairwiseStringEvalChain\n >>> model = ChatOpenAI(\n ... temperature=0, model_name=\"gpt-4\", model_kwargs={\"random_seed\": 42}\n ... )\n >>> chain = PairwiseStringEvalChain.from_llm(llm=model)\n >>> result = chain.evaluate_string_pairs(\n ... input = \"What is the chemical formula for water?\",\n ... prediction = \"H2O\",\n ... prediction_b = (\n ... \"The chemical formula for water is H2O, which means\"\n ... \" there are two hydrogen atoms and one oxygen atom.\"\n ... reference = \"The chemical formula for water is H2O.\",\n ... )\n >>> print(result)\n # {\n # \"value\": \"B\",\n # \"comment\": \"Both responses accurately state\"\n # \" that the chemical formula for water is H2O.\"\n # \" However, Response B provides additional information\"\n # . \" by explaining what the formula means.\\n[[B]]\"\n # }\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\comparison\\eval_chain.py",
"line": 248,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Initialize the PairwiseStringEvalChain from an LLM.\n\n Args:\n llm: The LLM to use (GPT-4 recommended).\n prompt: The prompt to use.\n criteria: The criteria to use.\n **kwargs: Additional keyword arguments.\n\n Returns:\n The initialized PairwiseStringEvalChain.\n\n Raises:\n ValueError: If the input variables are not as expected.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\comparison\\eval_chain.py",
"line": 264,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'gpt-4' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\comparison\\eval_chain.py",
"line": 266,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'This chain was only tested with GPT-4. Performance may be significantly worse with other models.' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
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"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\criteria\\eval_chain.py",
"line": 163,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'LLM Chain for evaluating runs against criteria.\n\n Parameters\n ----------\n llm : BaseLanguageModel\n The language model to use for evaluation.\n criteria : Union[Mapping[str, str]]\n The criteria or rubric to evaluate the runs against. It can be a mapping of\n criterion name to its description, or a single criterion name.\n prompt : Optional[BasePromptTemplate], default=None\n The prompt template to use for generating prompts. If not provided, a\n default prompt template will be used based on the value of\n `requires_reference`.\n requires_reference : bool, default=False\n Whether the evaluation requires a reference text. If `True`, the\n `PROMPT_WITH_REFERENCES` template will be used, which includes the\n reference labels in the prompt. Otherwise, the `PROMPT` template will be\n used, which is a reference-free prompt.\n **kwargs : Any\n Additional keyword arguments to pass to the `LLMChain` constructor.\n\n Returns:\n -------\n CriteriaEvalChain\n An instance of the `CriteriaEvalChain` class.\n\n Examples:\n --------\n >>> from langchain_anthropic import ChatAnthropic\n >>> from langchain_classic.evaluation.criteria import CriteriaEvalChain\n >>> model = ChatAnthropic(temperature=0)\n >>> criteria = {\"my-custom-criterion\": \"Is the submission the most amazing ever?\"}\n >>> evaluator = CriteriaEvalChain.from_llm(llm=model, criteria=criteria)\n >>> evaluator.evaluate_strings(\n ... prediction=\"Imagine an ice cream flavor for the color aquamarine\",\n ... input=\"Tell me an idea\",\n ... )\n {\n 'reasoning': 'Here is my step-by-step reasoning for the given criteria:\\n\\nThe criterion is: \"Is the submission the most amazing ever?\" This is a subjective criterion and open to interpretation. The submission suggests an aquamarine-colored ice cream flavor which is creative but may or may not be considered the most amazing idea ever conceived. There are many possible amazing ideas and this one ice cream flavor suggestion may or may not rise to that level for every person. \\n\\nN',\n 'value': 'N',\n 'score': 0,\n }\n\n >>> from langchain_openai import ChatOpenAI\n >>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain\n >>> model = ChatOpenAI(model=\"gpt-4\", temperature=0)\n >>> criteria = \"correctness\"\n >>> evaluator = LabeledCriteriaEvalChain.from_llm(\n ... llm=model,\n ... criteria=criteria,\n ... )\n >>> evaluator.evaluate_strings(\n ... prediction=\"The answer is 4\",\n ... input=\"How many apples are there?\",\n ... reference=\"There are 3 apples\",\n ... )\n {\n 'score': 0,\n 'reasoning': 'The criterion for this task is the correctness of the submission. The submission states that there are 4 apples, but the reference indicates that there are actually 3 apples. Therefore, the submission is not correct, accurate, or factual according to the given criterion.\\n\\nN',\n 'value': 'N',\n }\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\embedding_distance\\base.py",
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"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\embedding_distance\\base.py",
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"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
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"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\qa\\eval_chain.py",
"line": 50,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\scoring\\eval_chain.py",
"line": 151,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'A chain for scoring on a scale of 1-10 the output of a model.\n\n Attributes:\n output_parser (BaseOutputParser): The output parser for the chain.\n\n Example:\n >>> from langchain_openai import ChatOpenAI\n >>> from langchain_classic.evaluation.scoring import ScoreStringEvalChain\n >>> model = ChatOpenAI(temperature=0, model_name=\"gpt-4\")\n >>> chain = ScoreStringEvalChain.from_llm(llm=model)\n >>> result = chain.evaluate_strings(\n ... input=\"What is the chemical formula for water?\",\n ... prediction=\"H2O\",\n ... reference=\"The chemical formula for water is H2O.\",\n ... )\n >>> print(result)\n # {\n # \"score\": 8,\n # \"comment\": \"The response accurately states \"\n # \"that the chemical formula for water is H2O.\"\n # \"However, it does not provide an explanation of what the formula means.\"\n # }\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\scoring\\eval_chain.py",
"line": 249,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Initialize the ScoreStringEvalChain from an LLM.\n\n Args:\n llm: The LLM to use (GPT-4 recommended).\n prompt: The prompt to use.\n criteria: The criteria to use.\n normalize_by: The value to normalize the score by.\n **kwargs: Additional keyword arguments.\n\n Returns:\n The initialized ScoreStringEvalChain.\n\n Raises:\n ValueError: If the input variables are not as expected.\n\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\scoring\\eval_chain.py",
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"dimension": "D12",
"message": "Hardcoded model name: 'gpt-4' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\scoring\\eval_chain.py",
"line": 267,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'This chain was only tested with GPT-4. Performance may be significantly worse with other models.' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\evaluation\\scoring\\__init__.py",
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"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Scoring evaluators.\n\nThis module contains evaluators for scoring on a 1-10 the output of models,\nbe they LLMs, Chains, or otherwise. This can be based on a variety of\ncriteria and or a reference answer.\n\nExample:\n >>> from langchain_openai import ChatOpenAI\n >>> from langchain_classic.evaluation.scoring import ScoreStringEvalChain\n >>> model = ChatOpenAI(temperature=0, model_name=\"gpt-4\")\n >>> chain = ScoreStringEvalChain.from_llm(llm=model)\n >>> result = chain.evaluate_strings(\n ... input=\"What is the chemical formula for water?\",\n ... prediction=\"H2O\",\n ... reference=\"The chemical formula for water is H2O.\",\n ... )\n >>> print(result)\n # {\n # \"score\": 8,\n # \"comment\": \"The response accurately states \"\n # \"that the chemical formula for water is H2O.\"\n # \"However, it does not provide an explanation of what the formula means.\"\n # }\n' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\memory\\vectorstore.py",
"line": 74,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\output_parsers\\fix.py",
"line": 88,
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"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\output_parsers\\fix.py",
"line": 97,
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"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\output_parsers\\retry.py",
"line": 122,
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"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\output_parsers\\retry.py",
"line": 251,
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"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
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{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\contextual_compression.py",
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"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
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{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\multi_query.py",
"line": 199,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\multi_query.py",
"line": 224,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\re_phraser.py",
"line": 76,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\document_compressors\\chain_extract.py",
"line": 78,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\document_compressors\\listwise_rerank.py",
"line": 41,
"severity": "MEDIUM",
"dimension": "D12",
"message": "Hardcoded model name: 'Document compressor that uses `Zero-Shot Listwise Document Reranking`.\n\n Adapted from: https://arxiv.org/pdf/2305.02156.pdf\n\n `LLMListwiseRerank` uses a language model to rerank a list of documents based on\n their relevance to a query.\n\n !!! note\n Requires that underlying model implement `with_structured_output`.\n\n Example usage:\n ```python\n from langchain_classic.retrievers.document_compressors.listwise_rerank import (\n LLMListwiseRerank,\n )\n from langchain_core.documents import Document\n from langchain_openai import ChatOpenAI\n\n documents = [\n Document(\"Sally is my friend from school\"),\n Document(\"Steve is my friend from home\"),\n Document(\"I didn't always like yogurt\"),\n Document(\"I wonder why it's called football\"),\n Document(\"Where's waldo\"),\n ]\n\n reranker = LLMListwiseRerank.from_llm(\n llm=ChatOpenAI(model=\"gpt-3.5-turbo\"), top_n=3\n )\n compressed_docs = reranker.compress_documents(documents, \"Who is steve\")\n assert len(compressed_docs) == 3\n assert \"Steve\" in compressed_docs[0].page_content\n ```\n ' — no routing/fallback",
"remediation": "Use model routing or configuration instead of hardcoded names",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\document_compressors\\listwise_rerank.py",
"line": 95,
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"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 138,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 146,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 154,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 162,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 170,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 178,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
"line": 187,
"severity": "HIGH",
"dimension": "D9",
"message": "Empty exception handler — errors silently swallowed",
"remediation": "Log the exception or handle it explicitly",
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{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
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"remediation": "Log the exception or handle it explicitly",
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{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
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"remediation": "Log the exception or handle it explicitly",
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},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
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"remediation": "Log the exception or handle it explicitly",
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{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
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"remediation": "Log the exception or handle it explicitly",
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{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\retrievers\\self_query\\base.py",
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"remediation": "Verify tool result status/validity before using",
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{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\progress.py",
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"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
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"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
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"dimension": "D5",
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"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
"line": 1318,
"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
"line": 912,
"severity": "HIGH",
"dimension": "D15",
"message": "Tool result assigned directly without verification",
"remediation": "Verify tool result status/validity before using",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
"line": 922,
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"remediation": "Verify tool result status/validity before using",
"compliance": {}
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{
"layer": 1,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\smith\\evaluation\\runner_utils.py",
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{
"layer": 1,
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"remediation": "Verify tool result status/validity before using",
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{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\scripts\\check_imports.py",
"line": 29,
"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 1,
"scanner": "code_analyzer",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\scripts\\check_imports.py",
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"severity": "MEDIUM",
"dimension": "D5",
"message": "print() used instead of structured logging",
"remediation": "Use logging.* or structlog.* for structured, searchable logs",
"compliance": {}
},
{
"layer": 4,
"scanner": "secrets_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\memory\\entity.py",
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"remediation": "Move to secrets manager or .env file (excluded from VCS)",
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"eu_ai_act": "Article 15",
"owasp_llm": "LLM09"
}
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{
"layer": 7,
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"message": "No audit logging for tool calls detected",
"remediation": "Add audit logging for all tool/agent executions",
"compliance": {
"eu_ai_act": "Article 12"
}
},
{
"layer": 7,
"scanner": "audit_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain",
"line": 0,
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"message": "No log retention policy detected",
"remediation": "Configure log rotation and retention periods",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"dimension": "D8",
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"remediation": "Add role/permission checks before tool dispatch",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"remediation": "Track token usage and costs per agent execution",
"compliance": {}
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{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
"line": 55,
"severity": "MEDIUM",
"dimension": "D8",
"message": "Agent class 'BaseSingleActionAgent' has no defined lifecycle states",
"remediation": "Add state machine (ACTIVE/SUSPENDED/RETIRED) for agent lifecycle",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
"line": 221,
"severity": "HIGH",
"dimension": "D8",
"message": "Agent class 'BaseMultiActionAgent' has no permission model",
"remediation": "Add role/permission checks before tool dispatch",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"message": "Agent class 'BaseMultiActionAgent' has no cost tracking",
"remediation": "Track token usage and costs per agent execution",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
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"remediation": "Add state machine (ACTIVE/SUSPENDED/RETIRED) for agent lifecycle",
"compliance": {}
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{
"layer": 5,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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{
"layer": 5,
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"remediation": "Track token usage and costs per agent execution",
"compliance": {}
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{
"layer": 5,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"compliance": {}
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{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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{
"layer": 5,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"remediation": "Track token usage and costs per agent execution",
"compliance": {}
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{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"severity": "MEDIUM",
"dimension": "D8",
"message": "Agent class 'RunnableMultiActionAgent' has no defined lifecycle states",
"remediation": "Add state machine (ACTIVE/SUSPENDED/RETIRED) for agent lifecycle",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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{
"layer": 5,
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"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"message": "Agent class 'LLMSingleActionAgent' has no cost tracking",
"remediation": "Track token usage and costs per agent execution",
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{
"layer": 5,
"scanner": "agent_arch_scanner",
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"message": "Agent class 'LLMSingleActionAgent' has no defined lifecycle states",
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"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
"file": "C:\\Users\\gilad\\Projects\\warden\\gallery\\repos\\langchain\\libs\\langchain\\langchain_classic\\agents\\agent.py",
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"dimension": "D8",
"message": "Agent class 'Agent' has no permission model",
"remediation": "Add role/permission checks before tool dispatch",
"compliance": {}
},
{
"layer": 5,
"scanner": "agent_arch_scanner",
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{
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{
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{
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{
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{
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