### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

Using the example code in the tutorial provided the plugin usage from URL does not work anymore:

```python
from langchain_community.agent_toolkits.load_tools import load_tools
from langchain_community.tools import AIPluginTool
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.agents import AgentExecutor, create_structured_chat_agent

tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")

llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"])
tools += [tool]

prompt = hub.pull("hwchase17/structured-chat-agent")
agent = create_structured_chat_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools,handle_parsing_errors=False, verbose=True,include_run_info=False)
result = agent_executor.invoke({"input":"what t shirts are available in klarna?"})
```

The same with deprecated call structure.

```python
from langchain_community.tools import AIPluginTool
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI

tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")

llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"])
tools += [tool]

agent_chain = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent_chain.run("What are some t shirts available on Klarna?")
```


### Error Message and Stack Trace (if applicable)

Instead of executing the plugin at the designated URL the action input is simply the the plugin endpoint specification

### Description

```console
>> Entering new AgentExecutor chain...

Action:
{
  "action": "KlarnaProducts",
  "action_input": ""
}

Usage Guide: Assistant uses the Klarna plugin to get relevant product suggestions for any shopping or product discovery purpose. Assistant will reply with the following 3 paragraphs 1) Search Results 2) Product Comparison of the Search Results 3) Followup Questions. The first paragraph contains a list of the products with their attributes listed clearly and concisely as bullet points under the product, together with a link to the product and an explanation. Links will always be returned and should be shown to the user. The second paragraph compares the results returned in a summary sentence starting with "In summary". Assistant comparisons consider only the most important features of the products that will help them fit the users request, and each product mention is brief, short and concise. In the third paragraph assistant always asks helpful follow-up questions and end with a question mark. When assistant is asking a follow-up question, it uses it's product expertise to provide information pertaining to the subject of the user's request that may guide them in their search for the right product.

OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'countryCode', 'in': 'query', 'description': 'ISO 3166 country code with 2 characters based on the user location. Currently, only US, GB, DE, SE and DK are supported.', 'required': True, 'schema': {'type': 'string'}}, {'name': 'q', 'in': 'query', 'description': "A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. If the user speaks another language than English, translate their request into English (example: translate fia med knuff to ludo board game)!", 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'min_price', 'in': 'query', 'description': "(Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.", 'required': False, 'schema': {'type': 'integer'}}, {'name': 'max_price', 'in': 'query', 'description': "(Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.", 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}Action:
{
  "action": "Final Answer",
  "action_input": "You can use the Klarna Shopping API to search and compare prices of various t-shirts available in online shops. Please provide specific details such as the t-shirt brand, size, color, or any other preferences to narrow down the search results."
}

>> Finished chain.

Output: 'I have retrieved a list of t-shirts from Klarna. Please find the search results and product comparison in the provided link: [Klarna T-Shirts](https://www.klarna.com/us/shopping).'
```

### System Info

> langchain_core: 0.2.3
> langchain: 0.2.1
> langchain_community: 0.2.1
> langsmith: 0.1.65
> langchain_cli: 0.0.21
> langchain_openai: 0.1.8
> langchain_text_splitters: 0.2.0
> langchainhub: 0.1.14
> langserve: 0.0.41

Tested on mac and linux
Plugin execution not working anymore
This is a GitHub Issue
repo:langchain
owner:langchain-ai
Title : Plugin execution not working anymore
Issue date:

--- start body ---
### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

Using the example code in the tutorial provided the plugin usage from URL does not work anymore:

```python
from langchain_community.agent_toolkits.load_tools import load_tools
from langchain_community.tools import AIPluginTool
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.agents import AgentExecutor, create_structured_chat_agent

tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")

llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"])
tools += [tool]

prompt = hub.pull("hwchase17/structured-chat-agent")
agent = create_structured_chat_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools,handle_parsing_errors=False, verbose=True,include_run_info=False)
result = agent_executor.invoke({"input":"what t shirts are available in klarna?"})
```

The same with deprecated call structure.

```python
from langchain_community.tools import AIPluginTool
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI

tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")

llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"])
tools += [tool]

agent_chain = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent_chain.run("What are some t shirts available on Klarna?")
```


### Error Message and Stack Trace (if applicable)

Instead of executing the plugin at the designated URL the action input is simply the the plugin endpoint specification

### Description

```console
>> Entering new AgentExecutor chain...

Action:
{
  "action": "KlarnaProducts",
  "action_input": ""
}

Usage Guide: Assistant uses the Klarna plugin to get relevant product suggestions for any shopping or product discovery purpose. Assistant will reply with the following 3 paragraphs 1) Search Results 2) Product Comparison of the Search Results 3) Followup Questions. The first paragraph contains a list of the products with their attributes listed clearly and concisely as bullet points under the product, together with a link to the product and an explanation. Links will always be returned and should be shown to the user. The second paragraph compares the results returned in a summary sentence starting with "In summary". Assistant comparisons consider only the most important features of the products that will help them fit the users request, and each product mention is brief, short and concise. In the third paragraph assistant always asks helpful follow-up questions and end with a question mark. When assistant is asking a follow-up question, it uses it's product expertise to provide information pertaining to the subject of the user's request that may guide them in their search for the right product.

OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'countryCode', 'in': 'query', 'description': 'ISO 3166 country code with 2 characters based on the user location. Currently, only US, GB, DE, SE and DK are supported.', 'required': True, 'schema': {'type': 'string'}}, {'name': 'q', 'in': 'query', 'description': "A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. If the user speaks another language than English, translate their request into English (example: translate fia med knuff to ludo board game)!", 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'min_price', 'in': 'query', 'description': "(Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.", 'required': False, 'schema': {'type': 'integer'}}, {'name': 'max_price', 'in': 'query', 'description': "(Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.", 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}Action:
{
  "action": "Final Answer",
  "action_input": "You can use the Klarna Shopping API to search and compare prices of various t-shirts available in online shops. Please provide specific details such as the t-shirt brand, size, color, or any other preferences to narrow down the search results."
}

>> Finished chain.

Output: 'I have retrieved a list of t-shirts from Klarna. Please find the search results and product comparison in the provided link: [Klarna T-Shirts](https://www.klarna.com/us/shopping).'
```

### System Info

> langchain_core: 0.2.3
> langchain: 0.2.1
> langchain_community: 0.2.1
> langsmith: 0.1.65
> langchain_cli: 0.0.21
> langchain_openai: 0.1.8
> langchain_text_splitters: 0.2.0
> langchainhub: 0.1.14
> langserve: 0.0.41

Tested on mac and linux
--- end body ---
