Salesforce
A tool for interacting with Salesforce CRM using LangChain.
Overview
Thelangchain-salesforce package integrates LangChain with Salesforce CRM,allowing you to query data, manage records, and explore object schemasfrom LangChain applications.
Key Features
- SOQL Queries: Execute Salesforce Object Query Language (SOQL) queries
- Object Management: Create, read, update, and delete (CRUD) operations on Salesforce objects
- Schema Exploration: Describe object schemas and list available objects
- Async Support: Asynchronous operation support
- Error Handling: Detailed error messages
- Environment Variable Support: Load credentials from environment variables
Setup
Install the required dependencies:
pip install langchain-salesforce
Authentication Setup
These environment variables will be automatically picked up by the integration.
Getting Your Security Token
If you need a security token:
- Log into Salesforce
- Go to Settings
- Click on "Reset My Security Token" under "My Personal Information"
- Check your email for the new token
Environment Variables (Recommended)
Set up your Salesforce credentials as environment variables:
export SALESFORCE_USERNAME="your-username@company.com"
export SALESFORCE_PASSWORD="your-password"
export SALESFORCE_SECURITY_TOKEN="your-security-token"
export SALESFORCE_DOMAIN="login" # Use "test" for sandbox environments
Instantiation
import os
from langchain_salesforceimport SalesforceTool
username= os.getenv("SALESFORCE_USERNAME","your-username")
password= os.getenv("SALESFORCE_PASSWORD","your-password")
security_token= os.getenv("SALESFORCE_SECURITY_TOKEN","your-security-token")
domain= os.getenv("SALESFORCE_DOMAIN","login")
tool= SalesforceTool(
username=username, password=password, security_token=security_token, domain=domain
)
Invocation
defexecute_salesforce_operation(
operation, object_name=None, query=None, record_data=None, record_id=None
):
"""Executes a given Salesforce operation."""
request={"operation": operation}
if object_name:
request["object_name"]= object_name
if query:
request["query"]= query
if record_data:
request["record_data"]= record_data
if record_id:
request["record_id"]= record_id
result= tool.invoke(request)
return result
Query
This example queries Salesforce for 5 contacts.
query_result= execute_salesforce_operation(
operation="query", query="SELECT Id, Name, Email FROM Contact LIMIT 5"
)
Describe an Object
Fetches metadata for a specific Salesforce object.
describe_result= execute_salesforce_operation(
operation="describe", object_name="Account"
)
List Available Objects
Retrieves all objects available in the Salesforce instance.
list_objects_result= execute_salesforce_operation(operation="list_objects")
Create a New Contact
Creates a new contact record in Salesforce.
create_result= execute_salesforce_operation(
operation="create",
object_name="Contact",
record_data={"LastName":"Doe","Email":"doe@example.com"},
)
Update a Contact
Updates an existing contact record.
update_result= execute_salesforce_operation(
operation="update",
object_name="Contact",
record_id="003XXXXXXXXXXXXXXX",
record_data={"Email":"updated@example.com"},
)
Delete a Contact
Deletes a contact record from Salesforce.
delete_result= execute_salesforce_operation(
operation="delete", object_name="Contact", record_id="003XXXXXXXXXXXXXXX"
)
Chaining
from langchain_anthropicimport ChatAnthropic
from langchain_core.messagesimport HumanMessage
from langchain_salesforceimport SalesforceTool
# Initialize the Salesforce tool
tool= SalesforceTool(
username=username, password=password, security_token=security_token, domain=domain
)
# Initialize Anthropic LLM
llm= ChatAnthropic(model="claude-sonnet-4-20250514")
# First, let's query some contacts to get real data
contacts_query={
"operation":"query",
"query":"SELECT Id, Name, Email, Phone FROM Contact LIMIT 3",
}
contacts_result= tool.invoke(contacts_query)
# Now let's use the LLM to analyze and summarize the contact data
if contacts_resultand"records"in contacts_result:
contact_data= contacts_result["records"]
# Create a message asking the LLM to analyze the contact data
analysis_prompt=f"""
Please analyze the following Salesforce contact data and provide insights:
Contact Data:{contact_data}
Please provide:
1. A summary of the contacts
2. Any patterns you notice
3. Suggestions for data quality improvements
"""
message= HumanMessage(content=analysis_prompt)
analysis_result= llm.invoke([message])
print("\nLLM Analysis:")
print(analysis_result.content)
API Reference:ChatAnthropic |HumanMessage
API Reference
For comprehensive documentation and API reference, see:
Additional Resources
Related
- Toolconceptual guide
- Toolhow-to guides