LangChain

Build AI agents using LangChain’s framework with direct access to business data through StackOne’s infrastructure of pre-built tools, RPC orchestration, and MCP/A2A interfaces.

Supported languages: Python.

ReAct and OpenAI Functions agents with business tool access
Multi-step workflow automation
Conversational agents with memory
Advanced error handling and resilience

Calling actions

Install

# Requires Python 3.10 or higher
# uv or pip

# Using uv (recommended)
uv add 'stackone-ai[mcp]' langchain langchain-openai

# Using pip
pip install 'stackone-ai[mcp]' langchain langchain-openai

# Optional: install with example dependencies
uv add 'stackone-ai[mcp,examples]'
pip install 'stackone-ai[mcp,examples]'

export STACKONE_API_KEY=your_api_key_here

Fetch Tools

Two approaches to retrieving tools:

Fetch Tools (with filtering) — fetch all tools upfront with optional filters applied.
Search & Execute — hand the model just tool_search + tool_execute and let the agent discover what it needs at runtime.

See Tool Discovery for more details on which may be best in your scenario.

Fetch tools
Search & execute

By default this fetches every tool enabled for the account. Narrow it by passing providers (specific connectors) or actions — exact names or glob patterns (e.g. workday_*, or *_list_* for read-only tools).

from stackone_ai import StackOneToolSet

# Get account ID from your app's auth context or StackOne dashboard
account_id = "your-account-id"

# Initialize toolset
toolset = StackOneToolSet()

# Fetch tools dynamically for this account.
# Optionally narrow the fetch with filters:
#   providers=["hibob", "workday"]    — only these connectors
#   actions=["*_list_*", "*_get_*"]   — glob patterns, e.g. read-only actions
tools = toolset.fetch_tools(account_ids=[account_id])

# Convert to LangChain format
langchain_tools = tools.to_langchain()

The LLM receives only 2 tools and searches the catalog autonomously:

from stackone_ai import StackOneToolSet

toolset = StackOneToolSet(
    search={"method": "semantic", "top_k": 3},
    execute={"account_ids": ["your-account-id"]},
)

# LLM receives only 2 tools - framework handles execution.
# .langchain() returns LangChain tools directly, so no conversion is needed
langchain_tools = toolset.langchain(mode="search_and_execute")

Run the agent loop

Fetch tools
Search & execute

Bind the tools to your model, invoke it, and execute any tool calls:

from langchain_openai import ChatOpenAI

# Create model with tools
model = ChatOpenAI(model="gpt-5.4")
model_with_tools = model.bind_tools(langchain_tools)

# Use the agent
response = model_with_tools.invoke("List all employees in engineering")

# Handle tool execution
for tool_call in response.tool_calls:
    tool = tools.get_tool(tool_call["name"])
    if tool:
        result = tool.execute(tool_call["args"])
        print(f"Result: {result}")

Bind the two meta tools the same way — the model calls tool_search first, then tool_execute:

from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-5.4").bind_tools(langchain_tools)

response = model.invoke("List my upcoming Calendly events for the next week.")

Example

Multi-account usage

Each tool execution runs against a specific customer account. Set account IDs once on the toolset, or pass them per request.

# Set accounts on the toolset for all subsequent calls
toolset.set_accounts(["account-123", "account-456"])
tools = toolset.fetch_tools()

# Or pass account IDs per request
tools = toolset.fetch_tools(account_ids=["account-123", "account-456"])

# Loop over customer accounts dynamically
customer_accounts = ["account-1", "account-2", "account-3"]

for account_id in customer_accounts:
    tools = toolset.fetch_tools(
        actions=["workday_list_workers"],
        account_ids=[account_id],
    )
    employee_tool = tools.get_tool("workday_list_workers")
    if employee_tool:
        employees = employee_tool.call()
        print(f"Found {len(employees['data'])} employees")

Error handling

from stackone_ai.models import StackOneError, StackOneAPIError

try:
    result = employee_tool.call(id="employee-123")
    print("Success:", result)
except StackOneAPIError as e:
    print(f"API error: {e.message}")
except StackOneError as e:
    print(f"StackOne error: {e.message}")
except Exception as e:
    print(f"Unexpected error: {e}")

File downloads (binary responses)

Actions that download a file (for example googledrive_unified_download_file, documents_download_file, or any *_unified_download_file) return raw bytes plus metadata, not parsed JSON. The SDK decides from the response Content-Type: JSON is parsed as usual, and anything else is treated as a file download.call() and execute() both return the dict directly (it isn’t wrapped in a result object), so read its values with dict keys like result["content"].

tools = toolset.fetch_tools(actions=["googledrive_*"], account_ids=["your-account-id"])
download = tools.get_tool("googledrive_unified_download_file")

result = download.execute({"id": "file-id"})

# result describes the file. Write the bytes straight to disk:
with open(result["file_name"] or "download.bin", "wb") as f:
    f.write(result["content"])

The returned dict, from both call() and execute():

Key	Type	Description
`content`	`bytes`	Raw file bytes. Not JSON-serializable (see note).
`content_type`	`str`	File MIME type (for example `application/pdf`), or `application/octet-stream`.
`status_code`	`int`	HTTP status of the download response.
`headers`	`dict`	Response headers.
`file_name`	`str \| None`	Filename from `Content-Disposition` (RFC 5987 `filename*` aware), else `None`.

content is raw bytes and is not JSON-serializable. If you forward tool results to an LLM (or anything that re-serializes to JSON), handle or strip the content key. For example, base64-encode it on the LLM-facing path.

Troubleshooting

Execute a tool

Direct execution is useful for testing and debugging. In production, your agent framework handles tool calls automatically.

tools = toolset.fetch_tools(account_ids=["your-account-id"])
employee_tool = tools.get_tool("workday_list_workers")

# call() with keyword arguments
result = employee_tool.call(id="employee-123", include_details=True)
print(result["data"])

# execute() with dictionary payloads
result = employee_tool.execute({"id": "employee-123", "include_details": True})

# execute() with OpenAI-style arguments
payload = {"arguments": {"id": "employee-123"}}
result = employee_tool.execute(payload)

Calling actions

Example

Multi-account usage

Troubleshooting

Execute a tool

Next steps

Observability

Tool Defense

​Calling actions

​Example

​Multi-account usage

​Troubleshooting

​Execute a tool

​Next steps

Observability

Tool Defense

Calling actions

Example

Multi-account usage

Troubleshooting

Execute a tool

Next steps