Uipath functions

UiPath Python Coded Functions

What Python Coded Functions Are

Python Coded Functions are atomic, bespoke units of business logic — deterministic Python code packaged as a first-class UiPath artifact. Use them when generic activities don't cover the required logic: calling a third-party API with custom auth, processing documents with domain-specific rules, querying ERP systems via Integration Service connections, or transforming data in ways that no out-of-the-box activity handles.

A Coded Function is not an agent. It does not reason, route, or call LLMs. It takes typed input, executes deterministic code, and returns typed output.

Invocation surfaces

A Python Coded Function can be invoked from any UiPath surface:

Surface	How
Maestro BPMN	Service Task node
Maestro Flow	Coded Agent node or Service Task
Coded Agents (LangGraph / LlamaIndex / OpenAI Agents)	Called as a tool or step
Other Coded Functions	Direct Python call or Orchestrator job
Orchestrator API	POST /Jobs/StartJobs
CLI	uip function pack → uip function publish

Python Functions vs JS Functions

	Python Coded Function	JS/TS Function
Job semantics	Yes — Orchestrator job ID, audit trail, retry, scheduling	No — inline HTTP only, no job lifecycle
Invocation	Maestro, Flow, Agents, Orchestrator API	HTTP endpoint (BFF for Coded Apps)
Runtime	Serverless or Local Unattended Robot	Serverless HTTP shared tier
SDK access	Full UiPath Python SDK (assets, buckets, queues, connections)	Workload token forwarding only
Scaffold	uip function new <name> --language py	uip function new <name> --language ts (default)
Init	uip function init (generates entry-points.json)	Not needed
Local dev	uip function run	uip function serve + uip function run
Best for	Agentic process steps, ERP integration, document AI, data pipelines	Backend-for-Frontend for Coded Apps

Use Python when the logic needs job semantics, platform SDK access, or is invoked from Maestro/agents. Use JS when the caller is a Coded App frontend and low HTTP latency matters.

---

CLI Reference

All Python Coded Function lifecycle commands use uip function:

uip function new <name> -l py     # scaffold a new Python Functions project (--language py required)
uip function init                 # Python only — generate entry-points.json, bindings.json, project.uiproj
uip function pack                 # pack to .nupkg for deployment
uip function publish              # upload .nupkg to Orchestrator (prompts for feed, or use --feed-id)
uip function push                 # sync project to Studio Web

uip function run works for both Python and JS/TS. uip function serve is JS/TS only — it starts the local HTTP server that run invokes against.

---

Workflow

Step 1: Scaffold

uip function new <name> --language py       # Python Coded Function
uip function new <name> --language ts       # TypeScript Function (JS/TS, no job semantics)
uip function new <name> --language js       # JavaScript Function (JS/TS, no job semantics)

--language py is required for Python. The default language is TypeScript — omitting --language scaffolds a JS/TS project. Always pass -l py or --language py when building a Python Coded Function.

--empty skips the hello-world function (JS/TS only).

The scaffold follows the installed packages. With a framework package present in the environment (uipath-langchain, llama-index, openai-agents), uip function new -l py emits that framework's agent scaffold — langgraph.json plus an LLM main.py — not a function scaffold. Expected behaviour, not a broken flag. Recovery, in one pass:

1. Delete the framework config (langgraph.json and equivalents).
2. Replace main.py with the function template (Step 3).
3. Keep pyproject.toml's [project] metadata (Step 5) — swap dependencies for what the function needs.

Do not re-run new with different flag spellings, and do not read CLI or SDK internals to explain the scaffold. Reshape the project and move on.

Step 2: Define Function Schema

Use typed I/O. The SDK accepts pydantic BaseModel, pydantic.dataclasses.dataclass, a stdlib @dataclass, or a thin class with typed annotations. The shipped samples favor pydantic (BaseModel in csv-processor, pydantic.dataclasses.dataclass in calculator/greeter):

from pydantic import BaseModel

class Input(BaseModel):
    document_id: str = ""

class Output(BaseModel):
    vendor_name: str = ""
    total_amount: float = 0.0
    error_type: str = ""     # populated on failure, empty on success
    error_message: str = ""  # human-readable error detail

Step 3: Implement Business Logic

Do NOT make LLM calls inside a Coded Function. LLM calls introduce non-determinism and latency that break the function contract. If the step requires LLM reasoning or multi-step AI decisions, use a framework-based agent (LangGraph, LlamaIndex, OpenAI Agents) instead.

Minimal template

from __future__ import annotations

from pydantic import BaseModel
from uipath.tracing import traced
from uipath.platform import UiPath

class Input(BaseModel):
    document_id: str = ""

class Output(BaseModel):
    result: str = ""
    error_type: str = ""
    error_message: str = ""

# Lazy SDK singleton — never instantiate UiPath() at module level
_sdk: UiPath | None = None

def sdk() -> UiPath:
    global _sdk
    if _sdk is None:
        _sdk = UiPath()
    return _sdk

@traced(name="my_function", run_type="uipath")
def my_function(input: Input) -> Output:
    out = Output()
    try:
        # SDK calls, data processing, rule-based logic only
        asset = sdk().assets.retrieve("MY_ASSET", folder_path="Shared")
        out.result = str(asset.value)
    except Exception as exc:
        out.error_type = "FAILED"
        out.error_message = str(exc)
    return out

Key rules:

• Typed I/O — pydantic BaseModel, pydantic.dataclasses.dataclass, stdlib @dataclass, or a thin class with typed annotations; samples favor pydantic
• def or async def — both supported (csv-processor uses async def main); the function name is arbitrary
• Lazy SDK init — instantiate UiPath() inside a getter, never at module level
• Errors returned, not raised — populate error_type/error_message output fields and return; never let exceptions bubble out of the entrypoint
• @traced(name=..., run_type="uipath") — apply to the entrypoint and any sub-functions you want visible in LLM Ops Traces

Step 4: Register in uipath.json

{
  "runtimeOptions": { "isConversational": false },
  "functions": {
    "main": "main.py:my_function"
  }
}

The key is the entrypoint name — it can be any string and marks this as the callable entrypoint. The value is "<file>:<function_name>". Both the key and the function name are arbitrary.

This functions map is what identifies the project as a Coded Function — the runtime's determine_project_type() reads the entrypoint type from uipath.json.

Step 5: Declare dependencies in pyproject.toml

[project]
name = "my-function"
version = "0.1.0"
description = "..."
authors = [{ name = "Your Name", email = "[email protected]" }]
requires-python = ">=3.11"
dependencies = [
    "uipath",
    "httpx>=0.28",          # if making HTTP calls
    "pydantic-settings>=2", # if using Settings for env/asset config
]

authors is required — without it uip function pack rejects the package with Project authors cannot be empty.

No [build-system] section. The project is identified as a Coded Function by the functions map in uipath.json (Step 4).

Step 6: Generate Entry Points

uip function init

Python only. Discovers entrypoints and generates entry-points.json, bindings.json, and project.uiproj. Must run before pack or push. Re-run whenever Input/Output schemas or the entrypoint registration in uipath.json changes.

Step 7: SDK Capabilities

Full SDK reference: https://uipath.github.io/uipath-python/

Access UiPath platform resources via sdk():

from uipath.platform import UiPath
from uipath.platform.connections.connections import ActivityMetadata, ActivityParameterLocationInfo

# Assets — retrieve named credentials or config values
asset = sdk().assets.retrieve("ASSET_NAME", folder_path="Shared")
value = asset.string_value          # or credential_username / credential_password

# Buckets — download files for processing
sdk().buckets.download(
    name="BucketName",
    blob_file_path="relative/path/file.pdf",
    destination_path="/tmp/local.pdf",
    folder_path="Shared",
)

# Integration Service connections — invoke connector activities (ERP, CRM, etc.)
result = sdk().connections.invoke_activity(
    activity_metadata=ActivityMetadata(
        object_path="/executeSuiteQL",
        method_name="POST",
        content_type="application/json",
        parameter_location_info=ActivityParameterLocationInfo(body_fields=["q"]),
    ),
    connection_id="<connection-uuid>",
    activity_input={"q": "SELECT id FROM vendor WHERE ..."},
)

File attachment inputs

To accept a runtime file, type an Input field as Attachment (pydantic model, not a dataclass):

from pydantic import BaseModel
from uipath.platform.attachments import Attachment

class Input(BaseModel):
    attachment: Attachment

uip function init recognizes the Attachment type and emits x-uipath-resource-kind: JobAttachment in entry-points.json — the schema Studio Web and Orchestrator read to render a file picker for that field. Access fields snake_case: attachment.full_name, attachment.content.

Step 8: Pack and Publish

uip function pack                            # creates .nupkg
uip function publish                         # upload to Orchestrator (interactive feed picker)
uip function publish --feed-id <FEED_ID>     # CI/non-interactive

To sync to Studio Web instead of publishing to Orchestrator:

uip function push

Important Notes

• UiPath() must never be instantiated at module level — always inside a function body
• The functions map in uipath.json marks the project as a Coded Function (determine_project_type() reads the entrypoint type from uipath.json)
• uip function init must run before pack or push — it generates entry-points.json
• Python Functions have full job semantics: Orchestrator job ID, audit trail, retry, scheduling
• JS Functions have no job semantics and cannot be started as Orchestrator jobs — use Python when the caller is Maestro, a Flow, or an agent
• uip function run works for both Python and JS/TS local execution; uip function serve is JS/TS only (starts the local HTTP server that run invokes against)
• If cloud-backed work requires authentication, run uip login --organization "<ORG>" --tenant "<TENANT>" --output json.