inferencesh 0.7.9

inference.sh Python SDK

inferencesh — python sdk for ai inference api

official python sdk for inference.sh — the ai agent runtime for serverless ai inference.

run ai models, build ai agents, and deploy generative ai applications. access 250+ models including flux, stable diffusion, llms (claude, gpt, gemini), video generation (veo, seedance), and more.

installation

pip install inferencesh

client api

The Inference client exposes namespaced APIs:

Property	Purpose
client.tasks	Run and manage tasks
client.files	Upload files
client.agents	Create agents (agents.create() is the same as agent() without per-chat context)
client.sessions	List, inspect, and end sessions; use client.session() for a context manager

client.run() and client.upload_file() remain supported as legacy aliases for client.tasks.run() and client.files.upload().

client usage

from inferencesh import inference, TaskStatus

# Create client
client = inference(api_key="your-api-key")

# Simple synchronous usage - waits for completion by default
result = client.tasks.run({
    "app": "your-app",
    "input": {"key": "value"},
    "infra": "cloud",
    "variant": "default"
})

print(f"Task ID: {result.get('id')}")
print(f"Output: {result.get('output')}")

with setup parameters

Setup parameters configure the app instance (e.g., model selection). Workers with matching setup are "warm" and skip the setup phase:

result = client.tasks.run({
    "app": "your-app",
    "setup": {"model": "schnell"},  # Setup parameters
    "input": {"prompt": "hello"}
})

run options

# Wait for completion (default behavior)
result = client.tasks.run(params)  # wait=True is default

# Return immediately without waiting
task = client.tasks.run(params, wait=False)
task_id = task["id"]  # Use this to check status later

# Stream updates as they happen
from inferencesh import parse_status, is_terminal_status

for update in client.tasks.run(params, stream=True):
    status = parse_status(update.get("status"))
    print(f"Status: {status.name if status else 'unknown'}")
    if status == TaskStatus.COMPLETED:
        print(f"Output: {update.get('output')}")

task management

# Get current task state
task = client.tasks.get(task_id)
print(f"Status: {TaskStatus(task['status']).name}")

# Cancel a running task
client.tasks.cancel(task_id)

# Wait for a task to complete
result = client.tasks.wait_for_completion(task_id)

# Stream updates for an existing task
with client.tasks.stream(task_id) as stream:
    for update in stream:
        if parse_status(update.get("status")) == TaskStatus.COMPLETED:
            print(f"Result: {update.get('output')}")
            break
        if is_terminal_status(update.get("status")):
            break

# Access final result after streaming
print(f"Final result: {stream.result}")

task status values

Task statuses are integer enums. Streaming responses may return either integers or lowercase strings (for example "running").

from inferencesh import TaskStatus

TaskStatus.RECEIVED    # 1 - Task received by server
TaskStatus.QUEUED      # 2 - Task queued for processing
TaskStatus.DISPATCHED  # 3 - Task dispatched to a worker
TaskStatus.PREPARING   # 4 - Worker preparing environment
TaskStatus.SERVING     # 5 - Model being loaded
TaskStatus.SETTING_UP  # 6 - Task setup in progress
TaskStatus.RUNNING     # 7 - Task actively running
TaskStatus.CANCELLING  # 8 - Cancellation in progress
TaskStatus.UPLOADING   # 9 - Uploading results
TaskStatus.COMPLETED   # 10 - Task completed successfully
TaskStatus.FAILED      # 11 - Task failed
TaskStatus.CANCELLED   # 12 - Task was cancelled

status helpers

Use these helpers when handling streamed task or agent message updates:

from inferencesh import parse_status, is_terminal_status, is_message_ready, TaskStatus

# Tasks: parse int or string status values
status = parse_status(update.get("status"))  # TaskStatus or None
if status == TaskStatus.COMPLETED:
    ...

# Tasks: check for a terminal task state (completed, failed, cancelled)
if is_terminal_status(update.get("status")):
    ...

# Agent chat: check if a message has finished streaming
if is_message_ready(message.get("status")):  # ready, failed, or cancelled
    ...

is_terminal_status() is for task statuses. For chat message statuses, use is_message_ready() instead.

sessions (stateful execution)

Sessions allow you to maintain state across multiple task invocations. The worker stays warm between calls, preserving loaded models and in-memory state.

# Start a new session
result = client.tasks.run({
    "app": "my-stateful-app",
    "input": {"prompt": "hello"},
    "session": "new"
})

session_id = result.get("session_id")
print(f"Session ID: {session_id}")

# Continue the session with another call
result2 = client.tasks.run({
    "app": "my-stateful-app",
    "input": {"prompt": "remember what I said?"},
    "session": session_id
})

session context manager

For multi-step workflows, use client.session() to create a session and call app functions by name. The session ends automatically when the context exits.

# Optional kwargs for the initial run: input, function (default "run")
with client.session("my-stateful-app@abc123", input={"prompt": "hello"}) as session:
    # First argument is the app function name; second is input data
    session.call("process", {"step": 1})
    session.call("process", {"step": 2}, wait=False)  # same options as client.run()

    # Stream updates for a session call
    for update in session.call("run", {"prompt": "..."}, stream=True):
        print(update.get("status"))
    print(session.session_id)

session.call() forwards to client.run() with the session ID pinned, so it accepts the same keyword arguments: wait, stream, auto_reconnect, and related streaming options. With the default wait=True, it returns the completed task dict; with wait=False, task info; with stream=True, an iterator of status updates (same as client.run()).

On the handle itself you can also call session.info(), session.keepalive(), and session.end() without going through client.sessions.

session management

info = client.sessions.get(session_id)
sessions = client.sessions.list()
client.sessions.keepalive(session_id)  # extend idle timeout without a task call
client.sessions.end(session_id)

custom session timeout

By default, sessions expire after 60 seconds of inactivity. You can customize this with session_timeout (1-3600 seconds):

# Create a session with 5-minute idle timeout
result = client.tasks.run({
    "app": "my-stateful-app",
    "input": {"prompt": "hello"},
    "session": "new",
    "session_timeout": 300  # 5 minutes
})

# Session stays alive for 5 minutes after each call

Notes:

• session_timeout is only valid when session: "new"
• Minimum timeout: 1 second
• Maximum timeout: 3600 seconds (1 hour)
• Each successful call resets the idle timer

For complete session documentation including error handling, best practices, and advanced patterns, see the Sessions Developer Guide.

file upload

from inferencesh import UploadFileOptions

# Upload from file path
file_obj = client.files.upload("/path/to/image.png")
print(f"URI: {file_obj['uri']}")

# Upload from bytes
file_obj = client.files.upload(
    b"raw bytes data",
    UploadFileOptions(
        filename="data.bin",
        content_type="application/octet-stream"
    )
)

# Upload with options
file_obj = client.files.upload(
    "/path/to/image.png",
    UploadFileOptions(
        filename="custom_name.png",
        content_type="image/png",
        public=True  # Make publicly accessible
    )
)

Note: Files in task input are automatically uploaded. You only need files.upload() for manual uploads.

agent chat

Chat with AI agents using client.agents.create() or client.agent(). Both return the same Agent instance; use client.agent() when you need per-chat context variables (see below).

using a template agent

Use an existing agent from your workspace by its namespace/name@shortid:

from inferencesh import inference, is_message_ready

client = inference(api_key="your-api-key")

# Create agent from template
agent = client.agents.create("my-org/assistant@abc123")

# Send a message with streaming
def on_message(msg):
    content = msg.get("content", [])
    for c in content:
        if c.get("type") == "text" and c.get("text"):
            print(c["text"], end="", flush=True)

response = agent.send_message("Hello!", on_message=on_message)
print(f"\nChat ID: {agent.chat_id}")

# Or stream manually and stop when the message is ready
for message in agent.stream_messages():
    on_message(message)
    if is_message_ready(message.get("status")):
        break

creating an ad-hoc agent

Create agents on-the-fly without saving to your workspace:

from inferencesh import inference, tool, string

client = inference(api_key="your-api-key")

# Define a client tool (handler runs in your process)
weather_tool = (
    tool("get_weather")
    .describe("Get current weather")
    .param("city", string("City name"))
    .handler(lambda args: '{"temp": 72, "conditions": "sunny"}')
)

# Create ad-hoc agent (AgentConfig dict; export: from inferencesh import AgentConfig)
agent = client.agents.create({
    "core_app": {"ref": "infsh/claude-sonnet-4@abc123"},
    "system_prompt": "You are a helpful assistant.",
    "tools": [weather_tool],
})

def on_tool_call(call):
    print(f"[Tool: {call.name}]")
    # Tools with handlers are auto-executed

response = agent.send_message(
    "What's the weather in Paris?",
    on_message=on_message,
    on_tool_call=on_tool_call,
)

file attachments

Attach files to a message with the files argument (bytes or base64/data URI strings). Each item is uploaded via agent.upload_file() before the message is sent:

# Raw bytes
agent.send_message("Summarize this", files=[b"file contents"])

# Data URI (content type parsed from the URI)
agent.send_message("Analyze image", files=["data:image/png;base64,iVBORw0KGgo="])

Upload separately when you need the FileRef (for example, to reuse the same file across messages):

ref = agent.upload_file(b"notes", filename="notes.txt")
# ref["uri"], ref["filename"], ref["content_type"], ref["size"]

agent.upload_file() accepts bytes, base64 strings, or data:<mime>;base64,... URIs. It is separate from client.files.upload(), which also accepts local file paths.

structured output with run()

When the agent uses a finish tool, agent.run(text) sends a message, waits for completion, and returns the parsed chat.output. Returns None if the agent finished without calling the finish tool:

result = agent.run("Extract entities from the text above")
if result is not None:
    print(result)

per-chat context variables

Pass context when creating an agent with client.agent() (the context argument is not available on client.agents.create()). Values are available in HTTP/call tool URL templates as {{context.KEY}}:

from inferencesh import call_tool

agent = client.agent(
    "my-org/assistant@abc123",
    context={"tenant_id": "acme", "user_id": "42"},
)

# call_tool URL can reference context, e.g.:
# https://api.example.com/users/{{context.user_id}}/data
lookup = (
    call_tool("fetch_user", "https://api.example.com/users/{{context.user_id}}")
    .auth(bearer="API_TOKEN")
    .describe("Fetch user profile")
    .build()
)

tool builder

Define tools with the fluent API (tool, app_tool, agent_tool, call_tool, mcp_tool, webhook_tool):

from inferencesh import (
    tool, app_tool, agent_tool, call_tool, mcp_tool,
    string, optional, boolean,
)

# Client tool (runs in your code)
search = (
    tool("search")
    .describe("Search files")
    .param("pattern", string("Glob pattern"))
    .build()
)

# App tool (runs another inference app)
generate = (
    app_tool("generate", "infsh/flux-schnell@latest")
    .describe("Generate an image")
    .param("prompt", string("Image description"))
    .function("generate")          # multi-function apps
    .session_enabled()             # agent can pass session IDs
    .require_approval()            # human-in-the-loop
    .build()
)

# HTTP tool with auth (call_tool is an alias for http_tool)
notify = (
    call_tool("notify", "https://api.example.com/notify")
    .method("POST")
    .auth(api_key="MY_API_KEY")
    .header("X-Tenant", "{{context.tenant_id}}")
    .param("message", string("Notification body"))
    .build()
)

# MCP connector tool (integration must be connected in workspace)
web_search = (
    mcp_tool("web_search", "int-abc123", "search")
    .describe("Search via connected MCP server")
    .build()
)

See the Tool Builder reference for schema helpers and more examples.

generated tool types

The fluent tool builder produces JSON Schema objects. For lower-level typing (parsing LLM tool calls or building Tool / ToolParameters dicts by hand), import enums from inferencesh.types:

Enum	Purpose	Members
ToolCallType	Discriminator on tool calls and tool definitions	TOOL_TYPE_FUNCTION ("function")
ToolParamType	JSON Schema parameter types in ToolParameters	OBJECT, STRING, INTEGER, NUMBER, BOOLEAN, ARRAY, NULL

from inferencesh.types import ToolCallType, ToolParamType

# Tool / ToolCall wire format
assert ToolCallType.TOOL_TYPE_FUNCTION.value == "function"

# Parameter schema (matches JSON Schema "type" strings)
assert ToolParamType.STRING.value == "string"

ToolParamType is separate from ToolCallType. Parameter types such as "string" and "object" belong on ToolParamType, not on ToolCallType.

Package exports (Tool, ToolCall, ToolParameters, and related TypedDicts) are available from inferencesh; import ToolCallType and ToolParamType from inferencesh.types when you need the enums.

integration and instance enums

Workspace API responses use generated enums in inferencesh.types:

from inferencesh.types import (
    IntegrationProvider,
    IntegrationAuthType,
    IntegrationStatus,
    InstanceStatus,
)

IntegrationProvider.SLACK       # "slack"
IntegrationAuthType.O_AUTH      # "oauth"
IntegrationStatus.CONNECTED     # "connected"

InstanceStatus.CREATING         # "creating"
InstanceStatus.PENDING_PROVIDER # "pending_provider"
InstanceStatus.ACTIVE           # "active"
InstanceStatus.ERROR            # "error"

IntegrationProvider includes google, slack, notion, github, discord, gcp, mcp, and others. InstanceStatus covers the full lifecycle from creating through deleted.

requirements errors (HTTP 412)

When an app is missing secrets, integrations, or scopes, client.tasks.run() raises RequirementsNotMetError:

from inferencesh import RequirementsNotMetError

try:
    result = client.tasks.run({"app": "my-app", "input": {...}})
except RequirementsNotMetError as e:
    for err in e.errors:
        print(f"{err.type}: {err.key} — {err.message}")

agent methods

Method	Description
send_message(text, files=None, ...)	Send a message; optional files list (bytes or base64/data URI strings)
run(text, ...)	Send a message and return chat.output from the finish tool (or None)
upload_file(data, filename=None)	Upload bytes, base64, or a data URI; returns a FileRef
get_chat(chat_id=None)	Get chat history
stop_chat(chat_id=None)	Stop current generation
submit_tool_result(tool_id, result_or_action)	Submit result for a client tool (string or {action, form_data})
stream_messages(chat_id=None, ...)	Stream message updates
stream_chat(chat_id=None, ...)	Stream chat updates
reset()	Start a new conversation

async agent

from inferencesh import async_inference

client = async_inference(api_key="your-api-key")
agent = client.agents.create("my-org/assistant@abc123")

response = await agent.send_message("Hello!")

# Same helpers as sync: files on send_message, upload_file, run() for finish-tool output
output = await agent.run("Return structured data")

async client

from inferencesh import async_inference, TaskStatus

async def main():
    client = async_inference(api_key="your-api-key")

    # Simple usage - wait for completion
    result = await client.tasks.run({
        "app": "your-app",
        "input": {"key": "value"},
        "infra": "cloud",
        "variant": "default"
    })
    print(f"Output: {result.get('output')}")

    # Return immediately without waiting
    task = await client.tasks.run(params, wait=False)

    # Stream updates
    from inferencesh import parse_status

    async for update in await client.tasks.run(params, stream=True):
        if parse_status(update.get("status")) == TaskStatus.COMPLETED:
            print(f"Output: {update.get('output')}")

    # Task management
    task = await client.tasks.get(task_id)
    await client.tasks.cancel(task_id)
    result = await client.tasks.wait_for_completion(task_id)

    # Stream existing task
    async with client.tasks.stream(task_id) as stream:
        async for update in stream:
            print(f"Update: {update}")

    # Stateful session (async) — session() is async, so await before the context manager
    async with await client.session("my-app@abc123", input={"start": True}) as session:
        await session.call("step", {"x": 1})
        async for update in await session.call("run", {"prompt": "..."}, stream=True):
            print(update.get("status"))

file handling

the File class provides a standardized way to handle files in the inference.sh ecosystem:

from inferencesh import File

# Basic file creation
file = File(path="/path/to/file.png")

# File with explicit metadata
file = File(
    path="/path/to/file.png",
    content_type="image/png",
    filename="custom_name.png",
    size=1024  # in bytes
)

# Create from path (automatically populates metadata)
file = File.from_path("/path/to/file.png")

# Check if file exists
exists = file.exists()

# Access file metadata
print(file.content_type)  # automatically detected if not specified
print(file.size)       # file size in bytes
print(file.filename)   # basename of the file

# Refresh metadata (useful if file has changed)
file.refresh_metadata()

the File class automatically handles:

• mime type detection
• file size calculation
• filename extraction from path
• file existence checking

creating an app

to create an inference app, inherit from BaseApp and define your input/output types:

from inferencesh import BaseApp, BaseAppInput, BaseAppOutput, File

class AppInput(BaseAppInput):
    image: str  # URL or file path to image
    mask: str   # URL or file path to mask

class AppOutput(BaseAppOutput):
    image: File

class MyApp(BaseApp):
    async def setup(self):
        # Initialize your model here
        pass

    async def run(self, app_input: AppInput) -> AppOutput:
        # Process input and return output
        result_path = "/tmp/result.png"
        return AppOutput(image=File(path=result_path))

    async def unload(self):
        # Clean up resources
        pass

Input and output models inherit from BaseAppInput / BaseAppOutput (Pydantic v2). The runtime may pass a Metadata object (with app_id, worker_id, and extra fields) to app methods. JSON schemas preserve field definition order for the app store UI.

app lifecycle has three main methods:

• setup(): called when the app starts, use it to initialize models
• run(): called for each inference request
• unload(): called when shutting down, use it to free resources

resources

• documentation — getting started guides and api reference
• blog — tutorials on ai agents, image generation, and more
• app store — browse 250+ ai models
• discord — community support
• github — open source projects

license

MIT © inference.sh