Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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Python Sdk
Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh). Supports sync/async,...
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Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh). Supports sync/async,...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/agent-patterns.md, references/async-patterns.md, references/files.md, references/sessions.md, references/streaming.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.5
Provenance
- Publisher
- okaris
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Python SDK
Build AI applications with the inference.sh Python SDK.
Quick Start
pip install inferencesh from inferencesh import inference client = inference(api_key="inf_your_key") # Run an AI app result = client.run({ "app": "infsh/flux-schnell", "input": {"prompt": "A sunset over mountains"} }) print(result["output"])
Installation
# Standard installation pip install inferencesh # With async support pip install inferencesh[async] Requirements: Python 3.8+
Authentication
import os from inferencesh import inference # Direct API key client = inference(api_key="inf_your_key") # From environment variable (recommended) client = inference(api_key=os.environ["INFERENCE_API_KEY"]) Get your API key: Settings โ API Keys โ Create API Key
Basic Execution
result = client.run({ "app": "infsh/flux-schnell", "input": {"prompt": "A cat astronaut"} }) print(result["status"]) # "completed" print(result["output"]) # Output data
Fire and Forget
task = client.run({ "app": "google/veo-3-1-fast", "input": {"prompt": "Drone flying over mountains"} }, wait=False) print(f"Task ID: {task['id']}") # Check later with client.get_task(task['id'])
Streaming Progress
for update in client.run({ "app": "google/veo-3-1-fast", "input": {"prompt": "Ocean waves at sunset"} }, stream=True): print(f"Status: {update['status']}") if update.get("logs"): print(update["logs"][-1])
Run Parameters
ParameterTypeDescriptionappstringApp ID (namespace/name@version)inputdictInput matching app schemasetupdictHidden setup configurationinfrastring'cloud' or 'private'sessionstringSession ID for stateful executionsession_timeoutintIdle timeout (1-3600 seconds)
Automatic Upload
result = client.run({ "app": "image-processor", "input": { "image": "/path/to/image.png" # Auto-uploaded } })
Manual Upload
from inferencesh import UploadFileOptions # Basic upload file = client.upload_file("/path/to/image.png") # With options file = client.upload_file( "/path/to/image.png", UploadFileOptions( filename="custom_name.png", content_type="image/png", public=True ) ) result = client.run({ "app": "image-processor", "input": {"image": file["uri"]} })
Sessions (Stateful Execution)
Keep workers warm across multiple calls: # Start new session result = client.run({ "app": "my-app", "input": {"action": "init"}, "session": "new", "session_timeout": 300 # 5 minutes }) session_id = result["session_id"] # Continue in same session result = client.run({ "app": "my-app", "input": {"action": "process"}, "session": session_id })
Template Agents
Use pre-built agents from your workspace: agent = client.agent("my-team/support-agent@latest") # Send message response = agent.send_message("Hello!") print(response.text) # Multi-turn conversation response = agent.send_message("Tell me more") # Reset conversation agent.reset() # Get chat history chat = agent.get_chat()
Ad-hoc Agents
Create custom agents programmatically: from inferencesh import tool, string, number, app_tool # Define tools calculator = ( tool("calculate") .describe("Perform a calculation") .param("expression", string("Math expression")) .build() ) image_gen = ( app_tool("generate_image", "infsh/flux-schnell@latest") .describe("Generate an image") .param("prompt", string("Image description")) .build() ) # Create agent agent = client.agent({ "core_app": {"ref": "infsh/claude-sonnet-4@latest"}, "system_prompt": "You are a helpful assistant.", "tools": [calculator, image_gen], "temperature": 0.7, "max_tokens": 4096 }) response = agent.send_message("What is 25 * 4?")
Available Core Apps
ModelApp ReferenceClaude Sonnet 4infsh/claude-sonnet-4@latestClaude 3.5 Haikuinfsh/claude-haiku-35@latestGPT-4oinfsh/gpt-4o@latestGPT-4o Miniinfsh/gpt-4o-mini@latest
Parameter Types
from inferencesh import ( string, number, integer, boolean, enum_of, array, obj, optional ) name = string("User's name") age = integer("Age in years") score = number("Score 0-1") active = boolean("Is active") priority = enum_of(["low", "medium", "high"], "Priority") tags = array(string("Tag"), "List of tags") address = obj({ "street": string("Street"), "city": string("City"), "zip": optional(string("ZIP")) }, "Address")
Client Tools (Run in Your Code)
greet = ( tool("greet") .display("Greet User") .describe("Greets a user by name") .param("name", string("Name to greet")) .require_approval() .build() )
App Tools (Call AI Apps)
generate = ( app_tool("generate_image", "infsh/flux-schnell@latest") .describe("Generate an image from text") .param("prompt", string("Image description")) .setup({"model": "schnell"}) .input({"steps": 20}) .require_approval() .build() )
Agent Tools (Delegate to Sub-agents)
from inferencesh import agent_tool researcher = ( agent_tool("research", "my-org/researcher@v1") .describe("Research a topic") .param("topic", string("Topic to research")) .build() )
Webhook Tools (Call External APIs)
from inferencesh import webhook_tool notify = ( webhook_tool("slack", "https://hooks.slack.com/...") .describe("Send Slack notification") .secret("SLACK_SECRET") .param("channel", string("Channel")) .param("message", string("Message")) .build() )
Internal Tools (Built-in Capabilities)
from inferencesh import internal_tools config = ( internal_tools() .plan() .memory() .web_search(True) .code_execution(True) .image_generation({ "enabled": True, "app_ref": "infsh/flux@latest" }) .build() ) agent = client.agent({ "core_app": {"ref": "infsh/claude-sonnet-4@latest"}, "internal_tools": config })
Streaming Agent Responses
def handle_message(msg): if msg.get("content"): print(msg["content"], end="", flush=True) def handle_tool(call): print(f"\n[Tool: {call.name}]") result = execute_tool(call.name, call.args) agent.submit_tool_result(call.id, result) response = agent.send_message( "Explain quantum computing", on_message=handle_message, on_tool_call=handle_tool )
File Attachments
# From file path with open("image.png", "rb") as f: response = agent.send_message( "What's in this image?", files=[f.read()] ) # From base64 response = agent.send_message( "Analyze this", files=["data:image/png;base64,iVBORw0KGgo..."] )
Skills (Reusable Context)
agent = client.agent({ "core_app": {"ref": "infsh/claude-sonnet-4@latest"}, "skills": [ { "name": "code-review", "description": "Code review guidelines", "content": "# Code Review\n\n1. Check security\n2. Check performance..." }, { "name": "api-docs", "description": "API documentation", "url": "https://example.com/skills/api-docs.md" } ] })
Async Support
from inferencesh import async_inference import asyncio async def main(): client = async_inference(api_key="inf_...") # Async app execution result = await client.run({ "app": "infsh/flux-schnell", "input": {"prompt": "A galaxy"} }) # Async agent agent = client.agent("my-org/assistant@latest") response = await agent.send_message("Hello!") # Async streaming async for msg in agent.stream_messages(): print(msg) asyncio.run(main())
Error Handling
from inferencesh import RequirementsNotMetException try: result = client.run({"app": "my-app", "input": {...}}) except RequirementsNotMetException as e: print(f"Missing requirements:") for err in e.errors: print(f" - {err['type']}: {err['key']}") except RuntimeError as e: print(f"Error: {e}")
Human Approval Workflows
def handle_tool(call): if call.requires_approval: # Show to user, get confirmation approved = prompt_user(f"Allow {call.name}?") if approved: result = execute_tool(call.name, call.args) agent.submit_tool_result(call.id, result) else: agent.submit_tool_result(call.id, {"error": "Denied by user"}) response = agent.send_message( "Delete all temp files", on_tool_call=handle_tool )
Reference Files
Agent Patterns - Multi-agent, RAG, human-in-the-loop patterns Tool Builder - Complete tool builder API reference Streaming - Real-time progress updates and SSE handling File Handling - Upload, download, and manage files Sessions - Stateful execution with warm workers Async Patterns - Parallel processing and async/await
Related Skills
# JavaScript SDK npx skills add inference-sh/skills@javascript-sdk # Full platform skill (all 150+ apps via CLI) npx skills add inference-sh/skills@inference-sh # LLM models npx skills add inference-sh/skills@llm-models # Image generation npx skills add inference-sh/skills@ai-image-generation
Documentation
Python SDK Reference - Full API documentation Agent SDK Overview - Building agents Tool Builder Reference - Creating tools Authentication - API key setup Streaming - Real-time updates File Uploads - File handling
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- references/agent-patterns.md
- references/async-patterns.md
- references/files.md
- references/sessions.md
- references/streaming.md