runagents 1.4.2

RunAgents Python SDK — deploy, manage, and build AI agents

RunAgents Python SDK

Everything you need to build, test, and deploy AI agents on RunAgents — in one package.

The public source for this package lives in sdk/python/ in the runagents-io/runagents repository so the CLI, SDK, MCP server, docs, and skills ship together.

Install

pip install runagents        # Core SDK + CLI + runtime (zero deps)
pip install runagents[mcp]   # + MCP server for AI coding assistants
pip install runagents[dev]   # + hot-reload for local dev

Quickstart

runagents init my-agent      # Scaffold a project
cd my-agent
runagents dev                # Local dev server with mock tools
runagents deploy             # Ship to the platform

Python API

Client — manage platform resources

from runagents import Client

client = Client()  # reads ~/.runagents/config.json + env vars

agents = client.agents.list()
tools = client.tools.list()
runs = client.runs.list(agent="payment-agent")

result = client.agents.deploy(
    name="my-agent",
    source_files={"agent.py": open("agent.py").read()},
    required_tools=["stripe-api"],
    llm_configs=[{"provider": "openai", "model": "gpt-4o-mini", "role": "default"}],
)

Agent — write agent code

from runagents import Agent, tool

agent = Agent()  # reads TOOL_URL_*, LLM_GATEWAY_URL, LLM_MODEL from env

# Call a platform tool (platform egress layer handles auth)
result = agent.call_tool("echo-tool", "/echo", {"message": "hello"})

# Chat via LLM gateway
response = agent.chat("What is 2+2?", tools=[...])

# Custom handler (Tier 2)
def handler(request, ctx):
    message = request["message"]
    result = agent.chat(message)
    return result["choices"][0]["message"]["content"]

@tool decorator

from runagents import tool

@tool(name="calculator", description="Evaluate math expressions")
def calculate(expression: str) -> str:
    return str(eval(expression))

CLI Commands

Command	Description
runagents init [name]	Scaffold a new agent project
runagents dev	Start local dev server with mock tools
runagents deploy	Deploy an agent (delegates to Go CLI)
runagents catalog	Browse and deploy catalog agents
runagents policies	Manage governance policies
runagents approval-connectors	Manage approval routing connectors
runagents identity-providers	Manage workspace identity providers
runagents agents list	List agents
runagents tools list	List tools
runagents runs list	List runs
runagents context export	Export assistant/workspace context
runagents config	Manage configuration

Runtime

The runtime provides the HTTP server for deployed agents — tool calling loop, SSE streaming, health checks, OAuth consent, and JIT approvals. It runs automatically inside the platform; for local development use runagents dev.

Policy enforcement remains active at runtime. Agents need bound policy rules (allow or approval_required) for tool calls.

# Backward compatible — still works
import runagents_runtime

MCP Server

pip install runagents[mcp]
runagents-mcp  # starts on stdio

Assistant tools for deployment, catalog inspection, policies, approval connectors, runs, and approvals. See AI Assistant Setup.

Configuration

Reads from ~/.runagents/config.json with env var overrides:

Variable	Description	Default
RUNAGENTS_ENDPOINT	Platform API URL	http://localhost:8092
RUNAGENTS_API_KEY	API key or workspace key	—

Documentation

• RunAgents Docs
• Writing Agents
• Agent Runtime
• AI Assistant Setup
• CLI Commands

License

Apache-2.0