Framework Integrations

Agentspan works with the frameworks you already use. Pass your existing agent directly to runtime.run() — definitions, tools, and routing logic stay exactly as written. You get crash recovery, durable human-in-the-loop, and full execution history without changing a single node or handoff.

from agentspan.agents import AgentRuntime

with AgentRuntime() as runtime:
    result = runtime.run(your_existing_agent, "your prompt")

your_existing_agent can be a LangGraph compiled graph, an OpenAI Agents SDK Agent, a Google ADK pipeline, or a native Agentspan Agent. The API is the same.

Supported frameworks

LangGraph

Pass a compiled StateGraph or any graph produced by create_react_agent:

from langgraph.prebuilt import create_react_agent
from agentspan.agents import AgentRuntime

graph = create_react_agent(model="openai/gpt-4o", tools=[search, calculator])

with AgentRuntime() as runtime:
    result = runtime.run(graph, "Research the history of the Eiffel Tower")
    print(result.execution_id)

Note: Do not pass a checkpointer when wrapping with AgentRuntime — Agentspan manages execution state server-side and the two checkpointing models conflict. LangSmith observability is fully compatible and unaffected.

→ Full LangGraph example — code review bot

OpenAI Agents SDK

Pass an Agent from the agents package directly:

from agents import Agent as OAIAgent, WebSearchTool
from agentspan.agents import AgentRuntime

oai_agent = OAIAgent(
    name="support_agent",
    instructions="You are a helpful customer support agent.",
    tools=[WebSearchTool()],
)

with AgentRuntime() as runtime:
    result = runtime.run(oai_agent, "How do I reset my password?")
    print(result.output["result"])

Agent definitions, handoffs, and tool registrations stay exactly as written.

→ Full OpenAI Agents SDK example — support agent

Google ADK

Pass any ADK pipeline (SequentialAgent, ParallelAgent, LoopAgent, or a custom BaseAgent):

from google.adk.agents import SequentialAgent, LlmAgent
from agentspan.agents import AgentRuntime

researcher = LlmAgent(name="researcher", model="gemini-2.0-flash", ...)
writer = LlmAgent(name="writer", model="gemini-2.0-flash", ...)
pipeline = SequentialAgent(name="pipeline", sub_agents=[researcher, writer])

with AgentRuntime() as runtime:
    result = runtime.run(pipeline, "Research and summarize quantum computing trends")
    print(result.output["result"])

→ Full Google ADK example — research assistant

What Agentspan adds to any framework

Capability	Without Agentspan	With Agentspan
Process crash mid-run	Entire run lost	Resumes from last completed step
Human approval pause	State held in memory	Paused server-side, survives restarts
Execution history	None	Every run stored with inputs, outputs, token usage
Long-running agents	Risk of timeout or OOM	Runs detached from your process
Observability	Framework-specific	Unified across all frameworks

Tool locality

Regardless of which framework you use, tools in Agentspan run in one of two places:

Tool type	Where it runs	What you provide
`@tool` (Python function)	Your worker process	The function code
`http_tool()`	Agentspan server	A URL and optional headers
`api_tool()`	Agentspan server	An OpenAPI/Swagger spec URL
`mcp_tool()`	Agentspan server	An MCP server URL

When you wrap a LangGraph graph or OpenAI SDK agent with AgentRuntime, its tool functions become worker-executed tasks. Server-side tools (http_tool, api_tool, mcp_tool) run on the server regardless of framework.

See Tools for details.

Native Agentspan agents

If you’re not using an existing framework, define agents natively:

from agentspan.agents import Agent, tool, AgentRuntime

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

agent = Agent(
    name="researcher",
    model="openai/gpt-4o",
    tools=[search_web],
    instructions="Research topics thoroughly.",
)

with AgentRuntime() as runtime:
    result = runtime.run(agent, "What is quantum entanglement?")
    result.print_result()

→ Quickstart · Agents concept · Tools concept