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Framework Agent Bridges

A bridge is the SDK adapter that lets Conductor run an agent authored in another framework, such as OpenAI Agents, LangChain, LangGraph, or Google ADK. You keep the agent object your framework defines, and the bridge compiles and runs it as a durable Conductor execution. This page is the reference for the bridges: which frameworks and languages are supported, how a bridged agent becomes a deployable Conductor Agent, and where the maintained examples live for each pairing.

OpenAI Agents Google ADK LangChain LangGraph Vercel AI SDK Conductor Agents

Choose your bridge

Framework Start here
OpenAI Agents OpenAI Agents quickstart
Google ADK Google ADK quickstart
LangChain / LangChain4j LangChain quickstart
LangGraph / LangGraph4j LangGraph quickstart
Vercel AI SDK Vercel AI SDK examples on GitHub
Conductor Agents Your First Agent

Each route keeps the framework-specific code, dependencies, and executable examples in the owning Conductor SDK. The bridge is the boundary: your framework remains the authoring surface, while Conductor provides durable execution around it.

From framework object to workflow step

Every bridge follows the same path from your code to a reusable workflow step:

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flowchart LR
    obj["Your framework<br/>agent object"] --> bridge["SDK bridge<br/>compiles it to a workflow graph"]
    bridge -- "run (develop)" --> devrun["One durable execution<br/>visible in the UI"]
    bridge -- "deploy (release)" --> deployed["Deployed Conductor Agent<br/>named and versioned"]
    workers["serve: worker process<br/>executes the tools"] -.- deployed
    parent["Parent workflow<br/>AGENT task"] -- "invoke" --> deployed
  1. Run it while you iterate. Pass your framework's agent object to the SDK bridge and run it. The bridge compiles the agent and executes it on Conductor, so the durable execution is visible in the UI from the first run.
  2. Deploy it when it stabilizes. Deploying registers the compiled agent on the server as a named, versioned Conductor Agent. Callers can then invoke it without importing your framework or its dependencies.
  3. Serve its workers. Where the bridge runs your tools as local functions, a worker process must be running to execute them. Keep it running for as long as the deployed agent is in use.
  4. Invoke it from a workflow. A parent workflow calls the deployed agent with an AGENT task, the same way it calls any other durable step.

In the Python SDK, those steps are four calls on the same runtime. Here they are with the LangChain bridge:

from conductor.ai.agents import AgentRuntime
from langchain.agents import create_agent
from langchain_core.tools import tool

@tool
def check_token() -> str:
    """Check a token."""
    return "available"

agent = create_agent("openai:gpt-4o-mini", tools=[check_token],
                     system_prompt="You are a helpful assistant.")

with AgentRuntime() as runtime:
    runtime.run(agent, "Is the token set?")  # develop: compile and execute once
    runtime.plan(agent)                      # CI: inspect the compiled graph
    runtime.deploy(agent)                    # release: register without executing
    runtime.serve(agent)                     # operate: run tool workers and block

serve() blocks, so in production it belongs in its own long-lived worker process while deploy() runs in CI/CD. Once deployed, a parent workflow invokes the agent by name:

{
  "name": "run_agent",
  "taskReferenceName": "run_agent_ref",
  "type": "AGENT",
  "inputParameters": {
    "agentType": "conductor",
    "name": "<deployed-agent-name>",
    "prompt": "${workflow.input.prompt}"
  }
}

The Conductor Agents page covers the deployed agent's runtime behavior: invocation, waiting, resume, cancellation, and outputs.

Maintained SDK examples

Framework Python Java TypeScript / JavaScript C#
OpenAI Agents Examples Examples Examples Examples
Google ADK Examples Examples Examples Examples
LangChain Examples LangChain4j examples Examples —
LangGraph Examples LangGraph4j examples Examples —
Vercel AI SDK — — Examples —

Next steps