gora8-adapters

Wrap a LangGraph graph or CrewAI crew as an HTTP server gora8 can deploy — without hand-writing a FastAPI app yourself.

gora8's gora8 deploy only needs a public HTTPS endpoint that accepts a POST with {"task": "..."} and returns JSON. This package spins up that endpoint for you from an already-built graph or crew.

Install

pip install gora8-adapters[langgraph]   # or: crewai, openai-agents, google-adk, agno, semantic-kernel, autogen

LangGraph

from gora8_adapters.langgraph import serve

# graph = your_state_graph.compile()
serve(graph)

Defaults to the common messages-keyed state convention. If your graph uses a different state schema, pass your own mappers:

serve(
    graph,
    input_mapper=lambda task: {"my_input_key": task},
    output_mapper=lambda state: state["my_output_key"],
)

CrewAI

from gora8_adapters.crewai import serve

# crew = Crew(agents=[...], tasks=[...])
serve(crew)

Your crew's task descriptions should reference {task} (the default input variable name) — override with input_mapper if you use a different name.

OpenAI Agents SDK

from agents import Agent
from gora8_adapters.openai_agents import serve

agent = Agent(name="assistant", instructions="You are helpful.")
serve(agent)

Google Agent Development Kit (ADK)

from google.adk.agents import Agent
from gora8_adapters.google_adk import serve

agent = Agent(name="assistant", model="gemini-2.0-flash", instruction="You are helpful.")
serve(agent)

Each request gets its own throwaway in-memory ADK session — no multi-turn history is kept between calls, matching the stateless "task in, result out" contract of gora8's invoke gateway.

Agno

from agno.agent import Agent
from gora8_adapters.agno import serve

agent = Agent(name="assistant")
serve(agent)

Note: the package is agno (PyPI phidata is a frozen legacy snapshot from before the project's rename — don't install that one).

Semantic Kernel

from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from gora8_adapters.semantic_kernel import serve

agent = ChatCompletionAgent(service=OpenAIChatCompletion(), name="Assistant", instructions="You are helpful.")
serve(agent)

AutoGen

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from gora8_adapters.autogen import serve

model_client = OpenAIChatCompletionClient(model="gpt-4o")
agent = AssistantAgent(name="assistant", model_client=model_client)
serve(agent)

Targets Microsoft's official autogen-agentchat package. Not ag2 (its classic ConversableAgent/import autogen API moved to a separate ag2-classic package as of AG2 v1.0) and not legacy pyautogen~=0.2.0 (current pyautogen on PyPI is itself just a proxy onto autogen-agentchat).

Then deploy

Point endpoint: in your agent.yaml at wherever you host this server (e.g. https://my-agent.example.com/invoke), then run gora8 deploy as usual — no gora8-specific code beyond serve(...) is required.