Framework Integrations

LangChain

pip install agentshield langchain-core

from agentshield.integrations.langchain import AgentShieldCallback
from langchain_openai import ChatOpenAI

callback = AgentShieldCallback(
    agent_name="my-langchain-agent",
    session_id="optional-session-id",
)

llm = ChatOpenAI(model="gpt-4o")
llm.invoke("Hello", config={"callbacks": [callback]})

CrewAI

pip install agentshield crewai

from agentshield.integrations.crewai import AgentShieldCrewCallback
from crewai import Agent, Crew, Task

# Instantiating registers the listener automatically
callback = AgentShieldCrewCallback(agent_name="my-crew")

researcher = Agent(role="Researcher", goal="...", backstory="...")
task = Task(description="Research ...", agent=researcher)
crew = Crew(agents=[researcher], tasks=[task])
crew.kickoff()

AutoGen

pip install agentshield pyautogen

from agentshield.integrations.autogen import AgentShieldAutoGenHook
from autogen import AssistantAgent, UserProxyAgent

hook = AgentShieldAutoGenHook(agent_name="my-autogen-agent")

assistant = AssistantAgent("assistant", llm_config={"model": "gpt-4o"})
hook.register(assistant)  # attach hooks

user = UserProxyAgent("user", human_input_mode="NEVER")
user.initiate_chat(assistant, message="Research AI safety.")

LlamaIndex

pip install agentshield llama-index-core

from agentshield.integrations.llamaindex import AgentShieldLlamaIndexCallback
from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager

callback = AgentShieldLlamaIndexCallback(agent_name="my-llama-agent")
Settings.callback_manager = CallbackManager([callback])

# All LLM calls via LlamaIndex are now tracked
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is AgentShield?")

Direct API / Any framework

No framework? Use the decorator directly on any Python function that calls an LLM:

from agentshield import shield

@shield(agent="my-agent")
async def call_llm(prompt: str) -> str:
    # Your existing LLM call
    response = await openai.chat.completions.create(...)
    return response.choices[0].message.content