pip install "aegis-memory[langchain]"

from aegis_memory.integrations.langchain import AegisMemory
from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI

# Initialize Aegis Memory
memory = AegisMemory(
    api_key="your-aegis-key",
    agent_id="support-agent",
    namespace="customer-service"
)

# Use in a chain
chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory
)

# Memory is handled automatically
response = chain.predict(input="My name is John and I'm a Python developer.")
# Later...
response = chain.predict(input="What's my name?")
# Agent remembers: "Your name is John"

from aegis_memory import AsyncAegisClient

async def graph_node(state):
    async with AsyncAegisClient(api_key="your-aegis-key") as client:
        await client.add(state["message"], agent_id="planner")
        memories = await client.query("plan context", agent_id="planner")
        return {"memories": [m.content for m in memories]}

memory = AegisMemory(
    api_key="your-aegis-key",
    agent_id="assistant",
    namespace="my-app",
    scope="agent-private",        # Memory visibility
    return_messages=True,         # Return message objects
    k=5,                          # Number of memories to retrieve
    memory_key="history"          # Key in chain input
)

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from aegis_memory.integrations.langchain import AegisMemory

memory = AegisMemory(
    api_key="your-aegis-key",
    agent_id="tool-agent"
)

llm = ChatOpenAI(model="gpt-4")
agent = create_openai_tools_agent(llm, tools, prompt)

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True
)

# Agent now has persistent memory across sessions
result = agent_executor.invoke({"input": "Remember that I prefer Python"})