Geniffy for LangChain

Give a LangChain agent a memory of each of your users. Add one piece of middleware, and before every call the model is told what is known about the user, each line with where it came from; when the agent has its answer, the exchange is saved. When nothing is known, the model is told so, and says so instead of guessing.

pip install langchain-geniffy

Set GENIFFY_API_KEY from API keys in the Geniffy app. Keep it on your server.

The middleware

from dataclasses import dataclass

from langchain.agents import create_agent
from langchain_geniffy import GeniffyMemory


@dataclass
class User:
    id: str


agent = create_agent(
    "anthropic:claude-opus-5-5",
    system_prompt="You are a helpful assistant.",
    middleware=[GeniffyMemory(space=lambda runtime: f"user_{runtime.context.id}")],
    context_schema=User,
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Who signs the Lumen renewal?"}]},
    context=User(id=user.id),                     # from your own sign-in
)

• Before each model call, what is known that bears on the user's last message goes in the system prompt, after your own. In a run with tools, Geniffy is asked once, not once per step.
• When the agent has its answer, the exchange is saved to that user's space.
• If Geniffy can't be reached, the agent goes on without memory, and on_error hears about it.

Options: remember=False to only read, instructions to change what the model is told about the memory, and client / async_client to bring your own geniffy.Geniffy / geniffy.AsyncGeniffy. Async agents (ainvoke, astream) use the async client.

Tools

To let the agent decide when to look something up or save it:

from langchain_geniffy import geniffy_tools

agent = create_agent(
    "anthropic:claude-opus-5-5",
    tools=geniffy_tools(space=lambda runtime: f"user_{runtime.context.id}"),   # recall and remember
    context_schema=User,
)

The tools read the user from the runtime, so the model never sees or chooses whose memory it reads.

Retriever

from langchain_geniffy import GeniffyRetriever

docs = GeniffyRetriever(space="user_1042", k=5).invoke("the Lumen renewal")
docs[0].metadata    # {"id": ..., "kind": "people", "about": "Priya Nair", "said_at": "...", "source": "Call with Priya", ...}

One space per user

space is required: the user this is for, as a string or a function of the agent's runtime. Each space is a memory of its own, and nothing else can read it. space=None is your own memory, never your users' data. A blank space, or None from your function, is refused, so a user with no id never lands in your own memory. When a user deletes their account, forget them with Geniffy().forget_space(...) from the geniffy SDK.

Develop

pip install -e ".[test]" && pytest     # through LangChain's own create_agent, with a scripted model

Security

Report a vulnerability to ops@geniffy.com, not in a public issue. See the security policy.