langchain-engram

LangChain integration for Engram — cognitive memory infrastructure for AI agents.

Engram stores memories with confidence scores, detects contradictions, and manages memory lifecycle (decay, consolidation, tier promotion). Unlike a conversation buffer, it surfaces what the agent is most confident about — not just what was said most recently.

Installation

pip install langchain-engram

This also installs engram.to, the Engram Python SDK.

Setup

You need a running Engram server and an API key. Keys are prefixed mk_ (master, all scopes) or rk_ (restricted, user-chosen scopes).

export ENGRAM_BASE_URL=http://localhost:8080   # your Engram server
export ENGRAM_API_KEY=mk_your_key_here

Or pass them explicitly to each class.

EngramChatMemory

Drop-in replacement for ConversationBufferMemory. Stores each conversation turn as Engram memories and retrieves semantically relevant context on the next turn — not a flat buffer.

from langchain_engram import EngramChatMemory
from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI

memory = EngramChatMemory(
    agent_id="your-agent-uuid",
    # api_key and base_url read from env if not passed
)

chain = ConversationChain(llm=ChatOpenAI(), memory=memory)

chain.predict(input="I always prefer dark mode in all my tools")
chain.predict(input="I'm a backend engineer, mostly Go and Rust")

# Next session — memories are recalled from Engram, not kept in RAM
response = chain.predict(input="What do you know about my preferences?")

Return as messages

memory = EngramChatMemory(
    agent_id="your-agent-uuid",
    return_messages=True,   # returns List[BaseMessage] instead of a string
    min_confidence=0.7,     # only confident memories
    top_k=5,
)

Configuration

EngramRetriever

Use Engram as a retriever in any LangChain chain or agent. Retrieves memories using Engram's hybrid vector + knowledge-graph recall.

from langchain_engram import EngramRetriever
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

retriever = EngramRetriever(
    agent_id="your-agent-uuid",
    top_k=5,
    min_confidence=0.6,
)

qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever)
answer = qa.invoke({"query": "What are the user's display preferences?"})

Each retrieved Document carries full metadata:

docs = retriever.invoke("display preferences")
for doc in docs:
    print(doc.page_content)
    print(f"  confidence: {doc.metadata['confidence']:.0%}")
    print(f"  tier: {doc.metadata['tier']}")        # hot / warm / cold
    print(f"  type: {doc.metadata['memory_type']}")  # fact / preference / decision
    print(f"  score: {doc.metadata['score']:.3f}")   # combined recall score

Configuration

API key scopes

For LangChain usage, a rk_ key with read and write scopes is all you need.

Links

• Engram documentation
• Python SDK (engram.to)
• GitHub