Your AI engineering
team's memory
infrastructure.

# pip install 'memnos-sdk[langchain]'
from langchain.chains import ConversationChain
from memnos_sdk import MemnosClient
from memnos_sdk.integrations.langchain import MemnosMemory

client = MemnosClient(url="http://localhost:8766", api_key="...")

# Drop in as a LangChain memory backend
# Every conversation is persisted in memnos across sessions
memory = MemnosMemory(
    client=client,
    namespace="org:acme:agents",
    session_id="session-42",
)

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

# Past sessions are auto-injected as context — no extra code needed
chain.run("What database did we pick for the user service?")
# → remembers decisions made weeks ago across all prior sessions

# pip install 'memnos-sdk[llamaindex]'
from llama_index.core import VectorStoreIndex
from memnos_sdk import MemnosClient
from memnos_sdk.integrations.llamaindex import MemnosReader

client = MemnosClient(url="http://localhost:8766", api_key="...")
reader = MemnosReader(client=client, namespace="org:acme:engineering")

# Load memnos memories as LlamaIndex Documents
documents = reader.load_data(
    query="database and infrastructure decisions",
    top_k=20,
)

# Build a RAG index on top of your team's living memory
index = VectorStoreIndex.from_documents(documents)
engine = index.as_query_engine()
print(engine.query("What auth approach did we choose and why?"))

from memnos_sdk import MemnosClient

with MemnosClient(url="http://localhost:8766", api_key="...") as client:
    # Check a PR diff against your architecture constraints
    result = client.corpus.check(
        corpus_id="arch-docs",
        content=pull_request_diff,
    )

    # SHALL violations = hard failures → block the merge
    if result.shall_violations:
        print(result.format())
        raise SystemExit(1)

    # SHOULD violations = warnings → annotate the PR
    for v in result.should_violations:
        print(f"⚠ {v.rule}")

# Severity: SHALL/MUST NOT → block · SHOULD → warn · MAY → info