Python SDK

The Python SDK (memoturn on PyPI) is stdlib-only — no required dependencies. Install with pip install memoturn or uv add memoturn. Configure via the constructor or the env vars MEMOTURN_BASE_URL / MEMOTURN_PUBLIC_KEY / MEMOTURN_SECRET_KEY / MEMOTURN_ENVIRONMENT / MEMOTURN_MAX_BUFFER_SIZE (buffered-event cap, default 10000) / MEMOTURN_ALLOW_HTTP (suppress the cleartext-http warning for non-local hosts).

@observe decorator

The outermost @observe opens a trace; nested calls become child spans. Works for sync and async functions.

from memoturn import Memoturn, configure, observe

configure(Memoturn(base_url="http://localhost:3001", public_key="pk-mt-dev", secret_key="sk-mt-dev"))

@observe()
def retrieve(query): ...

@observe(as_type="generation")          # records a generation instead of a span
def answer(question, docs): ...

@observe(name="rag-pipeline")
def rag(question):
    return answer(question, retrieve(question))

Low-level API

mt = Memoturn()
trace = mt.trace(name="chat", user_id="u1", session_id="s1")
gen = trace.generation(name="answer", model="claude-sonnet-4-6", input=messages)
gen.end(output=reply, usage={"promptTokens": 100, "completionTokens": 20})
trace.score("user-feedback", value=1, comment="helpful")
mt.shutdown()  # flush (also flushed atexit)

OpenAI wrapper

from openai import OpenAI
from memoturn import wrap_openai

client = wrap_openai(OpenAI())
client.chat.completions.create(model="gpt-4o-mini", messages=[...])  # recorded

LangChain

from memoturn.langchain import MemoturnCallbackHandler

chain.invoke(inputs, config={"callbacks": [MemoturnCallbackHandler()]})

LlamaIndex

from memoturn.llamaindex import MemoturnLlamaIndexHandler
from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager

Settings.callback_manager = CallbackManager([MemoturnLlamaIndexHandler()])

Records query/retrieve/synthesize/LLM/tool/agent steps as a nested trace tree (using LlamaIndex’s own parent ids), including retrieved documents and embeddings.

Prompts

from memoturn import get_prompt, compile_prompt

prompt = get_prompt("support-reply", channel="production")
messages = compile_prompt(prompt, product="memoturn", question=q)