railtech-mme

Python SDK for MME — the Modular Memory Engine by Rail Tech.

Tag-graph memory for LLMs. Bounded retrieval. Hard token budgets. Learns from use. No vector DB.

pip install railtech-mme

Requires Python 3.9+.

Quick start

from railtech_mme import MME

mme = MME(api_key="mme_live_...")          # get one at https://mme.railtech.io

# Save a few facts — MME tags them automatically
mme.save("I prefer dark chocolate over milk chocolate.")
mme.save("I'm allergic to peanuts.")
mme.save("My favorite cuisine is Thai.")

# Recall them later — tag-graph activation matches keywords in the prompt
pack = mme.inject("What are my food preferences and allergies?", token_budget=1024)

for item in pack.items:
    print(f"- {item.title}: {item.excerpt}")

mme.feedback(pack_id=pack.pack_id, accepted=True)

That's the whole loop: save facts as they happen, inject them at prompt time, feedback to improve future packs.

Tip on prompt phrasing. MME's retrieval is tag-graph-based, not embedding-based: the prompt's keywords seed propagation across the tag graph. Prompts that share concrete words with your saved facts (food, chocolate, allergies) retrieve reliably even on a brand-new account; abstract paraphrases (dietary preferences) only start working after the graph has built up enough edges to bridge the gap. This is by design — it's why MME stays explainable and bounded.

Async

import asyncio
from railtech_mme import AsyncMME

async def main():
    async with AsyncMME(api_key="mme_live_...") as mme:
        await mme.save("hello")
        pack = await mme.inject("hello")
        print(pack.items[0].excerpt)

asyncio.run(main())

AsyncMME is a 1:1 mirror of MME. Same methods, same exceptions, same return types.

LangChain

pip install "railtech-mme[langchain]"

from railtech_mme import MME
from railtech_mme.langchain import MMESaveTool, MMEInjectTool

mme = MME(api_key="mme_live_...")
tools = [MMESaveTool(mme=mme), MMEInjectTool(mme=mme)]
# hand `tools` to your agent — see examples/langchain_agent.py for a runnable demo

The tools are LangChain BaseTool subclasses, so they drop into any agent that accepts tools (LangChain, LangGraph, AutoGen wrappers, etc.).

API surface

AsyncMME exposes the same surface with async def / await.

Filters

Narrow a retrieval to a section, a status, or a time window:

import datetime as dt
from railtech_mme import MME, InjectFilters

mme = MME()
pack = mme.inject(
    "what shipped this sprint?",
    filters=InjectFilters(
        section="work",
        since=dt.datetime(2026, 4, 1, tzinfo=dt.timezone.utc),
    ),
)

Auth

Get your mme_live_... API key at https://mme.railtech.io → API Key.

The SDK reads it from the RAILTECH_API_KEY environment variable if you don't pass it explicitly:

export RAILTECH_API_KEY=mme_live_...

from railtech_mme import MME
mme = MME()  # reads from env

The SDK exchanges your API key for a short-lived JWT on first use and caches it for the life of the client. Token refresh is automatic on 401.

Errors

All SDK errors inherit from MMEError:

import time
from railtech_mme import MME, MMEError, MMEAuthError, MMERateLimitError

mme = MME()
try:
    mme.save("...")
except MMERateLimitError as e:
    time.sleep(e.retry_after or 60)
except MMEAuthError:
    # API key is invalid or revoked — get a new one
    raise
except MMEError as e:
    print(e.status_code, e.response_body)

The full taxonomy: MMEError → MMEAuthError, MMEClientError, MMERateLimitError, MMEServerError, MMETimeoutError, MMEBudgetExceeded.

Architecture — what MME does differently

MME does not use vector embeddings. It uses a bounded tag-graph that learns from pack accept/reject events. Retrieval is:

1. Extract seed tags from the prompt
2. Spread activation across the tag graph (bounded depth, beam width, decay)
3. Score memories (activation + recency + importance + status − diversity penalty)
4. Pack into a hard token budget greedily

Every pack respects the budget exactly. Every retrieval is explainable — seed tags, bounds, and activation paths come back in the response. Read the whitepaper for the full picture.

Examples

Runnable scripts in examples/:

• basic.py — sync save / inject / feedback
• async_basic.py — async equivalent
• langchain_agent.py — minimal ReAct agent with both MME tools

Each one reads RAILTECH_API_KEY from the environment.

Links

• Dashboard & API key: https://mme.railtech.io
• Issues: https://github.com/gokulJinu01/railtech-mme-python/issues
• Changelog: CHANGELOG.md

License

Apache-2.0