Memotrix

Composable hybrid memory and RAG for AI agents.

Memotrix lets an agent remember files and free-text facts the same way LangChain-style tools compose: pick embeddings, pick a store, then add / search / delete. It is a Python library — not an LLM, not a chat UI, and not a hosted service.

It chunks documents, stores dense vectors (semantic) plus a keyword index (BM25 or Postgres full-text), and retrieves a small context window with hybrid search, optional rerank, and neighbor expansion.

• GitHub
• User guide

Why use it

Install

Python 3.10+. A bare pip install memotrix only installs python-dotenv. Use an extra:

pip install memotrix[memory]

Postgres + file extractors:

pip install memotrix[postgres,memory,extractors]

Quick start

from memotrix import Memory
from memotrix.embeddings import HuggingFaceEmbeddings
from memotrix.vectorstores import InMemoryStore

embeddings = HuggingFaceEmbeddings(model="BAAI/bge-small-en-v1.5")
memory = Memory(embeddings=embeddings, store=InMemoryStore(embeddings))

memory.add_text("User prefers dark mode.", memory_type="semantic", source_id="prefs")
hits = memory.search("what theme does the user want?", top_k=3)
memory.delete("prefs")
memory.close()

Files:

memory.add("report.pdf")
hits = memory.search("what is the revenue?", memory_type="semantic")
print(memory.list_sources())

Postgres:

import os
from memotrix.vectorstores import PostgresStore

embeddings = HuggingFaceEmbeddings(model=os.environ["EMBEDDING_MODEL"])
memory = Memory(
    embeddings=embeddings,
    store=PostgresStore(connection=os.environ["DATABASE_URL"], embeddings=embeddings),
)

Or from the environment (EMBEDDING_MODEL required; DATABASE_URL when MEMOTRIX_BACKEND=postgres):

from memotrix import Memory
memory = Memory.from_env()

Agent memory

memory.add_text("Shipped hybrid search.", memory_type="episodic", session_id="2026-09-03")
memory.add_text("Always cite source_path.", memory_type="procedural")
memory.search("how should answers be cited?", memory_type="procedural")

session_id and memory_type are exact-match payload filters.

What it can ingest

PDF, DOCX, PPTX, TXT, Markdown, HTML, EPUB, CSV, Excel (.xlsx), JSON (FHIR / GeoJSON / chat sniff), YAML, XML, SQL, images, video, audio ([audio]), source code, SCORM, knowledge graphs, GeoJSON, email (.eml / .mbox), chat exports, and .log files.

Generic .zip and BIFF .xls are not supported. Convert spreadsheets to .xlsx.

Plug in your own extractor:

memory = Memory(embeddings=embeddings, extract_file=my_extractor)

Links

• Source: github.com/Matrixxboy/memotrix
• Full API: User guide
• License: MIT