Unimem is a universal, self-improving AI memory layer for Python applications, powering personalized AI experiences that cut costs and enhance user delight.
Initialize a state-of-the-art vector memory bank in exactly two lines of Python. Unimem automatically handles database clustering, deduplication, and contextual semantic routing.
from unimem.core.memory_client import MemoryClient
# Initialize the AI Brain natively
client = MemoryClient(db)
# Inject long-term facts reliably
client.add("I hate mushrooms on pizza.", user_id="dev_1")
# Chat intelligently against historical memory
response = client.chat(
"What should I order for dinner tonight?",
user_id="dev_1"
)
print(response)Engineered to outperform standard cloud vector wrappers with extreme security heuristics.
Natively features a complex is_suspicious() algorithm preventing prompt-injection jailbreaks (e.g. "ignore previous") from manipulating your AI's root memories.
Don't just fetch data. Unimem exposes granular arrays explaining exactly how heavily Similarity, Recency, and Frequency weighted your results.
Features heuristic LLM-bypassing mechanisms. If a user asks a simple data query, Unimem circumvents the generative AI entirely, outputting pure structured vectors in 0.00ms.
Every single memory is hard-mapped to an isolated user_id and tagged natively into domain contexts (e.g. food:pizza) to prevent contextual overlap between facts.