Never pay twice for an LLM call.
Never serve a stale answer.
Blind semantic similarity is dangerous in production. MemoLM sits between your application and upstream LLMs (Groq, OpenAI, Gemini), subjecting every candidate match to an explainable Safety Gate before returning it in ~18ms.
Trace the Gate in Real-Time
Experience how MemoLM distinguishes safe semantic paraphrases from dangerous stale documentation leaks.
Why Naive Semantic Caching Breaks
Raw vector retrieval assumes similarity equals safety. Here is why enterprise teams replace raw vector caches with the MemoLM Firewall.
Blind vector caches continue serving outdated v12 return/pricing policies for weeks after business documentation updates.
Bump knowledge_version tag (v12 ➔ v13). The Safety Gate immediately blocks stale candidates with zero database purges.
Vector similarity has no concept of tenant boundaries. High-similarity queries cross organizations, leaking internal company data.
Every vector search applies cryptographic tenant isolation at the query execution level. Never crosses tenant partitions.
Once an LLM hallucinates an erroneous response, a naive cache stores it forever, repeatedly poisoning customer conversations.
Dynamic semantic TTL assigns zero-caching to high-risk domains and enforces strict similarity margins on sensitive categories.
When a customer complains about an incorrect answer, engineers have zero visibility into why the cache triggered or similarity scores.
Every response returns explicit telemetry: cosine similarity, required threshold, latency saved, and itemized safety gate verdicts.
How MemoLM Works
A deterministic, low-overhead pipeline ensuring no response is served without verified safety credentials.
01. Request Interception
Inbound requests target http://localhost:8000/v1/chat/completions. The gateway strips MemoLM headers and passes prompts down-pipeline.
02. Dual-Tier Retrieval
Sub-5ms exact hash lookup runs concurrently with Qdrant vector retrieval, enforcing strict tenant isolation filters.
03. The Safety Gate
Verifies candidate match against Knowledge Version, TTL expiration, tenant boundary, and domain risk margin.
04. Return or LLM Fallback
Verified hits return in ~18ms with 0 tokens billed. Stale or miss queries route to Groq/OpenAI with automatic writeback.
candidate = qdrant.search(embedding, filter=tenant_filter)
if candidate.payload.version != requested.version:
return REJECT("knowledge_version_mismatch")
if now() > candidate.payload.created_at + candidate.payload.ttl:
return REJECT("ttl_expired")
if candidate.score < risk_margin(requested.risk):
return REJECT("similarity_below_margin")
return SAFE_CACHE_HIT
Built for High-Throughput Workloads
Everything your engineering team needs to safely cache generative AI responses without operational surprises.
The Multi-Point Safety Gate
Every candidate is evaluated against Knowledge Version, TTL validity, tenant ownership, and risk policies before serving.
Dual-Tier Latency Optimization
Sub-5ms exact deterministic match for repeated prompts; sub-20ms semantic vector search in Qdrant for natural paraphrases.
Multi-Tenant Namespace Isolation
Partition vector collections by customer, organization, or user. Data never crosses tenant boundaries under any similarity score.
Zero-Downtime Cache Invalidation
Increment your version tag (v12 ➔ v13). Outdated records are safely blocked in milliseconds without expensive database purges.
100% Fail-Open Reliability
If the MemoLM gateway ever becomes unreachable, SDKs automatically fail open directly to your upstream LLM. Zero outage risk.
Explainable Decision Audit Stream
Programmatically inspect why every candidate passed or was rejected with similarity scores, latency speedups, and audit logs.
Integration in Under 60 Seconds
Integrate with Python, TypeScript, or any OpenAI-compatible client with zero architecture changes.
Install Package
Add the lightweight client to your project via pip or npm.
Point to Gateway
Set base URL to your local or deployed MemoLM instance.
Pass Safety Tags
Pass your knowledge version and tenant tag for protection.
# 1. Drop-in OpenAI Wrapper (Zero Code Rewrite)
from openai import OpenAI
from memolm import wrap_openai
# Wrap your existing OpenAI client in 1 line
client = wrap_openai(
OpenAI(),
gateway_url="http://localhost:8000",
knowledge_version="v12",
tenant_id="acme-corp",
fallback_to_upstream=True # Automatically fails open to OpenAI if gateway is offline
)
# Standard call sites remain completely untouched
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is the return policy?"}],
)
print(response.choices[0].message.content)
print(f"Safety Verdict: {response.memolm_stats.verdict}")
print(f"Latency Saved: {response.memolm_stats.latency_saved}s")Calculate Your Cloud LLM Savings
Estimate direct inference bill reductions and user latency saved across your production stack.