Native AI memory
Useful inside one assistant, but the memory usually stays trapped in that product. MemQ is built for the agent tools you already use.
Give every AI agent
a memory that
keeps learning.Give every AI agent
a memory that gets stronger
after every run.
That is MemQ.
MemQ is durable memory for anyone using ChatGPT, Claude, Codex, Cursor, Gemini, MCP clients, or autonomous agents. It saves the useful lessons from your work, then gives them back to the next AI session so mistakes, fixes, preferences, and decisions do not disappear.
Memory for AI agents
14-day trial
Works across tools
25 hosted MCP tools
Billing-backed MemQ access is live. Plan selection opens before checkout.
Kill the wrong categoryYou do not need another AI tab.
You do not need another AI tab.
You need your work to persist.
Every reset makes you rebuild briefs, constraints, decisions, fixes, and preferences. MemQ exists to make that context reusable through a memory layer, not another manual prompt ritual. Your AI should keep the lesson permanently.
Not a Vector database
Optimized for storage and similarity, not continuity across AI sessions.
Not a Retrieval layer
Finds nearby text, but does not by itself decide which decisions should carry forward.
Not a Memory cache
Helps with recent state, but durable context has to survive the tab closing.
Not a Static RAG
Requires humans to keep curating what the AI should have available automatically.
Simple version
It works like learning a boss fight.
Imagine fighting a boss in a game. Every failed attempt teaches the pattern. MemQ gives your AI the same loop: when a session fails, stalls, or solves something important, the lesson is saved permanently. Next time, your agent reaches into MemQ, pulls the advice back, and starts with the mistake already learned.
Run
Learn
Recall
Compound
Project history has to be pasted back into each tool
Decisions are hard to recover when a teammate takes over
Agents lose the preferences and constraints that made prior answers useful
Useful context competes with stale notes, transcripts, and noise
Market comparison
The market stores context.MemQ turns it into lessons.
Most AI memory options help with recall in one place. MemQ is positioned for people and teams who use many AI tools and need the lesson from one session to improve the next one.
Retrieval proof
+42 pts
primary@1 vs Mem0 OSS in the published retrieval snapshot.
Average latency
13ms
versus 2511ms for the Mem0 OSS adapter in the same harness.
Cost model
fewer resets
Sessions per week times re-explaining minutes is the hidden AI tax MemQ attacks.
Vector DB / RAG
Good infrastructure for finding similar documents. It does not automatically turn failures, fixes, and decisions into reusable agent lessons.
Agent memory libraries
Helpful for developers wiring one app. MemQ adds hosted MCP access, billing-backed setup, namespaces, proof, and team rollout paths.
MemQ
A persistent lesson layer for ChatGPT, Claude, Codex, Cursor, Gemini, MCP clients, automations, and team agents.
Performance numbers are artifact-backed snapshots, not universal workload guarantees. The buyer claim is simpler: stop paying people to re-explain the same context to every new AI session.
MemQ proof and product experience
Trust the memory layer before you start the trial.
The proof is concrete: hosted tool availability, reproducible retrieval results, and a clear path from individual use to team rollout.
Adaptive memory cycle
See what the memory layer actually does.
MemQ observes context, evaluates signal, reinforces useful memory, and lets stale context decay. The result is a workflow where important context can change the next answer.
01
Observe
Inputs, decisions, preferences
02
Evaluate
Signal, entropy, outcome value
03
Reinforce
Reuse what changes results
04
Decay
Let stale context disappear
Output: remembered context that changes the next answer
MemQ inside the work loop
MemQ is already inside the work loop.
MemQ is not a separate knowledge silo. It belongs in the same workflow where an AI assistant checks sources, changes files, and prepares the next branch action.
Live workflow proof
MemQ recall, repo inspection, and verification planning in one agent handoff.
12
retrieval cases
36
completed MemQ runs
13ms
avg latency
7
memory families
25
hosted tools
Fast path from curiosity to usage
Without: You re-explain goals, preferences, files, and prior answers every time
With MemQ: MemQ exposes hosted MCP tools for memory operations
MemQ currently exposes 25 hosted MCP tools. That gives your AI workflow a concrete setup path instead of a vague memory promise.
Tool count is pulled from verified product status.
100%
primary@1
Benchmarked retrieval, not brochure math
Without: You have to trust a generic memory claim
With MemQ: Retrieval results are tied to checked-in artifacts
The published retrieval snapshot reports MemQ MCP at 100% primary@1 across 36 completed retrieval runs in its reproduced corpus.
Source: the published MemQ retrieval benchmark snapshot.
+42 pts
vs Mem0 OSS
Proof you can inspect
Without: Performance claims are stranded on the page
With MemQ: Results link back to the public benchmark repository
MemQ is compared against Mem0 OSS and a keyword baseline in the checked-in retrieval snapshot, with the benchmark repo linked for deeper review.
Comparison scope is limited to retrieval benchmark results.
Shared
context layer
A path that scales
Without: One person remembers the context and everyone else starts from fragments
With MemQ: MemQ starts with personal continuity and can expand to shared team context
Start with self-serve plan selection when the pain is personal continuity. Use the demo path when the rollout spans multiple users or governed workflows.
Capability language follows the documented MemQ product scope.
Benchmark-backed claims only
Review the public benchmark repositoryHow MemQ fits the stackStart with memory.
Start with memory.
Add controls when the workflow needs them.
Start with MemQ when the immediate pain is lost project context. Add AIEGES Shield when the same workflow also needs prompt, file, browser, or tool-call controls before AI traffic reaches a model.
Shield
MemQ
Forge
Sentinel
Durable Contextual Parity
Give agents a reusable memory layer for prior decisions, preferences, and project context.
Institutional Wisdom
Expand from individual continuity into shared team context when more people need the same memory.
Controlled Execution
Use Shield controls when prompts, files, browser actions, or tools need review before execution.
Technical Integrity
Keep memory claims grounded in product status, documentation, and benchmark artifacts.
Security layer
Add Shield when memory touches sensitive work.
Start with MemQ for continuity. Bring in Shield when your team also needs prompt, file, and model-call controls around AI usage.
Block risky prompts
Before the model call
Redact sensitive data
Before it leaves the flow
Keep an audit trail
Trace every decision
Deploy on your terms
Cloud, VPC, or isolated
Keep private data out of prompts
Inspect and redact sensitive content before it reaches external models, tools, or agent actions.
Know exactly what happened
Every interaction gets a traceable decision path: allowed, blocked, redacted, routed, and why.
Fit your security boundary
Start with managed control, then move into VPC or isolated environments when your policies require it.
Choose the next stepStart with MemQ when your AI keeps
Start with MemQ when your AI keeps
relearning the same lesson.
Choose self-serve MemQ plan selection when you want the next agent session to inherit the fixes, preferences, and decisions the last one already earned.
Self-Serve · Deploy Now
Start with the MemQ Pro trial.
Use this path when you already feel the continuity problem: repeated briefs, lost decisions, copied background, and AI sessions that do not remember the lesson permanently.
14-day trial
25 hosted MCP memory tools
Plan selection opens before checkout
Billing required · Plan selection opens before checkout
Team / Enterprise · Talk to Engineering
Need team-wide memory and controls?
Use the demo path when multiple users, sensitive prompts, file handling, or Shield controls need review before rollout.
- Shared memory rollout for multiple users
- Architecture review for existing AI workflows
- Shield controls when prompt or file review is required
- Demo path for teams not ready for self-serve checkout