01
Recall that compounds
Cross-session memory with hybrid search. The engine decides what's worth keeping, so context carries from one conversation to the next without bloating your prompts.
Memory infrastructure for AI agents
Memory that
predicts — for your
AI agents.
A persistent, on-device memory layer that remembers across sessions, forecasts what happens next, and stays compliant. Drop it into Claude Code, Cursor, or Codex over MCP — or your own app via the SDK.
Open source · Patent-pending · Runs on-device
recall edges in ink · predicted next events in purple
One command
to install into Claude Code
MCP + SDK
drop-in for any agent
On-device
private by default, on SQLite
GDPR
export & erasure built in
Live demo
Same question. One remembers — and predicts.
A stateless assistant starts from zero every time. Your MemMesh MeshKey recalls what matters — and tells you what’s next.
M
Your MeshKey
PRIVATEENCRYPTEDPORTABLE
13,247
Entities
327
Agents
410K
Messages
1,021
Predictions
766
PDF
47
Video
199
Images
93
Skills
What's in my memory — and what's next?
AI
AI without MemMesh
ChatGPT · Claude · any LLM
MM
AI with your MemMesh
Claude Code · Cursor · your memory
Why MemMesh
Everything a memory layer should do — and one thing none of them do.
Most tools help an agent remember. MemMesh also forecasts what happens next, and keeps every claim governed and traceable.
02
Foresight, not just recall
MemMesh mines behavior into patterns and forecasts the next event. Every forecast carries a confidence score, checked against what actually happened and recalibrated over time. When it says 80%, it means it.
03
Governed by scope
Every memory has a scope — user, team, org — and provenance on every claim. GDPR export and erasure are built in, so you stay in control of what's remembered and why.
Products
Three ways to ship memory. One engine underneath.
Packaged products for people and teams — and the raw engine layers for builders who want the primitives.
Product→
MeshKey
One key. Your memory, in every AI.
A private, portable memory that unlocks in every AI tool you use — Claude Code, Cursor, ChatGPT. One key carries your context, preferences, and history wherever you work.
memory.observe · memory.search · MCP
Product→
Team Brain
Shared memory your whole team can trust.
A governed org memory: a shared knowledge graph with scopes, provenance, and compliance built in — so institutional knowledge stops disappearing every time someone switches context.
scopes · provenance · compliance
Product→
Mesh Router
The gateway that wires any agent into the mesh.
The infrastructure layer: an MCP gateway plus SDKs in five languages that connect any agent, tool, or app to MemMesh — one API for observe, recall, and predict.
MCP gateway · SDKs (TS · Py · Go · .NET · Rust)
The engine
Engine→
Memory
Recall that compounds.
Cross-session persistent memory with hybrid search and a knowledge graph. The engine decides what's worth keeping, so context carries between conversations without bloating your prompts.
memory.observe · memory.search · memory.reflect
Engine→
Predictions
Foresight, not just recall.
Declare any target and the engine predicts it from a subject's history — calibrated, provenanced, and abstaining when the signal is thin. This is the layer mem0 and MemoryLake have no answer for.
lattice.predict (event · numeric · time · anomaly)
Engine→
Behaviors
From patterns to next-best-action.
The engine mines behavior into emergent patterns and closes the loop: predict, let the agent act, record the outcome, and watch the patterns recalibrate. Decisions and outcomes are first-class.
lattice.mine · learning.recordDecision / recordOutcome
Quickstart
Wire up memory in minutes.
One command to install, three primitives to use: observe, recall, and search.
agent · terminal
# Wire MemMesh into your agent in one command
npx @thinkfleet/memmesh install
# Then, from inside your agent — three primitives:
memory_observe({ text: "Customer prefers email over phone." })
memory_recall({ subjectId: "cust_42" })
# -> prior context, with provenance
memory_search({ query: "communication preferences", limit: 5 })How it works
Observe, learn, recall.
The same loop runs at every level — user, team, org. Feed it raw text; the engine does the rest.
- STEP 01
Observe
Feed MemMesh raw text or typed observations from your agent. One call — memory.observe — and the engine decides what's worth saving.
- STEP 02
Learn
It distils observations into a knowledge graph, mines behavior into patterns, and calibrates predictions against what actually happened.
- STEP 03
Recall
Search, recall, and predict. Your agent retrieves relevant context — with provenance — and gets a calibrated forecast of what's next.
Solutions
Built for where memory pays off.
The same recall-plus-prediction loop, tuned for the industries that need it most.
Finance & investing
→From stateless advisors to trusted partners.
Portfolio-aware AI that remembers a client's holdings, risk profile, and prior calls — and makes calibrated buy/sell/hold decisions with a self-improving reconcile loop.
Healthcare & life sciences
→Memory that's audit-ready by construction.
Optional health estimators, provenance on every claim, and audit-ready retention with export and erasure — for teams that operate under real compliance requirements.
Commerce & retail
→Predict what each shopper wants next.
A memory of every shopper — preferences, history, intent — plus a forecast of the next purchase and the moment to make the offer. The predictive-wallet pattern, on your storefront.
Customer support & CX
→Every customer remembered across every channel.
Carry each customer's history and preferences across conversations and channels, self-correct when facts change, and surface what they're likely to need before they ask.
Use cases
One memory primitive, many surfaces.
The same observe-learn-recall loop powers very different products.
- 01
Engineering & dev agents
Drop persistent memory into Claude Code, Cursor, or Codex via MCP in one command. A new teammate opens their editor and the agent already knows the project.
- 02
Sales & revenue
Give every rep and every AI SDR a memory of the account: past calls, objections, commitments — and a calibrated forecast of what closes next.
- 03
Marketing & growth
Remember every segment, campaign, and creative decision — and predict the offer and send-time each user is most likely to act on.
- 04
Support & success
Carry every customer's history, preferences, and prior decisions across conversations — and surface what they're likely to need next.
- 05
Research & analytics
Turn scattered findings into a durable, queryable knowledge graph with provenance on every claim — and calibrated estimates where the data is thin.
- 06
Operations & compliance
Audit-ready retention, GDPR export and erasure, and scoped memory for teams that operate under compliance requirements.
The wedge
A prediction layer the others have no answer for.
Every prediction carries a confidence score that's checked against what actually happened, then recalibrated. When it says 80%, it means it.
| Capability | Typical memory tools | MemMesh |
|---|---|---|
| Cross-session memory | ||
| Hybrid search + knowledge graph | ||
| Provenance on every claim | — | |
| Calibrated forecast of what's next | — | |
| Runs on-device, private by default | — | |
| GDPR export + erasure / compliance pack | — |
Capability comparison, not a benchmark. Published benchmarks to follow as the calibration story matures.
See the architectureEnterprise & compliance
Governed by design, not as an afterthought.
MemMesh ships the controls regulated teams ask for: scoped access, data portability, audit trails, and right-to-erasure. On Growth and up, the compliance toolkit — export, audit, hard-delete — is on by default.
SOC 2-aligned describes our controls and roadmap, not a completed certification.
Governance by scope
Memories are scoped to user, team, or org, with provenance on every claim — so you can answer who knew what, and why.
Portability
Full GDPR-style export. Your memory is yours; take it with you at any time.
Auditability
Audit trail with configurable retention — up to seven years on Enterprise — plus right-to-erasure on compliance tiers.
Deployment control
Runs on-device and private by default, with VPC and on-device options for teams that need data to stay put.
Pricing
Start free. Scale on what you remember.
Subjects are the headline ladder, predictions are the value meter, and events are generous fair-use. Yearly is two months free.
Free
$0forever
Wire memory into your agent and ship your first project.
Growth
Popular$79/mo
Scaling teams — more projects, compliance, prediction overage.
Enterprise
Custom
Unlimited scale, on-device deployment, 7-year audit retention.