AI Long-Term Memory System Development Services
Long-term memory systems give AI agents persistent recall across sessions, storing semantic, episodic, and procedural memory so an agent remembers facts, past events, and learned procedures. Kodexo Labs builds these systems on Mem0, Zep, and Letta, backed by vector databases such as Pinecone.
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TRUSTED BY ENTERPRISES
Most AI agents forget everything the moment a session ends. Every conversation starts from zero, context rot sets in, and users repeat themselves. We build the persistent memory layer that finally fixes that.
Core Capabilities
Persistent state that survives restarts and redeploys via LangGraph checkpointing.
Semantic and episodic recall stored in Pinecone, Qdrant, or FAISS.
Procedural memory that lets agents repeat their learned tasks reliably.
Framework builds on Mem0, Zep, and Letta for production memory.
Relevance-ranked retrieval, so agents recall what matters, unlike plain RAG.
Governed memory with PII redaction and audit-ready access controls throughout.
IN THE NEWS
AI-powered products
AI Development Company
Expert Team
Client retention rate,
Inside Our AI Agent Memory Architecture
Memory is not one feature. It is five working layers, from state that survives a restart to governed recall that redacts sensitive data. Here is how we build each layer, and why each earns its place.
Persistent State & Checkpointing
When a server redeploys, the agent resumes right where it stopped. LangGraph checkpointing means a restart never sends any work back to zero.
It reloads full state after a crash, so work goes on with no gap.
State lives in a store, not memory, so a redeploy loses nothing at all.
Your Agents Forget. Ours Remember Every Single Session.
If your agents lose context when a session ends, users feel it first. We can scope a memory layer in one discovery sprint.
Memory Systems In Production
Extensiv
Extensiv's operations team waited on engineering for every data question. We built an agentic system on LangGraph, the same state architecture that underpins our memory work. It reads plain-English questions and answers across 207 tables and 4 databases at 90%+ accuracy, for an Inc. 5000 company. That foundation is what our memory layer uses.
90%+
Accuracy
207
Tables
04
Databases
SmartMedHx
Clinicians were losing an hour a day to note-taking during patient visits. Kodexo built a HIPAA-compliant documentation system that captures the patient interview and structures the clinical record, using the same compliance-first approach behind secure retrieval work. Today 42-plus providers use it daily, 493 patient interviews are processed, and the patent-pending AI stays HIPAA-compliant.
42+
Providers
493
Patient Interviews
HIPAA
Compliant
IFPG
IFPG's chatbot returned HTML-broken, inaccurate answers to every prospect, killing leads at first contact. Kodexo Labs rebuilt the reasoning layer with chain-of-thought prompting and eliminated all HTML errors.
85%
Accuracy Lift
100%
HTML Error Elimination
1000+
Franchise Listings
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- Patient history retainedNo re-asking patientsHIPAA-compliant by designStructured visit records
Long-Term Memory For AI Agents Across Regulated Industries
Memory needs differ by vertical. A clinic guards patient histories; a warehouse keeps cross-database context; a fleet shop recalls past diagnostics. We shape the persistent memory layer around each industry's data, its rules, and the recall patterns.
Build a HIPAA-compliant, agentic, or omnichannel chatbot. Let's scope it.
Sector-specific platforms either compound their data advantage or fall behind the operators investing now. Find out where yours sits.
Compliance Built Into The Memory Layer From Day One
Stored memory is regulated data, so we treat compliance as architecture, not paperwork. HIPAA with a signed BAA, SOC 2, GDPR, and PCI-DSS shape the design. We isolate memory in an AWS VPC, or on-prem, air-gapped Kubernetes, so your data stays under your control.
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
NIST AI RMF
PCI-DSS
NIST CSF
COPPA
FERPA
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
NIST AI RMF
PCI-DSS
NIST CSF
COPPA
FERPA
Why Engineering Teams Building AI Agents With Long-Term Memory Choose Kodexo Labs First
Plenty of teams can wire up a vector database. Fewer have shipped memory systems that hold up under regulation, load, and real production traffic. Here is what separates the work we deliver from a demo.
HIPAA-First Clinical Memory
SmartMedHx needed memory that regulators accept. We built for HIPAA from day one, not as a late patch. Patient notes persist across visits, so 42 staff log 493 interviews on a patent-pending system.
Memory Under Production Load
A demo memory layer folds under real traffic. Listen AI proved ours holds. Diesel Laptops runs its parts search in its own AWS VPC, at 160,000 records, lookup time down 85%.
A Proven State Architecture
Memory holds only if the state layer beneath it is sound. We built Extensiv, an Inc. 5000 firm, on LangGraph. It reads plain questions across 207 tables and 4 databases at 90%+ accuracy.
Deployed Inside Your Perimeter
Some memory stores can never touch a shared cloud. We deploy in your own AWS VPC or air-gapped Kubernetes. Diesel Laptops self-hosts 160,000 records, with lookups 85% faster, so it stays in view.
See What Persistent Memory Changed For Three Real Production Teams
Numbers from a slide deck are easy. These come from live systems our clients depend on every day. If your agents keep forgetting, we can scope a custom memory layer in one sprint.
Recognised, Not Self-Declared
Third-party reviewers, not our own marketing, put these badges on the wall. Each reflects verified client feedback and delivery track record across our AI development work, checked independently.
Overcoming AI Long-Term Memory Systems Challenges
Building persistent memory sounds simple until production exposes the hard parts. Data leaks between users, context vanishes on restart, and regulated records turn a memory store into a liability. We engineer around these three failures before they reach your users.
The Memory Stack We Build On
These are the frameworks, vector databases, and infrastructure our long-term memory systems run on in production today.
How We Build Your Long-Term Memory System
Discovery & Memory Scoping
We map what your agents must remember and for how long: which facts persist, which context expires, and where regulated data lives. You leave this phase with a memory model and a retention policy, not a vague brief.
Architecture Design
We choose the memory frameworks and vector databases that fit your load and rules: Mem0, Zep, or Letta, backed by Pinecone, Qdrant, or FAISS. You get a diagrammed architecture with isolation, retrieval, and governance decided before any code ships.
Build
We implement the persistence layer, wiring LangGraph checkpointing, semantic and episodic stores, and relevance-ranked retrieval into your agents. Each memory type gets built and reviewed in short sprints, so you see working recall early, not at the very end.
Testing, Load & Security Review
We test recall accuracy, run the store under production load, and probe for cross-tenant leaks and PII exposure. Isolation tests, penetration testing, and audit-log checks all run here, so the memory layer holds up before your users touch it.
Deploy & Handoff
We deploy inside your AWS VPC or on-prem Kubernetes, then hand over documentation, runbooks, and monitoring for the memory layer. Your team gets a system it can operate and audit, plus our support while it settles into production.
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Questions We Hear
Long-term memory lets an AI agent recall facts, past events, and learned procedures across sessions, instead of forgetting everything when a chat ends. For enterprise software, that means agents stop asking users to repeat themselves and can act on history. Kodexo Labs built exactly this for SmartMedHx, where structured patient histories persist across 493 interviews.