Memory infrastructure
for AI agents.
AI agents learn preferences, history, decisions, and context. Memgram turns those interactions into memory you can retrieve, inspect, and govern.
Memory gets harder over time.
The first few conversations usually work.
Then preferences change. Facts become outdated. Issues get resolved. New information conflicts with old information.
Most memory systems can store information. Very few help you understand what changed, what was retrieved, or why.
- Pilot signed: 50 seats on Growth plan, self-hosted evaluation.Week 1stored as account status
- Security review required before any production rollout.Week 3constraint added — supersedes 'review scheduled'
- Pilot expanded from 50 to 500 seats.Week 6status changed — old seat count still retrieved
- Security review completed and signed off.Week 10old blocker still surfaces in retrieval
- Two memories now contradict each other on rollout readiness.Week 14no signal which one is current
Do you actually need memory?
If all five are true, you're building a memory problem.
- Support agents
- Healthcare assistants
- Sales agents
- Research systems
- Legal workflows
- Your agent works across multiple sessions
- Your agent learns things from conversations
- Information changes over time
- Context doesn't naturally belong in a database
- Wrong context creates bad outcomes
What can you build with memory?
Memory becomes valuable when agents learn from interactions and improve over time.
Remembers patient history, preferences, and treatment context across visits.
Provides more personalized care without making patients repeat themselves.
Learns what works, what doesn't, and how conditions evolve over time.
Delivers support tailored to each patient's journey.
Builds on previous sessions, goals, and emotional context.
Creates continuity and trust across conversations.
Remembers strengths, weaknesses, and learning goals.
Adapts lessons as students progress.
Tracks mastered concepts and recurring mistakes.
Focuses attention where it matters most.
Builds a long-term understanding of each learner.
Creates continuity across every session.
Remembers preferences, interests, and purchase history.
Delivers recommendations that improve over time.
Learns favorite brands, styles, and sizing preferences.
Makes product discovery more relevant.
Maintains context across browsing, purchasing, and support.
Creates a more seamless customer experience.
Remembers previous issues and troubleshooting history.
Reduces repetitive questioning and speeds up resolution.
Tracks customer preferences and recurring problems.
Provides support with full context from the start.
Builds understanding across every interaction.
Helps teams deliver more proactive support.
Remembers stakeholders, objections, and buying signals.
Makes follow-ups more relevant and contextual.
Builds a living understanding of every customer relationship.
Maintains continuity across long sales cycles.
Tracks customer priorities and evolving requirements.
Helps teams make better decisions with better context.
Remembers findings, hypotheses, decisions, and source context across investigations.
Helps teams build knowledge that compounds over time instead of restarting every research session.
Learns sector trends, competitive moves, and signal patterns across reports.
Surfaces insights that emerge only from connecting many sources over time.
Builds a cumulative picture of risks, findings, and open questions.
Reduces redundant analysis and improves decision quality.
If your agent learns information that changes over time and doesn't naturally belong in a database, you're dealing with a memory problem.
Everything you need to run memory in production.
What happens to a memory after it is captured?
Watch extraction, classification, deduplication, and persistence happen in real time.
Why was this memory retrieved?
Every retrieval comes with a reason. See what surfaced, what was scored, what was rejected.
What does the system currently believe about this user?
Open any user, session, or agent and see what the system currently believes.
Who controls what persists and what expires?
Control what persists, what expires, what merges, and what stays isolated — per scope.
Can multiple agents share memory without leaking context?
Prevent cross-agent contamination. Inspect every transition between agents sharing a user.
Where does the memory infrastructure run?
Start on managed cloud. Self-hosted VPC coming soon — same APIs, same control plane.
Today's memory is a black box.
Memories get stored automatically, retrieved probabilistically, and you have no real answer to what your system believes or why.
- stores memories automatically
- retrieves probabilistically
- hides extraction logic
- no observability
- every decision is a traceable event
- retrieval comes with a reason
- inspect what the system believes
- policies you control
Inspectable runtime state for AI agents.
Trace every state transition. See what your agent currently believes. Replay any retrieval decision. Memgram gives you the visibility and control needed to run agent memory in production.
Why this memory was retained.
Every memory event opens into a full trace: the user message, extraction instructions, and every step of processing — extract, classify, deduplicate, decide.
- step-by-step pipeline replay
- per-event extraction reasoning
- reportable, shareable trace IDs
5a565eef…Skip pleasantries, greetings, and acknowledgements.
What the system currently believes.
Open any user, session, or agent to see the active belief state alongside every write, search, and rejection. Catch cross-agent contamination before it reaches production.
Watch memory evolve in real time.
Chat with an agent and see — turn by turn — what's extracted, what persists, what's retrieved, and what's rejected. Copy the integration straight into your stack.
One pipeline. Every decision, visible.
memgram sits between your application and your model runtime. Every memory event passes through a typed pipeline you can introspect and control.
Templates for the agents you actually ship.
Start from a memory profile tuned to your agent's job — support, personal assistant, coding copilot, sales, health. memgram configures extraction and retention automatically.
The failures you can't debug today.
Every team running agents in production has hit these. Without an inspectable cognition layer, the root cause looks like a hallucination — but it's a state transition no one saw.
Support agent quoted last quarter's billing flow to an enterprise customer.
A B2B SaaS team rolled out an LLM support agent for acme-enterprise. Two weeks in, it confidently referenced a deprecated metered-billing flow that had been replaced after the May 14 release — the old memory was never invalidated.
- what was extracted from the deprecated runbook
- why it persisted past the policy window
- which retrievals scored it above the new billing doc
Sales agent leaked an open support incident into an outbound email.
The support-agent and sales-agent shared the account scope without an isolation policy. The sales-agent retrieved an open incident memory (inc_8821, usage meter lag) during a renewal touchpoint — and personalised the outreach on it.
- which agent wrote which memory
- why the cross-scope retrieval matched
- the exact policy rule that should have blocked it
Coding copilot stopped recommending the team's actual stack.
Over 6,000 turns the embedding space drifted. The copilot started suggesting generic Express patterns because team-specific conventions (Next.js + PostgreSQL, feature flags required) fell below the retrieval threshold.
- confidence decay over time per memory
- rejected retrievals and their scores
- state transitions that changed the agent's belief
Managed cloud today. Self-hosted soon.
Start on managed cloud with enterprise-grade security. Self-hosted VPC and open-core distribution are landing for design partners — same APIs, same control plane.
Used by teams building production AI systems.
“Before memgram, debugging long-running conversations felt mostly probabilistic. We could see prompts and traces, but not how the system's memory state evolved over time. The retrieval traces changed that immediately.”
“We were chasing a hallucination for weeks. Turned out to be cross-agent contamination, a memory written by support surfacing in the sales agent. memgram showed us the exact transition.”
“We didn't need another vector database. We needed visibility into what our agents actually believed about users across sessions. memgram gave us a much clearer operational model for long-term memory.”
Simple plans. Scale when your agents do.
Every plan includes the full memgram cognition stack — observability, retrieval traces, and graph memory. You only pay for the writes your agents persist.
- Unlimited searches
- Unlimited agents
- Unlimited users
- Hybrid BM25 + vector search
- Graph memory
- Full observability
- Unlimited searches
- Unlimited agents
- Unlimited users
- Hybrid BM25 + vector search
- Graph memory
- Full observability
- Unlimited searches
- Unlimited agents
- Unlimited users
- Hybrid BM25 + vector search
- Graph memory
- Full observability
- Unlimited searches
- Unlimited agents
- Unlimited users
- Hybrid BM25 + vector search
- Graph memory
- Full observability
Unlimited scale, dedicated infrastructure, and policies tailored to your compliance posture. For teams running mission-critical agents in production.
Contact us- Unlimited memory adds
- Unlimited searches, agents & users
- Dedicated VPC / self-hosted
- Advanced auth & audit logs
- Custom policies & SLAs
- Priority support & onboarding
Stop guessing what your AI remembers.
memgram is the control plane for AI cognition — inspectable state, traceable retrieval, governable persistence. Built for production AI systems.