Your agents forget.Neotoma makes them remember.
Your agents pick up where they left off and coordinate across Claude, Cursor, ChatGPT, and everything else. No re-explaining, no lost context, no conflicting answers.
Durable memory for agents that persists across sessions and tools
Facts are stored privately under your control. Any agent can retrieve exactly what it needs, with full versioning and provenance.
Who this is for
You run AI agents seriously......and pay the tax for missing memory
The re-prompting wastes your time and tokens. The deeper risk is when your agent acts confidently on wrong facts, and you don’t find out until the damage is done.
You're the context janitor between tools
Every session starts from zero. You re-explain context, re-prompt corrections, re-establish what the agent already knew.
Stop acting as the human sync layer between tools. Start operating with continuity — steering instead of re-explaining.
You're babysitting inference variance
Your agent guesses entities every run. Corrections don’t persist. Regressions ship because the architecture can’t prevent them.
Stop babysitting inference variance. Build on solid ground — state that stays corrected from run to run.
You're the log archaeologist
Two runs. Same inputs. Different state. No replay, no diff, no explanation.
Stop reverse-engineering truth from logs. Debug from replayable state you can inspect, diff, and trust.
Already building your own memory system? Most developers start with SQLite, JSON, markdown, or a custom MCP server. Neotoma ships the guarantees you’d otherwise build and maintain yourself: versioning, conflict detection, schema evolution, and cross-tool sync.
Before & after
Same question, different outcome
Without shared memory, agents act on facts they can’t verify. With Neotoma, every response reads from versioned, structured history.
Silently overwritten, confidently wrong
You corrected a contact's email last week. A different agent session overwrote it with the old address. Your agent sends to the wrong person, and nobody notices until it's too late.
Product demo
Inspect, version, diff, and replay what your agents remember
The same operations work from the CLI, the REST API, the Inspector app, or through any MCP-connected agent. Toggle between views to try each interface.
Guarantees
Memory that stays correct from session one to month twelve
Chat memory fades. RAG drifts. Markdown and JSON files accumulate silent conflicts. Neotoma enforces versioning, provenance, and tamper detection that hold over months and years: not just between recent sessions.
Deterministic state
You run the same pipeline twice and get different results — no way to trace why.
Versioned history
A retry silently overwrites a preference. The original is gone.
Auditable change log
Your agent makes a bad call. You can’t trace what data it relied on.
Silent mutation prevention
Data changes without your knowledge. You find out when something breaks.
Schema constraints
An agent writes a malformed record. Nothing rejects it — errors compound silently.
Reproducible reconstruction
Your database corrupts. There’s no path back to a known-good state.
How it’s used
Running daily for 5+ months across Claude Code, Cursor, ChatGPT, and CLI. Same state graph from day one: every version preserved, every correction traceable. Contacts evolve, contracts get amended, tasks close and reopen. I ask my agents what changed on a deal since October or what I originally told an investor three months ago. The memory compounds; nothing silently drifts.
“State integrity, not retrieval quality.”
Agentic app builder
“Very relevant problem, most people rolling their own.”
Laurie Voss, npm co-founder
“Genuinely useful for production agents, overkill for hobbyist chatbots.”
Production agent evaluator
“CI/CD for agent state.”
What to store
Not sure where to start? Pick three.
Your contacts, tasks, and events disappear between sessions and tools. Store them once, versioned and queryable across every agent you run, and stop re-explaining your world.
Contacts
People, companies, roles, and the relationships between them.
contactcompanyaccountTasks
Obligations, deadlines, habits, and goals tracked across sessions.
taskhabitgoalEvents
Meetings, milestones, and the outcomes attached to them.
eventmeetingmilestoneTransactions
Payments, receipts, invoices, and ledger entries versioned instead of overwritten.
transactioninvoicereceiptContracts
Agreements, clauses, and amendments with the exact terms preserved over time.
contractclauseamendmentDecisions
Choices, rationale, and the audit trail that proves why an agent acted.
decisionassessmentreview
Evaluate it
Let your agent decide if Neotoma fits
Your agent can read the docs, inspect the evaluation criteria, and tell you whether Neotoma fits your actual workflow before you install anything.
Copy this prompt into any AI agent. It reads the evaluation page, inspects your context, and judges whether Neotoma fits.
Read https://neotoma.io/evaluate. Before you answer, inspect any broader tool, workspace, and configuration context you can access so you assess my real workflow rather than just this chat. Then assess whether Neotoma fits my workflow, what data would be highest value to persist first, and what I should do next.Frequently asked questions
Platform memory (Claude, ChatGPT) is good enough - why add another tool?
Platform memory stores what one vendor decides to remember, in a format you can't inspect or export. It doesn't version, doesn't detect conflicts, and vanishes if you switch tools. Neotoma gives you structured, cross-tool memory you control.
Can't I just build this with SQLite or a JSON file?
You can start there - many teams do. But you'll eventually need versioning, conflict detection, schema evolution, and cross-tool sync. That's months of infrastructure work. Neotoma ships those guarantees on day one.
Is this production-ready?
Neotoma is in developer preview — used daily by real agent workflows. The core guarantees (deterministic memory, versioned history, append-only change log) are stable. Install in 5 minutes and let your agent evaluate the fit.
Does Neotoma replace Claude's memory or ChatGPT's?
No — it works alongside them. Platform memory stores what one vendor decides to remember within that vendor's tool. Neotoma stores facts you control across all your tools. Keep using platform memory for quick context; use Neotoma when you need versioning, auditability, and cross-tool consistency.
Does Neotoma send my data to the cloud?
No. Neotoma runs locally by default. Your data stays on your machine in a local SQLite database. There is no cloud sync, no telemetry, and no training on your data unless you choose to expose the API (for example for remote MCP clients).
What's the difference between RAG memory and deterministic memory?
RAG stores text chunks and retrieves them by similarity. Neotoma stores structured facts and builds a versioned history for each one; the same inputs always produce the same result. RAG optimizes relevance; deterministic memory optimizes integrity, versioning, and auditability.
Does the memory degrade or drift over time?
No. Neotoma uses an append-only observation log with deterministic reducers. Nothing is overwritten or silently dropped. Facts stored six months ago are as retrievable and verifiable as facts stored today — with full version history and provenance intact. The memory compounds; it never decays.