Agent memory at the speed of a function call.
Records -- typed events with kind, tags, ts, and meta as first-class filters. Auto-embedded on ingest. GDPR-native forget by predicate. Portable as .neruva -- a single file you can export, diff, and own.
Records is one of four. They share a wallet, a namespace, a portable file.
Every layer runs on the same D=8192 HD bipolar substrate, deterministic from a seed, sub-millisecond, exportable as part of the same .neruva container.
Every agent team hits the same wall.
We've heard it from teams shipping agents into production. The shape of pain is always the same.
Your agents forget between sessions.
todayContext resets the moment the loop ends. Users restart and the agent has no idea who they are.
with neruvaTyped records keyed by namespace. Recall in a single call -- semantic + typed filters. No LLM round-trip.
Context-stuffing burns the same tokens every turn.
todayMost agents 'remember' by re-prepending recall text to every LLM call. ~1.25k input tokens at frontier rates, every turn, forever.
with neruvaOne records_query returns the same payload at $0.000002/turn. ~3,125x cheaper per recall slice.
LLM-as-memory hallucinates -- and bills you for it.
todayCalling a model to summarize, extract, and rerank on every write adds seconds of latency and per-token cost that compounds.
with neruvaNo model in the retrieval path. Deterministic, auditable, cheap by construction.
Filter dicts are gymnastics.
todayVector stores treat structured attributes as metadata, then ask you to express filters as $eq/$in dict-of-dicts. kind and tags should be query parameters, not metadata.
with neruvaRecords carry kind, tags, ts as first-class fields. Query by tag-any / tag-all / kind / time window directly.
Nobody can audit what your agent remembers.
todayCompliance asks 'show me what this agent knows about user X' and the answer is a black-box embedding.
with neruvaEvery record is a first-class row with kind, tags, ts, meta, and a content-addressable id. Inspect, export, delete.
Surgical forget is an afterthought.
todayA user revokes consent. Now you have to find their fingerprint scattered across embeddings, rebuilds, and caches.
with neruvaOne predicate -- by id, by tag, by user, by ts window. Free and never metered. Tombstones flush on next compaction.
Where teams reach for Neruva memory.
Multi-user customer-support agents
scenarioA SaaS support assistant serves 50,000 customers. Each customer needs an isolated namespace: past tickets, preferences, sentiment.
with neruvaOpen a Neruva namespace per customer. Marginal cost rounds to zero. Recall last 20 tickets in under a millisecond per turn -- typed by kind=ticket, filtered by tagsAny=[customer:abc-123].
Coding-agent persistent context
scenarioA Claude Code session ends, the next one starts cold. The agent has no idea what was decided, what broke, or where the project left off.
with neruvaneruva-record-install captures every prompt, tool call, decision and mistake as a typed record. Next session opens with records_query(kind=['decision','mistake','session_end']).
Compliance-grade agent audit trail
scenarioFinancial-services chatbot must prove which memory drove which response, retain for 7 years, support 'right to be forgotten' requests.
with neruvaEvery record is an inspectable row with kind, tags, ts, meta, content hash. Surgical delete by predicate. Export as .neruva to cold storage.
Write-heavy ingest pipelines
scenarioReal-time observability or log-analysis where thousands of events per minute become memory. HNSW indexes choke; tail latency explodes.
with neruvaAppend-only WAL with async indexing. Writes don't block on the index. Throughput stays flat as the corpus grows.
On-prem / edge agent deployments
scenarioAn agent that runs in a customer datacenter or on a developer's laptop. Managed indexes are off the table.
with neruvaExport the namespace as .neruva and ship it. The file is the deployment. Read it offline with the same client.
Cross-agent handoff
scenarioClaude Code finishes a refactor, Cursor picks it up, Codex reviews. Each tool today has its own memory store -- nothing carries across.
with neruvaOne namespace, one wallet, one MCP. Every harness reads the same typed records. Adopt the kind=session_end / handoff convention and the next agent picks up where the last one left off.
Stop paying frontier-model rates for the recall slice.
Most agent stacks "remember" by re-prepending recall text to every LLM call. That recall slice gets billed at frontier-model input rates, every turn, forever. Replace it with one records_query.
Numbers use Claude Opus 4.7 list pricing ($5/M input). Other models vary -- Sonnet is cheaper, Haiku cheaper still. The shape of the savings is what matters: every recall via the substrate is a recall you don't do via a context-stuffing prompt.
Designed for the workload you actually run.
kind, tagsAny, tagsAll, tsGte, tsLt, ids. Pass them directly. No metadata-dict gymnastics./v1/indexes/* for codebases that already speak the upsert / query / delete shape. New users should start with Records.Stop renting search infra.
Start owning agent memory.
$5 in credits on signup. No card. No subscription. No demo call. Wire it in and decide.