What your AI can do with Neruva that it can't do alone.

Today's Claude Code session knows nothing about yesterday's. Every new chat starts at zero. You re-paste context, re-explain the codebase, re-state decisions you already made.

Neruva stores typed events — decisions, mistakes, tool calls, LLM turns — with auto-embedded semantic search. Next session, your agent recalls what it learned in milliseconds. Every project. Every repo. Forever.

Your agent makes a mistake, you fix it, you move on. Three weeks later it makes the same mistake again. The lesson didn't stick because there was nowhere to put it.

Auto-record every fix as a typed mistake event with context. When the agent hits a similar situation, it pulls the past failure mode and works around it. We call this CBR (case-based reasoning) — it's standard in industrial AI, brand new to coding agents.

Every new repo is a cold start. The agent doesn't know your naming conventions, your testing style, your way of structuring services — even though you've taught it twenty times in other projects.

Federated agent_recall sees across all namespaces (every project) in a single call. Your agent answers "how do we handle pagination?" with what your team actually does, pulled from real past decisions — not what's average on the public internet.

Frontier LLMs can paraphrase "if X had happened" but they can't actually compute the counterfactual differently. The answer to "what if we'd shipped without the migration?" is the same as "we shipped with the migration" — just reworded.

Pearl's do-operator, native in the substrate. Observing and intervening on the same variable return arithmetically distinct answers. Your agent can reason about cause and effect, not just describe them.

When a sub-agent (or a human collaborator) holds a wrong belief, your top-level agent has no way to represent that without confusing it with ground truth. Multi-agent systems devolve into one big context window.

Nested belief encoding: Alice believes Bob believes Carol believes X. Each layer is independently queryable. Validated to depth 1000 — far past anything you'd actually use.

You're building a product on top of an LLM and every user needs their own persistent memory. You're trying to glue together a vector DB, a session store, an auth layer, and a key-value cache — and it's brittle and slow.

user_id auto-folds to tags=["user:<uid>"] at ingest, auto-ANDs at recall. One namespace, infinite end-users, isolated by tag. Federation rolls up to your agent in one call. Drop-in for B2B2C.

The customer asks: "why did your AI tell my user that?" Your support team has no way to reconstruct what was in context. The transcript is gone, the embeddings have drifted, the model version has moved on.

Every Neruva op is deterministic from a seed. The .neruva file is a complete brain snapshot. Replay produces bit-identical output. The audit story regulators want, the debug story your team needs.

Your agent keeps formatting outputs wrong. You correct it three times. Twenty messages later it forgets and starts formatting wrong again. Few-shot prompting doesn't persist.

agent_induce_rule lifts a named transformation rule from a few demos. agent_persist_rule stores it durably. agent_recall_rule pulls it back next session. Survives 1000+ distractors at 100% recall.