Governed memory for AI agents.
Every fact your agents rely on becomes a versioned, time-bound, access-controlled claim — so they act on what's true, see only what they're allowed, and you can prove what they knew.
Time-aware by default
Stale facts expire or get superseded — never acted on.
Deny-by-default access
The same query answers differently per agent identity.
Hash-chained audit
Replay exactly what any agent saw, at any moment.
Five operations. Nothing more.
Agents read through the Vault before they act, and write through it after.
Write a fact; the Vault decides how it fits.
Read only what's current & authorized.
Conflicts caught on write, never silent.
Confidence fades; stale history compresses.
Every read & write recorded immutably.
Recall is solved. Consistency isn't.
Vector search and agent-memory tools help an agent remember more. Context Vault makes that memory true, governed and provable — the gap that actually keeps agents out of production today.
Agents act only on current, authorized facts — so legal and risk can sign off.
Provider-neutral memory survives a move across Claude, GPT, Gemini or Llama.
Reconstruct exactly what an agent knew, and when — with a cryptographic receipt.
When a new fact arrives, the Vault decides what it means.
A six-way taxonomy classifies every write — so memory never silently corrupts. Pick a relation.
Augment your stack. Don't migrate it.
Front your vector store
Pinecone, Weaviate, Qdrant, pgvector — temporal, ACL & audit layered over your results.
Connect over MCP
One-click into Claude Code, Cursor and any MCP client — no agent rewiring.
Cross-LLM by design
Works equally against Claude, GPT, Gemini, Llama or Mistral. Switch freely.
Install in one line.
# connect over MCP — no rewiring $ uvx --from context-vault-ai context-vault-mcp from context_vault.app import build_vault from context_vault.sdk import VaultClient from context_vault.models.principal import Principal vault = build_vault(); vault.init() client = VaultClient(vault, Principal( id="advisor-7", role="agent", labels=["team:risk"])) # read only what's current & authorized ctx = client.resolve("risk for cust:8841") # write — conflicts handled, never silent client.assert_fact("cust:8841", "risk_rating", "elevated") # → CONTRADICTION vs apr-2 → SUPERSEDED ✓
From a laptop to the enterprise.
Enterprises
Governance, compliance packs and an audit your examiner accepts — for teams putting agents on regulated data.
Developers
One-line MCP install, Python & TypeScript SDKs, open core — drop governed memory into any agent.
Run it locally
Self-host the free core in-memory or on SQLite — no auth, no cloud, full control on your own machine.
Start free. Upgrade for governance.
- All five operations
- MCP server + SDKs
- Heuristic conflict detection
- Managed bitemporal store
- LLM-judge conflict engine
- Hash-chained audit + replay
- Federation adapters
- Compliance packs & attestations
- Human review queue + SLA
- SSO, multi-tenant, support
Paramind AI builds governed memory for enterprise agents.
Context Vault is our flagship product — the layer that makes an agent's knowledge versioned, time-bound, access-controlled and auditable, so autonomous systems can run on data teams couldn't risk before.
The layer everyone ships beneath their agents.
Bring an agent and a dataset — leave with an audit trail you can prove.