Who
Entities and relationships, extracted automatically and deduplicated over time.
Give your AI agents a memory worth trusting.
Vector databases were built for search. You were sold them as memory.
Similarity search can find related text. It does not understand what changed last Tuesday, why an agent approved the refund, or that "the client" in March and "Acme Corp" in June are the same entity with a renegotiated contract in between. Stuffing the context window is not remembering. It is a workaround for systems that forget.
When
Facts that know when they became true and when they stopped being true.
Why
Decisions traced back to inputs, policies, citations, approvals, and outcomes.
RAG retrieves. Kautious remembers.
Memory in three lines.
Every supported LLM call your app makes is now recorded. Conversations become episodes. Entities and relationships become graph structure.
python
from kautious import KautiousClient
client = KautiousClient(api_url="...", token="...")
client.enable(openai=True, anthropic=True)The first time your agent says, "Last time, you decided to ship the v2 schema, and these were the remaining blockers," the value is obvious. Not a dashboard tour. Not a six-week data project. A working memory loop your team can feel immediately.
Vector stores remember text. Kautious Graph remembers meaning.
Facts with a timeline, not a vibe
Every fact is temporal. Ask what your agent knew on March 3rd and get the March 3rd answer, not today's truth retrofitted into yesterday's context.
The right memories, sized to your budget
Smart context assembly classifies the task, ranks relevant memory, tracks work state, and packs the right context into the token budget you choose.
Receipts for every important decision
Decision traces capture inputs, policies, citations, exceptions, approvals, and outcomes. Answer 'why did the agent act?' with evidence, not archaeology.
MCP-native agent memory
Connect to assistants and agent runtimes through MCP. Agents store memories, search context, retrieve episodes, inspect facts, and evaluate policies.
Knowledge shaped to your domain
Domain packs specialize memory for the language of your business. Start with a known domain or generate a pack from a plain-English description.
Built like infrastructure, because it is
Multi-graph tenancy, org and per-user isolation, OAuth 2.1, scoped access, first-party API keys, signed webhooks, and REST, SDK, and MCP interfaces.
From raw interaction to usable memory.
Capture
Send conversations, documents, tickets, messages, app events, or decisions through REST, SDK, or MCP.
Structure
Kautious extracts entities, relationships, facts, timestamps, citations, work state, and decision traces.
Retrieve
Agents search the graph for relevant memory, temporal context, prior decisions, policies, and current work state.
Act
Your agent responds with continuity, context, and evidence instead of generic guesses.
Built for teams shipping real agent products.
Kautious Graph fits into the stack you already have instead of asking you to rebuild around it.
- REST API
- Python SDK (OpenAI · Anthropic · LiteLLM)
- MCP server
- OAuth 2.1 + first-party API keys
- Multi-tenant, multi-graph isolation
- Signed webhooks
- Temporal queries
- Decision traces & citations
Private design-partner access
Your agent shouldn't meet the same user for the first time twice.
We onboard design partners in focused cohorts, wiring the platform into your agent stack and tuning memory to your domain.