A layer that compiles user knowledge into a source-labelled context package alongside giving their agents intelligent memory capabilities which let you build autonomous AI agents that act with full awareness of your team's knowledge and context.
Powering AI infra for
AI agents need context, but it often comes from scattered, disconnected sources. Restrct compiles team knowledge and official documents into a single deterministic, source-labeled context package that agents can act on safely.
We extract your rules, policies, facts, permissions and more into retrieveable resourceful package.
A persistant, multi-tenant memory layer for your AI that captures facts, events, and relationships, enabling accurate recall for people while tracking changes over time.
It searches knowledge and memory in parallel, when needed, and it provides source labelled results which helps keep track of your work.
Export to LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or any REST API. Restrct sits underneath your agent stack as the context layer, your framework of choice on top, a deterministic and auditable context layer underneath.
import { Restrct } from '@Restrct/sdk'
// Ingest your compliance docs
const blueprint = await Restrct.Blueprint.ingest({
documents: ['compliance.pdf', 'sop.docx'],
framework: 'langgraph',
})
// Guardrails auto-generated from your docs
console.log(blueprint.guardrails.length) // e.g. N rules
console.log(blueprint.tests.length) // e.g. N cases
// Deploy an auditable, source-labeled agent
const agent = await blueprint.deploy()Ingest
Official documents - policies, contracts, compliance obligations - go in alongside team knowledge like past decisions and institutional memory.
Knowledge
Structured extraction turns documents into operational facts: permissions, business rules, compliance obligations, risks and workflows. This is the authoritative source of truth.
Context
Knowledge and Memory are searched in parallel and merged into one ranked, source-labeled package.
Harness
The output is compiled as a clear split: binding instructions versus background context, so the agent knows what it must obey and what is merely useful.
Audit
No LLM sits in Knowledge, Context or Harness, so the same query against the same data always returns the same result - and every claim traces back to the document or memory it came from.
Alongside the main path
Memory
A persistent, per-user layer that tracks how things change, reconstructs timelines from dated events, and builds a relationship graph of people, projects and technologies.
Conflict flagged
Where Knowledge and Memory disagree, the conflict is surfaced explicitly rather than silently resolved. Official documents always take precedence.
Approval gate
Points that need a human decision are flagged before the agent acts, not left for the agent to decide for itself.
An AI agent is only as reliable as the context beneath it. We built Restrct so every answer is source-labeled and reproducible - auditable by design, not as an afterthought.
Built for clarity, consistency, and accountability.
Restrct empowers teams to make confident decisions about how their AI systems operate. As intelligent systems scale, understanding and controlling their behavior should be part of the foundation not something added later.
Start on an auditable, source-labeled substrate from day one - no infrastructure to build.