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()Two sources go in. One deterministic, source-labeled context package comes out.
Feed in official documents (policies, contracts, compliance obligations) alongside team knowledge like past decisions and institutional memory.
Structured extraction turns your documents into operational facts: permissions, business rules, compliance obligations, risks, and workflows. This becomes the authoritative source of truth.
A persistent, per-user memory layer. Tracks how things change over time reconstructs timelines from dated events, and builds a relationship graph of people, projects, and technologies - blended into one result so the right memory surfaces no matter how you ask.
Knowledge and Memory are searched in parallel. Results are merged into one ranked, source-labeled package. Anywhere the two disagree, the conflict is flagged explicitly.
The final output is compiled as a clear split: binding instructions versus background context. Points requiring human approval are flagged automatically before the agent acts.
Knowledge, Context, and Harness run with no LLM: same query, same data, always the same result. Every piece of output keeps its source label end-to-end, auditable at every step.
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.
Every piece of context Restrct produces carries its source end-to-end: policy document or team memory, binding instruction or background context. When the two disagree, official documents always win. Points requiring human approval are flagged before the agent acts, not left for the agent to decide. And because there's no LLM in Knowledge, Context, or Harness, the same query against the same data always produces the same result, which means your compliance team can actually audit it.
Start on an auditable, source-labeled substrate from day one - no infrastructure to build.