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Documentation Index

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Focus on agents. We handle document intelligence.
Knowledge Stack is the document intelligence layer behind your agents — ingestion, chunking, permissions, versioning, and citation tracking — exposed through a stable MCP surface that plugs into LangChain, LangGraph, CrewAI, Temporal, the OpenAI Agents SDK, pydantic-ai, Claude Desktop, and Cursor. Build enterprise RAG and agent pipelines in minutes instead of weeks.

Watch the 90-second tour

Five short videos covering ingestion, agents, citations, and RBAC.

Book a demo

30 minutes on your own corpus with a founding engineer.

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What we are

  • A developer acceleration layer for enterprise RAG + agent pipelines.
  • An MCP-native retrieval surface that works with every major agent framework.
  • A permission-aware document store with citation-grounded reads.
  • Framework-agnostic — bring your own model, your own orchestration, your own UI.

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What we are not

  • Not an agent framework — use LangChain, LangGraph, CrewAI, or Temporal on top.
  • Not a model provider — bring your own OpenAI / Anthropic / open-source model.
  • Not a generic document store — we are specifically built for agent retrieval with permissions and citations.
  • Not a fine-tuning platform.

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Differentiators

Permission-aware retrieval

The same agent code returns different results per user — by construction, not by post-filtering. See Authorization.

Chunk-level citations

Every claim traces to a chunk UUID, page, and bounding box. Verifiable by auditors. See Citations.

Version-aware reads

Answer as of any point in time. Revisions, rollback, and time-travel queries are first-class.

MCP-native

Portable across every major agent framework without glue code. See MCP server.

Production pattern library

32 flagship demos across 10+ regulated verticals in ks-cookbook.

Stable, typed surface

First-party Python + TypeScript SDKs generated from one OpenAPI spec. See SDKs.

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Who it’s for

Teams building internal AI agents on large document collections where permissions, citations, and structured outputs matter. Especially:
  • Banking & insurance — policy and claim Q&A with audit trails.
  • Healthcare & pharma — protocol search with versioning and PHI-aware access.
  • Legal & accounting — contract and filings retrieval with citation-grounded answers.
  • Energy & government — regulated documents with strict role-based scoping.

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How it fits with your stack

You’re already usingHow Knowledge Stack plugs in
LangChain / LangGraphlangchain-mcp-adapters against our MCP server. See flagships/csv_enrichment.
CrewAIShared retrieval tool across the crew.
TemporalCall the MCP server from activities for durable agent workflows.
OpenAI Agents SDKFirst-party MCP support — point at uvx knowledgestack-mcp.
pydantic-aiMost cookbook flagships use this — native MCP plus schema-enforced output.
Claude Desktop / CursorAdd us as an MCP server — permission-scoped retrieval for your assistant.
Building from scratchStart with ks-cookbook — pick the flagship matching your vertical.

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The pitch, by role

Platform engineer — You’re already building ingestion pipelines, permission filtering, chunk storage, version tracking, and citation verification. Knowledge Stack does all of that behind an MCP surface. Your agent framework doesn’t change. Your team focuses on agent logic. ML / AI engineer — Skip the glue. Our MCP server plugs into LangChain, LangGraph, CrewAI, and the OpenAI Agents SDK. Chunk-level citations and structured output come for free. 32 production-grade flagships show the patterns. VP / director — Enterprise RAG usually takes 6-12 months to ship safely. Knowledge Stack collapses that to weeks. Permission-aware retrieval, audit-ready citations, pattern library across 10+ regulated verticals. Your team ships the agent; we handle the document layer.

Architecture

How it all fits together

Quickstart

First call in 5 minutes

Cookbook

32 flagship recipes