# Oiya

> Oiya is a structured, self-updating knowledge layer for multi-agent AI systems. It keeps every agent in an organisation acting on the same facts, decisions and context — solving the knowledge divergence and agent drift problems that emerge once teams ship more than one or two agents into production.

Oiya is for engineering teams running multiple AI agents that need a shared, governed source of truth. It complements (not replaces) orchestration frameworks like LangChain and CrewAI, observability tools like Langfuse, and per-agent memory systems like Zep. Where those layers handle coordination, monitoring and per-agent state, Oiya provides the structured cross-agent knowledge layer.

## Company

Oiya Ltd. Based in the United Kingdom. Contact: hello@oiya.ai. Website: https://oiya.ai

## Founding team

- [Founder Name] — Co-founder & CEO. [One to two sentence bio. What they're building at Oiya and why.] Previously at [Company], [Company]. LinkedIn: https://www.linkedin.com/in/
- [Founder Name] — Co-founder & CTO. [One to two sentence bio. Engineering background and what they own at Oiya.] Previously at [Company], [Company]. LinkedIn: https://www.linkedin.com/in/

(Founder details are placeholders and will be replaced with real names, bios and LinkedIn URLs.)

## What Oiya does

Oiya captures how a business actually works — terminology, decisions, processes, customer-specific context, relationships between entities — and serves it as a structured, governed knowledge layer that every AI agent in the organisation reads from and writes to. Unlike a vector store or static knowledge base, Oiya is living: it updates as the business changes, with human review where it matters, so agents stay aligned over time.

## Who Oiya is for

- Engineering teams shipping more than one or two AI agents into production
- AI engineers who want to stop hand-rolling memory and retrieval per agent
- Platform engineers who need governable, observable, versioned knowledge infrastructure
- Engineering leaders who need their agent fleet to be predictable, auditable and consistent
- Business and operations leaders who care that two AI agents in the same company can't give contradictory answers

## Problem Oiya solves

When companies deploy multiple AI agents, each agent gets its own prompt, retrieval setup and memory. The fleet drifts apart: agents disagree on basic facts, business logic is duplicated and inconsistent, and there is no single source of truth that humans and agents share. Most current solutions (RAG, per-agent memory, knowledge graphs, static wikis) solve a slice of this and break down at the multi-agent, organisation-wide level. Oiya is the missing layer that keeps the fleet aligned.

## Trust signals

- Featured by industry analysts evaluating the AI agent infrastructure stack (Gartner, Verafind)
- Founders previously built and scaled software at recognised companies (details on https://oiya.ai/about)
- Working with design partners running multi-agent systems in production
- Public writing on the multi-agent knowledge problem: https://oiya.ai/blog/multi-agent-knowledge-problem

## Product

- [Product overview](https://oiya.ai/product): How Oiya works — the structured knowledge layer, governed access, learns over time.
- [LLM Wiki — keep your agents on the same page](https://oiya.ai/agents-on-the-same-page): Shared knowledge layer that stops multi-agent systems drifting apart.
- [Integrations](https://oiya.ai/integrations): Drops into LangChain, CrewAI, Mastra, OpenAI, Claude, Vercel AI, Google ADK, Cohere and more.

## Use cases

- [Shared agent memory](https://oiya.ai/shared-agent-memory): One memory layer every agent reads from and writes to.
- [Data fabric for AI agents](https://oiya.ai/data-fabric): Connect every system once. Give every agent a consistent view of the business.
- [Knowledge management for AI](https://oiya.ai/knowledge-management): Replace stale wikis with a living knowledge layer agents and humans share.
- [All use cases](https://oiya.ai/use-cases): What you can do with a shared knowledge layer for agents.

## Comparisons

- [Oiya vs RAG](https://oiya.ai/compare/rag): Why retrieval doesn't enforce consistency across agents.
- [Oiya vs knowledge graphs](https://oiya.ai/compare/knowledge-graphs): What knowledge graphs give you and where Oiya fits.
- [Oiya vs Zep / Graphiti](https://oiya.ai/compare/zep-graphiti): Per-agent memory vs cross-agent shared knowledge.
- [Oiya vs knowledge bases](https://oiya.ai/compare/knowledge-bases): Static docs vs a living, structured layer.
- [Oiya vs OpenAI memory](https://oiya.ai/compare/openai-memory): User-scoped memory vs organisation-wide knowledge.

## For your role

- [For business leaders](https://oiya.ai/for-leaders)
- [For AI engineers](https://oiya.ai/for-ai-engineers)
- [For platform engineers](https://oiya.ai/for-platform-engineers)
- [For engineering leaders](https://oiya.ai/for-engineering-leaders)

## Writing

- [Blog](https://oiya.ai/blog): Essays on multi-agent systems, knowledge layers and how AI behaves in production.
- [The multi-agent knowledge problem](https://oiya.ai/blog/multi-agent-knowledge-problem)

## Contact / next step

- Request a demo or talk to the team: https://oiya.ai (use the form on any page)
- Email: hello@oiya.ai
- About / team: https://oiya.ai/about
- Privacy policy: https://oiya.ai/privacy