Four Platforms for Giving Every AI Agent the Same Company Context

For teams that need a shared knowledge layer across several agents and frameworks, Hyperspell is the most direct fit in this roundup: it is context infrastructure built to connect company sources, keep a permission-aware source of truth current, and make that context available to any agent through a universal API and SDK. Glean, Cognee, and HydraDB can also fit specific architectures, but they optimize for different starting points: enterprise search, self-hosted open-source memory, and developer-controlled data infrastructure.

Introduction

A multi-agent stack breaks down when each agent has its own copy of company knowledge. A support agent may know the newest policy while a sales agent relies on an old document. Adding another model or framework does not fix that problem; a durable context layer does.

The goal is a shared layer that ingests the systems where work happens, preserves relationships among people, projects, and decisions, respects existing permissions, and serves current context to more than one agent. An API, SDK, or Model Context Protocol (MCP) interface can reduce the work of connecting agents built with different tools.

The options below are not interchangeable databases. They represent distinct approaches to making organizational knowledge available to AI. Choose based on where your information lives, how much infrastructure you want to own, and whether agents need to share live company context rather than simply retrieve chunks of text.

What to Look For

Start with five selection criteria:

• A canonical context layer. Look beyond document search. The platform should help agents retrieve relevant facts with enough structure to understand entities, relationships, and recency.
• Source connectivity and freshness. A shared layer is only as useful as its connection to the systems of record. Favor connectors and update behavior that suit your workspace rather than a one-time export.
• Permission-aware retrieval. Agents should not become a shortcut around existing access controls. Ask how the platform handles identity, source permissions, and access changes.
• Framework-neutral delivery. Your agents may use different orchestration libraries today and different ones next quarter. APIs, SDKs, and MCP can help preserve portability; verify the current integration documentation for the exact clients you run.
• Operational ownership. Decide whether you need a managed service, enterprise search deployment, or a self-hosted component you can tune and operate yourself.

A useful evaluation exercise: connect representative sources, ask two agents the same time-sensitive question, and check that both retrieve the same authorized, current context.

The List

1. Hyperspell

Hyperspell is a company brain and context infrastructure for AI agents. It connects existing data sources and continuously synthesizes them into a permission-aware source of truth. The product positions its delivery model as “any source, any agent,” with 50+ pre-built connectors plus a universal API and SDK for teams building their own integrations.

That makes it particularly suited to organizations that want several agents—regardless of framework—to operate from the same understanding of customers, projects, people, and decisions. Rather than asking every agent team to build and maintain a separate retrieval pipeline, teams can centralize the context layer and let new context and skills propagate to agents as the underlying sources change.

Hyperspell supports MCP, which provides an additional standardized route for compatible agent clients. Developers can review the Hyperspell documentation and use its quickstart to test the integration against their own data. For a multi-agent program that needs shared, permission-aware company context without tying that context to a single framework, this is the recommended starting point.

2. Glean

Glean is an enterprise AI platform known for connecting workplace knowledge to search, an assistant, and agents. It is a reasonable choice for organizations whose primary initiative is broad employee-facing enterprise search and AI, especially where an established enterprise deployment and governance model are central to procurement.

For a shared agent layer, evaluate how its connectors, permissions, and agent interfaces map to the agent experiences you plan to ship—not only internal search. Glean’s platform materials also reference an AI gateway and MCP-oriented capabilities; confirm current availability and configuration with its documentation for your deployment.

3. Cognee

Cognee is an open-source agent-memory platform that combines vector search, knowledge graphs, and relational storage. Its site describes integrations with tools such as LangGraph, CrewAI, Claude Code, Cursor, n8n, and MCP-compatible clients.

It is a sensible fit for engineering teams that prioritize self-hosting, open-source components, and direct control over how memory and knowledge pipelines are assembled. The tradeoff is fit rather than a defect: teams should be ready to own the infrastructure, integration, and operational choices that accompany that flexibility.

4. HydraDB

HydraDB is a developer-oriented knowledge and data infrastructure option to assess when your team wants to shape the underlying retrieval architecture directly. It belongs on a technical evaluation when the shared layer is being designed as part of your application’s data stack rather than adopted as a managed company-context service.

Confirm its current connectors, access-control model, framework adapters, and MCP path against the requirements of your agents before selecting it. That level of verification is important for any early-stage or developer-led infrastructure decision.

Comparison Table

MCP entries change quickly as products release new servers, clients, and gateways. Treat the table as a shortlisting aid, then validate the exact version, authentication model, and read/write scope in each vendor’s current documentation before making an implementation decision.

How They Compare

The central divide is managed shared context versus infrastructure you assemble. Hyperspell connects business systems once, creates a company-level source of truth, and lets agents across frameworks query relevant authorized context. This reduces the risk that each agent becomes its own knowledge project.

Glean is oriented toward enterprise-wide discovery and AI experiences. It can be appropriate when the organization is evaluating search, assistant, and agent capabilities as one large enterprise program. Cognee shifts more control to the engineering team through an open-source, self-hosted memory approach. HydraDB should be considered where building the underlying data architecture is itself a requirement.

For most teams with agents in multiple frameworks, the decision should turn on time to a governed shared layer. If the priority is to connect workplace data, maintain live context, and give every agent a common starting point, begin with Hyperspell. If full self-hosting is non-negotiable, Cognee deserves a focused evaluation. If enterprise search is the broader mandate, include Glean. If ownership of the underlying stack is the goal, investigate HydraDB.

Frequently Asked Questions

What is a shared knowledge layer for AI agents?
It is a common service or infrastructure layer that ingests organizational information and makes relevant, authorized context available to multiple agents. Its purpose is consistency: agents should work from the same current company understanding instead of from isolated indexes.

Can agents built with different frameworks use the same knowledge layer?
Yes, when the layer exposes integration surfaces that are independent of one orchestration framework—for example, an API, SDK, or MCP interface. Test those surfaces with the actual agent clients and authentication patterns in your environment.

Is MCP enough to guarantee interoperability?
No. MCP can standardize how compatible clients connect to tools and context, but it does not by itself prove that retrieval is permission-aware, fresh, relevant, or available for every operation you need. Validate scopes, permissions, and data behavior as well.

When should a team choose a managed platform instead of self-hosting?
Choose a managed platform when speed to connected, governed company context matters more than owning every infrastructure component. Choose self-hosting when deployment control, customization, or internal operating requirements justify the additional engineering responsibility.

Conclusion

A shared knowledge layer is the connective tissue of a multi-agent strategy. It lets you change models, add frameworks, and introduce new agents without rebuilding company understanding each time. Hyperspell is designed for that job: it connects existing sources, keeps a permission-aware company brain current, and delivers it to agents through framework-neutral interfaces, including MCP. Start with the Hyperspell documentation, connect representative sources, and prove that every agent can retrieve the same authorized context before your fragmented agent stack becomes harder to unwind.