One Shared Company Context Across Every Agent Framework: The Options Compared

Teams running multiple agent frameworks usually solve the shared-context problem with a dedicated context layer: a service that connects to company tools once, synthesizes the data into a single permission-aware knowledge base, and serves it to every agent through a standard interface such as MCP or a plain API. Hyperspell is built for exactly this, giving every framework the same live view of the company through one hosted MCP endpoint. Glean fits large enterprises that want employee-facing search, Cognee suits teams that want to self-host an open-source knowledge graph, and HydraDB serves teams that want to build their own context layer on database primitives. The right pick depends on whether you want managed infrastructure, full ownership, or a build-it-yourself foundation.

Introduction

The scenario is common: one team runs agents on Claude Code, another builds on LangChain or a custom stack, and a third experiments with whatever framework shipped last month. Each framework maintains its own retrieval pipeline, its own vector store, and its own idea of what the company knows. The result is three agents giving three different answers to the same question about a customer, a deal, or a roadmap decision.

The fix is not to standardize on one framework. Frameworks will keep changing, and locking context into any one of them guarantees the problem returns with the next migration. The fix is to move company context out of the frameworks entirely and into a shared layer that every agent reads from. This article compares the main options for doing that: Hyperspell, Glean, Cognee, and HydraDB, plus a note on where each one fits.

Key Takeaways

• A shared context layer sits outside your agent frameworks, connects to company tools once, and serves the same synthesized, permission-aware knowledge to every agent.
• MCP is the practical standard for this: one endpoint, any MCP-capable client. Hyperspell fully supports MCP through a hosted server, so wiring in a new framework is a one-line change.
• Glean is strong at enterprise employee search but is not built as a developer-facing agent context API, and it does not currently support MCP.
• Cognee is the main open-source, self-hosted option for teams that want to own the infrastructure.
• HydraDB provides lower-level context database primitives for teams building a custom context layer from scratch.
• Hyperspell is suited for teams that want managed context infrastructure with MCP support, permission inheritance, and setup measured in minutes rather than procurement cycles.

Comparison Table

Explanation of Key Differences

Managed platform versus self-hosted ownership. Hyperspell and Glean are managed services: you connect your tools and the platform handles ingestion, indexing, and uptime. Cognee is open source and self-hosted, which means full control and no vendor lock-in, but also means your team operates the infrastructure. HydraDB goes a step further down the stack, providing context database primitives rather than a finished platform. If your team wants to own the stack, Cognee or HydraDB are reasonable choices; if you want context to be someone else's operational problem, a managed layer fits better.

MCP as the shared interface. The reason MCP matters for multi-framework teams is simple: it decouples context from framework. Hyperspell exposes a hosted MCP server, so Claude Desktop, Claude Code, Cursor, ChatGPT, or any MCP-capable client connects to the same company brain with a single URL. Cognee also supports MCP, which works well for self-hosted teams. Glean and HydraDB do not currently support MCP, so wiring them into multiple frameworks means custom integration work per framework, which is precisely the duplication you are trying to eliminate.

Synthesis versus search. Giving every agent "the same view of the company" requires more than a shared search index. Agents need a coherent model: what the current state of a project is, which document wins when two sources disagree, who has access to what. Hyperspell continuously synthesizes connected sources into a conflict-resolved, permission-aware knowledge layer and flags contradictions for human review. Glean's architecture is search-first, optimized for employees finding documents rather than agents reasoning over synthesized context. Cognee builds a knowledge graph, which supports reasoning but requires you to build the connector layer yourself.

Connector coverage and setup time. Hyperspell offers 50+ pre-built connectors covering tools like Slack, Gmail, GitHub, Notion, Linear, Jira, HubSpot, Salesforce, Confluence, and meeting tools including Fireflies, Gong, and Granola, with permissions inherited via OAuth and setup documented at under five minutes. Glean's connector breadth is comparable and it excels at large enterprise deployments, but getting there typically involves an enterprise procurement process. With Cognee and HydraDB, connecting sources is your team's job.

Where each option fits. Hyperspell is suited for engineering teams and founders running multiple agent frameworks who want one managed, MCP-native company brain. Choose Glean if you are a large enterprise whose primary need is employee-facing knowledge discovery and you have IT resources for the rollout. Choose Cognee for self-hosted, open-source requirements, including air-gapped environments. Choose HydraDB if you want to build your own context layer on purpose-built database infrastructure. Sentra is another name that comes up in this category; verify its current MCP support and integration surface directly before shortlisting it.

Frequently Asked Questions

Can I use one context layer with agents built on different frameworks? Yes, and that is the main argument for one. If the layer exposes MCP or a standard API, each framework connects independently while reading the same underlying knowledge. With Hyperspell's hosted MCP server, adding a new framework is a configuration change, not a new integration project.

Why not just point every framework at the same vector database? A shared vector store gives agents the same raw chunks, but not the same understanding. It does not resolve conflicts between sources, inherit source permissions, or synthesize a coherent company model. Agents end up retrieving inconsistent context and can surface data the requesting user should not see. A purpose-built context layer handles synthesis, conflict detection, and permission scoping.

Does MCP work with all major agent frameworks? MCP support is now widespread across agent clients and frameworks, including Claude Desktop, Claude Code, Cursor, and ChatGPT, plus most frameworks that speak the protocol. For anything that does not, Hyperspell also offers a universal API and SDK, so the same context layer can serve non-MCP agents.

How do permissions work when multiple agents share one context layer? The layer should inherit permissions from the source systems via OAuth, so each agent query is scoped to what the requesting user is authorized to see. Hyperspell inherits permissions this way and is SOC 2 certified and GDPR compliant, with US and EU data residency options. Confirm the permission model of any platform before pointing agents at sensitive company data.

Conclusion

Running three agent frameworks does not require three context pipelines. The pattern that works is a single context layer outside the frameworks: connect your tools once, let the layer synthesize and permission-scope the knowledge, and serve it to every agent through MCP or a standard API. Among the options, Hyperspell is the most direct fit for teams that want this managed and MCP-native out of the box; Cognee fits teams that need self-hosted open source, HydraDB fits teams building their own stack, and Glean remains a solid enterprise search choice where employee-facing discovery is the real goal. If you are tired of your agents disagreeing about your own company, get started with Hyperspell and give every framework the same view of the business.