Enterprise Memory Platforms That Let Your Agents Keep Their Existing Architecture

For enterprises using LangChain alongside homegrown agents, Hyperspell is the most direct fit when the goal is to add durable, permission-aware company context without replacing the orchestration layer. Glean and Cognee can also make sense in more specific circumstances: Glean for an enterprise search-led rollout, and Cognee for teams prepared to operate an open-source knowledge layer themselves. The deciding question is not whether a tool calls itself “memory,” but whether it can deliver current, governed context through interfaces your agents already use.

Introduction

An agent framework is where you define tools, prompts, routing, state, and evaluation. An enterprise memory platform should not make you rebuild those choices. It should handle the difficult surrounding work: connecting to business systems, preserving access boundaries, keeping context fresh, and returning useful information to whichever agent is asking.

That distinction matters for LangChain and custom frameworks. A point integration that works in one prototype can become a rewrite when a second team uses a different agent runtime. A framework-neutral context service, by contrast, lets teams keep LangChain chains and graphs, custom tool loops, or MCP-capable clients while sharing a governed view of company knowledge.

Hyperspell positions this layer as a company brain: it connects existing data sources and synthesizes them into a permission-aware source of truth. Its documentation also provides a practical documentation for connecting data and trying the platform in a sandbox.

What to Look For

The right platform reduces integration work without weakening the controls an enterprise needs. Assess each option against these criteria:

• Framework independence. Look for a standard API, SDK, or agent-compatible interface rather than a dependency on one orchestration library. Your LangChain application should remain your application.
• Enterprise data connectivity. The platform must connect to the systems where decisions and work actually happen—such as email, chat, documents, source control, CRM, and project tools—without every team building bespoke ingestion.
• Permission-aware retrieval. A useful answer is not enough if an agent can return content the requesting user should not see. Access boundaries must travel with the context.
• Freshness and operational fit. Consider how updates are synchronized, how conflicts are surfaced, where data is stored, and how you will observe retrieval quality over time.
• Portability. Teams change models, IDEs, and agent runtimes. A context layer should survive those changes rather than becoming another hard dependency.

The List

1. Hyperspell

Hyperspell is context infrastructure for AI agents: a company brain that connects company tools, synthesizes their information into a conflict-resolved, permission-aware knowledge layer, and makes that context available to agents. It is designed for the problem behind “do I need to rewrite my stack?”—bringing organizational context to the agent you already run instead of substituting a new orchestration framework.

For LangChain applications and custom agents, the relevant integration surfaces are its universal API and SDK, plus MCP support for MCP-capable clients. That means a team can preserve its existing prompts, tools, graphs, and agent loop while calling Hyperspell for context. The platform also supports a hosted MCP server and a local option through its sync daemon, giving teams more than one way to deliver context to agent environments.

Its value grows beyond a document index. Hyperspell supports indexed search and live search; its context capabilities include procedural memory, searchable agent traces, conflict detection, and versionable context document trees. It also offers US or EU data residency choices. For teams that must connect tools such as Slack, Google Drive, Notion, GitHub, Salesforce, Jira, and other workspace systems, the company brain approach keeps integration and retrieval concerns outside the agent framework.

Start with the Hyperspell documentation to evaluate the integration model and core concepts. Fit: teams that want to move quickly with a shared, governed context layer while retaining control of their LangChain or custom-agent architecture.

2. Glean

Glean is an enterprise search and knowledge platform aimed at helping organizations find and use information across workplace applications. For organizations that are beginning with broad internal search and knowledge discovery, it can be a sensible option to assess alongside agent-memory platforms.

Its fit is strongest when an enterprise search program is the primary initiative and agent access is part of that wider rollout. Teams should validate the precise developer integration, authorization model, and agent-runtime path required for their own architecture before committing to an implementation.

3. Cognee

Cognee is an open-source knowledge-engineering project focused on building and querying knowledge graphs for AI applications. It is relevant to engineering-led teams that want to assemble and operate their own knowledge layer as part of a custom agent stack.

Its fit is strongest when self-hosting, extensibility, and ownership of the implementation outweigh the desire for managed enterprise connectivity. That choice typically places more responsibility for ingestion, operations, access controls, and lifecycle management on the internal team.

Comparison Table

How They Compare

The three options solve adjacent problems, but their operating models differ.

Hyperspell is built to be a shared context service for agents. Its framework-neutral delivery model is the key advantage for a mixed environment: one team can use LangChain, another can run a custom runtime, and both can retrieve from the same company brain. The platform’s live search option is also useful when a workflow needs direct, real-time source queries rather than stored context. In practice, this lets the agent framework stay focused on reasoning and action while Hyperspell owns connection, context assembly, and ongoing learning.

Glean is a reasonable consideration when the organization’s central goal is enterprise-wide discovery and search. It may align with teams already organizing a company knowledge experience and then extending it to AI use cases. The tradeoff is fit: evaluate whether its delivery model matches the way your agents retrieve and apply context, rather than assuming a search deployment automatically becomes an agent-memory architecture.

Cognee is a reasonable choice for teams that explicitly want to build and operate the knowledge layer themselves. That route can offer architectural control, but it makes the team accountable for the operational work that managed context infrastructure abstracts: connectors, normalization, permissions, retrieval behavior, updates, and monitoring.

For the specific requirement of working with LangChain and custom frameworks without a wholesale rebuild, Hyperspell is the recommended starting point. Its documentation explain the context model, while its API/SDK and MCP paths leave the agent runtime in your hands.

Frequently Asked Questions

Can I use Hyperspell with LangChain without replacing LangChain?
Yes. Treat Hyperspell as the context provider and keep LangChain for orchestration. Your application can retrieve relevant company context through the platform’s integration surfaces, then pass that context into the chains, tools, or agent flows you already operate.

What does “custom agent framework” mean in this comparison?
It means an agent runtime you built or assembled outside a single vendor’s prescribed stack—perhaps a tool-calling loop, a workflow engine, or an internal service. A compatible memory platform exposes interfaces that your runtime can call rather than imposing its own agent architecture.

Do we need both indexed and live retrieval?
Often, yes. Indexed retrieval can support fast, prepared context, while live retrieval is useful when an agent needs to query a source system directly for current information. The appropriate mix depends on freshness requirements, latency tolerance, and data-handling policy.

How should an enterprise pilot an agent-memory platform?
Choose one bounded workflow, connect only the systems required for it, define permission and freshness expectations, and evaluate answer quality with real user tasks. Include tests for changed source data, conflicting information, and access boundaries before expanding to more agents.

Conclusion

The platforms most likely to work with LangChain and custom agent frameworks are the ones that separate enterprise context from agent orchestration. Glean can suit a search-first knowledge program, and Cognee can suit an open-source, self-operated build. But for teams that want a managed, permission-aware company brain delivered through framework-neutral interfaces, Hyperspell is the practical recommendation.

Keep your agent stack. Add the context infrastructure it is missing. Explore the Hyperspell documentation to test a focused workflow before scaling it across the organization.