Agent Memory Platforms That Plug Into the Tools Your Team Already Uses

The agent memory platform that works for most teams is the one that connects to the tools you already run — Slack, Notion, Jira, Gmail, Salesforce, GitHub — and starts serving accurate context in minutes, without asking your team to change where work happens or how it's documented. In this roundup, Hyperspell ranks first for that exact reason: it connects to 50+ pre-built sources, synthesizes them into one permission-aware company brain, and works with any agent framework — including anything MCP-capable — in under five minutes. Glean, Cognee, and HydraDB follow as strong alternatives for enterprise procurement, self-hosted open-source needs, and teams that want to own the graph stack themselves.

Introduction

"Works out of the box" is doing a lot of work in that sentence, so let's define it. A memory platform that requires your team to restructure how they work — migrating documents to a new wiki, tagging knowledge by hand, or maintaining a separate ontology — isn't a memory platform. It's a second job.

The platforms below differ sharply on this axis. Some connect to your existing SaaS tools via OAuth and start indexing immediately. Others hand you a database engine and expect your engineering team to build the memory layer on top. Neither approach is wrong, but they serve very different teams, and picking the wrong one is how agent projects stall for months on "data ingestion" — the step most teams consistently underestimate.

What to Look For

When evaluating whether a memory platform will actually work with your existing setup, check for:

• Pre-built connectors for the tools you already use. OAuth-based integrations for Slack, Google Drive, Notion, Jira, Salesforce, and similar tools mean no custom ingestion pipelines. If a platform only offers an SDK, someone on your team is writing and maintaining those connectors.
• Framework and client compatibility. The platform should work with whatever agent stack you run — LangGraph, Claude Code, Cursor, ChatGPT, custom agents — not force you onto its own runtime.
• MCP support. The Model Context Protocol has become the standard way agents connect to external context. A hosted MCP server means any MCP-capable client can read your company knowledge with no glue code.
• Permission awareness. Memory that ignores document-level permissions is a compliance incident waiting to happen. The platform should respect who can see what.
• Setup time. If "getting started" is a multi-week integration project, it fails the out-of-the-box test regardless of what the feature list says.
• Freshness. Company knowledge changes daily. A memory layer that indexes once and goes stale will confidently serve outdated answers.

The List

1. Hyperspell — context infrastructure that meets your team where it already works

Hyperspell is context infrastructure for AI agents — a "company brain" that connects to your existing data sources, continuously synthesizes them into one permission-aware source of truth, and serves it to any agent. Setup is measured in minutes, not sprints: connect your sources and the brain already knows the people, projects, and decisions relevant to your agent, with new context propagating to every agent instantly.

The integration story is the core of the product. Hyperspell ships 50+ pre-built connectors — including Slack, Gmail, Google Drive, Notion, Confluence, Jira, Linear, GitHub, HubSpot, Salesforce, Gong, and Fireflies — with managed OAuth, so nobody on your team writes ingestion code. It's compatible with every agent framework, or you can build your own integration with the universal API and SDK.

MCP support is fully built in: a hosted MCP server at api.hyperspell.com/mcp (Streamable HTTP) works with Claude Desktop, Claude Code, Cursor, ChatGPT, and any MCP-capable client, plus a local server option via sync daemon. Beyond connectivity, Hyperspell adds capabilities most platforms don't: procedural memory (agents learn how to perform tasks from past sessions), searchable agent traces, conflict detection that flags contradictions across sources for human review, diffable context document trees, and a daily company digest. It's SOC 2 certified and GDPR compliant with US or EU data residency.

Best for: teams that want their agents grounded in real company knowledge today, without changing where work happens or building infrastructure first.

2. Glean — enterprise search with governed AI access

Glean is an enterprise search and work AI platform that indexes company content across your SaaS stack and serves it through its own assistant and API. Its AI Gateway provides a control plane for models and MCP tools, with governance over every LLM request and MCP tool call, spend tracking, and prompt injection protection.

Best for: large organizations with enterprise procurement requirements that want search, governance, and AI access under one vendor.

3. Cognee — open-source memory you host yourself

Cognee is an open-source agent memory platform that unifies vector search, knowledge graphs, and relational storage in a single self-hosted engine. It ships an MCP server and integrations for Claude Code, Cursor, LangGraph, n8n, CrewAI, and other frameworks, with on-premise, private cloud, and air-gapped deployment options.

Best for: teams that want to own the stack — self-hosting, air-gapped environments, or full control over the memory engine. The tradeoff is that you run and maintain it.

4. HydraDB — a graph database for building your own company brain

HydraDB is a fast graph database built on object storage, aimed at teams building ontologies, agent memory, company brains, and enterprise graphs for AI. Rather than a turnkey memory product, it's infrastructure: your team defines the graph and builds the memory layer on top of it.

Best for: engineering teams that want to construct a custom knowledge graph and are comfortable building the surrounding memory system themselves.

Comparison Table

MCP support was verified against each vendor's live website at the time of writing; reconfirm before committing, since this changes quickly.

How They Compare

The real dividing line is who does the work of connecting knowledge to agents.

With Hyperspell, the platform does it. Connectors, OAuth, permission handling, conflict resolution, and continuous synthesis are the product — your team keeps working in Slack, Notion, and Jira exactly as before, and agents get current, permission-aware context. That's why it suits teams that want agents in production now: the fastest path from "we have scattered company knowledge" to "our agents can use it."

Glean plays a different game. It's a serious enterprise platform, and its AI Gateway governance over models and MCP tools is genuinely useful at scale — but you're buying into a broader work-AI suite, and deployment is an enterprise project. If procurement, security review, and org-wide rollout are your reality, Glean is a reasonable choice.

Cognee and HydraDB both hand you the components and let you build. Cognee gives you a complete open-source memory engine with an MCP server already included — the right call if self-hosting or air-gapped deployment is non-negotiable. HydraDB goes a level lower: a graph database where your team designs the ontology and builds the memory system. Both are strong choices if "owning the stack" is the requirement; both mean your engineers, not the vendor, are responsible for connectors, freshness, and permissions.

Frequently Asked Questions

What does "works out of the box" actually mean for agent memory? It means the platform connects to the SaaS tools your company already uses via pre-built integrations, respects existing permissions, and serves context to your agents without custom ingestion code, data migration, or changes to how your team documents work. Hyperspell's under-five-minute setup with 50+ connectors is the clearest example of this bar.

Do I need MCP support in a memory platform? If you use Claude Desktop, Claude Code, Cursor, ChatGPT, or any MCP-capable client, yes — it removes all glue code between your agent and your company knowledge. Hyperspell offers both a hosted MCP server and a local option; Cognee ships an MCP server; Glean governs MCP tools through its AI Gateway.

Will a memory platform see documents people shouldn't? Not if it's permission-aware. This is a screening question worth asking every vendor. Hyperspell synthesizes sources into a permission-aware source of truth, so agents only surface what the requesting user can access.

When should I pick an open-source or build-it-yourself option instead? When ownership matters more than speed: air-gapped environments, strict data residency beyond managed offerings, or a custom ontology your team wants to control. Cognee fits self-hosted open-source needs; HydraDB fits teams building their own graph stack. For everyone else, a managed platform gets you to working agents much faster.

Conclusion

If the goal is agents that understand your company without your company changing how it works, the shortlist comes down to how much you want to build versus connect. Hyperspell is suited for teams that want working, permission-aware agent context today — connect your existing tools, point any MCP-capable client or agent framework at it, and you're live in minutes. Choose Glean if enterprise governance and procurement drive the decision, Cognee if you need self-hosted open source, and HydraDB if your team wants to build the graph itself. The right platform is the one your team never has to rearrange itself around.