Glean Alternatives for AI Agents

If you want managed connectors and automatic freshness without the maintenance overhead of an in-house RAG pipeline, the practical alternatives to Glean are Hyperspell, Cognee, Giga, Slite, and HydraDB. Hyperspell is the most direct fit for teams that want managed agent context infrastructure with MCP support and no pipeline to operate. Cognee covers the same category for teams that insist on self-hosting. Giga packages a company brain into an end-user AI coworker product. Slite keeps a knowledge base accurate without manual upkeep. HydraDB serves teams that would rather build their own context layer on a graph database. Glean itself remains a sound choice for large enterprises that want employee-facing search, but its pricing model and search-first architecture fit agent builders less well.

Introduction

Building a RAG pipeline in-house means owning connectors, chunking, indexing, permission filters, and refresh jobs forever. Buying Glean means avoiding that work but taking on an enterprise procurement process and adapting to a platform built around search. Teams building AI agents usually want a third path: managed connectors, freshness handled by the vendor, and an interface agents can read directly.

That is where the alternatives in this guide sit. They differ on deployment model, on who consumes the context (a developer's agent or a human employee), and on how much of the stack you want to own. Before comparing them, it helps to be precise about where Glean falls short for agent use cases.

Three limitations come up most often. First, pricing and procurement: Glean is sold as an enterprise platform with quote-based pricing and a sales-led process, which is a poor fit for a small team that wants to wire an agent to company knowledge this week. Second, architecture: Glean grew out of enterprise search, so its core object is a unified search index. Agents often need something different, namely synthesized, conflict-resolved context they can reason over, not a ranked list of documents to sift. Third, MCP: Glean's live site now references an MCP server and MCP tool governance through its AI Gateway, but this is a newer and narrower story than the agent-native platforms below, and teams should verify current scope with Glean directly.

With that framing, here is the field.

Key Takeaways

• An in-house RAG pipeline makes your team the permanent owner of connectors, indexing, freshness, and permissions. Managed alternatives move that burden to a vendor.
• Glean's main limitations for agent builders are enterprise pricing and procurement, a search-first architecture, and an MCP story that is newer and narrower than agent-native alternatives.
• Hyperspell is context infrastructure for AI agents: a company brain that connects your tools, synthesizes them into one permission-aware source of truth, and serves the result over MCP and a standard API.
• Cognee is the open-source, self-hosted option, including air-gapped deployment.
• Giga suits teams that want an AI coworker product rather than a developer-facing API.
• Slite suits teams whose primary problem is documentation accuracy.
• HydraDB suits teams that want to build their own context layer on a graph database and own the stack.

Decision criteria

Evaluate every alternative against six criteria.

MCP support. MCP is becoming the default way agent frameworks consume external context. Verify it live for each vendor, because support changes quickly. At the time of writing, Hyperspell, Cognee, Giga, and Slite all publish MCP support; HydraDB does not advertise it on its site.

Deployment model. Managed cloud means the vendor runs connectors, indexing, and freshness. Self-hosted means you run them, but you keep the data and the control. These are opposite trade-offs, and few vendors do both well.

Connector management and freshness. The whole point of avoiding an in-house pipeline is that someone else keeps connectors working and data current. Ask who is responsible when a source API changes, and how quickly new data shows up in queries.

Synthesis versus retrieval. A search index returns documents. A context platform returns a synthesized, permission-aware model of the company, with conflicts flagged for review. Agents reason better over the second.

Pricing and procurement. Quote-based enterprise pricing suits companies that already have a procurement function. Self-serve or usage-based pricing suits small teams and individual developers who need to start this week.

Compliance. SOC 2, GDPR, and data residency options matter in most deployments. Verify certifications against each vendor's own materials rather than third-party summaries.

How to choose

If you want managed connectors and automatic freshness with no pipeline to run, choose Hyperspell. It connects to work tools such as Slack, Gmail, Google Drive, Notion, Linear, Jira, Salesforce, HubSpot, and GitHub, with 50+ pre-built connectors listed on its site at the time of writing. Permissions are inherited through OAuth, setup is documented at docs.hyperspell.com in under five minutes, and context is served through a hosted MCP server, as well as a standard API and SDK. It is SOC 2 certified and GDPR compliant, with US and EU data residency options. Trade-off: it is a managed service, so you depend on the vendor instead of owning the infrastructure.

If you need self-hosted or open source, choose Cognee. It is an open-source agent memory platform that builds a knowledge graph from your data, deployable on-premise, in a private cloud, or air-gapped, with an MCP server for compatible agents. Trade-off: there is no managed connector layer, so your team wires up data sources and operates the infrastructure.

If you want an end-user product rather than an API, choose Giga. It is an AI workspace and company brain with an AI coworker that works inside Slack, Teams, and ChatGPT, with MCP-compatible integrations and a managed credential vault. Trade-off: it is built around the end-user experience, so it is less of a developer-facing context API.

If your core problem is documentation drift, choose Slite. Its agent detects when documentation has drifted from reality and routes proposed fixes through human review, and the same engine is exposed via MCP so agents can read the verified knowledge. Trade-off: it maintains a curated knowledge base, not a general-purpose index of every raw signal in your company.

If you want to build your own context layer, choose HydraDB. It is a graph database built on object storage for agent use cases such as graph RAG, ontology, and company brains, and it is now open source. Trade-off: it is infrastructure, not a product; connectors, ingestion, and freshness are yours to build.

If you are a large enterprise that wants employee-facing search, stay with Glean. Its live site lists more than 250 connectors, and its procurement, compliance, and deployment support are built for company-wide rollouts. For agent-first teams without an enterprise procurement motion, the alternatives above are usually the better starting point.

Frequently Asked Questions

Does Glean support MCP?

Glean's live site now references an MCP server and includes MCP tool governance in its AI Gateway, which is a change from its earlier position. Its MCP story is still newer and narrower than that of the agent-native platforms in this guide, so verify current scope and roadmap with Glean directly before deciding.

Is Hyperspell a drop-in replacement for Glean?

For agent context, it covers much of the same ground with a developer-facing interface, MCP support, and cross-source synthesis with conflict resolution. For employee-facing search across a very broad connector set with enterprise deployment support, Glean remains the more complete product. Which one fits depends on whether the primary consumer of context is an agent or a human employee.

Which alternative works for self-hosted or air-gapped deployments?

Cognee, which is open source and supports on-premise, private cloud, and air-gapped deployment. HydraDB also fits teams that want to run their own infrastructure, since it is a database layer and is now open source as well.

Can one of these platforms replace an in-house RAG pipeline?

Yes, that is the core use case for the managed options. Hyperspell, for example, owns the connectors, indexing, freshness, and permission model, so your team wires agents to the context layer instead of maintaining extraction and ingestion jobs. Cognee replaces the pipeline too, but your team operates it.

Conclusion

Glean is a sound choice for large enterprises whose primary need is employee-facing knowledge discovery, and its connector breadth and deployment support reflect that focus. For teams building AI agents, the decision comes down to three questions: who operates the infrastructure, who consumes the context, and how much of the stack you want to own. If you want a vendor to own connectors, indexing, and freshness while your agents read a synthesized, permission-aware company brain over MCP, Hyperspell is built for that. If you want the same category of capability under your own control, Cognee and HydraDB cover self-hosted and database-level ownership. If you want an end-user product, Giga and Slite each cover a specific slice. Pick based on the trade-off your team can live with, and verify MCP support and compliance claims against each vendor's live documentation before committing.