Best Glean Alternatives for Teams and AI Agents (2026)

TL;DR

• Glean is AI-powered enterprise search with a knowledge graph, 100+ connectors, and permissions-aware results. It finds documents that already exist, and it carries a $7.2B valuation to prove the model works.
• Teams leave Glean over a ~$50K minimum contract, no true self-hosting, closed model choices, and agentic features that are still new.
• Dust wins when you want no-code agents built over existing tools, with model-agnostic orchestration starting at €29/user/month.
• Mem0 and Zep give individual agents persistent memory, scoped to a single agent or session rather than the whole company.
• Sentra is the pick when teams and agents need one shared memory that stays correct over time, with bi-temporal facts that retire when they stop being true.

What Glean Does Well — and Where It Falls Short

Glean built one of the strongest enterprise search products on the market, and the case for it is real. Its proprietary Knowledge Graph and hybrid semantic search index across 100+ pre-built connectors, covering Google Workspace, Microsoft 365, Slack, Salesforce, Jira, and ServiceNow. Search respects source permissions, so employees only see what they already have access to. A Series F valuing the company at $7.2 billion signals serious enterprise traction, and Glean Assistant answers questions directly instead of returning a wall of links.

The friction shows up in four places. Pricing starts high, with minimum annual contracts reported at $50,000 to $60,000 and a 500-person org landing near $300K to $400K per year before implementation fees. Renewal escalations of 7 to 12 percent are common, and Glean does not publish pricing publicly.

Self-hosting is the second gap. Glean's "Customer-Hosted" option runs as a Glean-managed VPC inside your cloud, and their documentation states they do not support customers deploying, patching, or altering the architecture. The codebase stays closed, so regulated and security-conscious buyers cannot audit it.

Model choice is the third constraint. Glean controls which models power its features, with no path to Anthropic Claude, Llama, DeepSeek, or locally hosted models. The fourth gap is agentic maturity. Glean Agents are newer additions, and production-grade multi-step agent workflows still need careful evaluation. Glean finds what already exists well. It was not designed to remember what changed over time.

The Category Split: Three Different Problems, Three Different Tool Types

The tools people call "Glean alternatives" actually solve three different problems, and picking the wrong category wastes months. Sort your real need first, then shortlist.

Enterprise search answers "where does this exist?" You point it at your documents, tickets, and wikis, and it returns the relevant passage with permissions intact. Glean and Onyx live here. The job is retrieval, and the answer is only as current as the underlying source.

A memory layer answers "what happened, and is it still true?" Instead of fetching documents, it captures decisions, commitments, and facts over time, then tracks when each one became true and when it stopped. One graph serves both your people and your agents, so an agent never restates a deprecated number as current. Sentra and Zep work at this layer.

Agent orchestration answers "what should the agents do?" You build specialized assistants, connect them to your tools, and they execute tasks like updating a CRM record or posting a summary. Dust is built for this. Orchestration coordinates action, but most platforms here read from indexed data rather than remembering across sessions, which is why a memory layer often sits underneath them.

The Best Glean Alternatives at a Glance

• Sentra — Best for teams that need one shared memory their agents and people both query, with facts that stay correct as they change over time.
• Mem0 — Best for developers adding lightweight, persistent memory to a single AI agent or user session.
• Zep — Best for engineers who want a graph-based agent memory with some temporal tracking, scoped to a user or conversation.
• Dust — Best for teams that want to build no-code agents connected to existing tools like Notion, Slack, and Salesforce.
• Coworker.ai — Best for teams that want an end-user AI coworker app that executes tasks, rather than an infrastructure memory layer.
• Onyx — Best for teams that want self-hostable, model-agnostic enterprise search as an open-source Glean substitute.

Sentra — Shared Memory Layer for Teams and Agents

Sentra solves a problem Glean doesn't touch. Glean finds documents that already exist. Sentra remembers what your organization decided, when each fact became true, and when it stopped being true. The mechanism behind that distinction is a bi-temporal knowledge graph. Every fact carries two timestamps, the moment it became true and the moment it was superseded. When a decision changes, the old fact is invalidated rather than deleted, so an agent never restates a deprecated policy as current.

Compare that to how retrieval-augmented generation works under most enterprise search. RAG stores text as embeddings in a flat haystack where old facts sit next to new ones, equally weighted. Vector search returns what is close, not what is correct, so a six-month-old pricing doc and yesterday's update both surface with no signal about which one is live. Sentra resolves meaning at write time instead. It builds the graph against a per-organization ontology as data arrives, and it runs confidence-scored identity resolution so Sarah Chen in HubSpot, S. Chen in Gmail, and @schen in Slack collapse into one person.

One graph serves the whole organization. Engineering, Sales, Finance, Ops, People, and Legal query the same memory, and so does every agent you run. That shared scope is what separates Sentra from per-agent memory tools. A decision logged once is visible to your Cursor agent, your Claude assistant, and the analyst asking a question in Slack, with no re-ingestion per surface.

The benchmark numbers back the correctness claim. On the MEME benchmark from KAIST, Sentra is the only system above 30% on both Cascade and Absence, scoring 40% on Cascade where the field average sits at 3%, and 43% on Absence where Mem0 scores 0%. Those two categories measure exactly what trips up flat retrieval, tracking facts through a chain of changes and knowing when something is no longer true.

Sentra runs under the tools you already use, not over them. It connects to 200+ integrations through a single REST or MCP surface, ingests without tagging or manual filing, and feeds memory to Glean, Cursor, Claude, and Slack rather than replacing any of them. Deployment covers cloud, isolated VPC, or fully air-gapped on-prem, and the platform holds SOC 2 Type II and ISO 27001. Sentra does not train models on your data.

The honest limitation is scope. Sentra is not an enterprise search UI. If you want a polished search box for employees to look up files and pages, Glean does that better, and Sentra sits underneath it as the memory layer.

Best forteams that need shared, always-current memory for both humans and agents, where stating a stale fact as current is the failure that costs you.

Mem0 — Per-Agent Persistent Memory

Mem0 gives a single AI agent a lightweight memory it can write to and recall across sessions. You drop it into an agent, and it stores user preferences, past instructions, and conversation history so the agent stops asking the same question twice. For a developer building one assistant or chatbot, that scope is the right amount of machinery. Mem0 is open source, has a generous free tier, and integrates with the common LLM frameworks, so you can ship persistent memory in an afternoon.

The scope that makes Mem0 simple also caps what it can do. Memory lives per agent or per user, so two agents in the same company hold two separate, unsynchronized pictures of reality. Nothing reconciles them, and nothing tells one agent that a fact another agent learned has since changed. When your sales agent and your support agent disagree about a customer's contract terms, Mem0 has no mechanism to settle it.

The harder gap is temporal correctness. Mem0 stores facts but does not track when a fact stopped being true, so a deprecated decision sits in memory ready to be restated as current. On the MEME benchmark from KAIST, Mem0 scored 3% on Cascade and 0% on Absence, the two categories that test whether a system tracks changing truth and notices what is missing. Sentra scored 40% and 43% on the same tests, because it invalidates old facts at write time instead of letting them pile up next to new ones.

Best fordevelopers building a single AI agent or chatbot that needs to remember one user across sessions.

Key limitationno temporal invalidation and no shared truth across agents, so it cannot serve as an org-wide memory layer for both teams and agents.

Zep — Temporal Memory Graph for Agents

Zep gives developers a graph-based memory store that tracks how facts change over time, which puts it closer to Sentra's category than Mem0's flat key-value approach. Where Mem0 stores discrete memories per agent, Zep builds a temporal knowledge graph that records when relationships formed and shifted. For a team building a single agent that needs to recall a user's history across sessions, that temporal structure pays off in fewer stale or contradictory answers.

The graph model is Zep's real strength. It links entities and events, and it timestamps edges so an agent can reason about sequence and recency rather than treating every stored fact as equally current. Developers integrate it through an SDK and shape memory into the agent's workflow, which suits engineering teams who want control over how context gets stored and retrieved.

The limitation is scope. Zep anchors memory to a session or a user, so it remembers what one person told one agent, not what the whole company knows. Two agents built on Zep do not share a single source of truth, and a fact learned in one workflow stays trapped in that workflow's memory. Sentra takes the opposite approach with one org-wide graph that every team and every agent reads from, so a decision logged in Slack surfaces for a coding agent in Cursor without anyone rebuilding the context.

Zep also resolves meaning at query time rather than at write time, so it inherits some of the same drift Sentra avoids by comprehending facts at ingestion. The graph gives Zep a real edge over per-agent stores, but it stops short of being a shared company brain.

Best fordevelopers building a single agent that needs temporal, session-scoped memory with graph structure, who do not yet need org-wide shared knowledge.

Dust — Agent Orchestration Over Company Data

Dust solves orchestration, not memory. Founded in 2023 by ex-OpenAI researcher Stanislas Polu and ex-Alan CPO Gabriel Hubert, the Paris-based company built a no-code platform for spinning up specialized agents connected to your existing tools. You name an agent, connect data sources like Notion or Salesforce, write instructions, and deploy in about five minutes. No engineering required.

The orchestration is model-agnostic, which is the real draw. Dust routes across GPT-4, Claude, Gemini, and Mistral, so you pick the right model per task instead of getting locked into one vendor. Around 30 native connectors pull from Google Drive, Slack, GitHub, Jira, Zendesk, and others, and agents can act, creating tickets, updating CRM records, and posting summaries on a schedule.

The traction backs the approach. Doctolib reports 70% weekly usage across 3,000 employees, and Qonto credits Dust with 50,000 hours saved annually, roughly 24 full-time equivalents (SaaStr). Clay hit 100% adoption while scaling 4×. For a 200-to-1,000-person knowledge-work company with tribal knowledge scattered across five or more tools, those numbers are real.

The limitation is structural. Dust agents do not carry persistent memory across sessions, so they reach the indexed knowledge base each time but never accumulate context about your preferences or workflow patterns (Vybe). The connectors are read-focused, pulling and indexing data with limited write-back to external tools. Agents respond to prompts rather than surfacing risk or drift on their own.

That gap is exactly where Dust and Sentra fit together. Dust coordinates what agents do, and Sentra gives those agents a shared, bi-temporal memory that stays correct over time. Run Dust agents on top of a Sentra graph, and a support agent stops restating a deprecated policy as current because the underlying memory knows when that fact stopped being true.

Best formid-sized knowledge-work teams that want non-technical staff building model-agnostic agents over existing tools. Pro pricing runs €29 per user per month, with custom enterprise pricing above 100 users.

Coworker.ai — AI Coworker App

Coworker.ai sits at a different layer than every other tool on this list. It is an end-user app, not infrastructure. You hire an AI coworker with a persona, hand it a task, and watch it work through your tools the way a junior teammate would. The pitch targets non-technical buyers who want a finished assistant they can talk to, not a memory graph or an orchestration framework they have to wire up.

The strength is the experience. Coworker.ai packages task execution into a familiar chat-and-assign workflow, so a marketer or operations lead can delegate research, drafting, or routine multi-step work without touching an API. For teams that want a working AI teammate on day one, that polish matters more than any architecture decision underneath.

The limitation is what it does not try to solve. Coworker.ai focuses on getting a task done in the moment, not on keeping organizational truth correct across time. It carries no bi-temporal model, so it does not track when a fact stopped being true or flag a deprecated decision an agent might restate as current. Treat it as the front-end coworker, and pair it with a shared memory layer when correctness over weeks and months becomes the real risk.

Best forteams that want a ready-to-use AI coworker for everyday tasks, where ease of delegation matters more than long-term memory or temporal accuracy.

Side-by-Side Comparison

How to Choose

Pick the tool that matches the problem you actually have, not the one with the broadest feature list.

If you need a search box your whole company uses to find documents, tickets, and threads across connected apps, choose Glean or the self-hostable, MIT-licensed Onyx. Both index your tools and return permission-aware answers. Onyx fits regulated teams that need to audit the codebase or deploy air-gapped, and it cuts roughly 60% off Glean's three-year cost.

If your agents need to remember what happened across time and stay correct as facts change, choose Sentra. Its bi-temporal graph tracks when each fact became true and when it stopped, so an agent never restates a deprecated decision as current. One org-wide graph serves both your people and every agent, and it sits under Glean, Cursor, and Claude rather than replacing them.

If you want non-technical staff to build specialized agents over existing data without code, choose Dust. Its no-code builder ships a working agent in minutes, and Qonto reports 50,000 hours saved annually with it. Pair Dust with Sentra when those agents need memory that persists across sessions, since Dust indexes data but does not accumulate it.

FAQ

Is Sentra a replacement for Glean?

No. Glean is an enterprise search interface that helps people find documents and answers across connected tools. Sentra is the memory layer underneath your agents and teams, holding what is true over time. Most teams run both, with Glean for human search and Sentra for shared memory that agents query directly.

What is the difference between a memory layer and enterprise search?

Enterprise search finds what already exists in your documents and indexes it for retrieval. A memory layer remembers what happened across time, including when a fact became true and when it stopped being true. Sentra gives your humans and agents one shared record of decisions, commitments, and current truth rather than a list of matching files.

Does Sentra work alongside Glean?

Yes. Sentra connects through REST or MCP and integrates with 200+ tools, so it sits under your existing stack rather than replacing it. You keep Glean for search, and Sentra supplies correct, time-aware context to the agents you build on Claude, Cursor, or ChatGPT.

Which tool is best for AI agents that need to remember org context?

Sentra holds one org-wide graph that every agent shares, instead of the per-agent memory in tools like Mem0 or Zep. On the MEME benchmark from KAIST, Sentra scored 40% on Cascade where Mem0 scored 3%. That shared graph means a fact updated by one agent is correct for all of them.

What makes bi-temporal memory different from RAG?

RAG returns text that is close to your query, weighting old and new facts equally. Sentra resolves meaning at write time and tracks when each fact was valid, so a deprecated decision never gets restated as current. Your agents act on what is true now, not what merely sounds similar.