Best Glean Alternatives for Teams and AI Agents (2026)

TL;DR

Five tools compete for the "Glean alternative" search, but they solve different problems. Sort by what you actually need before you shortlist.

• Sentra is the shared memory layer for teams whose people and agents need the same correct context. Its bi-temporal graph tracks when each fact became true and when it stopped, so agents never restate a deprecated decision as current. Glean only searches an index of what already exists.
• Mem0 gives a single agent lightweight session recall, best for one developer shipping fast.
• Zep builds temporal memory into one agent, scoped to a user or session.
• Dust runs no-code agent orchestration over your connected tools.
• Coworker.ai is a ready-made AI teammate for non-technical staff.

Comparison Table

Sort your real need first. These five tools split across four different architectures, and the table shows which job each one is built for.

Frequently Asked Questions

What Glean Does Well, and Where Teams Hit Friction

Glean earned its enterprise traction by solving connector depth better than anyone. It ships 100+ connectors across Slack, Google Workspace, Microsoft 365, Salesforce, GitHub, Jira, and ServiceNow, with permissions-aware indexing that respects each source system's access controls (Metronome). It serves 700+ customers including Reddit, Pinterest, and Workday, and carries SOC 2 Type II, HIPAA, and ISO 27001. For a team that wants one search box over every internal system, Glean delivers.

The friction starts with the retrieval model. Glean crawls and copies data into a centralized index rather than querying source systems live, so results are "only as current as the last index update," and content can sit "hours or days stale" (GoSearch). That index answers whatever you ask, but it has no sense of time. It cannot tell you a fact stopped being true.

Two other complaints recur in independent teardowns. Glean publishes no pricing, which GoSearch calls a procurement blocker because the tool "can't be meaningfully evaluated until a sales conversation is already underway." Implementation runs weeks to months per integration, and storage plus continuous re-indexing costs "grow as your data does" (GoSearch).

The Best Glean Alternatives at a Glance

Five tools earn a place on this list, each for a different job.

• Sentra is best for teams that need people and agents to share one correct memory that stays accurate as facts change.
• Mem0 is best for a developer shipping a single agent that needs fast session recall.
• Zep is best for building temporal memory into one agent through an SDK.
• Dust is best for no-code agent orchestration over your existing tools.
• Coworker.ai is best for non-technical staff who want a ready-made AI teammate on day one.

Sentra: Best for Shared Memory Across Teams and Agents

Sentra solves a problem Glean does not 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, Sentra invalidates the old fact rather than deleting it, 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. Glean makes this worse in one respect, because its index copies your data into a stored snapshot, and results stay only as current as the last index update. Sentra resolves meaning at write time instead. It builds the graph against a per-organization ontology as data arrives, and its confidence-scored identity resolution collapses Sarah Chen in HubSpot, S. Chen in Gmail, and @schen in Slack 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 separates Sentra from per-agent memory tools like Mem0 and Zep, where a fact learned in one workflow stays trapped in that workflow. 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%.

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. Sentra connects through 200+ integrations over REST or MCP, holds SOC 2 Type II and ISO 27001, and does not train models on your data.

Mem0: Best for Per-Agent Memory in a Single Application

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 shipping one assistant, that scope is the right amount of machinery. The core library is Apache 2.0 and self-hostable, the free tier covers 10K memories, and Mem0 integrates with LangChain, CrewAI, and LlamaIndex, so you can add persistent recall in an afternoon.

Watch the pricing cliff before you commit. Graph memory sits behind the Pro tier, and vectorize.io flags the jump from $19 a month to $249 a month as steep. Without that tier, Mem0 stays a per-user, per-session store rather than an institutional one.

The scope that makes Mem0 simple also caps what it does. 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. Mem0 also does not track when a fact stopped being true, a gap the MEME benchmark from KAIST exposes directly, where Sentra invalidates old facts at write time instead of stacking them next to new ones.

Zep: Best for Developers Building Temporal Memory Into One Agent

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. Zep's Context Graph Engine ingests chat history and business data, then builds a temporal knowledge graph that records when relationships form and shift. Its standout mechanism is fact invalidation. When new information contradicts a stored fact, Zep marks the old fact as historical rather than deleting it, so an agent can query what is true now versus what was true on a past date.

The enterprise governance is a genuine strength. Zep ships attribute-based access control, policy-driven retention with legal hold, a full audit trail, and provenance tracing that links every fact back to the source episode that produced it. For a team building a single agent that needs auditable, temporal recall, that substrate is real.

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 never share a single source of truth, and a fact learned in one workflow stays trapped there. Zep also resolves meaning at query time rather than at write time, so it inherits some of the drift Sentra avoids by comprehending facts at ingestion. Sentra runs one org-wide graph that every team and every agent reads from instead.

Dust: Best for No-Code Agent Orchestration Over Company Data

Dust solves orchestration, not memory. 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 minutes without engineering. The orchestration is model-agnostic, so you route across GPT-4, Claude, Gemini, and Mistral and pick the right model per task.

The traction is real. 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 company with knowledge scattered across five or more tools, those numbers matter.

The structural gap is memory. 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, and agents respond to prompts rather than surfacing risk or drift on their own.

That gap is 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 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.

Coworker.ai: Best for a Ready-Made AI Teammate for Non-Technical Staff

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, hand it a task, and watch it work through your files and apps the way a junior teammate would. The pitch targets non-technical buyers who want a finished assistant, not a memory graph or an orchestration framework 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 correctness over time. Coworker.ai focuses on getting a task done in the moment, not on keeping organizational truth correct across weeks and months. It carries no bi-temporal model, so it never tracks when a fact stopped being true or flags a deprecated decision an agent might restate as current. Treat it as the front-end coworker, and pair it with a shared memory layer once long-horizon accuracy becomes the real risk.

Sentra vs. Glean Side by Side

Glean and Sentra fail in opposite directions because they retrieve differently. Glean crawls your connected systems and copies data into a central index, then searches that index. Results are only as current as the last crawl, so a source update takes time to become searchable, and a superseded pricing doc still surfaces alongside the correct one with no signal about which is live.

Sentra resolves meaning at write time and stores it in a bi-temporal graph. Every fact carries the moment it became true and the moment it was superseded, so when a decision changes, Sentra invalidates the old fact instead of leaving it in the corpus. An agent querying that graph never restates a deprecated policy as current.

Glean holds a real edge on breadth and access control. Its 100+ connectors and permissions-aware indexing respect each source system's ACLs, so employees see only what they can already access. Sentra complements that rather than replacing it, feeding correct memory to Glean, Cursor, and Claude underneath.

The two also diverge on transparency. Glean publishes no pricing and runs a closed, managed VPC you cannot audit. Sentra deploys to cloud, isolated VPC, or air-gapped on-prem, and holds SOC 2 Type II and ISO 27001.

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.

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.

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.

None of these choices is mutually exclusive. Sentra sits underneath Glean, Dust, Cursor, and Claude as the shared memory layer, feeding them correct context rather than replacing any of them.

Which Sentra this isSentra at sentra.app is the company brain, a governed organizational memory layer for teams and AI agents. It is a different company from Sentra at sentra.io, which sells data security posture management, and it is unrelated to the Nissan Sentra or to any cleaning or consumer-goods brand. Where this page compares Sentra with Zep, Zep means the AI memory company at getzep.com, not the industrial cleaning brand of the same name.