Glen
Organizational memory that makes every AI agent work smarter on fewer tokens.
Glen is the most complete answer to siloed agent knowledge we've seen, with provenance and RBAC that make it enterprise-ready. The free tier is a nice toe-dip, but if you run multiple MCP agents, this is worth a serious look; if you don't, it's overkill.
Verified 3d ago · liveness 70/100 · cite: rightaichoice.com/tools/glen
- Teams running multiple MCP-compatible AI coding agents (Claude Code, Codex, Cursor) that need shared context
- Support organizations wanting consistent answers across reps by learning from top performers
- Engineering teams needing shared runbooks and conventions across PRs and debugging
- Companies needing compliance-ready agent memory with audit trails and provenance
- Solo developers without team collaboration or agent workflows
- Teams needing on-premise deployment without Enterprise buy-in
- Organizations looking for a simple Q&A bot without agent integration
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Glen if you don't run MCP-compatible agents like Claude Code, Codex, or Cursor, or if you're a solo developer working alone—you'll get little value and pay for features you don't need. Also skip if you need on-premise deployment without waiting for an Enterprise plan.
The Team tier at $250/mo and Scale at $750/mo are billed monthly, but going beyond the free tier's usage limits may require upgrading, and the exact usage caps aren't published.
Glen's freemium model starts at $0, which is generous for small teams. But for scaling features, the Team tier at $250/mo is steeper than some competitors like Notion AI (add-on ~$10/user) or basic memory tools. If you're a heavy MCP user, the token savings (29% fewer) can offset the cost, but for casual users it may not be worth it.
In short
Glen — Organizational memory that makes every AI agent work smarter on fewer tokens. Best for Teams running multiple MCP-compatible AI coding agents (Claude Code, Codex, Cursor) that need shared context, Support organizations wanting consistent answers across reps by learning from top performers, Engineering teams needing shared runbooks and conventions across PRs and debugging. Free to start; paid plans from $250/mo.
What's new in Glen
Checked 9 days agoAcross the latest 1 update: 1 feature update.
What people actually say about Glen — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +Org-wide shared memory eliminates silos between agents.
- +Automatic capture reduces manual documentation effort.
- +Provenance tracking shows who contributed each knowledge piece.
- +Observation-level RBAC provides granular access control.
- +Model-agnostic through MCP integration with many tools.
- −No real community feedback to validate claims.
- −Requires MCP-compatible agents (limited ecosystem).
- −Early access may have stability and feature gaps.
- −Automatic capture may bloat storage with noise.
- −Manual mode needed for sensitive interactions – can be forgotten.
Viability Score
How well maintained and how widely used is Glen? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Continuous capture from Claude Code, Codex, and Cursor sessions
- Ingests Slack, GitHub, tickets, calls, and docs
- Automatic context injection into prompts
- Company brain queryable from Slack, MCP, or web app
- PR reviewer with why-any-line explanations
- Skills distilled from team work, surfaced automatically
- Shared transcript library with search and takeover
- Provenance tracking on every observation
- Observation-level RBAC for security
- Private mode for sensitive conversations
- MCP protocol integration for any MCP-compatible agent
- Command line tool for full access
- Web app included with account
- Slack bot
- Code search and clone query for agent context
About Glen
Glen is an organizational memory platform that captures knowledge from your team's AI agent sessions and everyday work tools, then automatically feeds the right context back into every prompt. It's built for teams running MCP-compatible coding agents like Claude Code, Codex, and Cursor, but extends beyond developers—support, sales, and design teams use it to standardize answers, templates, and workflows. Glen continuously ingests sessions, Slack messages, GitHub activity, tickets, calls, and documents into a single shared store, eliminating the need for manual documentation. The core value is automatic context injection: when an agent starts a task, Glen finds prior work that relates and steers the model with it. This results in faster results and fewer tokens. The company's benchmark, replaying 100 PRs with Codex 5.5, showed 29% fewer tokens and 21% faster completion, with quality at parity or better. The company brain is queryable from Slack, any MCP agent, or the web app, giving every employee the same complete answer to questions like "What did we decide about pricing?" or "How does our support lead handle this ticket?" Beyond retrieval, Glen offers a PR reviewer that explains why any line of code exists by querying a clone of the agent that wrote it, and a shared transcript library where sessions can be searched, shared, or taken over mid-task. Skills automatically distill from your team's work and surface when someone encounters a relevant task, so best practices spread without anyone hunting for them. Every observation carries provenance—who made a decision, when, and why—and observation-level RBAC keeps sensitive material secure. Glen is invite-only, onboarding teams in waves via waitlist, with a free tier for small teams. It positions itself as a cognitive layer that lives with your team, not inside any single vendor's client, keeping transcripts, skills, and artifacts portable across harnesses and providers to prevent lock-in. For organizations that
Behind the Verdict
Glen attacks a real problem: AI agents start from zero on every task, even when the company already solved it. Instead of pasting context into prompts manually, Glen captures everything automatically and injects relevant history. That's the core value, and the benchmark data backs it up—29% fewer tokens and 21% faster completion on real PR replays. For teams running heavy agent workflows, that's a direct productivity win. We'd reach for Glen when you have several MCP-compatible agents in play, and particularly when knowledge is scattered. Slack threads, tickets, calls, and old sessions—Glen pulls it all together. The PR reviewer is a clever touch: it explains why a line exists by querying a clone of the agent that wrote it. The company brain feature makes institutional knowledge queryable across the org, which is huge for onboarding and consistency. Watch out for the invite-only model. Glen onboard teams in waves via waitlist, so you might wait before getting in. Also, if you're a solo dev or a small team without agent sprawl, the value is thin. And while RBAC and provenance are solid, you'll need Enterprise for on-premise. That's a dealbreaker for some regulated orgs. Compared to alternatives like Copilot Workspace or other agent logs, Glen is more of a cross-tool memory layer. It doesn't lock you into one client—transcripts and skills are portable, so you can switch providers without losing context. That's a differentiator. Pricing is freemium, with a free tier for small teams. Paid tiers scale up, but current numbers aren't public. If you're evaluating, budget for the Team tier at $250/mo as a starting point. In practice, the setup is fast—under 5 minutes—and the integrations sync once. The auto-surfaced skills are a nice touch, but they only work if the source
Researching Glen? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Glen actually fits — and what changes day-one when you adopt it.
Managing a team of 10 devs using Claude Code and Cursor; wants to reduce duplication of effort and accelerate PR reviews.
Outcome: Configure Glen to capture all sessions and connect Slack/GitHub. In a week, the team sees automatic context injection on PRs, and the PR reviewer explains why lines exist, cutting review time by 21% based on the benchmark.
Leading a support team of 15; wants consistent answers to policy questions across reps and to onboard new hires faster.
Outcome: Use Glen to ingest Slack, tickets, and calls. Reps ask Slack bot 'How do we handle refunds?' and get the same complete answer with provenance. New hires use skills distilled from top performers, reducing ramp time.
Building an internal agent ecosystem on MCP; wants to give agents shared context across multiple harnesses.
Outcome: Expose Glen via MCP server to all agents, regardless of harness. Agents automatically pull relevant prior work, reducing token spend by 29% and improving consistency across all sessions.
Use Cases
- Capture and reuse organizational knowledge across all your AI agents automatically
- Answer customer support queries consistently based on past decisions and policies
- Onboard new agents with the same expertise as your senior team members
- Triage bugs using shared runbooks learned from on-call engineers
- Generate documentation in a consistent style modeled after top contributors
- Provide every agent with context from decisions made months ago
- Manage commitments to customers by pulling every promise from Slack, tickets, and calls
- Plan sprints using what shipped last quarter
Models Under the Hood
as of 2026-08-26
Limitations
- Glen is invite-only and onboards teams in waves via a waitlist.
- It integrates with MCP clients such as Claude Code, Codex, and Cursor, and the benchmark mentions Codex 5.5.
- Specific model names and performance details for all underlying models are not disclosed on the site.
as of 2026-08-24
Verification history
We have re-verified Glen 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Glen tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Small teams (up to 5) just starting with agent memory, wanting to test Glen with one or two MCP agents and limited usage.
What this tier adds
Starting tier: full functionality but with limited usage caps, access to the company brain—a low-commitment entry point.
Team
$250/mo
Ideal for
Growing teams (around 10-25) that need increased capture and injection volumes across multiple agents and a shared transcript library.
What this tier adds
Adds scaling features: increased usage limits, shared transcript library, and access for more users compared to Free.
Scale
$750/mo
Ideal for
Larger organizations (50+) that need enhanced security with observation-level RBAC, advanced compliance features, and priority support.
What this tier adds
Adds advanced features for larger orgs: stronger RBAC, security controls, and priority support versus Team.
Enterprise
Custom
Ideal for
Enterprises needing custom deployment options (including possible on-prem), advanced compliance and audit trails, and dedicated support.
What this tier adds
Custom deployment, advanced compliance/audit, and dedicated support—highest tier with tailored solutions.
Where the pricing makes sense
The company stage and team size where Glen's pricing actually pencils out — and where peers do it cheaper.
Glen's freemium model starts at $0, which is generous for small teams. But for scaling features, the Team tier at $250/mo is steeper than some competitors like Notion AI (add-on ~$10/user) or basic memory tools. If you're a heavy MCP user, the token savings (29% fewer) can offset the cost, but for casual users it may not be worth it.
Setup time & first value
How long it actually takes to get something useful out of Glen — broken out by persona, not the marketing-page minute.
For engineering teams: under 5 minutes—add the Glen plugin to Claude Code, Codex, or Cursor; sync integrations once (one person does it). For support/sales: similar—Slack bot and web app are ready immediately. Full functionality is available from day one.
Switching to or from Glen
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a manual knowledge base: start using Glen's automatic capture; you can seed it with existing docs and Slack history, and over time it becomes the single source of truth.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Featured Head-to-Head Comparisons
Glen vs Spider Cloud
Spider Cloud and Glen solve completely different problems. Choose Spider Cloud if you need a fast, cost-effective web scraping API for feeding live data into AI agents or RAG pipelines. Choose Glen if your team runs multiple MCP-compatible coding agents (Claude Code, Cursor) and needs a shared memory layer to keep them consistent. They are complementary, not competitive.
Glen vs Presto Voice
Presto Voice and Glen serve entirely different domains: Presto automates drive-thru ordering for QSR chains with proven ROI (up to 6% revenue lift), while Glen provides a shared memory layer for AI agents and humans in organizations using MCP-compatible tools. Your choice depends on whether you need voice order automation or agent coordination. If you run a QSR chain, choose Presto; if you manage multiple AI coding agents, choose Glen.
Glen vs Temporal Ai
Temporal AI and Glen solve entirely different problems. Temporal is the go-to for teams building fault-tolerant, long-running workflows and AI agents that survive crashes — ideal for mission-critical orchestration. Glen excels at creating a shared memory layer so multiple MCP-compatible agents (like Claude Code) recall the same organizational knowledge. Choose Temporal if you need durable execution; choose Glen if your main pain point is knowledge silos across agents. They are complementary rather than direct competitors.
Popular in Agent Memory & Runtimes
Frequently Asked Questions
Best-of guides
Topics
Used Glen? Help shape our editorial sentiment research.