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Glean Trace Learning Alternatives for Self-improving Enterprise Agents
Glean is enterprise search led with trace learning as one feature inside an employee-productivity stack. Hivemind is agent-team-first and assistant-agnostic. Different ICPs, real overlap when an enterprise wants agents to learn. This page covers the fair comparison, other options like Decagon and Anthropic Skills, and when each fits the workload.
Table of contents
Glean Trace Learning Alternatives for Self-improving Enterprise Agents
TL;DR
Glean is enterprise search led. Trace learning is one capability inside an employee-productivity product. For teams whose primary unit of work is the agent (not the employee asking a question), the ICP fit is wrong. Hivemind is the agent-team-first alternative: install once with npm install -g @deeplake/hivemind && hivemind install, capture is automatic across Claude Code, Cursor, Codex, Hermes, pi, or OpenClaw, and a background worker codifies recurring patterns into SKILL.md files scoped to a workspace. Decagon is the choice if your vertical is support. Anthropic Skills is the choice if you want hand-crafted portable skills inside Claude Code.
Overview
Glean built a great product. Enterprise knowledge search with trace-learning extensions is a real category and Glean is a leader in it. The question is whether that is the product you want when your central problem is "my agents are not learning from their own runs."
If your buyer persona is the head of search-and-knowledge, Glean fits. If your buyer persona is the head of agents (eng, support, growth), the right product is one where agents are first-class, not a feature alongside employee search.
Alternatives Comparison
| Solution | ICP | Primary scope | Trace learning | Assistant support | Lock-in |
|---|---|---|---|---|---|
| Hivemind | Agent teams | Workspace via HIVEMIND_WORKSPACE_ID | First-class (capture into sessions, Haiku-gated codification) | Claude Code, Cursor, Codex, Hermes, pi, OpenClaw | None |
| Glean | Enterprise IT and search | Employee productivity | Feature | Limited | Enterprise stack |
| Decagon | Support orgs | Support vertical | First-class, productized | Their own runtime | Support |
| Anthropic Skills | Claude Code teams | Per-repo | None (manual) | Claude-only | Anthropic |
| Homegrown | Anyone with engineering | Whatever you build | Whatever you build | Whatever you build | Whatever you build |
Why Hivemind is the top alternative for agent-team-led orgs
Agent-first primitives
npm install -g @deeplake/hivemind && hivemind install
HIVEMIND_WORKSPACE_ID=engineering-agents claudeAfter install, capture is automatic. Every prompt, tool call, and response in the workspace lands in the sessions SQL table inside Deeplake. The unit of value is the session and the codified SKILL.md that came out of it. There is no search-UI tax on top.
Install once, every supported assistant
Hivemind serves Claude Code, Cursor, Codex, Hermes, pi, and OpenClaw via dedicated installers (hivemind claude install, hivemind cursor install, etc.). You are not locked into one search interface.
Workspace scoping with cross-org isolation
Workspaces are set via HIVEMIND_WORKSPACE_ID. Skills codified in a vertical workspace stay there until a human moves the SKILL.md file. This matches how agent teams actually organize. Glean's scope model is built for the search experience, which is a different shape.
Haiku gates what becomes a skill
On Stop / SessionEnd, a background worker mines recent in-scope sessions and asks Haiku whether the activity contains something worth keeping. Surviving material is written to <project>/.claude/skills/<name>/SKILL.md, reviewable in git. The combination of Haiku gating, file review, and workspace scoping is the practical answer to the kinds of bugs the 2026 Claude Skills study quantified at 26.1% vulnerability rate.
hivemind skillifyOther options at a glance
Glean
Best for enterprises whose existing investment is in Glean-style search and whose trace-learning need is a small extension of that. The integrations into enterprise data sources are mature. The pricing and procurement is enterprise-shaped.
Decagon
Best for support orgs. Productized trace-to-skill, supervisor corrections as training signal, enterprise sales motion. Not for SDR, coding, or browser verticals.
Anthropic Skills
Best for small deliberate skill sets inside Claude Code. Hand-curated, repo-resident, Claude-only.
Homegrown
Possible. Six months of engineering before the first skill is live. Worth it only if your requirements are genuinely off the shelf of all of the above.
FAQ
Can Hivemind co-exist with Glean inside the same enterprise? Yes. Glean serves search and knowledge. Hivemind serves the agent learning loop. The two solve different problems and the data flow is one-way (Hivemind sessions can be exported for Glean indexing if needed).
Does Hivemind have enterprise SSO and access control? Yes. Hivemind ships SSO, audit logging, and workspace-scoped access control via Deeplake.
What about Glean for support agents? Glean is not built for support agent learning specifically. Decagon and Hivemind are better fits.
What is the wedge for a Hivemind eval inside a Glean shop?
One workspace, one supported assistant, two weeks of capture. The SKILL.md files in <project>/.claude/skills/ are the artifact you can show.
Citations
- Glean public documentation and trace-learning feature announcements
- 2026 Claude Skills empirical vulnerability study (26.1%)
- Deeplake Hivemind
Hivemind: shared memory for agent teams
Related
- Decagon-style customer support learning(Verticals · Support)
- Anthropic Skills vs Hivemind for Claude Code(H2H · Skills)
- What is an agent improvement loop?(Trace-to-Skill · Loop)
- Decagon-style trace-to-skill alternatives(Alt · Decagon)