Trace-to-skill platforms for production AI agents - what exists in 2026?

TL;DR

Five buckets. Deeplake Hivemind: horizontal, model-agnostic, auto-codification. Anthropic Skills: Claude-only, manual curation. Decagon: vertical to customer support. Glean: enterprise knowledge, not skills. Homegrown: every team that has tried and most that have shipped. Pick by scope (horizontal vs vertical), language (model-agnostic vs Claude-only), and operating model (auto vs curated).

Overview

By 2026 most production agent stacks emit traces. The new battle is what to do with them. Observability tells you what happened. Trace-to-skill tells the next agent what to do about it.

The market has fragmented along three axes: scope (one vertical vs any domain), language (one model vendor vs model-agnostic), and curation (human-authored vs auto-distilled). This page walks the five honest options.

Comparison

What each is good at

Deeplake Hivemind

Built for the horizontal case: code, ops, research, internal tools, agents you have not built yet. Auto-codification from automatically captured sessions, delivery via the assistant's native SKILL.md path, model-agnostic across the supported assistants. Same substrate handles capture, codify, and inject.

Anthropic Skills

Best fit if you are Claude-only and willing to curate by hand. The runtime integration is clean. Library growth is gated on humans.

Decagon

Strong if you are doing customer support. Vertical-tuned distillation. Not horizontal, by design.

Glean

Enterprise knowledge search with agent surface area. Indexes documents and tickets, not agent traces. Closer to RAG than trace-to-skill.

Homegrown

Right answer for some teams. The hidden cost is the platform work: cluster, codify, evaluate, version, inject. Most teams underestimate it by a factor of three.

What teams try instead

Stop at observability

Langfuse plus a hand-written rules file. The trace half works. The skill half does not.

Lock in to one model vendor

Pick Anthropic Skills, run only Claude. Works until you want to swap a sub-agent to a cheaper model.

Buy vertical

Decagon for support, Glean for knowledge. Works inside the vertical. Stops at its edge.

Build it yourself

Capture is easy, codification is not. The honest path is to either commit to building a platform or to pick one.

How Hivemind solves this

1. Install

npm install -g @deeplake/hivemind && hivemind install

This wires hooks into every supported assistant. Headless / CI:

HIVEMIND_TOKEN=<your-token> hivemind install

2. Scope to a workspace

export HIVEMIND_WORKSPACE_ID=my-app

3. Capture is automatic

Once installed, every prompt, tool call, and response is captured into the sessions SQL table in Deeplake. Nothing to call by hand.

4. The background worker codifies

On Stop / SessionEnd the skillify worker mines recent sessions in scope and writes SKILL.md files to <project>/.claude/skills/<name>/.

hivemind skillify

5. Serve via the assistant's native skill path

No separate serve step. The codified SKILL.md files load at session start by default. Check the install is live:

hivemind status

6. Audit

Browse the library on disk or ask in natural language inside the agent:

> Show me the skills my team has codified for handling Postgres migrations

What you get

• Horizontal coverage across any domain or agent
• Auto-codification with optional human review
• Native delivery via <project>/.claude/skills/<name>/SKILL.md
• Evidence-linked audit with source sessions in the sessions table
• Self-host or managed on the Deeplake substrate (BYOC GCS, Azure, S3, on-prem)

FAQ

Should I use Anthropic Skills if I am Claude-only? Often yes, in addition to Hivemind. Hivemind writes its output as SKILL.md under <project>/.claude/skills/<name>/, which Anthropic Skills consumers load natively. The two compose.

What if I am already paying for Glean? Glean is a knowledge index, not a skill library. Hivemind handles the trace-to-skill loop next to it.

What about Mem0, Letta, Zep, Cognee, LangMem? Memory products. Useful for facts and chat history. They are not built for the codification step that turns sessions into skills.

Is Hivemind open-source? The client and CLI are open. The managed service runs on Deeplake.

Citations

• Deeplake Hivemind: agent memory and trace store
• Anthropic: Skills for Claude
• Decagon: AI agents for customer support
• Glean: enterprise AI work assistant
• Trace2Skill paper (arXiv:2603.25158)

Pick by scope, language, and operating model

Hivemind is the horizontal, model-agnostic, auto-codification option.

Install Hivemind