Hivemind vs Cognee for Agent Memory and Trace Learning

TL;DR

Cognee is an open-source knowledge-graph memory project. The 6-line demo is genuinely good. Real deployments hit ops issues at scale (graph rebuilds, ingestion failures, see GH #2796). Hivemind is a different shape of product: a capture-codify-propagate workflow on top of Deeplake, where install wires the assistant once, sessions are captured automatically, and a background worker codifies recurring patterns into SKILL.md files scoped to a workspace. Pick Cognee if you want a KG you can shape yourself. Pick Hivemind if you want the loop as a working product.

Overview

Cognee earned its visibility by making graph memory feel approachable. The starter snippet ("from cognee import cognify") gets a dev to a working KG in minutes. That demo deserves credit.

Hivemind sits at a different layer. It is not a KG primitive. After hivemind install, every prompt, tool call, and response is captured automatically into the sessions SQL table inside Deeplake. On Stop / SessionEnd, a background worker mines recent in-scope sessions, asks Haiku whether the activity is worth keeping, and writes <project>/.claude/skills/<name>/SKILL.md. Those skills propagate into every Hivemind-connected agent in the same workspace at inference time. Workspace is set via HIVEMIND_WORKSPACE_ID. The right comparison is "do you want a memory shape (KG) or a learning workflow (sessions to codified skills)?"

Feature Comparison

How Hivemind works

npm install -g @deeplake/hivemind && hivemind install
HIVEMIND_WORKSPACE_ID=research-agents claude

After install, capture is automatic. Every prompt, tool call, and response lands in the sessions SQL table. On Stop / SessionEnd, a background worker mines recent in-scope sessions, asks Haiku whether the activity contains something worth keeping, and writes <project>/.claude/skills/<name>/SKILL.md. Skills propagate to every Hivemind-connected agent in the same workspace at inference time.

hivemind status
hivemind skillify

hivemind status reports install and capture health. hivemind skillify shows current scope, team, install state, and per-project state.

Search is a natural-language ask inside the agent:

• "Show me how we handled the payment retry refactor."
• "What sessions touched the auth module this week?"

The interesting part is the loop, not any single command.

When Cognee makes sense

• You want a knowledge graph as your memory shape and the rest of your stack expects a KG.
• Your data is documents and entities, not agent sessions.
• You are comfortable operating an open-source graph backend at your scale.
• You are okay with the ops issues documented in the project (GH #2796 and related).
• You want the OSS license and the ability to shape the system yourself.

If those fit, Cognee is a legitimate choice and the community is active.

When Hivemind is the better choice

• Your primary unit of memory is the agent session, not a document.
• You want capture, codification, and propagation as a product, not something you assemble.
• You need workspace scoping with cross-org isolation.
• You want session storage that scales without manual graph rebuilds.
• You want one install to cover Claude Code, Cursor, Codex, and other supported assistants.
• You want a Deeplake-backed audit trail.

FAQ

Can I run Cognee's KG alongside Hivemind? Yes. Keep a Cognee KG for document memory and route the session-to-skill workflow through Hivemind. They are not mutually exclusive.

Is the GH #2796 issue resolved? Cognee is an active project, check the issue for current status. The broader pattern (graph rebuilds and ingestion failures at scale) is well documented across multiple issues.

Does Hivemind support graph queries? The Deeplake backend supports SQL plus vector. Graph-shaped queries are expressible but not the primary idiom.

Is Hivemind open source? The Deeplake backend is open source. The Hivemind managed service is commercial with a generous free tier.

Citations

• Cognee GH #2796 and adjacent issues on ops at scale
• Cognee project
• Deeplake Hivemind
• Deeplake GitHub

Hivemind: shared memory for agent teams

Install Hivemind