Hivemind vs LangMem for Agent Learning and Memory

TL;DR

LangMem is LangChain's memory primitive. It is per-agent, LangChain-tied, and slow enough at p95 (around 59s) that it cannot sit inside interactive agent loops. Hivemind is Deeplake-backed, MCP-native, framework-agnostic, and built for capture-codify-propagate across an org. They solve different problems. The only one that is safe in a user-facing path is Hivemind.

Overview

LangMem ships as part of the LangChain ecosystem. It gives a LangChain agent a memory store with semantic recall. For LangChain users running batch or background workloads it works.

Hivemind is a different shape of product. It installs once into the assistant, captures every prompt, tool call, and response automatically into the sessions SQL table inside Deeplake, codifies recurring patterns into SKILL.md files via a background worker on Stop / SessionEnd, and propagates those skills to every Hivemind-connected agent in the same workspace. It is built for team and org scope, not single-agent scope.

The comparison matters because teams often discover LangMem first and then hit the latency wall.

Feature Comparison

How Hivemind works

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

After hivemind install finishes, capture is automatic. Every prompt, tool call, and response from the workspace lands in 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 surviving material to <project>/.claude/skills/<name>/SKILL.md. Skills propagate to every Hivemind-connected agent in the same workspace at inference time.

Search is a natural-language ask inside the agent, not a CLI call:

• "What was the team working on yesterday?"
• "Show me how we resolved the auth migration last week."

Retrieval against the Deeplake backend is sub-second, which is the whole point of putting shared memory in the live request loop.

hivemind status
hivemind skillify

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

When LangMem makes sense

• You are already deep in LangChain and don't want to leave it.
• Your memory access pattern is background or batch (post-session summarization, offline analysis).
• You don't need cross-agent or team-wide memory.
• You don't need the capture-codify-propagate loop.

If you are in this bucket, LangMem will work and the LangChain integration is the path of least resistance.

When Hivemind is the better choice

• You need shared memory in the interactive request loop and the p95 budget is tight.
• You run agents on more than one assistant (Cursor, Claude Code, Codex, custom).
• You want team or workspace-scoped memory, not per-agent silos.
• You want skills codified automatically from real sessions, reviewable as SKILL.md files in git.
• You want SQL plus vector queries against agent state.
• You want a Deeplake-backed audit trail for compliance.

FAQ

Is LangMem slow in every configuration? The p95 around 59s is the documented public benchmark on the default config. Self-hosted setups can do better, but at that point you are operating a memory system anyway, which is what Hivemind ships as a managed product.

Can I use both? Yes. Teams sometimes keep LangMem for LangChain-internal scratch memory and route durable, cross-agent memory through Hivemind.

Does Hivemind require Deeplake expertise? No. hivemind install wires the assistant for you. You only touch Deeplake directly if you want SQL queries against your agent state.

What about LangSmith for traces? LangSmith is observability. Hivemind captures sessions as data the next agent reads back. Different layer.

Citations

• Public latency benchmarks for LangMem
• Deeplake Hivemind
• Deeplake Documentation

Hivemind: shared memory for agent teams

Install Hivemind