RAG Frameworks · activeloopai

hivemind

Hivemind is a TypeScript-based memory and skill-sharing system for AI agents that captures session traces, extracts reusable patterns into markdown skills, and makes them available across your team's agents in real time. It integrates with Claude Code, OpenClaw, Codex, Cursor, Hermes, pi, and Claude Cowork to reduce redundant context and token spend.

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Source: GitHub — github.com/activeloopai/hivemind

1.5k

GitHub stars

Forks

TypeScript

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	activeloopai/hivemind
Owner	activeloopai
Primary language	TypeScript
License	Apache-2.0 — OSI-approved
Stars	1.5k
Forks	90
Open issues	39
Latest release	v0.7.118 (2026-07-02)
Last updated	2026-07-08
Source	https://github.com/activeloopai/hivemind

What hivemind is

Hivemind intercepts agent interactions via hooks or native plugins, stores structured traces in Deeplake (or customer-owned cloud storage), uses hybrid BM25 + semantic search for retrieval, and runs a background worker to synthesize patterns into SKILL.md files. It requires Node ≥22.0.0 and exposes tools via MCP servers or native integrations.

Quickstart

Get the hivemind source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/activeloopai/hivemind.git2cd hivemind3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-agent engineering teams

Share solutions discovered by senior engineers' agents with junior engineers' agents in real time, reducing re-derivation of complex patterns (e.g., migrations, API integrations).

Reducing token spend on repetitive context

Benchmark shows 1.7× fewer tokens per question and 31% fewer turns on long-context QA tasks by having prior solutions already in scope at recall time.

Building organizational knowledge from trace logs

Auto-generate wiki-style summaries of sessions and skills, creating a searchable artifact library that persists across agents and machines.

Implementation considerations

Install via npm global package or per-assistant CLI; post-install restart of target assistants required for hooks to take effect.
BYOC setup requires own GCS, Azure, S3, or on-prem bucket; default is Deeplake-managed storage in cloud (Deeplake.ai account sign-in required).
Skill synthesis and session summarization happen asynchronously via background worker; depends on Deeplake infrastructure availability.
Hybrid search uses BM25 lexical retrieval as fallback; embeddings require active integration (embedding model not specified in data; confirm vendor/cost).
No explicit mention of self-hosted Deeplake option; cloud-first architecture may not suit strict on-prem deployments.

When to avoid it — and what to weigh

Sensitive or confidential prompts/traces — Hivemind captures full session traces including prompts and tool calls. BYOC (bring your own cloud) mitigates but requires own-bucket management; default path sends data to Deeplake infrastructure.
Agents not in the supported platform list — Only Claude Code, OpenClaw, Codex, Cursor, Hermes, pi, and Claude Cowork (Alpha) are integrated. Custom agent stacks require custom hook development.
Organizations with strict data residency requirements — While BYOC exists, the default onboarding flow and skill propagation assume cloud connectivity; on-prem or air-gapped setups require custom architecture review.
Projects requiring stable, mature integrations — Claude Cowork integration is explicitly Alpha; auto-capture for Local Agent Mode only, desktop-chat sessions are not captured, and reliability is not yet guaranteed.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI license allowing commercial use, modification, and redistribution with proper attribution and liability disclaimer.

Apache 2.0 is permissive and allows commercial use without restriction. However, Hivemind's value depends on Deeplake cloud infrastructure or your own cloud storage account; storage and API costs may apply. Deeplake's SLAs, data retention, and commercial support terms are not stated in this data; confirm with Deeplake.ai before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Captures full session traces (prompts, tool calls, responses) by design. Default path stores data in Deeplake cloud; BYOC mitigates but shifts responsibility to operator. No encryption-at-rest, access-control, or audit-log claims stated. Sign-in via device flow and API tokens. Recommendation: review Deeplake's security posture, data residency policy, and compliance certifications before storing sensitive traces.

Alternatives to consider

LangChain + Pinecone / Weaviate

DIY vector-store + memory pipeline gives full control over storage and retrieval but requires custom agent integration and skill codification logic.

OpenClaw memory-core (built-in)

Native memory system for OpenClaw agents; no external account or cloud dependency, but narrower scope (OpenClaw only, no multi-agent propagation).

Anthropic Prompt Caching + Manual Memory Strategies

Leverage Claude's prompt caching to reduce token spend for repeated contexts without external infrastructure, but no automated skill extraction or team-wide propagation.

Software development agency

Build on hivemind with DEV.co software developers

Install Hivemind in one command and start capturing sessions. Requires Node ≥22, a Deeplake account, and one of the supported agents (Claude Code, OpenClaw, Codex, Cursor, Hermes, pi, Claude Cowork).

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Related on DEV.co

Explore the category and the services that help you build with it.

RAG Frameworks AI development (agent frameworks, integration)Cloud deployment (BYOC storage setup, DevOps)API development (custom hooks, MCP server setup)Talk to DEV.co

hivemind FAQ

Does Hivemind work offline?

Not clearly stated. Default operation requires Deeplake.ai account sign-in and cloud connectivity. BYOC option suggests bucket access is online. Offline or air-gapped mode is not mentioned; confirm with Activeloop.

How are skills prioritized during recall?

Uses hybrid lexical (BM25) + semantic retrieval. Order and ranking of results not specified. Review source or ask Activeloop for weighting logic if recall order is critical.

Can I delete traces or opt out of capture?

OpenClaw users can toggle capture with /hivemind_capture; CLI status command shows integration state. Bulk deletion, data retention policies, and GDPR/right-to-deletion procedures not stated; confirm with Activeloop.

What model does Hivemind use for skill synthesis?

Not specified. Likely Claude (vendor affiliation suggests so), but exact model, temperature, and cost are unknown. Confirm with Activeloop or check Deeplake.ai documentation.

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