activeloopai/hivemind
hivemind
One brain for all your agents
Usage guide
hivemind is an open-source project around ai-agents, ai-memory, anthropic with 1,015 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating hivemind for TypeScript AI workflows.
- Comparing a GitHub project with 1,015 stars and current repository activity.
Pros
- hivemind has visible GitHub traction with 1,015 stars. Topics: ai, ai-agents, ai-memory.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
hivemind should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
hivemind architecture preview
hivemind's main path starts at the entry surface, runs through Coding agent runtime, combines OpenAI / Claude, PostgreSQL, GitHub, and returns Code changes / developer feedback.
Entry
CLI / terminal entry
hivemind is primarily entered through a developer command or terminal workflow.
npm install -g @deeplake/hivemind && hivemind install
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
OpenAI / Claude
Model calls are likely routed through OpenAI, Claude based on README and topic signals.
OpenAI, Claude
Context
PostgreSQL
Context comes from PostgreSQL, which constrains what the model or runtime can use.
PostgreSQL
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
Code changes / developer feedback
The final result is code edits, explanations, repository actions, or developer-facing feedback.
coding output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
hivemind uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/activeloopai/hivemind.git ~/.codex/hivemindInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npm install -g @deeplake/hivemind && hivemind installAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
One brain for all your agents
This is one of the documented reasons to evaluate hivemind before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate hivemind before choosing a stack.
AI Agents project comparison
Compare hivemind with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official hivemind setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented hivemind example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is hivemind?
hivemind is an open-source ai agents project. One brain for all your agents
How do I install hivemind?
Start with the official README. The first detected setup step is: git clone https://github.com/activeloopai/hivemind.git ~/.codex/hivemind.
Is hivemind beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can hivemind be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does hivemind need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt hivemind?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.