200db04fc7
* feat(config): add user ID retrieval and workspace scoping for authenticated users - Implemented `read_authenticated_user_id` to extract the user's ID from `auth-profiles.json`, avoiding a dependency cycle with the credentials module. - Introduced `maybe_scope_workspace_to_user` to create user-specific workspace directories based on the authenticated user ID, ensuring isolated workspace data. - Updated the configuration loading process to call `maybe_scope_workspace_to_user`, enhancing user data management. - Added unit tests for the new functionality, ensuring correct behavior in various scenarios. This change improves user experience by providing personalized workspace management based on authentication status. * feat(config): enhance user management with active user state handling - Added functions to manage the active user state, including `read_active_user_id`, `write_active_user_id`, and `clear_active_user`, allowing for user-specific configuration and workspace isolation. - Introduced `default_root_openhuman_dir` to standardize the retrieval of the root directory for user data. - Updated configuration loading to support user-scoped directories, improving the overall user experience by ensuring personalized settings and workspace management. This change enhances the OpenHuman platform by enabling better user data management and isolation. * feat(credentials): enhance user directory management during session storage - Added logic to create and activate user-scoped directories based on the resolved user ID when storing session data, ensuring credentials are saved in the correct location. - Implemented error handling for directory creation and active user ID writing, with appropriate logging for failures. - Updated the configuration loading process to reflect the newly activated user directory, improving user-specific settings management. - Enhanced the `get_data_dir` function to return user-scoped directories if an active user is set, streamlining data access. This change improves user experience by ensuring that session data is correctly organized and accessible based on user context. * refactor(tests): update user ID handling and improve test coverage - Renamed and refactored tests to better reflect functionality, focusing on active user ID management. - Removed the `write_auth_profiles` helper function and replaced it with direct calls to `write_active_user_id` for clarity. - Enhanced tests to cover scenarios for reading and clearing active user IDs, ensuring accurate behavior in user-specific configurations. - Added a new test for building user directory paths, improving overall test coverage for user management features. This change streamlines the testing process and enhances the clarity of user ID handling in the configuration schema. * refactor(paths): streamline model and binary path resolution - Introduced a new `shared_root_dir` function to centralize the logic for determining the shared root openhuman directory, improving code clarity and reducing duplication. - Updated `workspace_ollama_dir` and `workspace_local_models_dir` functions to utilize the new shared root directory, ensuring consistent path resolution for user-specific and shared resources. - Enhanced the `model_artifact_path` function to leverage the new directory structure, improving the organization of model artifacts. This refactor enhances maintainability and clarity in the path management for local AI resources. * style: apply cargo fmt formatting Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(paths): streamline directory management for model artifacts - Updated the `model_artifact_path` function to utilize a new `shared_root_dir` function, which centralizes the logic for determining the root openhuman directory. - Enhanced the `config_root_dir` function to improve clarity and maintainability. - Adjusted the `workspace_ollama_dir` and `workspace_local_models_dir` functions to leverage the new shared directory logic, ensuring consistent path resolution across the application. These changes improve the organization of directory management and enhance the overall clarity of the codebase. --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
OpenHuman
The age of super intelligence is here. OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.
Discord • Reddit • X/Twitter • Docs
"The Tet. What a brilliant machine" — Morgan Freeman as he reminisces about alien superintelligence in the movie Oblivion
Early Beta — Under active development. Expect rough edges.
To install or get started, either download from the website over at tinyhumans.ai/openhuman or run
# For MacOS/Linux
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
# For Windows
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
What is OpenHuman?
OpenHuman is an open-source agentic assistant that is designed to integrate with you in your daily life. Here's what makes OpenHuman special:
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Simple, UI-first — A clean desktop experience and short onboarding paths so you can go from install to a working agent in a few clicks, without a config-first setup. You don't need a terminal to run OpenHuman.
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One subscription, many providers — You only need one account to get access to many agentic APIs (AI Models, Search, Webhooks/Tunnels and other 3rd party APIs etc..), simplifying the experience to get a powerful agent going.
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Rich Skills — Plug into Gmail, Slack, Notion, and the rest of your stack via rich, feature-backed skills. Connections are typically one click through setup wizards instead of wiring APIs by hand. Workflow data is kept on device, encrypted locally, and treated as yours: encryption and sensitive context stay on your machine. Webhooks give instant feedback into the agent when external systems or skills emit events, so the loop stays tight without constant polling.
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Local knowledge base — Built from your data and your activity. How you work across tools, sessions, and connected services—so the agent gets rich, workflow-aware context, not a one-off chat transcript. Everything is stored on your machine and compounding over time without becoming a cloud dossier. Channels, skills and ongoing conversations feed the same loop so day-to-day context does not reset every session.
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Local AI model — The Rust core exposes local AI paths (and the desktop bundle can ship local/bundled runners where applicable) for the workloads above—vision snippets, speech helpers, summarization, tooling—so sensitive steps can stay off the cloud when you choose.
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Deep desktop integrations — OpenHuman is a native desktop assistant, not a web-only chat: memory-aware keyboard autocomplete, voice (STT listening and TTS replies), screen intelligence that understands what is on screen and feeds your local context, plus windowing and OS-level permissions—so the agent meets you on the machine, not trapped in a browser tab.
Architecture: docs/ARCHITECTURE.md. Contributor orientation: CONTRIBUTING.md.
OpenHuman vs other agents
High-level comparison (products evolve—verify against each vendor). OpenHuman is built to minimize vendor sprawl, keep workflow knowledge on-device, and ship deep desktop features—not only chat.
| Claude Code/Cowork | OpenClaw | Hermes Agent | OpenHuman | |
|---|---|---|---|---|
| Open-source: Is the codebase open to review? | 🚫 Proprietary client | ✅ MIT License | ✅ MIT License | ✅ GNU License |
| Simple: Is it simple to get started? | ✅ Simple Desktop App + CLI | ⚠️ Terminal first and often complex | ⚠️ Terminal first and often complex | ✅ Simple, Clean UI/UX. Get started within minutes |
| Cost: How expensive is to run? | ⚠️ Subscription + add-on tool/API costs | ⚠️ Tied to models & hosting you choose | ⚠️ Tied to models & hosting you choose | ✅ Cost optimized with the option to run many things locally for free |
| Memory & Knowledge Base (KB): Does the agent know you and your world? | ✅ Built-in memory; mostly chat/session scoped | ⚠️ Has a local memory but often needs plugins for richer behavior | ✅ Self-learning / task loops (typical) | 🚀 Local KB + Self-learning from your activity & data (GMail, Notion etc... via skills) & prompts |
| API spagetti: How complex is it to hook mulitple features together? | 🚫 Claude bill + often extra keys for MCP/tools | 🚫 BYOK / multi-vendor common | 🚫 Multiple providers common | ✅ One account get access to many bundled platform APIs |
| Extensibility: Can you add rich features into it? | ✅ MCP (different model than sandboxed skills) | ✅ Plugin Architecture (SKILL.md) | ✅ Plugin Architecture (SKILL.md) | 🚀 Rich Skills with ability to have realtime updates, local DB & more |
| Desktop integrations: Can it integrate into your desktop completely? | ⚠️ Desktop app & access to folders | ⚠️ Often lighter native surface | ⚠️ Often lighter native surface | ✅ STT, TTS, screen intelligence, memory-aware autocomplete and a whole lot more |
Contributors Hall of Fame
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