Steven EnamakelandGitHub
7db71408bd
* Enhance draft message handling in streaming edits
- Introduced a new `draft_sent` field in the `StreamingState` struct to track when a draft message has been posted, decoupling the existence of a draft from the ability to edit it.
- Updated the `flush_streaming_edit` function to set `draft_sent` upon posting a draft, ensuring that duplicate messages are not sent if the backend fails to return an ID.
- Modified the `finalize_channel_reply` function to prevent sending a fresh message if a draft was already posted without an ID, improving user experience by avoiding duplicate bubbles.
These changes enhance the robustness of message handling during streaming edits, ensuring a smoother interaction for users.
* feat(onboarding): enhance LocalAIStep for low-RAM device handling
- Added logic to determine if the device is below the RAM threshold, defaulting to a cloud AI fallback if necessary.
- Introduced a new UI for low-RAM devices, informing users about the cloud mode and providing options to continue with cloud AI or force-enable local AI.
- Updated the `ensureRecommendedLocalAiPresetIfNeeded` function to return a `recommend_disabled` flag based on device RAM.
- Enhanced tests to cover new cloud fallback UI and local AI consent handling.
These changes improve the onboarding experience by providing clearer options based on device capabilities.
* style: apply prettier formatting to LocalAIStep
* feat(local_ai): unlock model selection + add Disabled/cloud fallback option
- Remove MVP ceiling that clamped selection to the 2-4 GB tier, in bootstrap
and in the apply_preset RPC — users can now pick any tier.
- Add special "disabled" tier string to apply_preset that toggles
config.local_ai.enabled = false so the app uses the cloud summarizer.
- Presets RPC now returns local_ai_enabled so the UI can render the
currently-active state correctly.
- Rewrite DeviceCapabilitySection: tiers are now clickable buttons that
call apply_preset; adds a "Disabled — Cloud fallback" card at the top
marked "Recommended" on low-RAM devices and "Active" when enabled=false.
- Remove the MVP message copy from the model tier panel.
- Remove unread-count badge from the bottom tab bar.
* refactor(onboarding): streamline LocalAIStep and update local AI preset handling
- Removed the `ensureRecommendedLocalAiPresetIfNeeded` function call from `LocalAIStep`, replacing it with `openhumanLocalAiPresets` to directly fetch preset information.
- Updated the logic in `ensureRecommendedLocalAiPresetIfNeeded` to ensure the recommended tier is applied correctly based on user consent, improving clarity in the local AI setup process.
- Enhanced tests to mock the new `openhumanLocalAiPresets` function, ensuring coverage for low-RAM device scenarios and local AI consent handling.
- Updated documentation in the `schemas.rs` file to reflect changes in the data structure returned by the presets RPC.
These changes improve the onboarding experience by providing a more direct and efficient method for managing local AI presets based on device capabilities.
* test(LocalAIStep): improve mock implementation for local AI presets
- Refactored the mock for `openhumanLocalAiPresets` to enhance readability and maintainability.
- Ensured the mock returns consistent device information and preset details for testing scenarios.
- This change supports better test coverage and clarity in the LocalAIStep component's behavior during onboarding.
* fix(local_ai): preserve hadSelectedTier semantics in bootstrap return
hadSelectedTier / selectedTier represent the incoming state ("was a tier
already selected before this call"), not the post-apply state. Setting
both to the just-applied tier broke the existing localAiBootstrap
contract and the unit test that encodes it.
The Rust-side persistence fix is independent: removing the
recommend_disabled short-circuit ensures openhumanLocalAiApplyPreset
actually writes the selected tier to disk on the opt-in path, so
config_with_recommended_tier_if_unselected() honors the user's choice.
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:
-
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.
-
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.
-
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.
-
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 |
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