1836c7691f
* chore: update OpenHuman version to 0.52.9 and add intelligent routing functionality - Bumped OpenHuman version from 0.52.7 to 0.52.9 in Cargo.lock files. - Introduced a new routing module that implements intelligent model routing based on task complexity and local model health. - Added a health checker for the local Ollama model server to improve routing decisions. - Enhanced the provider to classify tasks and determine the appropriate backend (local or remote) for processing requests. - Updated related files to support the new routing logic and ensure seamless integration with existing functionalities. * refactor(routing): streamline provider and enhance routing logic - Removed the `build_tool_instructions` function from the public API, simplifying the routing module. - Updated the `IntelligentRoutingProvider` to utilize a more efficient model resolution process, improving routing decisions based on task complexity and local model health. - Introduced a new `quality` module to assess response quality, enabling better fallback decisions when local responses are deemed low quality. - Enhanced the `RoutingHints` struct to provide more granular control over routing behavior, including privacy requirements and cost sensitivity. - Added tests to validate the new routing logic and quality assessment, ensuring robust functionality across various scenarios. * feat(tests): add live end-to-end routing tests for real backend integration - Introduced a new test file `live_routing_e2e.rs` containing end-to-end tests for routing against a live backend. - Tests require a valid backend URL, user session JWT, and real network interactions, hence marked as `#[ignore]`. - Implemented functionality to set up environment variables, write configuration files, and perform JSON-RPC calls to validate routing behavior. - Added assertions to ensure correct handling of various routing cases, enhancing test coverage for the routing module. * refactor(format): ran format command * feat(routing): add IntelligentRoutingProvider and enhance LocalHealthChecker - Introduced a new `factory.rs` file containing the `new_provider` function to construct an `IntelligentRoutingProvider` that integrates local AI capabilities with remote backend providers. - Enhanced the `LocalHealthChecker` in `health.rs` by adding a `reqwest::Client` for improved health probing, including better logging for cache hits and misses, and streamlined cache updates. - Updated health check logic to utilize the new client, ensuring more reliable health status checks for local AI services. * refactor(routing): move new_provider function to factory module - Moved the `new_provider` function from `mod.rs` to a new `factory.rs` module to improve code organization and maintainability. - Updated public exports to include the new location of `new_provider`, ensuring continued accessibility for constructing `IntelligentRoutingProvider` instances. - Removed the old implementation from `mod.rs`, streamlining the routing module's structure. * refactor(routing): simplify local task routing logic - Removed redundant conditions for routing medium tasks locally, streamlining the decision-making process in the `decide` function. - Updated comments to reflect the simplified logic, enhancing code clarity and maintainability. * docs(tests): clarify comments in json_rpc_e2e.rs regarding hint overrides logic. * refactor(tests): enhance live routing end-to-end tests with timeout handling - Introduced a timeout mechanism for reading SSE events to prevent indefinite blocking. - Updated environment variable management in tests to ensure safe access and cleanup. - Improved comments for clarity regarding the safety of environment variable mutations during tests. * refactor(tests): update SSE event reading in live routing tests. * refactor(routing): enhance medium task routing logic and update comments - Updated the routing logic for medium tasks to utilize hints for local bias, ensuring more accurate routing decisions. - Revised comments throughout the code to clarify the behavior of task categories and routing preferences. - Adjusted test cases to reflect the new routing logic, ensuring they accurately validate the expected behavior for medium tasks. * refactor(tests): implement timeout handling for dictation event reception - Added a timeout mechanism to the dictation event test to prevent indefinite blocking while waiting for the "pressed" event. - Enhanced the test logic to consume events until the expected event type is received, improving reliability and clarity in the test flow. --------- Co-authored-by: Steven Enamakel <31011319+senamakel@users.noreply.github.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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