Steven EnamakelandGitHub
4cf608c2be
* feat: replace fastembed with candle for local embeddings - Introduced a new `CandleEmbedding` provider using the `candle` ML framework, eliminating C++ dependencies and enhancing performance. - Updated configuration to default to `candle` for embedding provider settings, replacing the previous `fastembed` references. - Added new modules for embedding providers, including `candle_embed`, `noop`, and `openai`, to support various embedding strategies. - Enhanced the `EmbeddingProvider` interface to accommodate the new `CandleEmbedding` implementation. - Refactored related tests to ensure comprehensive coverage of the new embedding functionalities and maintain backward compatibility with existing configurations. * feat: update embedding provider to Ollama - Replaced the default embedding provider from Candle to Ollama, enhancing local embedding capabilities with improved model management and GPU acceleration. - Updated configuration defaults for embedding model and dimensions to align with Ollama specifications. - Introduced a new Ollama embedding module, including necessary constants and functionality for embedding requests. - Refactored related code and tests to ensure compatibility with the new provider, maintaining backward compatibility with existing configurations. * refactor: remove Candle embedding provider and related dependencies - Deleted the `CandleEmbedding` module and its associated files, streamlining the embedding provider architecture. - Updated `Cargo.toml` and `Cargo.lock` to remove references to Candle-related packages, ensuring a cleaner dependency tree. - Refactored the `EmbeddingProvider` interface to eliminate support for the Candle provider, maintaining compatibility with existing providers. - Adjusted tests and documentation to reflect the removal of the Candle embedding functionality, ensuring clarity and consistency across the codebase. * feat: add SQLite-backed vector store for embeddings - Introduced a new `store` module to implement a local vector store backed by SQLite, enabling efficient storage and retrieval of text embeddings. - Added functionality for inserting, updating, and searching embeddings using cosine similarity, enhancing the embedding management capabilities. - Updated the `mod.rs` file to include the new `store` module and expose relevant functions for cosine similarity and vector operations. - Enhanced documentation to provide usage examples and clarify the integration of the vector store with existing embedding providers. * Add fs2 dependency for improved file locking in ComposeIO trigger history * test: boost embeddings module coverage to 97%+ Add tests for batch insert mismatch error path, invalid metadata JSON handling, disk store parent directory creation, and fake embedding dimensions accessor. 95 tests total across the module, all files at 97-100% line coverage. * style: apply cargo fmt to embeddings module * feat: enhance embedding provider creation with error handling - Updated the `create_embedding_provider` function to return an `anyhow::Result`, allowing for immediate error reporting on unrecognized provider names. - Added support for a "none" provider that returns a no-op embedding. - Enhanced tests to cover new error handling paths and validate behavior for known and unknown providers, improving overall test coverage. * feat: enhance Ollama and OpenAI embedding providers with improved handling for blank inputs and response validation - Updated the `embed` method in both `OllamaEmbedding` and `OpenAiEmbedding` to skip blank inputs while preserving their positions in the output as zero-vectors. - Added validation to ensure the response count matches the input count, with appropriate error handling for dimension mismatches. - Enhanced tests to cover new behavior for blank inputs and response validation, ensuring robustness in embedding functionality. * fix: address PR review findings for embeddings module - Factory returns Result for unknown providers instead of silent noop - Ollama: preserve positional alignment when blank texts are filtered - Ollama: validate response count and dimensions before returning - OpenAI: strict parsing errors on non-numeric embedding values - OpenAI: skip Authorization header when api_key is empty - OpenAI: validate response count and dimensions - OpenAI/Ollama: add tracing at entry, success, and error paths - Store: add store_meta table to persist and validate embedding provider/dimensions on open — errors on dimension mismatch - Store: propagate row decode errors instead of filter_map(ok) - Store: add tracing to search, insert, delete, and count paths - Update factories.rs caller for Result-returning factory 101 tests pass, all files at 97%+ coverage.
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 |
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