Steven EnamakelandGitHub
4afa751024
* feat(memory): add CLI support for memory commands - Introduced a new `memory` subcommand in the CLI for memory ingestion, graph inspection, and debugging. - Implemented various subcommands including `ingest`, `docs`, `graph`, `query`, and `namespaces` for comprehensive memory management. - Updated the CLI entry point to route the `memory` command appropriately, enhancing the command-line interface functionality. * refactor(memory_cli): streamline memory command ingestion and improve error handling - Simplified the ingestion process by removing unnecessary workspace directory creation and embedding logic. - Updated the ingestion function to utilize the `create_memory_client` for better client management. - Changed the limit parameter type from `usize` to `u32` for consistency and improved error handling in command arguments. - Enhanced logging for ingestion start to focus on model name only, removing redundant extraction mode information. * refactor(memory): implement global memory client singleton for improved resource management - Introduced a new `global.rs` module to manage a process-global memory client singleton, ensuring consistent access across subsystems. - Updated `create_memory_client` to utilize the global client, enhancing memory management and reducing resource contention. - Refactored various modules to replace local memory client instances with the global singleton, improving performance and reliability. - Adjusted CLI and screen intelligence components to leverage the global memory client for document persistence and ingestion operations. This refactor enhances the architecture by centralizing memory client management, leading to better resource utilization and simplified code structure. * feat(memory): add put_doc_light for screen-intelligence, skip vectors/graph Screen-intelligence captures are too frequent and ephemeral to justify vector embedding and GLiNER graph extraction per frame. Adds a lightweight storage path (put_doc_light) that persists the document row and markdown file without chunking, embedding, or graph extraction. Three-tier storage: - put_doc_light: DB + markdown only (screen-intelligence) - put_doc: DB + markdown + vectors + background graph (skill sync) - ingest_doc: full synchronous pipeline (CLI, debugging) * style: apply cargo fmt formatting * fix: add missing window_id field in AppContext test helper * fix(test): fall back to per-call MemoryClient when global not initialized In tests with isolated OPENHUMAN_WORKSPACE, the process-global singleton may not be initialized or may point at the wrong directory. Fall back to creating a client from Config (which respects env vars) when the global is not ready. * fix(test): use per-call MemoryClient for screen-intelligence persistence put_doc_light does no background work (no vectors, no graph), so a per-call client created from Config is safe and avoids the global singleton which may point at a different workspace in test suites.
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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