Steven EnamakelandGitHub
4ee518cf31
* feat(tree-summarizer): implement hierarchical summarization engine and event handling - Introduced a new `tree_summarizer` module to manage hierarchical time-based summaries, organizing data into a tree structure (root → year → month → day → hour). - Added functionality to ingest raw content, summarize it into hour leaves, and propagate summaries upward through the tree. - Implemented event handling for summarization completion and tree rebuild events, enhancing observability and modularity. - Created RPC operations for ingesting content, triggering summarization, querying the tree, and retrieving tree status. - Added comprehensive tests to ensure the reliability of the summarization process and event handling. This update significantly enhances the summarization capabilities of the system, allowing for efficient data organization and retrieval. * feat(tree-summarizer): add CLI support for tree summarization commands - Introduced a new `tree-summarizer` command to the CLI, allowing users to ingest content, run summarization jobs, query the summary tree, check status, and rebuild the tree. - Updated the CLI help documentation to include the new command and its subcommands. - Added a new module `tree_summarizer_cli` to encapsulate the tree summarization functionality. This enhancement improves the usability of the summarization features, providing a streamlined interface for managing hierarchical summaries directly from the command line. * style: apply cargo fmt to tree_summarizer module * feat(tree-summarizer): implement TreeSummarizerEventSubscriber for observability logging - Added a new `TreeSummarizerEventSubscriber` to log events related to tree summarization, enhancing observability. - Updated the `start_channels` function to register the new subscriber. - Refactored the `run_summarization` function to group buffered entries by hour and publish events upon completion of summarization. - Improved documentation and added tests for the new subscriber functionality. This update aims to provide better insights into the summarization process and facilitate future cross-module workflows. * refactor(tree_summarizer): streamline buffer backup and function signature - Simplified the buffer backup process by consolidating the rename operation with context handling for better error reporting. - Cleaned up the function signature of `derive_node_ids_from_hour_id` for improved readability. These changes enhance code clarity and maintainability within the tree summarization engine. * feat(tree_summarizer): enhance tree summarization with metadata support - Updated the `tree_summarizer_ingest` function to accept an optional metadata parameter, allowing users to include additional context during content ingestion. - Refactored related functions to validate and handle metadata, improving the overall robustness of the summarization process. - Adjusted the buffer write functionality to store metadata alongside content, enhancing the data structure for future retrieval and processing. These changes aim to enrich the summarization capabilities and provide more context for ingested content. * refactor(tree_summarizer): improve error message formatting in node ID validation - Enhanced the formatting of error messages in the `validate_node_id` function for better readability and consistency. - Adjusted the string formatting to use multi-line syntax, improving clarity in error reporting. - Minor formatting changes in the `strip_buffer_frontmatter` function to enhance code readability. These changes aim to improve the maintainability and clarity of error handling within the tree summarization module. * refactor(tree_summarizer): enhance buffer management and summarization process - Replaced the buffer draining mechanism with a non-destructive read approach, allowing for safer data handling during summarization. - Introduced a new `buffer_delete` function to explicitly manage the deletion of buffer entries after successful processing. - Updated the `run_summarization` function to reflect these changes, ensuring that buffer entries are only deleted after durable writes are confirmed. - Improved the backup process for the buffer directory during tree rebuilds, ensuring it is preserved outside the tree structure. These modifications aim to improve data integrity and clarity in the summarization workflow. * refactor(tree_summarizer): improve markdown parsing and timestamp handling - Updated the `parse_node_markdown` function to trim trailing whitespace from the body after splitting frontmatter, enhancing data cleanliness. - Modified test cases to use specific timestamps instead of the current time, ensuring consistent and predictable test results. - Adjusted assertions in tests to reflect the new timestamp-based ordering of entries. These changes aim to improve the robustness of markdown parsing and the reliability of test outcomes in the tree summarization module.
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.
-
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.
-
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 |
Contributors Hall of Fame
Show some love and end up in the hall of fame