Steven EnamakelandGitHub
e5f571cec7
* feat(tokenjuice): implement core functionality for terminal-output compaction engine - Introduced the `tokenjuice` module, which includes the classification and reduction of tool outputs based on JSON-configured rules. - Added new dependencies for Unicode handling: `unicode-segmentation` and `unicode-width`. - Implemented the `classify` module to match tool execution inputs against predefined rules, enhancing the ability to process and summarize terminal outputs. - Created a comprehensive set of types and utilities for managing tool execution inputs and classification results. - Established a built-in rule set for common tools, improving the initial setup and usability of the `tokenjuice` engine. - Enhanced testing framework with integration tests to ensure the accuracy of output compaction and classification. These changes lay the groundwork for a robust terminal-output management system, facilitating better interaction with various tools and improving overall user experience. * feat(tokenjuice): implement tokenjuice module for terminal output compaction - Introduced the `tokenjuice` module, which includes functionality for classifying and reducing terminal output based on JSON-configured rules. - Added new dependencies: `unicode-segmentation` and `unicode-width` to support text processing. - Created a new `classify.rs` file for rule classification logic, including matching helpers and scoring functions. - Implemented a `reduce.rs` file to handle the main reduction pipeline and text normalization. - Established a structured approach for loading and compiling rules from multiple sources, including built-in and user-defined rules. - Added integration tests to ensure the correctness of the output reduction process. These changes enhance the application's ability to manage and compact verbose tool outputs, improving overall efficiency and user experience. * test(tokenjuice): enhance test coverage for classification and reduction logic - Added a series of unit tests to `classify.rs` to validate the behavior of tool name filters and argument matching, ensuring correct classification of tool executions. - Introduced tests for edge cases in `reduce.rs`, including command tokenization and normalization of execution inputs, to improve robustness against various input formats. - Expanded tests in `builtin.rs` to cover duplicate ID reporting and compile issues, enhancing error handling and reporting mechanisms. - Implemented additional tests in `compiler.rs` to verify regex handling in rule definitions, ensuring invalid patterns are correctly ignored. These enhancements improve the overall test coverage and reliability of the tokenjuice module, facilitating better maintenance and future development. * test(tokenjuice): add edge-case tests for gh table and reduction pipeline * style(tokenjuice): apply cargo fmt * feat(tokenjuice): wire into agent tool loop output compaction Add `tokenjuice::compact_tool_output` helper and call it in the agent tool loop after credential scrubbing (and on error paths with exit=1), before any optional payload_summarizer. Derives argv/command heuristically from JSON tool arguments (command / args / argv / cmd shapes) so shell-wrapping tools still match upstream family rules (git/*, package/*, tests/*, etc.). Pass-through safe: outputs under 512 bytes or where compaction saves <5% are returned untouched. * fix(tokenjuice): address coderabbit review comments - classify: derive command from argv join when input.command is unset, so commandIncludes* rules still match argv-only callers - rules/loader: log read_dir / file_type / read_to_string failures at debug level so permission or filesystem issues are observable rather than silently skipped - text/ansi: add trace log at strip_ansi entry/exit with lengths (no text content) per the project debug-logging rules - tests: remove orphan src/openhuman/tokenjuice/tests/integration.rs which was never wired into any module declaration; the real fixture- parity runner lives at tests/tokenjuice_integration.rs and asserts hard when the fixtures directory is missing Vendored-rule issues (docker-ps / kubectl-describe / git/branch / grep casing / counter-pattern overbreadth / etc.) come from upstream and are left as-is; this module is a straight port of the upstream rule set and should not fork from it in v1.
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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