Steven EnamakelandGitHub
8057f2283c
* refactor(composio): restructure toolkit metadata handling and onboarding steps - Removed the old `toolkitMeta.ts` file and replaced it with a new `toolkitMeta.tsx` file that includes updated metadata handling for Composio toolkits, enhancing the integration with React components. - Updated the `ComposioConnectModal` to directly render icons without additional markup, streamlining the component structure. - Modified the `Skills` page to utilize the new icon rendering method, improving consistency across the application. - Enhanced the onboarding process by introducing a new `ContextGatheringStep` component, which gathers user context from connected integrations, improving the onboarding experience. - Updated the `SkillsStep` to reflect changes in toolkit connection handling and display, ensuring a smoother user interaction during onboarding. * feat(apify): introduce Apify integration tools for actor execution and status retrieval - Added new tools for running Apify actors and fetching their run statuses, enhancing automation capabilities. - Updated the integration schema to include an `apify` toggle for user configuration, allowing for flexible integration management. - Enhanced the onboarding experience by modifying the SkillsStep to focus on Gmail integration, streamlining user interactions. - Improved documentation and comments for clarity on the new Apify functionalities and their usage. * feat(learning): add LinkedIn enrichment module and schemas - Introduced a new `linkedin_enrichment` module for enriching user profiles by scraping LinkedIn data from Gmail. - Implemented the `run_linkedin_enrichment` function to handle the enrichment pipeline, including Gmail search, scraping via Apify, and data persistence. - Added controller schemas for the learning domain, enabling integration with the existing controller framework. - Updated the `all.rs` file to register the new learning controllers and schemas, enhancing the overall functionality of the learning system. * feat(linkedin): implement PROFILE.md generation for LinkedIn enrichment - Added functionality to generate a PROFILE.md file from scraped LinkedIn data, summarizing user profiles for agent context. - Updated the `run_linkedin_enrichment` function to write PROFILE.md to the workspace, enhancing data persistence. - Introduced helper functions for rendering and summarizing LinkedIn profiles, improving the overall enrichment process. - Ensured minimal PROFILE.md creation even when scraping fails, maintaining essential user context. * feat(onboarding): enhance ContextGatheringStep for LinkedIn enrichment pipeline - Updated the ContextGatheringStep to integrate a new pipeline for LinkedIn enrichment, replacing the previous Gmail profile fetching stages. - Implemented a progress animation and logging for the enrichment process, improving user feedback during data retrieval. - Refactored stage definitions to align with the new pipeline structure, enhancing clarity and maintainability. - Introduced error handling and status updates for each stage of the enrichment process, ensuring robust user experience. * feat(instructions): refactor tool instruction generation for clarity and flexibility - Introduced helper functions `tool_instructions_preamble` and `append_tool_entry` to streamline the construction of tool instructions. - Updated `build_tool_instructions` to utilize the new helper functions, improving code readability and maintainability. - Added `build_tool_instructions_filtered` to allow for generating instructions from a filtered list of tools, enhancing flexibility in tool usage. - Adjusted the startup process to use the filtered instructions, ensuring only relevant tools are included in the system prompt. * refactor(toolkitMeta): simplify component structure and improve readability - Refactored the `BrandIcon` component to streamline its props definition, enhancing code clarity. - Consolidated SVG path definitions in various icons for better readability and maintainability. - Updated the `ContextGatheringStep` to simplify the RPC call syntax, improving code conciseness. - Enhanced logging in the LinkedIn enrichment process for clearer tracking of Gmail searches and scraping stages. * fix: address PR review — USER.md→PROFILE.md consistency, Composio tool filtering, and quality fixes - Replace USER.md with PROFILE.md across all prompt paths: channels_prompt.rs, subconscious/prompt.rs, workspace/ops.rs bootstrap, and channel tests - Remove Composio tool description from main agent system prompt (tool_descs) so skills_agent is the only agent that sees Skill-category tools - Add "learning" namespace description for CLI help discovery - Fix react-hooks/set-state-in-effect: wrap synchronous setState in queueMicrotask in ContextGatheringStep - Derive SkillsStep displayToolkits from backend allowlist with error/retry UI - Add KNOWN_COMPOSIO_TOOLKITS alternate slug variants (google_calendar, etc.) - Add namespaced debug logging to onboarding handlers and pipeline - Deduplicate MemoryClient creation in linkedin_enrichment persist functions * fix: use setTimeout instead of queueMicrotask for ESLint compatibility * refactor(onboarding): streamline debug logging in handleContextNext function - Consolidated debug logging in the handleContextNext function to improve clarity and reduce verbosity. - Removed unnecessary line breaks for a more concise code structure. * refactor(linkedin): enhance enrichment pipeline with structured stage results - Introduced a new `EnrichmentStage` struct to capture detailed results for each stage of the LinkedIn enrichment process. - Updated the `LinkedInEnrichmentResult` to include a vector of stages, allowing for structured reporting of success, failure, and skipped stages. - Improved error handling and logging throughout the enrichment pipeline, ensuring better traceability of issues during execution. - Adjusted the API response to include stage results, enhancing the frontend's ability to display detailed enrichment outcomes. * chore(workflows): update macOS E2E test configuration and comment out Linux E2E job - Modified the description and default values in the macOS E2E test input options for clarity. - Commented out the entire Linux E2E job configuration to prevent execution while maintaining the setup for future use.
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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