sanil-23andClaude Opus 4.6
990a72647c
Follow-up to #522 that addresses a rough UX edge in the welcome agent prompt: users who arrive with only an API key configured (no channels, no Composio integrations, no web search / browser / local AI) were getting a "gentle suggestion" to connect something without any concrete picture of what they'd actually unlock. The welcome agent would finish onboarding, hand off to orchestrator, and leave the user staring at a functional-but-empty assistant with no roadmap for how to make it useful. This commit rewrites the prompt to handle sparse setups explicitly. No Rust changes — this is prompt.md only, picked up at build time via the existing `include_str!` loader in `agents/mod.rs`. Changes to `src/openhuman/agent/agents/welcome/prompt.md`: * Step 2's "point out what's missing" sub-point is rewritten from a single "gently suggest" line into a four-case decision tree keyed off `check_status` state: - No API key → critical, block completion. - Integrations yes, channels no → note the Tauri-only reach limitation, suggest a messaging platform. - Channels yes, integrations no → degraded assistant, nudge toward Composio. - Nothing beyond the API key → the "bare install" case, gets the new Step 2.5 treatment. * New **Step 2.5: Handling a bare install** section added after Step 2. Spells out what the user DOES have (sandboxed reasoning + coding assistant with memory), what they're MISSING (any external action), and how to structure the message: state the current capability honestly, pitch 2-3 specific integrations with concrete example prompts, point to Settings → Integrations / Channels, and leave room for the user to opt into the coding-only experience if that's what they actually want. For bare-install users the word budget stretches to 250-400 words (up from 200-350) so the concrete pitches and example prompts actually fit without cramming. * New **Integration capability reference** section giving the LLM a menu it can draw from when pitching integrations. Each entry is a one-line "connect X → I can Y" with a concrete example prompt the user could send next: - Gmail: "Summarise the most important emails that came in overnight and flag anything that needs a reply today." - Google Calendar: "What's on my calendar tomorrow, and do I have a 30-minute gap before 2pm?" - GitHub: "List open issues on my main project tagged 'bug' and summarise which ones look newest or most urgent." - Notion: "Pull up my 'Ideas' Notion database and show me the three newest entries." - Slack / Discord / Linear / Jira / etc. with similar shapes. Plus a sub-section for messaging platforms (Telegram / Discord / Slack / iMessage / WhatsApp / Signal / web-fallback) that clarifies which each is best for, and a sub-section for the other capabilities (web search, browser automation, HTTP requests, local AI) that explains what breaks without them. The LLM is told NOT to list everything — just pick 2-3 most likely to matter, defaulting to Gmail + GitHub + one of {Calendar, Notion} as the top-3 pitch when no profile context is available. * Tone guidelines updated to document the stretched word budget for bare installs (200-350 for configured users, 250-400 for bare installs). * "What NOT to do" list updated: - Explicitly allows product-tour-style listing ONLY in the bare-install case (Step 2.5), forbids it elsewhere. - Clarifies that describing what WOULD unlock with integration X is fine and encouraged; claiming a capability the user doesn't have is still forbidden. - Adds a new "Don't gloss over a bare install" entry that pins the rule: API-key-only users get concrete pitches and example prompts, not vague suggestions. Scope note: this commit does NOT change the completion logic. `complete_onboarding(complete)` still accepts API-key-only as the minimum bar — that's a separate design question for the maintainer about whether zero-integration users should be gatekept. This change improves what the welcome agent SAYS to those users, not whether they're allowed to proceed. Tests: all 14 `agent::agents::tests` pass (including `welcome_has_onboarding_and_memory_tools` which validates the welcome agent's declarative shape is unchanged). The prompt.md edit is pure content — no schema changes, no tool additions. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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 |
Contributors Hall of Fame
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