ff918f030d
## Summary - Bundles the glibc dynamic linker that `sharun` needs inside the Linux AppImage when the post-build AppImage cleanup runs. - Repackages and re-signs AppImage artifacts when the only mutation is adding the missing loader, not just when graphics libraries were stripped. - Keeps the existing Mesa/libdrm/libva stripping behavior intact and adds a release smoke item for the Ubuntu 24.04 `Interpreter not found!` regression. ## Problem - `OpenHuman_0.54.0_amd64.AppImage` can exit immediately on Ubuntu 24.04 with `Interpreter not found!` because the AppImage contains a `sharun` launcher but no `lib/ld-linux-x86-64.so.2`. - The existing post-processing script only repacked when graphics libraries were removed, so it had no guard for a missing `sharun` interpreter. ## Solution - Detect `sharun`-style launchers by checking the extracted AppDir launcher binaries for the `Interpreter not found!` marker. - Resolve the expected loader from the build target architecture and copy it into `squashfs-root/lib/` when missing. - Fail the post-processing step if the AppImage uses `sharun` but the build host cannot provide the required loader. - Track all modified AppImages, whether modified by stripping graphics libs or by adding the loader, so updater tarballs and signatures stay in sync. ## Submission Checklist - [x] Tests added or updated (happy path + at least one failure / edge case) per [Testing Strategy](../gitbooks/developing/testing-strategy.md#failure-path-requirement) — release smoke checklist updated; local function smoke covers loader injection path. - [x] **Diff coverage >= 80%** — N/A: shell release packaging script and docs are not covered by Vitest/cargo diff coverage. - [x] Coverage matrix updated — N/A: release packaging behavior, not a product feature row. - [x] All affected feature IDs from the matrix are listed in the PR description under `## Related` — N/A: no feature matrix row. - [x] No new external network dependencies introduced (mock backend used per [Testing Strategy](../gitbooks/developing/testing-strategy.md#mock-policy)) - [x] Manual smoke checklist updated if this touches release-cut surfaces ([`docs/RELEASE-MANUAL-SMOKE.md`](../docs/RELEASE-MANUAL-SMOKE.md)) - [x] Linked issue closed via `Closes #NNN` in the `## Related` section ## Impact - Linux AppImage release artifacts should launch on clean Ubuntu 24.04 hosts without requiring users to extract the AppImage and manually copy `ld-linux-x86-64.so.2`. - Packaging fails earlier if a future build cannot locate the required loader, preventing a known-broken AppImage from shipping. ## Related - Closes #2297 - Follow-up PR(s)/TODOs: N/A --- ## AI Authored PR Metadata (required for Codex/Linear PRs) ### Linear Issue - Key: N/A - URL: N/A ### Commit & Branch - Branch: `codex/2297-appimage-sharun-loader` - Commit SHA: `4260df24f5355a63df02e748e276586ebed0c53a` ### Validation Run - [x] `pnpm --filter openhuman-app format:check` — N/A: no frontend files changed. - [x] `pnpm typecheck` — N/A: no TypeScript files changed. - [x] Focused tests: Git Bash smoke test for `ensure_sharun_interpreter` copies `ld-linux-x86-64.so.2` into a synthetic sharun AppDir. - [x] Rust fmt/check (if changed): N/A: no Rust files changed. - [x] Tauri fmt/check (if changed): N/A: no Tauri Rust files changed. - [x] Shell syntax/check: `C:\Program Files\Git\bin\bash.exe -n scripts/release/strip-appimage-graphics-libs.sh`; `git diff --check`. ### Validation Blocked - `command:` Full AppImage rebuild and launch on Ubuntu 24.04. - `error:` No produced Linux AppImage artifact is available in this local Windows workspace. - `impact:` CI release packaging will exercise the changed script; manual release smoke checklist now covers the Ubuntu 24.04 launch regression. ### Behavior Changes - Intended behavior change: AppImage post-processing now bundles the missing `sharun` dynamic linker when needed. - User-visible effect: Linux users should no longer hit `Interpreter not found!` on affected AppImages. ### Parity Contract - Legacy behavior preserved: existing graphics-library stripping, re-signing, and updater tarball rebuild behavior remain intact. - Guard/fallback/dispatch parity checks: AppImages unchanged by stripping or loader injection are still left untouched; missing host loader now fails packaging instead of shipping a broken artifact. ### Duplicate / Superseded PR Handling - Duplicate PR(s): N/A - Canonical PR: this PR - Resolution (closed/superseded/updated): N/A <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * AppImages now remove incompatible host graphics libraries and auto-include a missing bundled dynamic loader when needed, preventing "Interpreter not found" failures on clean Ubuntu 24.04 hosts. * **Chores** * Added a smoke-test checklist item to validate AppImage launches on Ubuntu 24.04. * Release tooling now only repacks and re-signs AppImages that were actually modified. <!-- review_stack_entry_start --> [](https://app.coderabbit.ai/change-stack/tinyhumansai/openhuman/pull/2307?utm_source=github_walkthrough&utm_medium=github&utm_campaign=change_stack) <!-- review_stack_entry_end --> <!-- end of auto-generated comment: release notes by coderabbit.ai --> Co-authored-by: aqilaziz <gonzes7@gmail.com> Co-authored-by: Steven Enamakel <enamakel@tinyhumans.ai>
OpenHuman
OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.
Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator)
🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch
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
# Download DMG, EXEs over at https://tinyhumans.ai/openhuman or run in from your terminal
# For macOS or Linux x64
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 designed to integrate with you in your daily life. Each bullet links to the deeper writeup in the docs.
-
Simple, UI-first & Human A clean desktop experience and short onboarding paths take you from install to a working agent in a few clicks — no config-first setup, no terminal required. The agent has a face: a desktop mascot that speaks, reacts to its surroundings, joins your Google Meets as a real participant, remembers you across weeks, and keeps thinking in the background even when you've stopped typing.
-
118+ third-party integrations with auto-fetch: plug into Gmail, Notion, GitHub, Slack, Stripe, Calendar, Drive, Linear, Jira and the rest of your stack with one-click OAuth. Every connection is exposed to the agent as a typed tool, and every twenty minutes the core walks each active connection and pulls fresh data into the memory tree. No prompts, no polling loops you have to write, so the agent already has tomorrow's context this morning.
Managed integrations are backend-proxied through OpenHuman's Composio connector layer. If you want to run Composio directly instead of using the managed backend path, configure direct mode with your own Composio API key; real-time trigger webhooks then need to be hosted and wired by you.
-
Memory Tree + Obsidian Wiki: a local-first knowledge base built from your data and your activity. Everything you connect is canonicalized into ≤3k-token Markdown chunks, scored, and folded into hierarchical summary trees stored in SQLite on your machine. The same chunks land as
.mdfiles in an Obsidian-compatible vault you can open, browse and edit, inspired by Karpathy's obsidian-wiki workflow. -
Batteries included: web search, a web-fetch scraper, a full coder toolset (filesystem, git, lint, test, grep), and native voice (STT in, ElevenLabs TTS out, mascot lip-sync, live Google Meet agent) are wired in by default. Model routing sends each task to the right LLM (reasoning, fast, or vision) under one subscription. No "install a plugin to read files" friction. Optional local AI via Ollama for on-device workloads.
-
Smart token compression (TokenJuice): every tool call, scrape result, email body, and search payload is run through a token compression layer before it touches any LLM Model. HTML is converted to Markdown, long URLs are shortened, and verbose tool output is deduped and summarized via a configurable rule overlay etc... CJK, emoji, and other multi-byte text are preserved grapheme-by-grapheme — never stripped. You get the same information but at a fraction of the tokens. Reducing cost & latency by up to 80%.
-
Messaging channels and privacy & security: inbound/outbound across the channels you already use, with workflow data that stays on device, encrypted locally, treated as yours.
Contributing from source
New contributor? Start with CONTRIBUTING.md for the fork/PR workflow and local validation commands, or use the copy-paste AI-agent prompt in CONTRIBUTING-BEGINNERS.md. The short path is:
- Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.93.0 (
rustfmt+clippy), CMake, Ninja, ripgrep, and the platform desktop build prerequisites. - Fork and clone the repo, then run
git submodule update --init --recursivebeforepnpm installso the vendored Tauri/CEF sources are present. - Use
pnpm devfor web-only UI work,pnpm --filter openhuman-app dev:appfor the desktop shell, and focused checks such aspnpm typecheck,pnpm format:check, andcargo check -p openhuman --libbefore opening a PR.
Deeper docs: Architecture · Getting Set Up · Cloud Deploy.
Context in minutes, not weeks
OpenHuman is the first agent harness that gets to know you in minutes. Inspired by Karpathy's LLM Knowledgebase. Most agents start cold. Hermes learns by watching you work; OpenClaw waits for plugins to ferry context in. Either way, you spend days or weeks before the agent knows enough about your stack to be genuinely useful.
OpenHuman summarizes and compresses all your documents, emails & chats; and creates a memory graph that lets your agent remember everything about you.
OpenHuman skips the wait. Connect your accounts, let auto-fetch pull data locally on a 20-minute loop, and then have Memory Trees compress everything into Markdown files stored intelligently in a Karpathy-style Obsidian wiki.
In just one sync pass, the agent has full (compressed) context of your inbox, your calendar, your repos, your docs, your messages. No training period. No "give it a few weeks.". It becomes you, controlled by you.
Already self-host agentmemory across other coding agents? OpenHuman ships an optional Memory backend that proxies to it — set memory.backend = "agentmemory" in config.toml and the same durable store powers OpenHuman alongside Claude Code, Cursor, Codex, and OpenCode. See the agentmemory backend page for setup.
OpenHuman vs Other Agent Harnesses
High-level comparison (products evolve, so verify against each vendor). OpenHuman is built to minimize vendor sprawl, keep workflow knowledge on-device, and give the agent a persistent memory of your data, not only chat.
| Claude Cowork | OpenClaw | Hermes Agent | OpenHuman | |
|---|---|---|---|---|
| Open-source | 🚫 Proprietary | ✅ MIT | ✅ MIT | ✅ GNU |
| Simple to start | ✅ Desktop + CLI | ⚠️ Terminal-first | ⚠️ Terminal-first | ✅ Clean UI, minutes |
| Cost | ⚠️ Sub + add-ons | ⚠️ BYO models | ⚠️ BYO models | ✅ One sub + TokenJuice |
| Memory | ✅ Chat-scoped | ⚠️ Plugin-reliant | ✅ Self-learning | 🚀 Memory Tree + Obsidian vault, optional agentmemory backend |
| Integrations | ⚠️ Few connectors | ⚠️ BYO | ⚠️ BYO | 🚀 118+ via OAuth |
| Auto-fetch | 🚫 None | 🚫 None | 🚫 None | ✅ 20-min sync into memory |
| API sprawl | 🚫 Extra keys | 🚫 BYOK | 🚫 Multi-vendor | ✅ One account |
| Model routing | 🚫 Single model | ⚠️ Manual | ⚠️ Manual | ✅ Built-in |
| Native tools | ✅ Code-only | ✅ Code-only | ✅ Code-only | ✅ Code + search + scraper + voice |
Star us on GitHub
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Contributors Hall of Fame
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