35be5e99e8
* chore(workflows): comment out Windows smoke tests in installer and release workflows * feat: add usage field to ChatResponse structure - Introduced a new `usage` field in the `ChatResponse` struct across multiple files to track token usage information. - Updated various test cases and response handling to accommodate the new field, ensuring consistent behavior in the agent's responses. - Enhanced the `Provider` trait and related implementations to include the `usage` field in responses, improving observability of token usage during interactions. * feat: introduce structured error handling and event system for agent loop - Added a new `AgentError` enum to provide structured error types, allowing differentiation between retryable and permanent failures. - Implemented an `AgentEvent` enum for a typed event system, enhancing observability during agent loop execution. - Created a `ContextGuard` to manage context utilization and trigger auto-compaction, preventing infinite retry loops on compaction failures. - Updated the `mod.rs` file to include the new `UsageInfo` type for improved observability of token usage. - Added comprehensive tests for the new error handling and event system, ensuring robustness and reliability in agent operations. * feat: implement token cost tracking and error handling for agent loop - Introduced a `CostTracker` to monitor cumulative token usage and enforce daily budget limits, enhancing cost management in the agent loop. - Added structured error types in `AgentError` to differentiate between retryable and permanent failures, improving error handling and recovery strategies. - Implemented a typed event system with `AgentEvent` for better observability during agent execution, allowing multiple consumers to subscribe to events. - Developed a `ContextGuard` to manage context utilization and trigger auto-compaction, preventing excessive resource usage during inference calls. These enhancements improve the robustness and observability of the agent's operations, ensuring better resource management and error handling. * style: apply cargo fmt formatting Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(agent): enhance error handling and event structure - Updated `AgentError` conversion to attempt recovery of typed errors wrapped in `anyhow`, improving error handling robustness. - Expanded `AgentEvent` enum to include `tool_arguments` and `tool_call_ids` for better context in tool calls, and added `output` and `tool_call_id` to `ToolExecutionComplete` for enhanced event detail. - Improved `EventSender` to clamp channel capacity to avoid panics and added tracing for event emissions, enhancing observability during event handling. * fix(agent): correct error conversion in AgentError implementation - Updated the conversion logic in the `From<anyhow::Error>` implementation for `AgentError` to return the `agent_err` directly instead of dereferencing it. This change improves the clarity and correctness of error handling in the agent's error management system. * refactor(config): simplify default implementations for ReflectionSource and PermissionLevel - Added `#[derive(Default)]` to `ReflectionSource` and `PermissionLevel` enums, removing custom default implementations for cleaner code. - Updated error handling in `handle_local_ai_set_ollama_path` to streamline serialization of service status. - Refactored error mapping in webhook registration and unregistration functions for improved readability. * refactor(config): clean up LearningConfig and PermissionLevel enums - Removed unnecessary blank lines in `LearningConfig` and `PermissionLevel` enums for improved code readability. - Consolidated `#[derive(Default)]` into a single line for `PermissionLevel`, streamlining the code structure. --------- 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.
OpenHuman is an open-source agentic assistant that is designed to integrate with you in your daily life. Here's what makes OpenHuman special:
-
One subscription, many providers — One assistant wired to skills and backend models so you are not juggling a separate subscription stack for every integration surface.
-
Incredible memory — Rust-side memory (store / recall / namespaces) plus optional TinyHumans Neocortex-backed context when configured, so the agent can retain and retrieve more than a single chat window. Channels and ongoing conversations feed the same loop so day-to-day context does not reset every session.
-
Screen intelligence — Regular screen capture (on a cadence or when triggered) feeds an on-device pipeline that understands what is on screen, distills it into memory (facts, UI state, workflows), and can propose actions the agent executes for you. OS permissions and capture APIs vary by platform; the goal is your machine first, not shipping raw frames to the cloud by default.
-
Voice & meetings — A Local-model speech stack (listen / TTS) let the assistant talk back and capture or work with meeting audio with a privacy-first default when you route inference locally. Transcripts and summaries land in the same memory + agent loop so OpenHuman can follow up: tasks, drafts, calendar nudges, or skill-backed workflows—without treating a meeting as a one-off chat.
-
Memory-aware autocomplete — Keyboard autocomplete is built for right-context suggestions: it consults memory namespaces and recent context so completions stay aligned with you, your workspace, and prior sessions—not a blank model every keystroke.
-
Runs a 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.
-
Simple or advanced — Skill setup wizards and defaults for common tools, with room to go deeper via settings, credentials, and core RPC when you need control and privacy.
Architecture: docs/ARCHITECTURE.md. Contributor orientation: CONTRIBUTING.md.
Download
Early Beta — Under active development. Expect rough edges.
You can download the latest desktop build from the website at tinyhuman.ai/openhuman. You can also grab it from the latest GitHub release, which includes all current artifacts (.dmg, .deb, .AppImage, .app.tar.gz, and more).
If you need an older version, browse all releases.
If you want to build from source, see docs/BUILDING.md.
Install with one command:
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
On Windows, use PowerShell:
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
What setup does:
- Resolves the latest stable release for your OS/arch
- Verifies release digest when available
- Installs locally without requiring system-wide admin rights by default
- macOS: installs
OpenHuman.appin~/Applications - Linux: installs
openhumanAppImage in~/.local/bin/openhumanand creates a desktop entry - Windows: installs from latest release MSI/EXE in per-user mode where supported
Under the hood (Architecture)
OpenHuman is a desktop monorepo: Rust owns business logic and execution; the UI owns interaction, layout, and OS integration.
Rust (openhuman / openhuman_core). The repo root src/ crate is the brain: JSON-RPC over HTTP (core_server), domain modules (auth, config, memory, skills, channels, screen intelligence, local AI, cron, …), and a QuickJS runtime for sandboxed JavaScript skills. The openhuman binary is built and staged next to the Tauri app so the desktop shell can spawn it as a sidecar. Heavy work—SQLite, sockets, crypto, skill lifecycle—runs there under Tokio, not in the WebView.
UI (app/). Vite + React (TypeScript) implements screens, onboarding, settings, and realtime UX. Redux Toolkit holds client state; Socket.io and the MCP-style client stack stay in sync with the core’s realtime surface. Tauri v2 (app/src-tauri/) is a thin Rust host: windowing, filesystem hooks where needed, and core_rpc_relay—forwarding JSON-RPC from the WebView to the openhuman process so the UI never re-implements domain rules.
Controllers and the RPC surface. Features are exposed as registered controllers: each domain declares schemas (namespace, function name, parameter shapes) and a handler. At runtime, calls are validated, dispatched by method name (e.g. openhuman.auth_get_state, openhuman.local_ai_agent_chat), and return structured outcomes. CLI and HTTP share the same controller catalog, so automation, tests, and the app all hit one contract.
What ties it together: one registry of controllers, one sidecar process for execution, Tauri IPC for shell-only capabilities, and HTTP JSON-RPC for everything else—plus skills and dual-socket behavior documented in the architecture guide.
Read more: docs/ARCHITECTURE.md · Frontend tree: docs/src/README.md · Tauri commands: docs/src-tauri/README.md
Star us on GitHub
Building toward AGI and artificial consciousness? Star the repo and help others find the path.
Contributors Hall of Fame
Show some love and end up in the hall of fame