sanil-23andGitHub
305c58a6ec
* fix: reduce agent loop hallucination and improve tool call reliability - Strengthen tool-use instructions with explicit anti-hallucination rules: "NEVER narrate tool use without emitting tags", "use exact tool names", "only respond without tool call when no tool is needed" - Wire context guard into tool loop: check utilization before each LLM call, abort on context exhaustion (>95% with circuit breaker tripped) - Add 120-second timeout on tool execution to prevent hangs - Add debug/warn/error logging at all loop boundaries: LLM request, response (with token counts), tool call parsing, tool execution, unknown tools, timeouts, and final response Closes #144 * fix: implement chat(ChatRequest) on ReliableProvider and add bracket tool call parser Root cause: ReliableProvider did not implement the chat(ChatRequest) trait method. The agent loop called provider.chat() which fell through to the default trait implementation — this used chat_with_history() which strips native tool support and sends raw tool-role messages without the required assistant tool_calls, causing the backend Jinja template to reject the request with "Message has tool role, but there was no previous assistant message with a tool call!" Fixes: - Add chat(ChatRequest) to ReliableProvider with full retry/failover logic, matching the existing chat_with_system/chat_with_history implementations. Delegates to inner provider's chat() which properly converts messages to native OpenAI format with tool_calls. - Add [TOOL_CALL]/[/TOOL_CALL] bracket format to the tool call parser (parse.rs) — some models emit this format instead of <tool_call> XML. - Add parse_bracket_tool_call() for the pseudo-syntax format: {tool => "name", args => { --key "value" }} Verified with real staging backend (agentic-v1 model): - Shell tool calls execute successfully - File read tool calls return real content - Knowledge questions return without tool calls - No Jinja template errors Closes #144
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:
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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.
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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.
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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.
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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.
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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.
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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.
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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
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