ed83cae117
* refactor(agent): update default model configuration and pricing structure - Changed the default model name in `AgentBuilder` to use a constant `DEFAULT_MODEL` instead of a hardcoded string. - Introduced new model constants (`MODEL_AGENTIC_V1`, `MODEL_CODING_V1`, `MODEL_REASONING_V1`) in `types.rs` for better clarity and maintainability. - Refactored the pricing structure in `identity_cost.rs` to utilize the new model constants, improving consistency across the pricing definitions. These changes enhance the configurability and readability of the agent's model and pricing settings. * refactor(models): update default model references and suggestions - Replaced hardcoded model names with a constant `DEFAULT_MODEL` in multiple files to enhance maintainability. - Updated model suggestions in the `TauriCommandsPanel` and `Conversations` components to reflect new model names, improving user experience and consistency across the application. These changes streamline model management and ensure that the application uses the latest model configurations. * style: fix Prettier formatting for model suggestions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(agent): introduce multi-agent harness with archetypes and task DAG - Added a new module for the multi-agent harness, defining 8 specialized archetypes (Orchestrator, Planner, CodeExecutor, SkillsAgent, ToolMaker, Researcher, Critic, Archivist) to enhance task management and execution. - Implemented a Directed Acyclic Graph (DAG) structure for task planning, allowing the Planner archetype to create and manage task dependencies. - Introduced a session queue to serialize tasks within sessions, preventing race conditions and enabling parallelism across different sessions. - Updated configuration schema to support orchestrator settings, including per-archetype configurations and maximum concurrent agents. These changes significantly improve the agent's architecture, enabling more complex task management and execution strategies. * feat(agent): implement orchestrator executor and interrupt handling - Introduced a new `executor.rs` module for orchestrated multi-agent execution, enabling a structured run loop that includes planning, executing, reviewing, and synthesizing tasks. - Added an `interrupt.rs` module to handle graceful interruptions via SIGINT and `/stop` commands, ensuring running sub-agents can be cancelled and memory flushed appropriately. - Implemented a self-healing interceptor in `self_healing.rs` to automatically create polyfill scripts for missing commands, enhancing the robustness of tool execution. - Updated the `mod.rs` file to include new modules and functionalities, improving the overall architecture of the agent harness. These changes significantly enhance the agent's capabilities in managing multi-agent workflows and handling interruptions effectively. * feat(agent): implement orchestrator executor and interrupt handling - Introduced a new `executor.rs` module for orchestrated multi-agent execution, enabling a structured run loop that includes planning, executing, reviewing, and synthesizing tasks. - Added an `interrupt.rs` module to handle graceful interruptions via SIGINT and `/stop` commands, ensuring running sub-agents are cancelled and memory is flushed. - Implemented a `SelfHealingInterceptor` in `self_healing.rs` to automatically generate polyfill scripts for missing commands, enhancing the agent's resilience. - Updated the `mod.rs` file to include new modules and functionalities, improving the overall architecture of the agent harness. These changes significantly enhance the agent's ability to manage complex tasks and respond to interruptions effectively. * feat(agent): add context assembly module for orchestrator - Introduced a new `context_assembly.rs` module to handle the assembly of the bootstrap context for the orchestrator, integrating identity files, workspace state, and relevant memory. - Implemented functions to load archetype prompts and identity contexts, enhancing the orchestrator's ability to generate a comprehensive system prompt. - Added a `BootstrapContext` struct to encapsulate the assembled context, improving the organization and clarity of context management. - Updated `mod.rs` to include the new context assembly module, enhancing the overall architecture of the agent harness. These changes significantly improve the orchestrator's context management capabilities, enabling more effective task execution and user interaction. * style: apply cargo fmt to multi-agent harness modules Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: resolve merge conflict in config/mod.rs re-exports Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address PR review findings — security, correctness, observability Inline fixes: - executor: wire semaphore to enforce max_concurrent_agents cap - executor: placeholder sub-agents now return success=false - executor: halt DAG when level has failed tasks after retries - self_healing: remove overly broad "not found" pattern - session_queue: fix gc() race with acquire() via Arc::strong_count check - skills_agent.md: reference injected memory context, not memory_recall tool - init.rs: run EPISODIC_INIT_SQL during UnifiedMemory::new() - ask_clarification: make "question" param optional to match execute() default - insert_sql_record: return success=false for unimplemented stub - spawn_subagent: return success=false for unimplemented stub - run_linter: reject absolute paths and ".." in path parameter - run_tests: catch spawn/timeout errors as ToolResult, fix UTF-8 truncation - update_memory_md: add symlink escape protection, use async tokio::fs::write Nitpick fixes: - archivist: document timestamp offset intent - dag: add tracing to validate(), hoist id_map out of loop in execution_levels() - session_queue: add trace logging to acquire/gc - types: add serde(rename_all) to ReviewDecision, preserve sub-second Duration - ORCHESTRATOR.md: add escalation rule for Core handoff - read_diff: add debug logging, simplify base_str with Option::map - workspace_state: add debug logging at entry and exit - run_tests: add debug logging for runner selection and exit status Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- 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:
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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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