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Steven EnamakelandGitHub
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* Add built-in agent definitions and prompts for orchestrator, planner, code executor, skills agent, researcher, critic, archivist, and tool maker - Introduced a new module for built-in agent definitions, allowing for easy addition of agents through a structured format. - Created TOML configuration files for each agent, detailing their properties such as id, display name, usage context, and tool specifications. - Developed corresponding prompt files that outline the responsibilities and rules for each agent, enhancing their functionality and usability. - Implemented a loading function to parse these definitions into usable agent structures, ensuring all agents are correctly initialized and validated. * Remove deprecated archetypes and related components from the multi-agent harness - Deleted the `archetypes.rs`, `dag.rs`, `executor.rs`, and `types.rs` files, which contained the definitions and implementations for agent archetypes, task DAGs, and execution logic. - Updated the `builtin_definitions.rs` and `definition.rs` files to remove references to the now-removed archetypes, streamlining the agent definition process. - Refactored the `mod.rs` file to eliminate unused modules and clarify the structure of the multi-agent harness. - Adjusted comments and documentation to reflect the removal of the DAG orchestration flow, emphasizing the current sub-agent delegation model. * Remove unused `ArchetypeConfig` from schema exports in `mod.rs` to streamline configuration management. * Implement context guard and memory management features in the harness module - Introduced `ContextGuard` to monitor context utilization and trigger auto-compaction when usage exceeds defined thresholds. - Added `scrub_credentials` function to sanitize sensitive information from tool outputs. - Implemented history management functions to trim conversation history and build compaction transcripts. - Created `build_tool_instructions` to generate tool usage guidelines for the system prompt. - Developed `memory_context` functions to manage user memory and relevant context for conversations. - Enhanced the `tool_loop` to integrate the new context management and tool invocation logic. This update improves the agent's ability to manage context effectively, ensuring efficient memory usage and secure handling of sensitive data. * Refactor agent module imports to use the harness instead of loop_ - Updated import paths in `dispatcher.rs`, `memory_loader.rs`, `mod.rs`, `dispatch.rs`, and `startup.rs` to replace references from the `loop_` module to the `harness` module. - This change enhances module organization and aligns with the recent architectural updates in the agent structure. * Remove cost tracking and context assembly modules from the agent structure - Deleted `cost.rs` and `context_assembly.rs` files, which handled token cost tracking and context assembly for the multi-agent harness, respectively. - Updated `mod.rs` files to remove references to the deleted modules, streamlining the agent's organization and focusing on essential components. - This cleanup enhances maintainability and aligns with the current architectural direction of the project. * Remove identity module and related configurations from the agent structure - Deleted the `identity.rs` file, which contained the AIEOS identity handling logic and related structures. - Updated `mod.rs` and other files to remove references to the deleted identity module, streamlining the agent's organization. - This change simplifies the codebase and aligns with the decision to rely solely on OpenClaw markdown files for identity management. * Enhance agent prompts with improved formatting and clarity - Added spacing for better readability in the prompts of the Archivist, Code Executor, Critic, Orchestrator, Researcher, Skills Agent, and Tool Maker agents. - Updated the table formatting in the Orchestrator prompt for consistency and clarity. - These changes improve the overall presentation and usability of agent documentation, making it easier for users to understand agent responsibilities and rules. * Remove REPL functionality and related components from the agent structure - Deleted the `repl.rs` file, which contained the implementation for the interactive REPL. - Removed references to the REPL in `cli.rs`, `mod.rs`, and other related files, streamlining the agent's organization. - Eliminated unused REPL session handling logic from the agent schemas and local AI operations, enhancing maintainability and focusing on essential components. - This cleanup aligns with the current architectural direction of the project, simplifying the codebase. * Refactor imports and clean up configuration exports - Updated import paths in `startup.rs` to include `host_runtime` from the correct module. - Streamlined the export statements in `mod.rs` and `schema/mod.rs` for better organization and clarity. - Removed unnecessary line breaks in the export lists to enhance readability and maintainability of the configuration schema. * Remove unused history management functions and clean up agent module exports - Deleted the `history.rs` and `session.rs` files, which contained functions for managing conversation history and session handling. - Updated `mod.rs` to remove references to the deleted modules and streamline the agent's organization. - Cleaned up import statements in `mod.rs` and other files to enhance clarity and maintainability of the codebase. * Add agent harness module with builder, runtime, and turn management - Introduced a new `harness` module for the `Agent`, encapsulating the `AgentBuilder`, runtime accessors, and turn lifecycle management. - Implemented the `AgentBuilder` fluent API for constructing `Agent` instances with customizable configurations. - Developed the `turn` method to handle user interactions, tool execution, and context management within the agent. - Created a `runtime` module for public accessors and utility functions related to agent operations. - Added comprehensive unit and integration tests to ensure the functionality of the new agent structure and its components. * Remove classifier and traits modules from the agent structure - Deleted the `classifier.rs` and `traits.rs` files, which contained the classification logic and core agent traits, respectively. - Updated `mod.rs` to remove references to the deleted modules, streamlining the agent's organization. - This cleanup enhances maintainability and focuses on essential components of the agent architecture. * Refactor agent prompt structure and remove unused files - Updated the `prompt.rs` file to streamline the orchestrator's logic by removing unnecessary tool documentation references and simplifying the file list. - Deleted several prompt files (`AGENTS.md`, `BOOTSTRAP.md`, `CONSCIOUS_LOOP.md`, `MEMORY.md`, `README.md`, `TOOLS.md`) to declutter the project and focus on essential components. - Adjusted the `harness/mod.rs` file to change module visibility from public to crate-level for better encapsulation. - These changes enhance maintainability and align with the current architectural direction of the project. * Refactor bootstrap file handling and update tests - Simplified the list of bundled prompt files in `prompt.rs` by removing references to `AGENTS.md` and `TOOLS.md`, focusing on essential identity files. - Adjusted the logic for injecting `MEMORY.md` to be optional, ensuring it only appears if it exists. - Updated test cases in `common.rs`, `identity.rs`, and `prompt.rs` to reflect the changes in the bootstrap file structure and ensure accurate validation of the new logic. - These modifications enhance clarity and maintainability of the codebase while aligning with the current project architecture. * Implement agent delegation tools and browser automation features - Introduced new tools for agent delegation, including `ArchetypeDelegationTool`, `AskClarificationTool`, `DelegateTool`, and `SkillDelegationTool`, enhancing the agent's ability to manage tasks through specialized sub-agents. - Added `SpawnSubagentTool` for delegating tasks to sub-agents, allowing for more complex workflows and improved task management. - Implemented browser automation capabilities with `BrowserOpenTool`, enabling secure opening of approved URLs in the Brave Browser. - Enhanced the `BrowserTool` with pluggable backends for improved automation and user interaction. - Added `ImageInfoTool` for extracting metadata from images, supporting future multimodal capabilities. - Organized tools into a new `impl` module structure for better maintainability and clarity in the codebase. * Refactor imports and clean up tool implementations - Updated import statements across various tool implementations to ensure consistency and clarity, particularly in the `traits.rs` file. - Removed unnecessary line breaks and adjusted module paths for better organization. - Cleaned up test files by removing trailing whitespace, enhancing code readability and maintainability. - These changes streamline the codebase and improve the overall structure of tool-related components. * Update built-in definitions and prompt handling for clarity and consistency - Revised the test for built-in agent definitions to dynamically reference the length of `BUILTINS`, enhancing maintainability. - Clarified documentation for the `system_prompt` field in `AgentDefinition`, emphasizing the default behavior and usage of inline prompts. - Simplified the `build_system_prompt` function by removing unnecessary conditional logic, ensuring a more straightforward return of the prompt string. * Enhance agent definition loading and documentation clarity - Updated the `load_file` function to reject definitions with missing or empty `system_prompt`, ensuring custom definitions are properly validated. - Added a new test to verify that definitions lacking a `system_prompt` are correctly rejected, improving robustness. - Clarified documentation for built-in definitions and TOML parsing, emphasizing compile-time guarantees and runtime checks. - Improved the logic for checking the existence of `MEMORY.md` to prevent errors from stray directories, enhancing file handling reliability.
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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