Steven EnamakelandGitHub
3a20599f45
* feat(agent): add support for connected integrations in system prompts - Introduced a new `fetch_connected_integrations` method to retrieve and populate active Composio integrations for the agent. - Updated the `Agent` struct to include a `connected_integrations` field, allowing the system prompt to display available external services. - Enhanced the `build_system_prompt` method to incorporate connected integrations, improving the context provided to users during interactions. - Added a `ConnectedIntegrationsSection` to the prompt rendering, ensuring visibility of active integrations in the system prompt output. - Overall, these changes enhance the agent's ability to leverage connected services, improving user experience and interaction capabilities. * feat(debug_dump): integrate connected integrations into agent prompt dumps - Added a new `fetch_connected_integrations_for_dump` function to retrieve active integrations for the agent during prompt dumps. - Updated the `render_main_agent_dump` function to include connected integrations, enhancing the context provided in the debug output. - Improved the overall structure and clarity of the debug dump process, ensuring that connected integrations are accurately represented in the agent's prompt context. * refactor(agent): streamline integration fetching for system prompts - Refactored the `fetch_connected_integrations` method in the `Agent` struct to delegate integration fetching to a new centralized function in the `composio` module, enhancing code clarity and maintainability. - Updated the `fetch_connected_integrations_for_dump` function to utilize the new centralized fetching logic, ensuring consistent integration retrieval across different contexts. - Improved the overall structure of integration handling, allowing for better error management and logging during the fetching process. * feat(agent): add connected integrations support to parent execution context - Introduced a new field `connected_integrations` in the `ParentExecutionContext` struct to store active Composio integrations. - Updated relevant functions to utilize the new `connected_integrations` field, ensuring that system prompts and agent dumps reflect the current integrations. - Enhanced the integration cache management by implementing cache invalidation logic when connections are created or deleted, improving the accuracy of integration data across sessions. - Overall, these changes enhance the agent's ability to leverage connected services, providing users with better context during interactions. * feat(agent): initialize connected integrations in subagent context - Added a `connected_integrations` field to the `ParentExecutionContext` and `Agent` struct, allowing for the storage and retrieval of active Composio integrations. - Updated the `dispatch_target_agent` function to populate the `connected_integrations` field when creating a new sub-agent context. - Enhanced the `fetch_connected_integrations` method to return an `Option<Vec<ConnectedIntegration>>`, improving error handling and caching logic. - These changes improve the agent's ability to manage and utilize connected integrations, enhancing user interactions and context awareness. * refactor(agent): improve caching logic in fetch_connected_integrations - Updated the `fetch_connected_integrations` function to handle caching more effectively by using a match statement. - The function now caches results only when the backend is reachable, preventing unnecessary caching when the client is unavailable. - This change enhances error handling and ensures that subsequent calls with different configurations can retry without stale data. * style: apply cargo fmt formatting * feat(agent): enhance connected integrations handling and caching - Updated the `dispatch_target_agent` function to initialize connected integrations for sub-agents, ensuring they have access to the latest integrations. - Improved the caching mechanism for connected integrations by using a `HashMap` keyed by configuration identity, allowing for user-specific caching and better isolation of integration data. - Refactored the `invalidate_connected_integrations_cache` function to clear the entire cache instead of setting it to `None`, enhancing cache management. - Added a new method `load_from_default_paths` in the `Config` struct to reliably load user configurations without being affected by environment variable overrides, improving the debug dump process. - Enhanced the rendering of connected integrations in system prompts to provide clearer instructions based on available tools, improving user interaction clarity. * feat(agent): add method to set connected integrations - Introduced a new `set_connected_integrations` method in the `Agent` struct to allow for replacing the agent's connected integrations from external sources, enhancing flexibility in integration management. - Updated the caching mechanism for connected integrations to utilize `LazyLock`, improving initialization efficiency and thread safety. * style: apply cargo fmt
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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