Steven EnamakelandGitHub
ace0006c85
* feat(transcript): implement session transcript management for KV cache stability - Added support for session transcript persistence, allowing the exact `Vec<ChatMessage>` to be stored as human-readable `.md` files. - Introduced methods for loading previous transcripts to pre-populate messages for KV cache reuse, enhancing session continuity. - Updated `AgentBuilder` to include an agent definition name for transcript file naming. - Refactored `Agent` to handle transcript loading and persistence during session turns, ensuring byte-identical message handling across sessions. - Created a new `transcript.rs` module to encapsulate transcript-related functionality, improving code organization and maintainability. * feat(usage): enhance API response structure with usage and billing metadata - Added `usage` and `openhuman` fields to the `ApiChatResponse` struct to capture standard OpenAI usage metrics and OpenHuman backend metadata. - Introduced `ApiUsage`, `OpenHumanMeta`, and related structs to encapsulate detailed usage and billing information. - Updated `parse_native_response` and `extract_usage` methods to process and return usage data, improving the integration of usage tracking in chat responses. - Modified the `UsageInfo` struct in traits to include new fields for cached input tokens and charged amount, enhancing the overall usage reporting capabilities. * feat(provider): integrate usage extraction into OpenAiCompatibleProvider responses - Added usage extraction functionality to the `OpenAiCompatibleProvider`, enhancing the API response structure to include usage metrics. - Updated the `parse_native_response` method to utilize a new `wrap_message` function for improved testability and clarity in response handling. - Refactored tests to accommodate the new usage extraction logic, ensuring comprehensive coverage of the updated response structure. * feat(transcript): implement sub-agent transcript persistence and usage tracking - Added functionality to persist sub-agent conversation transcripts, enabling inspection for debugging and KV cache analysis. - Introduced a new `persist_subagent_transcript` function to handle transcript writing, including metadata for input/output tokens and charged amounts. - Updated the `Agent` implementation to accumulate usage statistics across iterations and include them in the session transcript. - Enhanced the `write_transcript` function to include additional metadata fields, improving the detail of stored transcripts. - Refactored tests to validate the new transcript persistence and usage tracking features, ensuring comprehensive coverage. * refactor(transcript): improve code readability and structure - Reformatted the `write_transcript` and `read_transcript` functions for better clarity by adjusting line breaks and indentation. - Enhanced the `parse_meta` function to improve readability by restructuring the `get` closure. - Updated test cases to maintain consistency with the new formatting, ensuring clarity in the test structure. - Overall, these changes aim to enhance code maintainability and readability without altering functionality. * fix(provider): return Result from parse_native_response, use Option<u64> for OpenHumanUsage fields - parse_native_response now returns Result and propagates an error when choices is empty instead of silently returning text: None - OpenHumanUsage token fields changed to Option<u64> so missing keys yield None and allow fallback to standard OpenAI usage block - extract_usage now logs which source (openhuman vs standard) provided token counts via structured tracing - run_inner_loop returns AggregatedUsage so subagent transcripts reflect real usage instead of zeros - Document escape_content/unescape_content edge case with pre-existing escape sequences
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 |
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