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* feat(memory): phase 1 memory tree - multi-source ingestion & canonical chunks (#707) Adds an isolated memory tree layer under src/openhuman/memory/tree/ implementing Phase 1 of the new memory architecture (umbrella #711). Zero edits to existing memory/*.rs files - the new layer coexists with the legacy TinyHumans-backed client. - Source adapters: chat / email / document -> canonical Markdown - Token-bounded chunker with deterministic SHA-256 chunk IDs - SQLite persistence at <workspace>/memory_tree/chunks.db with full provenance metadata (source_kind, source_id, owner, timestamps, tags, time_range) and back-pointer to raw source - Unified JSON-RPC ingest (dispatches on source_kind + JSON payload): openhuman.memory_tree_ingest, _list_chunks, _get_chunk - DataSource enum covering the 8 providers from m.excalidraw step 1 (Discord/Telegram/Whatsapp/Gmail/OtherEmail/Notion/MeetingNotes/DriveDocs) - ~40 unit tests (chunk ID stability, UTF-8-safe splitting, canonicalisation idempotence, store round-trip, filter behavior) Additive only: new tables in a new DB file, new JSON-RPC namespace, no existing behavior changes. Feeds #708 (scoring), #709 (summary trees), #710 (query tools). Closes #707. Parent: #711. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(memory): phase 2 memory tree - preprocessing, scoring, admission gate (#708) Adds the scoring / admission layer between Phase 1's chunker and store. Stacked on feat/707-memory-ingestion (PR #732) - depends on Phase 1's chunk substrate. - Pluggable EntityExtractor trait + CompositeExtractor chain - RegexEntityExtractor: mechanical entities (emails, URLs, @handles, #hashtags) - Always on, deterministic, zero deps, UTF-8-safe char spans - Five weighted signals: token count, unique-word ratio, metadata weight, source weight (per-DataSource), interaction (reply/sent/mention/dm tags), entity density - Exact-match entity canonicalisation (email lowercased, @ and # stripped) - Admission gate drops chunks below configurable threshold (default 0.3) - Score rationale persists for EVERY chunk (kept or dropped) for debugging - Entities indexed for KEPT chunks only - Two new SQLite tables added to the memory_tree DB: - mem_tree_score: per-chunk score rationale with all signal values - mem_tree_entity_index: inverted index entity_id -> node_id - Idempotent ALTER TABLE migration adds embedding BLOB column to mem_tree_chunks (used in Phase 3 retrieval, wired but not populated here) - Ingest pipeline converted to async to accommodate the extractor trait; blocking SQLite work isolated on spawn_blocking; JSON-RPC surface unchanged (same memory_tree_ingest / list / get methods) - Phase 2 deliberately ships without GLiNER/semantic NER - per-chunk semantic entities land later behind a cargo feature flag; the composite extractor interface keeps that drop-in trivial Additive only: new tables, new columns, new module. Existing Phase 1 behavior unchanged except that low-signal chunks are now dropped before reaching mem_tree_chunks. Raise score_drop_threshold to 0 to disable the gate and restore Phase-1-identical behavior. Closes #708. Parent: #711. Depends on: #707 (#732). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Fix memory tree scoring persistence issues * Fix memory tree scoring robustness issues from PR review - ingest: fail fast if scorer returns fewer/more results than chunks (silent zip truncation would drop chunks or their score rationale) - score::persist_score{,_tx}: clear stale entity-index rows before re-indexing a re-scored chunk, since INSERT OR REPLACE never deletes rows whose entity_id is no longer in the new extraction - score::store::lookup_entity: clamp limit to i64::MAX before casting to prevent a large usize wrapping into a negative LIMIT Adds clear_entity_index_drops_stale_rows regression test. --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: Steven Enamakel <enamakel@tinyhumans.ai>
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. Running from source: docs/install.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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