48897af9b3
Phase 3b of the memory architecture (umbrella #711). Adds a singleton cross-source `global` tree whose purpose is time-windowed recap ("what did I do in the last 7 days?") via a time-axis-aligned hierarchy: L0 = day, L1 = week (7 dailies), L2 = month (4 weeklies), L3 = year (12 monthlies). ## What's in this PR - `global_tree/registry.rs` — singleton `get_or_create_global_tree` with the same UNIQUE-race recovery pattern Phase 3a uses for source trees (scope is the literal `"global"`). - `global_tree/digest.rs` — `end_of_day_digest(config, day, summariser)` walks every active source tree, picks one representative contribution per tree (latest L1+ intersecting the day, fallback to root), folds them with the Summariser trait into one L0 daily node, inserts it into `mem_tree_summaries`, and triggers the L0→L1→L2→L3 cascade. Idempotent on re-run (returns `DigestOutcome::Skipped` when an L0 already exists for the day). - `global_tree/seal.rs` — count-based cascade-seal with thresholds 7 (weekly), 4 (monthly), 12 (yearly). Transactional append with idempotency on (tree_id, level, item_id) to survive partial retries. - `global_tree/recap.rs` — `recap(config, window)` maps the window to a level (<2d→L0, <14d→L1, <60d→L2, else L3), fetches covering summaries, and falls back downward when the chosen level has no sealed material yet (reports `level_used` so callers can surface "best available"). Empty tree returns `None`. - `source_tree/store.rs` — adds `list_trees_by_kind` used by the digest to enumerate source trees. - `tree/mod.rs` — exports the new `global_tree` module. ## Reuses from Phase 3a - `source_tree::store` CRUD for `mem_tree_trees` / `mem_tree_summaries` / `mem_tree_buffers` (no new schema tables). - `source_tree::summariser::{Summariser, SummaryContext, SummaryInput, InertSummariser}` — honest-stub entity/topic semantics kept as-is. - `source_tree::registry::new_summary_id` for id generation. - `source_tree::types::{Tree, SummaryNode, Buffer, TreeKind::Global, TreeStatus}` — `TreeKind::Global` was already declared in Phase 3a. - Entity backfill via `tree::score::store::index_summary_entity_ids_tx` so Phase 4 retrieval can resolve "summaries mentioning X" through the same inverted index as leaves. ## Design decision: seal.rs kept as parallel impl `source_tree::bucket_seal::cascade_all_from` uses a token-budget seal policy (`TOKEN_BUDGET`), not pluggable. The global tree needs count-based thresholds per level. Rather than refactoring the stable Phase 3a seal pipeline to accept a policy (regression risk), we keep `global_tree/seal.rs` as a parallel count-based implementation that routes through the same `source_tree::store` primitives. The shape of the transaction is intentionally identical so future consolidation is straightforward when we're ready to touch the Phase 3a seal path. ## Tests 15 new tests; 176 total `memory::tree::*` passing (was 161). Coverage: - registry idempotency, ID prefix, UNIQUE-race recovery - digest empty-day, populated-day, rerun idempotency, 7-day weekly cascade - seal below-threshold, weekly-threshold, append idempotency - recap level selection across all four bands, empty-tree, L0 fallback, L1 when sealed ## Stacked on #789 Base is `feat/709-summary-trees` (Phase 3a). Merge #789 first, then rebase this branch. PR body should flag the stack order. Co-authored-by: Claude Opus 4.7 (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.
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.
Highlights
- Neocortex — local-first knowledge base that learns from your data and activity, compounding context across tools and sessions.
- The Subconscious — background self-learning loops that turn everyday usage into workflow-aware intelligence.
- Screen Intelligence — the agent sees what's on your screen and feeds it into your local context.
- Inline Autocomplete — memory-aware keyboard autocomplete anywhere on your desktop.
- Voice (STT + TTS) — speak to OpenHuman and hear it reply, natively on the desktop.
- Skills & Integrations — one-click skills for Gmail, Slack, Notion and the rest of your stack, with local encryption and webhooks for instant feedback.
- Messaging Channels — inbound/outbound across the channels you already use, routed through your agent.
- Teams & Organizations — shared workspaces for collaborating with an agent across a team.
- Rewards & Achievements — gamified progression as your agent grows with you.
- Privacy & Security — workflow data stays on device, encrypted locally, and treated as yours.
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 | 🚫 Proprietary | ✅ MIT | ✅ MIT | ✅ GNU |
| Simple to start | ✅ Desktop + CLI | ⚠️ Terminal-first | ⚠️ Terminal-first | ✅ Clean UI, minutes |
| Cost | ⚠️ Sub + add-ons | ⚠️ BYO models | ⚠️ BYO models | ✅ Local-friendly |
| Memory & KB | ✅ Chat-scoped | ⚠️ Plugin-reliant | ✅ Self-learning | 🚀 Local KB + learning |
| API sprawl | 🚫 Extra keys | 🚫 BYOK | 🚫 Multi-vendor | ✅ One account |
| Extensibility | ✅ MCP | ✅ SKILL.md | ✅ SKILL.md | 🚀 Rich Skills |
| Desktop integration | ⚠️ Basic | ⚠️ Light | ⚠️ Light | ✅ STT/TTS/screen/more |
Contributors Hall of Fame
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