sanil-23andClaude Opus 4.6
9c1c51a00f
Replaces the explicit `empty_filter: HashSet::new()` in `render_main_agent_dump` with a real visibility whitelist derived from the orchestrator's `AgentDefinition`. The "main" dump path now mirrors the runtime dispatch path from commits 4a/4b — same definition, same delegation tool synthesis, same filter — so `openhuman agent dump-prompt main` shows what the LLM actually sees in production instead of the unfiltered global registry. Before this commit, `dump-prompt main` always rendered every tool in the registry regardless of which agent was supposed to run, which is exactly the symptom #526 reports: "agent prompt tool scopes leak full GitHub tool catalog". The runtime fix in commit 4b stops the leak in production but the dump path was the user's primary observability tool for inspecting prompts, so leaving it unscoped would mask future regressions and confuse debug sessions. Changes in `src/openhuman/context/debug_dump.rs`: * `dump_agent_prompt` now loads `AgentDefinitionRegistry` once at the top (previously only loaded inside the sub-agent branch). When the request is for "main" or "orchestrator_main", it looks up the "orchestrator" definition from the registry and passes it into `render_main_agent_dump` along with the registry itself. Missing orchestrator entry → structured error listing known agents instead of silently rendering an unfiltered prompt. * `render_main_agent_dump` signature gains `registry: &AgentDefinitionRegistry` and `orchestrator_def: &AgentDefinition`. Inside, it: - calls `collect_orchestrator_tools(orchestrator_def, registry, connected_integrations)` to synthesise the same per-turn delegation tools (`research`, `plan`, `delegate_<toolkit>`, …) that dispatch generates; - extends `prompt_tools` with the synthesised extras so they contribute to the rendered tool catalogue; - builds `visible_filter: HashSet<String>` from `orchestrator_def.tools` (the `[tools] named` list) ∪ the names of the synthesised extras, falling back to an empty HashSet when the orchestrator definition uses `ToolScope::Wildcard` (which the prompt builder treats as "no filter, every tool visible") so dump consumers that supply a wildcard orchestrator (custom workspace overrides, tests) retain the legacy unscoped behaviour; - replaces `visible_tool_names: &empty_filter` in the `PromptContext` with `&visible_filter`; - filters the returned `tool_names` and `skill_tool_count` by the same predicate so the `DumpedPrompt` summary fields match what the prompt text actually contains. Tests: * Replaces the previous `render_main_agent_dump_includes_tool_ instructions_and_skill_count` test with two more focused cases: 1. `render_main_agent_dump_wildcard_scope_shows_full_tool_set` — regression guard for the legacy wildcard path. Builds a wildcard-scoped orchestrator definition, asserts every tool from `tools_vec` survives, and checks the standard system- prompt skeleton (Tools section, Tool Use Protocol, cache boundary) still renders. 2. `render_main_agent_dump_named_scope_filters_to_whitelist` — the #526 regression guard. Builds an orchestrator with `ToolScope::Named(["query_memory", "ask_user_clarification"])` and a `tools_vec` containing `shell`, `query_memory`, and `GMAIL_SEND_EMAIL`. Asserts the dump's `tool_names` is exactly `["query_memory"]` — `shell` and `GMAIL_SEND_EMAIL` are in the global registry but NOT in the whitelist, so they MUST be excluded. If a future change reintroduces the unfiltered behaviour this test fails immediately. * Adds two test helpers: `wildcard_orchestrator_def()` builds a minimal orchestrator definition with all `omit_*` flags set and `ToolScope::Wildcard`, and `registry_with_orchestrator(orch)` wraps it in an `AgentDefinitionRegistry` so the tests can call `render_main_agent_dump` without going through the full TOML loader. 11/11 debug_dump tests pass. The two new guards plus the 9 existing sub-agent / filter / composio-stub tests all run clean. Co-Authored-By: Claude Opus 4.6 (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.
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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