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Steven EnamakelandGitHub
6465f3d314
* Enhance agent architecture with sub-agent support and memory optimizations - Refactored the `Agent` struct to use `Arc` for shared ownership of the provider, tools, and tool specifications, enabling efficient memory management and concurrent access. - Introduced a `NullMemoryLoader` to optimize memory usage for sub-agents, allowing them to operate without incurring the cost of memory recall. - Added new methods in the `Agent` implementation to facilitate sharing of the provider, tools, and tool specifications with sub-agents, enhancing their operational efficiency. - Implemented a new `SystemPromptBuilder` method for constructing prompts specifically for sub-agents, ensuring they receive tailored context while minimizing unnecessary information. - Established a framework for loading custom agent definitions from TOML files, allowing for dynamic agent configuration and specialization. - Introduced a `ForkContext` to support efficient sub-agent execution in fork mode, leveraging shared resources for improved performance and reduced token usage. * Enhance agent definition management and sub-agent functionality - Introduced a global `AgentDefinitionRegistry` to manage built-in and custom agent definitions from TOML files, ensuring idempotent initialization. - Added new RPC handlers for listing, fetching, and reloading agent definitions, improving the flexibility of agent management. - Refactored the `Agent` struct to streamline sub-agent execution, including enhancements to the task execution flow and context handling. - Updated the orchestrator configuration to support fork mode for sub-agents, optimizing resource usage and performance. - Improved error handling and logging for agent definition loading and initialization processes, enhancing system reliability. * Add end-to-end test for sub-agent spawning and response integration - Implemented a new asynchronous test to validate the full path of a parent agent issuing a `spawn_subagent` tool call. - The test ensures that the sub-agent's output is correctly folded into the parent's response, verifying the interaction between the parent agent and the sub-agent. - Enhanced the `AgentDefinitionRegistry` to support global initialization of built-in agents, ensuring consistent behavior across tests. - Updated the handling of tool calls and memory configuration to facilitate the new test scenario, improving overall test coverage for agent interactions. * Enhance agent tool filtering with category support - Introduced a new `category_filter` in `AgentDefinition` to restrict tool visibility based on their category (System or Skill). - Updated the `from_archetype` function to apply the category filter for the `SkillsAgent` archetype. - Modified `SubagentRunOptions` to include a `category_filter_override` for dynamic filtering during sub-agent execution. - Enhanced the `filter_tool_indices` function to incorporate category filtering logic, ensuring tools are correctly filtered based on their defined categories. - Updated relevant tests to validate the new category filtering functionality, improving overall test coverage for agent interactions. * Implement layered context reduction pipeline for agent - Introduced a new `context_pipeline` module to manage a layered context reduction strategy, enhancing memory efficiency during agent interactions. - Added stages for tool-result budgeting, history trimming, microcompaction, autocompaction, and session memory extraction, each with specific triggers and cache implications. - Updated the `Agent` struct to include a `context_pipeline` field, ensuring state persistence across turns. - Enhanced the `AgentBuilder` to initialize the context pipeline by default. - Implemented tests to validate the functionality and stability of the context pipeline, ensuring consistent behavior across agent sessions. - Refactored relevant components to integrate the new context management features, improving overall agent performance and memory handling. * Enhance agent context pipeline with tool result budgeting and microcompaction - Renamed variable for clarity in tool execution result handling. - Implemented a new stage in the context pipeline to apply a byte budget to tool results, ensuring efficient memory usage. - Added logging for budget application, including details on original and final byte sizes. - Integrated microcompaction stages before tool calls to manage history and reduce memory footprint, with appropriate logging for outcomes. - Updated the agent's session memory management to track turn counts, facilitating better resource handling across iterations. * Refactor agent context pipeline for session memory extraction and tool call management - Simplified method calls in the `Agent` struct for clarity and efficiency. - Enhanced session memory extraction logic to spawn a background archivist sub-agent when thresholds are met. - Improved context pipeline handling for tool call recording and usage tracking. - Updated documentation and comments for better understanding of session memory extraction process. - Refactored microcompact function for cleaner code structure and readability. * refactor(agent): split agent.rs into focused submodules - Convert agent.rs (1988 lines) into agent/ folder with six files: * types.rs — Agent + AgentBuilder struct defs * builder.rs — AgentBuilder fluent API + Agent::from_config factory * turn.rs — turn lifecycle, tool dispatch, context pipeline wiring * runtime.rs — public accessors, run_single/run_interactive, helpers * tests.rs — integration tests with shared fakes * mod.rs — glue + top-level `run` convenience function - Drop misc external inspiration references from doc comments in the context_pipeline module and fork_context — the files now stand on their own design language. * fix(agent): address review comments on sub-agent + context pipeline PR Inline comment fixes: - definition.rs: YAML/TOML inconsistency — module doc, PromptSource, source bookkeeping, and load() all now uniformly document the TOML format. - subagent_runner.rs: render_subagent_system_prompt previously only appended PromptSource::Inline bodies, silently dropping PromptSource::File content. Thread the preloaded archetype_body through as an explicit &str parameter so both source variants render. Drops the unused SystemPromptBuilder + tools_for_prompt wiring while we're here. - prompt.rs: remove DateTimeSection from SystemPromptBuilder::for_subagent. Local::now() would make the sub-agent system prompt change per call and break KV-prefix cache stability. Document the invariant. Nitpick fixes: - agent/turn.rs: drop the no-op mark_extraction_started() call — the immediately-following mark_extraction_complete() clears the in-flight flag anyway. - context_pipeline/pipeline.rs: call guard.record_compaction_success() on microcompact success so a prior streak of autocompaction failures doesn't leave the circuit breaker tripped after a successful reduction. - context_pipeline/tool_result_budget.rs: remove the unnecessary out.clone() in the truncation return — capture final_bytes first, then move out. - definition_loader.rs: replace brittle reg.len() == 10 with assert!(reg.len() > 1) plus the existing targeted .get() checks. - executor.rs: tracing::warn! on unknown sandbox override values so typos surface during development; explicitly accept "none" and empty string as valid defaults. - fork_context.rs: add parent_context_visible_inside_scope test mirroring fork_context_visible_inside_scope, with minimal stub Provider/Memory impls so the test stays self-contained. - schemas.rs: drop redundant serde_json::to_value(serde_json::json!(...)) wrapping in handle_list_definitions + handle_get_definition. - event_bus/events.rs: add SubagentSpawned/SubagentCompleted/SubagentFailed cases to all_variants_have_correct_domain. - tools/ops.rs: add all_tools_includes_spawn_subagent regression test. - tools/spawn_subagent.rs: sort/dedup the known skill list in place instead of cloning into a second vec. Tests: 2316 passed / 0 failed (up from 2314; two new tests added). fmt + clippy clean on all touched files. * udpate prompts * Enhance AgentBuilder and runtime with event context and interactive CLI improvements - Added `event_context` method to `AgentBuilder` for setting `session_id` and `channel` for `DomainEvent`s, improving event tagging and correlation. - Updated `run_interactive` method in `Agent` to dispatch messages through `run_single`, ensuring consistent lifecycle event handling and error sanitization for interactive turns. * Optimize configuration handling in AgentBuilder by lazily creating Arc for full config in reflection hook. This change reduces unnecessary cloning when learning is enabled, improving performance. * Add fork-mode test for sub-agent spawning in agent - Introduced a new test, `turn_dispatches_spawn_subagent_in_fork_mode`, to validate the behavior of the agent when spawning a sub-agent in fork mode. - The test ensures that the parent agent correctly processes the sub-agent's output and maintains the expected response sequence. - Enhanced the test setup with a mock provider and memory configuration to simulate the agent's environment effectively. * Refactor sync RPC handling in skills to treat missing onSync as no-op - Updated the `handle_sync` function to log a debug message and return a no-op response when a skill does not implement the `onSync` handler, preventing unnecessary RPC errors for skills that do not require periodic syncs. - This change improves logging clarity and reduces error noise in logs and dashboards for skills that are not designed to handle sync operations.
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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