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# sero-labs/sero ## Metadata - **Stars:** ~2,000 - **License:** AGPL-3.0 - **Last pushed:** 2026-06 (actively maintained) - **Topics:** agent-os, desktop, electron, pi-framework - **Fork:** No - **Author:** Sero Labs (Mario Zechner / badlogic) ## Archetype: agent-workspace-platform A personal agent operating system (Agent OS) built on the Pi coding agent framework. Electron desktop app that consolidates chat, terminals, file explorer, visual browser, and self-extending plugins into a unified local-first workspace where agents maintain persistent awareness across sessions. ## Structure - `apps/desktop/` — Electron + React shell (main process + renderer) - `packages/` — Shared runtime, UI, and common libraries - `plugins/` — Built-in and example plugins (scout, reviewer, test-writer) - `eval/` — Promptfoo-based evaluation framework ## Tech Stack - TypeScript (96%), Electron, React, Module Federation - Built on Pi coding agent framework (@earendil-works/pi-*) - Node.js 22, pnpm 10 - Platforms: macOS (Apple Silicon), Linux (x64/arm64), Windows (x64) ## Key Concepts ### Agent-as-Workspace The fundamental inversion: instead of an agent living in a sidebar/tab, the agent IS the workspace. Files, terminals, browser, and plugins all exist as native context the agent can see and reason about without manual context injection. ### Self-Extending Plugin Paradigm The agent builds plugins on demand during conversation. User describes a workflow → agent scaffolds plugin (manifest + tools + optional React UI panel) → plugin is live immediately via Module Federation, no restart. Plugins persist across sessions and can be iteratively refined. ### Content vs Details Tool Response Pattern Tool results separate LLM context from UI rendering: - `content`: Clean text for the model to reason about - `details`: Structured data (JSON, images, charts) for the UI to render Neither pollutes the other. Solves the "parsing text for UI" problem at the source. ### Session Tree (Not Linear Chat) Conversation history is a tree with fork/clone/branch semantics. Each message has a parentId. Users can explore alternative approaches without losing the original path. Context is linearized root-to-leaf for LLM APIs. Dead-end branches can be compacted independently. ### Composable Hook Interceptors Extensions intercept the agent loop at surgical points (beforeToolCall, afterToolCall, message_start, turn_end). Enables: plan mode (approve before executing), git checkpointing (auto-commit before mutations), custom compaction triggers, safety filters. Hooks compose — multiple extensions can register for the same event. ### Markdown Specialist Agents Built-in agents (scout, reviewer, test-writer) are plain markdown files with YAML frontmatter (name, tools, model, permissions) and markdown body (system prompt). Editable, forkable, composable. The agent can create new specialist files during conversation. ### Git-Checkpointed Compaction Before summarizing old conversation turns, create a git commit. The agent can reconstruct state via `git log` and `git diff` after compaction. Compaction becomes reversible — information moves from conversation context to git history. ### Visual Reasoning Loop Built-in browser enables closed-loop visual feedback: agent writes code → code renders in browser → agent takes annotated screenshot (elements get numbered labels) → agent reasons about visual layout → agent fixes issues → repeat. ## Extraction Priority: High ### Extractable Patterns | Pattern | Priority | Notes | |---------|----------|-------| | Self-extending plugin paradigm | High | `createPlugin` agentic tool + Module Federation hot reload | | Content/details tool response | High | Clean separation of LLM context from UI rendering | | Session tree with fork/clone | High | Non-linear conversation exploration | | Composable hook interceptors | Medium | Plugin-registered beforeToolCall handlers | | Visual reasoning loop | Medium | Annotated screenshots + vision-based feedback | | Git-checkpointed compaction | Medium | Auto-commit before context compression | | Markdown agent creation | Medium | Agent creates specialist agents mid-conversation | | Mosaic workspace layout | High | Split-pane desktop (chat + terminal + files + browser) | ### What We Already Have (No Extraction Needed) | Sero Feature | Our Equivalent | |---|---| | Multi-provider LLM | adapters/core — 25+ providers, transport-adapter proxy | | Plugin system | genty/platform/plugins — sandbox, permissions, marketplace | | Memory/persistence | sdk/profiles + sdk/session — user+project profiles, session state | | Subagent framework | genty/core/subagent — 3 invocation modes, oversight | | MCP integration | genty/platform/mcp — full client with channels, permissions | | Container isolation | Docker, SSH, K8s transports, secure-sandbox | | Agent definitions | .claude/agents/ + AgentPersona/Soul/Definition CRDs | | Multi-platform UI | genty/{desktop,web,tui,mobile,tv,watch} — 7+ surfaces | ### What We Have That Sero Doesn't - Kubernetes-native forge (Kradle) with 89+ CRD types - Process definitions + babysitter orchestration with breakpoints - Multi-agent Jitsi meeting integration - Agent identity system (Persona, Soul, Appearance, Voice profiles) - Atlas knowledge graph - 7 platform surfaces (vs Sero's 1) - CI/CD integration (GitHub Actions workflows) - Governance layer (authority chains, mandates, posture-based permissions) - Agent marketplace / process library ## Related Files - [sero-gap-analysis.md](./sero-gap-analysis.md) — Feature-by-feature gap analysis - [sero-concepts-and-opportunities.md](./sero-concepts-and-opportunities.md) — Deep architecture concepts and build opportunities
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