MyAgents
An open-source, local-first desktop Agent workbench that unifies conversations, workspaces, files, tools, models, and task scheduling into one desktop system for real workflows.
README asserts local-first design, per-session Sidecar isolation, Tauri/Rust file IO, and Rust-proxied control plane under CORS/CSP; SECURITY.md advises reviewing tool permissions and stores keys locally in ~/.myagents/. However, no evidence shows a concrete permission model, tool-call confirmation flow, key encryption, or dependency auditing — these are assertions, so least_privilege/user_confirmation/sensitive_data_handling/dependency_security score 1. setup-no-egress.ts and CI notes show a strong test-layer zero-egress guard (externalResourceGuard, WidgetRenderer transport invariants), but that is a test constraint, not runtime protection; credentialed tests are properly separated from default CI. data_flow_transparency gets 2. No rollback/recovery mechanism is evidenced beyond task failure recovery mentions: rollback 1. AGPL plus third-party notices are complete: source_attribution 2.
README, package., and CI workflows are mutually consistent (version 0.4.15, Node 24, pinned rust-toolchain, matching layered test names): self_consistency 2. Lockfiles and --locked cargo commands support reproducibility: dependency_availability 2. No evidence in the provided files on error/failure message quality: failure_messages 1.
README explicitly names multiple audiences (developers, creators, researchers, PMs, educators) and concrete scenarios (workspace, task center, IM bots, cron): audience_and_scenarios 3. Capability boundaries are partially flagged (Lab runtimes, in-app model list authority) but thin: 2. Cron and low-cost command sensors with wake-only-on-match describe trigger precision adequately: 2. Explicit OS version requirements and per-platform setup notes: environment_fit 2.
Clear project structure and a full specs documentation system (architecture plus module docs): information_architecture 3. Detailed dev setup including platform differences, but end-user install relies on an external site: install_notes 2. Consistent naming (myagents, Mino workspace): 2. Screenshots and usage descriptions exist but no FAQ: examples_and_faq 2. Known limitations only scattered (Lab features): known_limitations 1. AGPL-3.0-only with LICENSING/COMMERCIAL/THIRD_PARTY_NOTICES and a verify:license script: license 3. No CHANGELOG evidence: versioning_changelog 1. Maintenance responsibility rests on one email and a personal repository; SECURITY.md timelines partially support it: maintenance_responsibility 1.
Feature tables and screenshots show usable output forms (task state, execution logs, audit): output_usability 2. Clear differentiation from plain chat windows: marginal_value 2. Cost/benefit claims are qualitative only, no data on API cost or resource usage: cost_benefit 1.
Architecture claims trace to named specs/ documents; CI test names map one-to-one to package. scripts: claim_traceability 2. Key facts (key storage path, test layering, version) cross-corroborate across README, SECURITY.md, package., and workflows: cross_source_corroboration 2. README distinguishes Lab features from stable ones and defers the model list to the in-app page, showing good fact/inference discipline: 2.
- This is a static review (low confidence); no builds or runs were executed, and test claims rest on CI configuration and script names only.
- Publisher is unverified and identity is unknown; the repo is maintained under a personal account with uncertain long-term commitment.
- No implementation evidence shows the runtime permission model, tool-call confirmation flow, or API key encryption; audit src-tauri and src/server security-boundary code before use.
- No CHANGELOG evidence; changes between versions cannot be traced from repository files.
- The release workflow is disabled in favor of local publish scripts; update-distribution auditability and signing chain should be verified independently.
- The xlsx dependency is pinned to a SheetJS CDN tarball rather than the npm registry; note the supply-chain provenance.
What does this agent do, and when should you use it?
MyAgents (GitHub: hAcKlyc/MyAgents) is an open-source desktop workspace for personal AI Agents, built with Tauri v2 + Rust + React 19 and licensed under AGPL-3.0. Rather than being a chat window, it puts multi-tab sessions, workspace file trees, an embedded terminal, an embedded browser, a task center, and local full-text search into a single application. Execution runs through a Node.js v24 Sidecar with the built-in Claude Agent SDK, and a Lab mode lets sessions be driven by Claude Code CLI, OpenAI Codex CLI, or Google Gemini CLI instead. Data is local-first by default: conversations, tasks, config, and generated artifacts stay on your machine. It suits advanced users who want to configure their own model providers, skills, and automations, and want AI deeply integrated with local files and the terminal.
MyAgents organizes Agents around workspaces: each Tab is bound to a real folder, sessions reference files with @, invoke Skills with /, and call MCP tools (STDIO/HTTP/SSE), while a split-panel embedded terminal (xterm.js + portable-pty) and embedded browser (Tauri multi-Webview) let you execute and verify in place. The Node.js Sidecar embeds the Claude Agent SDK by default, with Claude Code CLI / OpenAI Codex CLI / Google Gemini CLI available as external runtimes in the Lab. Model providers are configurable in-app: Anthropic subscription or API, plus DeepSeek, Moonshot, Zhipu, MiniMax, Google Gemini, Volcengine, SiliconFlow, ZenMux, OpenRouter, Xiaomi MiMo, Alibaba Cloud Bailian, and more. The Ideas + Task Center turns rough thoughts into executable tasks, supporting one-shot and recurring tasks via Cron expressions, optional local-command sensors that wake the AI only on condition match, execution logs, and failure recovery. IM integration ships with Telegram and DingTalk bots, with other platforms available through OpenClaw plugins. Local full-text search (Tantivy + jieba) indexes session history and workspace files. Architecturally, Session:Sidecar is 1:1, control-plane HTTP/SSE goes through a Rust proxy, and workspace file IO goes through Tauri/Rust.
- A developer has the Agent work inside a real project folder — reading code, running terminal commands, and verifying results in a split panel instead of a detached chat box
- An individual captures scattered thoughts in the Ideas center, aligns on goals with the AI, and converts them into scheduled, state-tracked tasks
- A heavy Agent user runs several independent sessions in parallel via Chrome-style tabs, each with its own Sidecar
- A user who wants a custom model mix (e.g., Anthropic subscription + DeepSeek + OpenRouter) without being locked to one vendor
- A user who wants the Agent to handle messages via a Telegram or DingTalk bot, or execute tasks automatically on a Cron schedule
- A developer familiar with Claude Code CLI / Codex CLI / Gemini CLI who wants one desktop interface to switch between Agent runtimes
What are this agent's strengths and limitations?
- Local-first architecture: sessions, tasks, config, and artifacts stay on your machine; Sidecars are isolated per Session and workspace file IO goes through Tauri/Rust rather than Sidecar HTTP
- Open on two axes: model providers are swappable, and the runtime can switch between the built-in Claude Agent SDK and Claude Code CLI / Codex CLI / Gemini CLI, avoiding vendor lock-in
- A complete task system: idea capture, Cron scheduling, local-command sensors (cheap condition checks that wake the AI only on match), execution logs, and failure recovery — all on one shared scheduler across Chat, IM bots, and background tasks
- Extensible tool ecosystem: MCP (STDIO/HTTP/SSE), built-in/user/workspace-level Skills, OpenClaw plugin bridge, and a
myagentsCLI - Embedded terminal and browser in a split panel close the execute-and-verify loop inside one window
- AGPL-3.0-only license: closed-source modification, OEM, or hosted offerings require a separate commercial license, adding compliance cost for business integration
- Heavy developer dependencies: self-building requires Node.js 22+, a Rust toolchain, and CMake 3.28+; Linux builds additionally need a set of WebKit/GTK system packages
- Model capability depends on external providers: you must configure your own API keys or subscriptions, and the external CLI runtimes are explicitly Lab-stage features
- The multi-process architecture (Tauri + Rust proxy + Node Sidecar + plugin bridge) is complex; contributors must read specs/tech_docs/ first, raising the customization bar
- Real provider/SDK smoke tests (test:credentialed) are excluded from default CI and must be run locally, so out-of-the-box reliability is partly on the user to verify
How do you install or deploy this agent?
End users can download the app from https://myagents.io; it supports macOS 13.0+ (Apple Silicon and Intel) and Windows 10+. For a developer build you need Node.js >=22.0.0 (Node 24 recommended; the repo declares [email protected]), Rust installed via rustup (toolchain pinned by rust-toolchain.toml), and for cold native-inference builds CMake 3.28+ plus a platform C/C++ toolchain (macOS additionally needs Git, Python 3.10+, and Apple Clang).
macOS / Linux:
git clone https://github.com/hAcKlyc/MyAgents.git
cd MyAgents
./setup.sh
./start_dev.shWindows:
powershell
git clone https://github.com/hAcKlyc/MyAgents.git
cd MyAgents
.\setup_windows.ps1
.\build_windows.ps1On first run you must configure at least one model provider (e.g., an Anthropic API key or subscription) in the in-app Model Providers page, otherwise the Agent cannot execute anything.
How do you use this agent?
- Launch the app and open or create a workspace (each Agent binds to a real folder). 2. Configure models in the Model Providers page (Anthropic subscription/API, or DeepSeek, Moonshot, OpenRouter, etc.). 3. In a session, reference files with
@, invoke Skills with/, or connect MCP servers (STDIO/HTTP/SSE). 4. Use the split-panel embedded terminal and browser to verify results. 5. Capture thoughts in the Ideas center, discuss them with the AI, and convert them into one-shot or Cron-recurring tasks tracked in the Task Center with status, run counts, and execution logs. 6. Optionally switch the runtime to Claude Code CLI / Codex CLI / Gemini CLI in Lab settings, or interact with the same work context via IM bots (Telegram/DingTalk). For development use./start_dev.sh; before contributing runnpm run typecheck,npm run lint,npm run test:classification, andnpm run test:unit.
How does this agent compare with similar options?
The README positions the product as "not another chat window" and supports Claude Code CLI, OpenAI Codex CLI, and Google Gemini CLI as external runtimes — meaning it can act as a unified desktop shell for these command-line agents; the repo also tags "manus" as a topic, but no concrete comparison data is given.