Agenvoy is a free, open source ai interaction & interfaces project written in Go and released under AGPL-3.0. It has 555 GitHub stars, 44 forks and 2 open issues, and was last pushed 34 hours ago. On this registry it ranks #138 of 143 tracked projects in AI Interaction & Interfaces, with 5 head-to-head comparisons available.

What is Agenvoy?

Agenvoy is an open-source, self-hosted 24/7 personal AI agent written in Go that runs on your own machine, keeping memory, schedules, tools and credentials local while it turns conversations into completed work.

What it is

Agenvoy is an AI Agent Harness — a single Go binary, licensed AGPL-3.0, developed in Taiwan by Pardn Chiu — that coordinates models, context, tools, real-time data, task routing, memory, schedules and execution in one workflow. It ships with a TUI, a Web dashboard, and a Web interface that supports hands-free voice interaction with natural speech, wake-word detection and interruptible spoken replies, with the full result still available in the chat. It integrates with external services through stdio/HTTP MCP servers and OAuth, and exposes sandboxed tools to other agents over MCP.

The concrete problem it addresses is the gap between chat and completed work: a chat gives an answer, whereas work needs a result on your computer. Agenvoy breaks requests into steps, calls tools and delivers outcomes, while the operator retains control of files, tools, schedules and working context. In doing so it replaces ad-hoc, one-off scripting and hand-driven agent sessions with a persistent local daemon that researches live data, organizes files and completes multi-step tasks, and it keeps your memory, schedules, tools and credentials on your own machine rather than in a hosted service.

Key capabilities

  • Breaks a request into steps, calls tools and reports the outcome, covering live research, file organisation and multi-step tasks.
  • Fills capability gaps itself: it creates, tests and keeps a new tool when no suitable one exists, ready for reuse next time.
  • Shares one sandboxed tool library across agents, so Agenvoy, Claude Code, Codex and other agents use the same tools instead of rebuilding them.
  • Creates schedules in one sentence, running recurring work in your environment and pushing the result.
  • Streams command output to the TUI and Web dashboard, while sensitive paths and restricted actions still require confirmation.
  • Routes models by task and configures image generation, STT, TTS, stdio/HTTP MCP servers and OAuth.
  • Connects outward to Telegram and Discord from the local daemon without making the host public or opening inbound ports.

Who uses it and how

  • Developers who want an agent that acts on their own filesystem while keeping tools, credentials and working context local rather than in a hosted assistant.
  • Users of Claude Code or Codex who share a single sandboxed tool library with Agenvoy through MCP instead of maintaining separate toolsets for each agent.
  • People running unattended automation, where schedules created in one sentence execute recurring work in their own environment and push results back to them.
  • Operators who want remote interaction through Telegram or Discord while keeping their machine unexposed, with no inbound ports opened.
  • Users who drive the agent hands-free via the Web interface, speaking with wake-word detection and interrupting replies, then reading the full result in chat.

Getting started

The README presents Agenvoy as a single Go binary, which you run locally to get the TUI and Web dashboard, with further details published at https://agenvoy.com/; the excerpt gives no package manager or container image name.

How it compares

Agenvoy stands alongside Claude Code and Codex rather than replacing them: those are the agents it hands sandboxed tools to over MCP, while Agenvoy itself supplies the persistent local harness, memory, schedules and execution layer that coordinates them. It differs from them in being the always-on, self-hosted coordinator that keeps the tool library, memory and credentials on your machine and reaches you through Telegram, Discord or voice.

When to use it — and when not

Choose it if you want a long-running local agent and are willing to run a daemon yourself, configuring model routing, MCP servers, OAuth credentials and schedules on your own host; the AGPL-3.0 licence also carries copyleft obligations if you modify or redistribute it. It is a poorer fit if you want a managed, hosted assistant with nothing to operate, or if you need inbound access from the public internet, since the design deliberately avoids opening inbound ports.

project readme (upstream, from github) — read inline

Make AI do the work on your computer—not just talk about it

Agenvoy is an open-source, self-hosted 24/7 personal AI agent that runs on your own computer — your memory, schedules, tools and credentials stay local.
From live research and file work to automation, it takes action and delivers results in a single Go binary; through MCP, it shares sandboxed tools with Claude Code, Codex, and other agents.

agenvoy%2FAgenvoy | Trendshift

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Taiwan-developed AI Agent Harness

Agenvoy is a Taiwan-developed AI Agent Harness by Pardn Chiu, built to turn conversation into completed work on your computer. It coordinates models, context, tools, real-time data, task routing, memory, schedules, and execution in one workflow, while keeping control of your files and environment in your hands. Through the Web interface, it also supports hands-free voice interaction with natural speech, wake-word detection, and interruptible spoken replies; the full result remains available in the chat.

Why Agenvoy

A chat can give you an answer; work needs a result. Agenvoy breaks requests into steps, calls tools, and delivers outcomes on your computer—while you retain control of files, tools, schedules, and working context.

  • Turns conversation into deliverable work — Research live data, organize files, and complete multi-step tasks with an agent that acts and reports the result.
  • Fills capability gaps itself — Creates, tests, and keeps a new tool when no suitable one exists, ready to reuse next time.
  • Shares one tool library across agents — Agenvoy, Claude Code, Codex, and other agents use the same sandboxed tools instead of rebuilding them.
  • Keeps automation running — Create schedules in one sentence; recurring work runs in your environment and pushes the result.
  • Makes every step visible and controllable — Command output streams to the TUI and Web dashboard, while sensitive paths and restricted actions still require confirmation.
  • Connects models and external services freely — Route models by task and configure image generation, STT, TTS, stdio/HTTP MCP servers, and OAuth.
  • Provides private access from anywhere — The local daemon connects outward to Telegram and Discord, without making your host public or opening inbound ports.

Validated designs

Agenvoy has already implemented the design directions below, and other teams have since adopted similar approaches, confirming that these directions solve real problems agents face in practice. Every milestone links to a public commit or release, so you can check it yourself.

Shipped in Agenvoy Design Same direction elsewhere
2025-06-28 Implement core application
2025-06-29 add traditional memory structure
Summary-based memory for unbounded context 2025-11-24 Claude client-side compaction
2026-01-04 bubblewrap sandbox
2026-02-01 harden bubblewrap sandbox
2026-03-07 block sensitive paths and credentials
2026-03-18 sandbox execution with bubblewrap
Sandbox mode for secure agents 2026-03-16 NVIDIA NemoClaw
2026-02-07 Copilot device code login
2026-04-04 OpenAI Codex (OAuth) provider
Sign in with an existing AI subscription 2026-09-29 Pi 0.99.0 Sign in with ChatGPT
2026-02-27 LLM-driven agent routing
2026-03-10 configurable dispatcher model selection
2026-04-15 strengthen agent tier routing
2026-06-10 tiered routing across providers
2026-09-11 configurable model tiers for agent and subagent routing
2026-09-11 model tier controls on fallback priority
Route each task to the model best suited to it 2026-09-14 Copilot auto model selection tiers: efficiency / balance / intelligence
2026-09-29 Pi 0.99.0 virtual models: per-request model and thinking level
2026-09-29 Pi 0.99.0 classifier models
2026-03-23 script tool support with sandboxed execution
2026-05-14 script tool scaffolding skill
2026-06-05 script tool runtime metadata
Custom JS / Python tools run in the sandbox 2026-09-29 Pi 0.99.0 codemode: model-written JavaScript in a QuickJS sandbox
2026-04-02 deferred tool loading with search_tools
2026-05-05 MCP client adapter with stdio/HTTP transports
2026-06-07 tool search registry
2026-08-08 MCP OAuth flow
Load tools on demand instead of declaring them all 2026-09-29 Pi 0.99.0 built-in MCP + tool_search
2026-04-17 invoke_subagent tool
2026-04-25 session action logs
2026-04-25 session log streaming endpoint
2026-04-28 subagent name dispatch
Invoke sub-agents and track their logs 2026-08-03 Claude Code cross-session messaging
2026-05-28 extension tool loading
2026-05-28 extension install and upload skills
2026-05-29 extension marketplace
2026-06-08 ext_ extension tools and scaffolding
Everything is a plugin 2026-08-13 DeepSeek Harness: everything is a plugin
2026-06-03 ask_user interruption async resume
2026-06-04 preserve in-progress action memory across interruptions
2026-06-29 pending task resume
2026-07-03 write_todo checklist flow
Resume long tasks from a checkpoint 2026-08-04 pi harness v2 in-memory storage
2026-08-05 pi indexed harness recovery queries
2026-08-05 pi validate harness recovery record logs
2026-08-05 pi harness v2 jsonl backend
2026-08-06 pi atomic writes + torn-tail truncation
2026-08-14 pi AgentHarness R3 generation recovery
2026-08-14 pi AgentHarness R4 tool execution

What you can do with it

Ask live questions and get live answers (Web Search / Tool Generate)

What's the weather in Taipei?

The agent finds current data, calls tools, and gives you the answer.

If a tool doesn't exist, it builds one.

Web Search / Tool Generate demo

Turn one sentence into automation (Scheduler)

Report TSMC stock price every morning at 8am

The agent asks:

  • Where to push results
  • What format you want
  • When to run

Then creates the schedule automatically.

Scheduler demo

Ask questions about your local files (File Search / RAG)

Find all invoices from last year

Which document mentions Prompt guide?

The agent searches your local files and answers directly.

File Search / RAG demo

Finish multi-step work (Skills / Sub-agents)

Summarize today's GitHub commits and generate a progress report

The agent breaks down the task, calls tools, combines results, and replies.

Skills / Sub-agents demo

Work with the agents you already use (MCP Server)

Agenvoy is also an MCP server.

Claude Code, Codex, OpenCode, and other AI agents can connect and:

  • Use all your sandboxed tools
  • Auto-build new tools when none exist
  • Share every tool across all agents

One line of config. Instant shared tool library. Tools created in the demo: fetch_weather · fetch_crypto_price

Claude Code creates a weather tool (1)
Codex reuses it and creates a crypto tool (2)
Agenvoy tests both tools (3)

Who it's for

If you want AI to complete work—not merely respond—within an environment you control, Agenvoy is for you:

  • People who want to turn research, file work, and recurring reporting into reusable automation
  • Developers who want a self-hosted agent with local data control and sandbox guardrails
  • Teams that want Claude Code, Codex, and other agents to share tools instead of rebuilding them
  • Technical operators who need private access to a local agent through the Web, Telegram, or Discord

Drive Your Agent From the Browser

Manage sessions, tools, schedules, and memory from a browser. The dashboard ships inside the binary — start the daemon and open http://127.0.0.1:17989. It is served by your own machine, so nothing leaves your device.

Agenvoy Web Dashboard demo


Chatbot Integrations

Agenvoy currently supports Telegram and Discord as chatbot channels. The local daemon initiates outbound connections to these platforms, so you only need to configure a bot token—without exposing inbound ports, setting up a reverse proxy, or making your host public.

Since v0.34.4, Telegram and Discord have paused the default flow that automatically replies to voice input with voice output. You can still use STT/TTS tools to generate audio and send the resulting audio files to either channel.


One-line install

macOS / Linux distributions

Run this in a terminal:

curl -fsSL https://agenvoy.com/scripts/install.sh | bash

macOS tip: If you run schedules on a MacBook, also run:

sudo pmset -c sleep 0

This prevents sleep from interrupting schedules.

Windows (via WSL)

First open PowerShell as an administrator, then list and install a Linux distribution:

wsl --online --list
wsl --install <distribution-name>

After installation, restart your computer, open a WSL terminal, and run:

curl -fsSL https://agenvoy.com/scripts/install.sh | bash

Developer Recommendations

A cost-effective model setup to get started:

  1. Choose a subscription model for everyday primary use, such as:
    • GitHub Copilot ($10/mo) — pick gpt-5.6-luna: its quota drains slowly enough for daily use and it is capable enough for most work
    • OpenAI ChatGPT Plus ($20/mo)
    • SuperGrok ($30/mo)
  2. To try it for free, this works without a subscription:
    • Ollama Cloud — create a free API key, then pick Ollama Cloud in /model add and add gemma4:31b. The free plan has a usage cap; Shift+U shows how much of it is left.

Core capabilities

Capability Description
Auto tool generation Builds and saves tools when they're missing
Self-scheduling Create cron jobs with a single sentence
Long-term memory Retains key info and context
File search Answers from your local files
Sub-Agent Multi-agent collaboration
MCP client Connect to external MCP services via official go-sdk (live tool refresh)
MCP server Expose sandboxed tools to any MCP-compatible agent
Reasoning guides On-demand rules via reasoning_guide(topic=...)
Tool Market Share and install tools
Image generation Generate images through a configured provider
Live command output Stream run_command progress to the TUI and Web dashboard
Secure file boundary Confirm sensitive paths and out-of-home access before granting them
MCP OAuth Log in to HTTP MCP servers and persist tokens in the OS keychain
Transcription Audio and video to text
Self-improvement Auto-fixes after execution failures

Docs

Full documentation at agenvoy.com/docs

License

This project is dual-licensed.

  • Open source — GNU Affero General Public License v3.0. You may use, modify and distribute it, provided derivative works and any network-accessible service built on it are released under the same license, source included.
  • Commercial — for use that cannot meet the AGPL-3.0 source-disclosure requirement, a commercial license is available. It covers rights to the software only; any work on the project requested from the developer is quoted and charged separately. See COMMERCIAL.md.

Under section 7(b) of the AGPL-3.0, the License and Source entries under Settings > Other in the web interface must be preserved: they carry the copyright notice, the warranty disclaimer, the license statement, and the corresponding-source link that section 13 requires. They may be relocated or restyled while remaining prominently visible, but not removed or hidden. A commercial license removes this requirement.

Author

Just open an issue to share an idea.

Agenvoy contributors

©️ 2026 邱敬幃 Pardn Chiu

Frequently asked questions

Is Agenvoy free to use?

Agenvoy is open source under the AGPL-3.0 licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does Agenvoy do?

Self-hosted 24/7 personal AI agent that runs on your own machine — memory, schedules, tools and credentials stay local. Single Go binary with MCP.

What is Agenvoy written in?

Agenvoy is primarily written in Go. Its source is publicly available at https://github.com/agenvoy/Agenvoy, and it has 555 GitHub stars.