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Agenvoy

Self-hosted AI agent harness in a single Go binary — writes, sandbox-tests and repairs its own tools, and lets Claude Code, Codex and any MCP client build and share them.

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README

From the repo.

Agenvoy

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

Open source, single Go binary that runs on your computer. From live research and file work to automation,
Agenvoy takes action and delivers results; through MCP, it shares sandboxed tools with Claude Code, Codex, and other agents.

agenvoy%2FAgenvoy | Trendshift

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English · 繁體中文

Taiwan-developed AI Agent Harness

Agenvoy is a Taiwan-developed AI Agent Harness 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 AgenvoyDesignSame direction elsewhere
2025-06-28 Implement core application
2025-06-29 add traditional memory structure		Summary-based memory for unbounded context2025-11-24 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 agents2026-03-16 NVIDIA NemoClaw
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 logs2026-08-03 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 plugin2026-08-13 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 checkpoint2026-08-04 harness v2 in-memory storage
2026-08-05 indexed harness recovery queries
2026-08-05 validate harness recovery record logs
2026-08-05 harness v2 jsonl backend
2026-08-06 atomic writes + torn-tail truncation
2026-08-14 AgentHarness R3 generation recovery
2026-08-14 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

CapabilityDescription
Auto tool generationBuilds and saves tools when they're missing
Self-schedulingCreate cron jobs with a single sentence
Long-term memoryRetains key info and context
Knowledge notesReads the notes you keep, before it answers
File searchAnswers from your local files
Sub-AgentMulti-agent collaboration
MCP clientConnect to external MCP services via official go-sdk (live tool refresh)
MCP serverExpose sandboxed tools to any MCP-compatible agent
Reasoning guidesOn-demand rules via reasoning_guide(topic=...)
Tool MarketShare and install tools
Image generationGenerate images through a configured provider
Live command outputStream run_command progress to the TUI and Web dashboard
Secure file boundaryConfirm sensitive paths and out-of-home access before granting them
MCP OAuthLog in to HTTP MCP servers and persist tokens in the OS keychain
TranscriptionAudio and video to text
Self-improvementAuto-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

Collected info

  • ★ 525 stars
  • ⎇ 46 forks
  • Language: Go
  • Source updated: 9/21/2026

Config for your environment

Replace {MCP_ENDPOINT_URL} with this MCP’s endpoint URL (from its repo or docs above). No API key — you connect directly.

Tool

OS

Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "mcp-server": {
      "url": "{MCP_ENDPOINT_URL}"
    }
  }
}

Paste into mcpServers in the config file. Restart Cursor after saving.

If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.