DEV Community: Jason Huang

Building an AI Agent in Go: What I Learned

Jason Huang — Mon, 18 May 2026 12:25:11 +0000

Hey DEV community! 👋

I'm an undergraduate developer who recently shipped OpenAgent — a local AI Agent that runs as a single binary. No dependencies, no Docker, just download and double-click.

This post isn't about marketing. It's about the technical decisions, mistakes, and lessons from building an AI Agent in a language most people don't associate with AI.

Project: github.com/the-open-agent/openagent

Why Go for an AI Agent?

The obvious question: "Isn't AI Python's territory?"

Here's what I realized: AI Agents spend 90% of their time waiting on LLM APIs, not crunching numbers. The bottleneck isn't language performance — it's architecture. How you orchestrate tools, manage state, handle concurrency, and ship to users.

Go excels at exactly those things:

Static compilation → single distributable binary
Goroutines → handle concurrent tool calls without memory explosion
Cross-platform → Windows/Mac/Linux from one codebase
Zero runtime dependencies → users don't install anything

For a "download and run" experience, these trade-offs beat Python's ecosystem richness.

Lesson 1: Embedding Frontend into a Go Binary

I wanted a web UI without shipping separate static files. Go 1.16+ makes this trivial with the embed package:

import "embed"

//go:embed all:dist
var distFS embed.FS

func main() {
    // Serve React build directly from binary
    http.Handle("/", http.FileServer(http.FS(distFS)))
    log.Fatal(http.ListenAndServe(":14000", nil))
}

What I learned: The all: prefix is crucial. Without it, files starting with _ or . get skipped, and your React build might mysteriously break.

Build process:

# Build React app
cd frontend && npm run build

# Go embeds the dist folder automatically
cd .. && go build -o openagent .

One file. Frontend and backend. No path resolution bugs, no "where did my assets go" issues.

Lesson 2: Shell Access Without Footguns

Giving an AI Agent shell access is powerful but dangerous. I spent significant time on safety boundaries in tool/shell.go:

type ShellConfig struct {
    DefaultTimeout time.Duration // 30s default
    MaxTimeout     time.Duration // 300s hard limit
    EnablePTY      bool          // Interactive mode
    AuditLog       bool          // Log every command
}

type ShellSession struct {
    ID      string
    State   SessionState // idle | running | waiting_input
    Timeout time.Time
}

Key design decisions:

Session-based flow — poll/write/submit pattern instead of streaming stdout
Timeouts at two levels — default prevents runaways, max prevents abuse
Optional PTY — interactive programs work, but only when explicitly enabled
Audit logging — every command logged with timestamp and output hash

What I learned: Users will accidentally run rm -rf / or fork bombs. Design for the worst case, not the happy path.

Lesson 3: Memory-Conscious Concurrency

I ran a stress test: 80 concurrent health checks. Memory grew by 10 MB.

Here's why Go's model works for Agents:

// Each tool call gets its own goroutine
func (a *Agent) ExecuteTool(ctx context.Context, tool Tool) (Result, error) {
    ctx, cancel := context.WithTimeout(ctx, tool.Timeout)
    defer cancel()

    resultChan := make(chan Result, 1)

    go func() {
        resultChan <- tool.Execute(ctx)
    }()

    select {
    case result := <-resultChan:
        return result, nil
    case <-ctx.Done():
        return Result{}, ctx.Err()
    }
}

What I learned:

Goroutines are cheap (~2KB stack), but channels need buffering to prevent leaks
context.Context is your friend for cancellation and timeouts
Always defer cancel() — goroutine leaks are subtle and painful to debug

Compare to Node.js: each concurrent operation holds the entire event loop. Memory grows with concurrency. Go's model scales horizontally.

Lesson 4: Streaming LLM Responses

Users expect typing effects, not wall-of-text responses. Implementing SSE (Server-Sent Events) with Go:

func (s *Server) ChatStream(w http.ResponseWriter, r *http.Request) {
    w.Header().Set("Content-Type", "text/event-stream")
    w.Header().Set("Cache-Control", "no-cache")
    w.Header().Set("Connection", "keep-alive")

    flusher, ok := w.(http.Flusher)
    if !ok {
        http.Error(w, "Streaming not supported", http.StatusInternalServerError)
        return
    }

    stream := s.agent.ChatStream(r.Context(), r.Body)
    for chunk := range stream {
        fmt.Fprintf(w, "data: %s\n\n", chunk)
        flusher.Flush()
    }
}

What I learned:

http.Flusher interface is required — not all ResponseWriters support it
The \n\n after data: is mandatory per SSE spec
Always handle client disconnects — r.Context() cancels when they close the tab

Lesson 5: Tool Calling Architecture

The heart of an Agent is deciding which tool to use and when. I settled on this interface:

type Tool interface {
    Name() string
    Description() string
    Schema() json.RawMessage // JSON Schema for LLM
    Execute(ctx context.Context, input json.RawMessage) (Result, error)
}

type Agent struct {
    tools   map[string]Tool
    llm     LLMClient
    history []Message
}

func (a *Agent) Run(ctx context.Context, userInput string) error {
    // 1. Add user message to history
    a.history = append(a.history, UserMessage(userInput))

    for {
        // 2. Ask LLM what to do
        response, err := a.llm.Complete(ctx, a.history, a.availableTools())
        if err != nil {
            return err
        }

        // 3. If LLM wants to use a tool
        if response.ToolCall != nil {
            result := a.executeTool(ctx, response.ToolCall)
            a.history = append(a.history, ToolMessage(result))
            continue // Loop back for next decision
        }

        // 4. Final answer
        a.history = append(a.history, AssistantMessage(response.Content))
        return nil
    }
}

What I learned:

The loop pattern (LLM decides → tool executes → LLM decides again) is surprisingly robust
Tool schemas must be precise — vague descriptions lead to wrong tool selection
History management is critical — context windows fill up fast

Lesson 6: Error Handling in Distributed Systems

An Agent is essentially a distributed system: LLM API, local tools, browser automation, file I/O. Things fail constantly.

My error taxonomy:

var (
    ErrToolTimeout     = errors.New("tool execution timed out")
    ErrToolNotFound    = errors.New("tool not found")
    ErrLLMRateLimit    = errors.New("LLM rate limited")
    ErrLLMContextLimit = errors.New("context window exceeded")
    ErrUserCancel      = errors.New("user cancelled")
)

func (a *Agent) executeWithRetry(ctx context.Context, tool Tool, input json.RawMessage) (Result, error) {
    for attempt := 0; attempt < 3; attempt++ {
        result, err := tool.Execute(ctx, input)
        if err == nil {
            return result, nil
        }

        // Don't retry user cancellations
        if errors.Is(err, ErrUserCancel) {
            return Result{}, err
        }

        // Exponential backoff for rate limits
        if errors.Is(err, ErrLLMRateLimit) {
            time.Sleep(time.Duration(attempt+1) * time.Second)
            continue
        }

        // Non-retryable error
        return Result{}, err
    }
    return Result{}, ErrMaxRetriesExceeded
}

What I learned:

errors.Is() and errors.As() are essential for error classification
Not all errors are retryable — know when to fail fast
Context cancellation should propagate immediately, not retry

Lesson 7: Testing Agents is Hard

How do you test something that calls external LLMs? My approach:

// Mock LLM for deterministic tests
type MockLLM struct {
    Responses []LLMResponse
    CallCount int
}

func (m *MockLLM) Complete(ctx context.Context, history []Message, tools []Tool) (LLMResponse, error) {
    if m.CallCount >= len(m.Responses) {
        return LLMResponse{}, errors.New("unexpected LLM call")
    }
    resp := m.Responses[m.CallCount]
    m.CallCount++
    return resp, nil
}

func TestAgentToolLoop(t *testing.T) {
    mock := &MockLLM{
        Responses: []LLMResponse{
            {ToolCall: &ToolCall{Name: "calculator", Input: `{"expr": "2+2"}`}},
            {Content: "The answer is 4"},
        },
    }

    agent := NewAgent(mock, []Tool{&CalculatorTool{}})
    err := agent.Run(context.Background(), "What's 2+2?")

    assert.NoError(t, err)
    assert.Equal(t, 2, mock.CallCount)
}

What I learned:

Mock the LLM, test the orchestration logic
Integration tests with real LLMs are flaky and slow — keep them minimal
Record/replay patterns work well for regression testing

What I'd Do Differently

1. Start with the binary constraint
I initially prototyped in Python, then rewrote in Go. Waste of time. If the constraint is "single binary," start with Go (or Rust).

2. Design state management earlier
I underestimated how complex conversation state gets. Tool results, errors, user corrections, context window management — it piles up fast.

3. Invest in observability from day one
Debugging an Agent is like debugging a distributed system blindfolded. Structured logging and tracing are non-negotiable.

The Bottom Line

Building an AI Agent in Go was the right call for this project. The language's strengths (static binaries, concurrency, simplicity) aligned perfectly with the goal of "download and run."

Is Go the right choice for every AI project? No. If you're training models or doing heavy data science, Python's ecosystem is unmatched. But for shipping a tool that uses AI? Go is surprisingly effective.

If you're curious, check out the code: github.com/the-open-agent/openagent

I'd love to hear your thoughts — especially if you've built Agents in other languages. What worked? What didn't?

Built with Go, excessive amounts of coffee, and the stubborn belief that software should just work. ☕

[Boost]

Jason Huang — Mon, 18 May 2026 12:20:45 +0000

Jason Huang

May 18

Just Download and Run: OpenAgent Launches, Possibly the Most Hassle-Free Local AI Agent

#ai #productivity #opensource #security

Comments 1

6 min read

I Built a Single-File AI Agent in Go — Zero Dependencies, Double-Click to Run

Jason Huang — Mon, 18 May 2026 12:20:08 +0000

Hey DEV community! 👋

I'm an undergrad developer who spent the last few months hacking on something I'm excited to share: OpenAgent — a local AI Agent that ships as a single binary. No Docker. No Node.js. No Python environments. Just download the .exe and double-click.

GitHub: github.com/the-open-agent/openagent ⭐

The Problem I Was Trying to Solve

Like many of you, I've been experimenting with AI Agents for everything from code generation to automating tedious tasks. But I kept hitting the same friction:

# Typical "getting started" experience
npm install -g some-agent
# 847 packages installed...
# Oh, you need Python 3.9+ too
pip install ...
# Also Docker for the vector DB
docker-compose up
# 12 minutes later...
# Wait, why is this 400MB?

Sound familiar? I wanted something that felt more like:

# Download openagent.exe (23MB)
# Double-click
# Done ✅

So I built it.

What Makes OpenAgent Different

Single Binary, Zero Runtime Dependencies

I wrote OpenAgent in Go with a specific constraint: everything must compile into one executable. The React frontend gets embedded at build time. The backend is pure Go. One process, one port (14000), one file.

The result:

23 MB executable (vs 400MB+ for typical setups)
~2.7s cold start (vs 30s for alternatives)
~110 MB idle memory

It's Not Just "Hello World"

Despite being a single file, OpenAgent packs real capabilities:

Feature	Details
🤖 Models	30+ providers: OpenAI, Claude, DeepSeek, Gemini, Mistral, Grok...
🌐 Browser	Real browser automation (navigation, clicks, forms, screenshots)
🖥️ Shell	Local command execution with PTY support
📄 Office	Read/write Word, Excel, PowerPoint
🔗 MCP	Plug-and-play with any MCP-compatible server
📚 RAG	PDF/Word/Excel → automatic chunking, embedding, indexing
⚡ Workflows	BPMN-style visual orchestration
📊 Dashboard	Token usage, activity logs, tool management

Why Go Instead of Python?

"But isn't AI/ML Python's domain?"

Here's the thing: Agent performance is bottlenecked by LLM API calls, not language execution speed. The magic happens in the architecture — how you orchestrate tools, manage state, handle concurrency.

Go gives me:

Static compilation → single distributable binary
Goroutines → efficient concurrency without the memory bloat
Cross-platform → Windows/Mac/Linux from one codebase
No runtime → users don't install anything

For a "personal local Agent," these trade-offs beat Python's dynamic flexibility.

Quick Start

Windows (easiest):

Download openagent_Windows_x86_64.exe from Releases
Double-click
Open http://localhost:14000

macOS/Linux:

curl -fsSL https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.sh | bash

Configure your model:
The UI has a one-click setup for OpenAI, Claude, DeepSeek, or any OpenRouter-compatible provider.

The Engineering Decisions I'm Proud Of

1. Shell with Guardrails

Shell access is powerful but dangerous. In tool/shell.go, I built in:

Default 30s timeout (max 300s)
Session-based poll/write/submit flow
Optional PTY for interactive programs
Audit logging for every command

It's designed as a production tool, not a foot-gun.

2. Memory-Conscious Concurrency

I ran an 80-concurrent health check stress test. Memory grew by 10 MB. Go's goroutine + channel model makes this trivial compared to the memory curves I've seen in Node.js-based agents.

3. Frontend Embedded, Not Served

The React build gets embedded into the binary with Go's embed package. No separate static files to manage, no path resolution issues, no "where did my assets go" bugs.

Real Talk: What's Not Perfect

I want to be transparent about where we stand:

Metric	OpenAgent	OpenClaw
Lighthouse Score	45	87
Health Check P50	~33ms	~20ms

Our frontend loading needs work. The Lighthouse gap is real — we're optimizing it. The health check difference is milliseconds, but still.

If you find cases where we're slower or more expensive, file an issue. I genuinely want to know.

Who This Is For

Developers who want a Claude Code alternative that runs locally with their own API keys
Privacy-conscious users who don't want data leaving their machine
Resource-limited setups where running multiple agents used to be impossible
Security folks who need audit trails and controlled shell access

Get Involved

OpenAgent is Apache 2.0 licensed. The code is on GitHub, and I review issues/PRs within 12 hours.

If the project helps you, a ⭐ means a lot. If you want to contribute, there are plenty of good first issue labels waiting.

GitHub: github.com/the-open-agent/openagent

Discord: discord.gg/5rPsrAzK7S

The Bigger Picture

I believe personal AI Agents should be lighter, not heavier. One file. Double-click. Done.

No 400MB installs. No dependency hell. No "it works on my machine."

Just an Agent that does the job and gets out of your way.

That's OpenAgent.

Built with Go, caffeine, and the stubborn belief that software should just work. ☕

OpenAgent - My Journey Building a Zero-Config Local AI Agent

Jason Huang — Mon, 18 May 2026 12:13:55 +0000

Hi everyone! I'm an undergraduate student and a developer on the OpenAgent open-source team.

We spent two months building a truly zero-configuration, zero-dependency, double-click-to-run personal local single-file Agent 🦞 with Go. It truly works out of the box, and it has garnered significant attention in the community.

OpenAgent is an open-source local AI Agent for individual developers and tech enthusiasts. Similar to OpenClaw and Hermes in the "personal local assistant" space, but we've taken a completely different approach: written in Go as a single binary executable — download the .exe, double-click to run, zero configuration out of the box.

Project: https://github.com/the-open-agent/openagent

If this project's features or architecture help you, we'd appreciate a star ⭐

1. Why We Built This

More and more people are using AI to get work done — not just coding, but creating presentations, running scripts, researching, organizing documents. But after using these tools for a while, most people hit the same wall: deployment costs.

Many well-known Agents are structural dependency monsters — a complete environment requires Node.js, Python, Docker, WSL, nested layer by layer. The problem usually isn't the model itself, but the Agent's delivery format: dependency bloat, tens of thousands of scattered files, complex configuration, difficult migration. Each layer drains user patience, and the monthly bill delivers the final lesson.

OpenAgent's trade-offs were clear from day one: make "single-file zero-config" a top-level design goal, not an afterthought patch. We chose a harder path — rewriting from scratch in Go as a single binary, with no runtime dependencies, no installer, no Docker. The frontend React is embedded directly into the binary, the backend is pure Go, one process listening on port 14000.

This is OpenAgent today.

2. More Than Lightweight — A Complete Agent Work Platform

OpenAgent isn't just a fast single-file executable. It comes with a full suite of capabilities for daily workflows:

🤖 30+ Model Providers: OpenAI, Claude, DeepSeek, Gemini, Mistral, Grok... Switch anytime without code changes
🌐 Browser-Use: Drive real browsers for navigation, clicking, form filling, screenshots
🖥️ Shell Execution: Local command execution with PTY interactive session support
📄 Office Automation: Read and write Word, Excel, PowerPoint
🔗 MCP Integration: Any MCP-compatible server, plug and play
📚 RAG Knowledge Base: Automatic slicing, embedding, and indexing of PDF/Word/Excel
⚡ Workflow Orchestration: BPMN-style visual drag-and-drop orchestration
📊 Admin Dashboard: Token usage statistics, activity monitoring, tool management, request logs

3. Why Better Performance and Lower Latency — 3 Engineering Decisions

Not "cutting features for performance" — every layer made the right choice.

① Single Binary: One File, Zero Dependencies

Go static compilation, frontend React build embedded directly into the binary. Windows users download the .exe and double-click to run — no WSL, no Docker, no Node.js. Mac/Linux one-command install. This is what a "personal local Agent" delivery should look like.

② Shell Execution with Boundaries, Safe and Controllable

At the source code level, tool/shell.go defines default timeout 30 seconds, max 300 seconds, optional PTY, session-based poll/write/submit mechanisms. Shell capability is strongly constrained production-grade tooling by default, not unlimited remote execution. Also supports audit logs, SSO, request logs, and enterprise-grade observability.

③ Go Native Concurrency, Memory Controllable

Go's goroutine + channel model keeps memory growth very restrained in high-concurrency scenarios. 80-concurrent health stress test, memory grew only 10 MB. Compare to Node.js memory curves — this is a structural advantage at the language level.

4. About the Go Rewrite

Some might ask: Isn't AI Agent Python's territory? Why Go?

Agent bottlenecks are in LLM calls, not language performance — what makes an Agent run well is Harness-layer architecture design, not underlying language execution speed. Go's advantages: static compilation single file, controllable memory, clean concurrency model, native cross-platform support. These characteristics matter more than Python's dynamic flexibility for the "personal local Agent" use case.

We wrote in Go for three months, from zero to one, building this core.

5. Finally

OpenAgent is an Apache 2.0 open-source project, all code on GitHub, welcome to review, Star, and file issues.

We firmly believe: The future of personal local Agents isn't getting heavier, it's getting lighter. One file, double-click to run — that's what users really want.

GitHub: https://github.com/the-open-agent/openagent ⭐

Just Download and Run: OpenAgent Launches, Possibly the Most Hassle-Free Local AI Agent

Jason Huang — Mon, 18 May 2026 12:07:15 +0000

Hi DEV community, I'm a developer from the OpenAgent team and a long-time lurker here.

Today, I want to introduce a project we've been refining for quite some time — OpenAgent, an open-source local AI Agent for individual developers and tech enthusiasts. Similar to OpenClaw and Hermes in the "personal local assistant" space, but we've taken a completely different approach: written in Go as a single binary executable — download the .exe, double-click to run, zero configuration out of the box.

One-liner positioning: A ready-to-use single-file local Agent with better stability, lower latency, and minimal resource footprint.

GitHub: github.com/the-open-agent/openagent (Stars appreciated ⭐)

Official Website: openagentai.org

1. Why We Built This

More and more people are using AI to get work done — not just coding, but creating presentations, running scripts, researching, organizing documents, even security scanning. But after using these tools for a while, most people hit the same wall: deployment costs.

Many "well-known" Agents are structural dependency monsters — a complete environment requires Node.js, Python, Docker, WSL, nested layer by layer. The problem usually isn't the model itself, but the Agent's delivery format: dependency bloat, tens of thousands of scattered files, complex configuration, difficult migration. Each layer drains user patience, and the monthly bill delivers the final lesson.

OpenAgent's trade-offs were clear from day one: make "single-file zero-config" a top-level design goal, not an afterthought patch. We chose a harder path — rewriting from scratch in Go as a single binary, with no runtime dependencies, no installer, no Docker. The frontend React is embedded directly into the binary, the backend is pure Go, one process listening on port 14000.

This is OpenAgent today.

2. More Than Lightweight — A Complete Agent Work Platform

OpenAgent isn't just a fast single-file executable. It comes with a full suite of capabilities for daily workflows:

🤖 30+ Model Providers: OpenAI, Claude, DeepSeek, Gemini, Mistral, Grok... Switch anytime without code changes
🌐 Browser-Use: Drive real browsers for navigation, clicking, form filling, screenshots
🖥️ Shell Execution: Local command execution with PTY interactive session support
📄 Office Automation: Read and write Word, Excel, PowerPoint
🔗 MCP Integration: Any MCP-compatible server, plug and play
📚 RAG Knowledge Base: Automatic slicing, embedding, and indexing of PDF/Word/Excel
⚡ Workflow Orchestration: BPMN-style visual drag-and-drop orchestration
📊 Admin Dashboard: Token usage statistics, activity monitoring, tool management, request logs

3. Let's Look at the Data: Performance and Latency Benchmarks vs OpenClaw

Architecture done — but how does it actually perform? We spent over ten days on horizontal benchmarking, putting OpenAgent and OpenClaw on the same starting line. Both use deepseek/deepseek-v4-flash as the underlying model, currently the most cost-effective model that best exposes the true capabilities of each Agent Harness.

3.1 Agent Tool Invocation Benchmark (Core Performance)

12 tasks × 3 rounds = 36 scoring runs, same model, same prompt:

Metric	OpenAgent	OpenClaw
Success Rate	83.33% (30/36)	61.11% (22/36)
Avg. Time	14,183 ms	26,128 ms
Avg. Tokens	1,312	2,362
Compliance Rate	90.0	76.67
Coverage Rate	97.5	91.67
Overall Score	91.14	78.41

One-sentence summary: Higher success rate, faster speed, fewer tokens. OpenAgent averages ~11.9 seconds less per round, with ~1,050 fewer tokens consumed. 36 real execution rounds, scored by automated scripts, zero human intervention.

3.2 Conversation Scenario Performance

Metric	OpenAgent	OpenClaw
Completion Rate	100%	95%
Format Accuracy	100%	82.5%
Factual Accuracy	100%	92.5%
Avg. Latency	3,991 ms	26,781 ms

OpenAgent achieves 100% completion rate, format accuracy, and factual accuracy in conversation scenarios, with average latency around 4 seconds; OpenClaw averages around 26.8 seconds.

3.3 Project Performance

Dimension	OpenAgent	OpenClaw
Cold Start Ready	~2.7 s	~29.2 s
Resident Memory	~110 MB	~215–307 MB
Install Size	exe ~23 MB	~382 MB / 42,000+ files
TTFT (Time to First Token)	~23 ms	~44 ms

Cold Start: OpenAgent median 2.7 seconds to pass health check and return HTTP 200, OpenClaw needs 29.2 seconds.

Size: OpenAgent single file ~23 MB, OpenClaw entire directory ~382 MB, over 40,000 files. This isn't "trading features for size" — it's the architectural advantage of Go static compilation + frontend embedding.

Memory: Idle OpenAgent ~110 MB, OpenClaw ~215–245 MB. Under load testing OpenAgent ~120 MB, OpenClaw ~307 MB.

4. Why Better Performance and Lower Latency — 3 Engineering Decisions

Not "cutting features for performance" — every layer made the right choice.

① Single Binary: One File, Zero Dependencies

② Shell Execution with Boundaries, Safe and Controllable

③ Go Native Concurrency, Memory Controllable

5. Let's Be Honest, Challenges Welcome

How far from "perfect"? Let's be clear about a few things:

Lighthouse Overall Score: OpenClaw 87, we're at 45. This is frontend loading performance — we used Lighthouse 11, OpenClaw's test version may differ, scores shouldn't be compared across versions. But it does indicate our frontend has optimization room.
Sequential Health P50: OpenClaw ~20 ms, we're ~33 ms. Millisecond-level difference, limited reference value for personal local Agents.
Configuration Prerequisites: Chat API requires Authorization: Bearer <provider_key>, not "completely zero steps". But compared to installing Node.js, configuring Docker, pulling tens of thousands of files, our friction is already minimal.

We Welcome Your Challenge:

Download OpenAgent, use your own OpenRouter Key
Run the same prompts, compare the bills
Beat us on speed/efficiency? PRs welcome. Find us more expensive? File an issue and we'll fix it.

6. About the Go Rewrite

Some might ask: Isn't AI Agent Python's territory? Why Go?

We wrote in Go for three months, from zero to one, building this core.

7. Who Benefits — Typical Use Cases

🔧 Programmers / Developers: CLI form directly replaces Claude Code, BYOK with your own Key, local Shell, Browser, Office tools one-click invocation.

🏠 Individual Users / Enthusiasts: Same machine specs, where running one Agent used to cause lag, now easily run multiple — memory footprint less than half the competition.

💼 Freelancers / One-Person Companies: Client proposals, consulting reports, competitive analysis, document organization — run locally, data never leaves your machine, the savings are peace of mind.

🔒 Security Professionals: Shell tool supports nmap, subfinder, httpx and other security CLI calls (requires local installation), with audit logs for traceable attack/defense exercises.

8. Getting Started

Windows (Easiest, Recommended)

Download openagent_Windows_x86_64.exe from GitHub Releases
Double-click to run, environment/dependencies auto-configured
Open http://localhost:14000

macOS / Linux / WSL

curl -fsSL https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.sh | bash

Windows PowerShell

irm https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.ps1 | iex

Model Integration

One-click configuration in the project startup frontend interface, supports almost all mainstream LLMs, including locally deployed models.
Download and docs: openagentai.org

9. Finally

OpenAgent is an Apache 2.0 open-source project, all code on GitHub, welcome to review, Star, and file issues.

We firmly believe: The future of personal local Agents isn't getting heavier, it's getting lighter. One file, double-click to run — that's what users really want.

GitHub: github.com/the-open-agent/openagent ⭐

Discord: discord.gg/5rPsrAzK7S