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
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.
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?
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.
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.exefrom 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
Top comments (1)
As you can see, we don't need dependencies, we don't need deployment — just download and double-click to run. It's incredibly convenient. We hope you'll check out our GitHub!