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Run OpenManus on Windows: 2026 Installation Guide (uv, Conda, Ollama)

Published • Updated • 10 min read
Run OpenManus on Windows: 2026 Installation Guide (uv, Conda, Ollama)

Last updated April 2026 — refreshed for current model/tool versions.

OpenManus is the actively maintained open-source agent framework that lets you reproduce most of what proprietary Manus AI does — autonomous web browsing, multi-step planning, code and shell execution, file I/O — on your own Windows machine, with the model of your choice. This guide is a clean, ordered, 2026-current install for Windows 10 and 11, with verified config for GPT-4o / Claude 3.7 Sonnet / DeepSeek / local Ollama models.

What changed in 2026 (read this first if you used the original guide)The repo moved from mannaandpoem/OpenManus to FoundationAgents/OpenManus. The old URL still redirects, but new contributions, releases, and issues live on the new org.Python 3.12+ is now required (modern type-hint syntax, asyncio.TaskGroup). The old python=3.9 conda command will fail at install.The default model in config.example.toml is now Claude 3.7 Sonnet via Anthropic. gpt-4o and Ollama-hosted local models are commented as drop-in alternatives. The original guide's model = "gpt-4" string still works but is no longer the recommended default — GPT-4 is deprecated on the OpenAI Chat Completions endpoint as of mid-2025.The latest tagged release is v0.3.0 (April 10, 2026). The repo is at roughly 56k GitHub stars and is MIT-licensed.The Manus / Meta deal is dead. China's NDRC blocked Meta's $2 billion acquisition of Manus on April 27, 2026, and Bloomberg reported the deal "officially dead" on April 29. That accelerates demand for the open-source fork — OpenManus is now the de facto path for anyone who wants Manus-style agent behavior outside Chinese cloud or Meta's stack.Browser automation now ships via Playwright (run playwright install after pip). The old Chromium-CDP path still works but is no longer the default.

Want the full picture? Read our continuously-updated Qwen 3.5 Complete Guide (2026) — flavors, licensing, benchmarks, and on-device usage.

TL;DR

QuestionShort answer
Repo to clonehttps://github.com/FoundationAgents/OpenManus.git
Python version3.12 or higher
Recommended installeruv — 5–10× faster than pip in the dependency-resolve phase
Default LLM in shipped configClaude 3.7 Sonnet (claude-3-7-sonnet-20250219)
Cheapest cloud optionDeepSeek V3.2 via https://api.deepseek.com/v1 (use gpt-4o for tool-call reliability if budget allows)
Fully local optionOllama with a strong tool-use model (Qwen 3.5-Coder 32B, Llama 4 70B, GLM-4.7)
LicenseMIT
Latest releasev0.3.0 (April 10, 2026)

Why OpenManus, and why now

Manus AI launched in March 2025 and immediately attracted attention for outperforming GPT-4 on the GAIA general-agent benchmark while behind a Chinese-cloud waitlist. The MetaGPT research group at FoundationAgents shipped OpenManus as an open replica within hours of that launch. Twelve months later, the picture has flipped:

  • Meta agreed to acquire Manus for $2B and migrated about 100 staff into its Singapore office; on April 27, 2026 the National Development and Reform Commission ordered the deal unwound, citing strategic-technology export controls. Two Manus founders are reportedly under exit bans.
  • Manus access for non-Chinese users is now uncertain. OpenManus is the only path that doesn't depend on a contested cross-border corporate transaction.
  • The open project itself has matured — v0.3.0 added Playwright-based browser tooling, an MCP server bridge, optional sandboxed execution, and a configurable Data Analysis Agent.

If you previously bookmarked the proprietary Manus and waited for an invite, the pragmatic 2026 move is to install OpenManus locally and bring your own model. (If you're a hiring manager evaluating engineers who can ship agentic systems on Windows, Codersera maintains a roster of vetted remote developers with this exact stack.)

System requirements (2026)

  • OS: Windows 10 22H2 or Windows 11 (24H2 recommended). WSL 2 works and is friendlier for the browser-automation stack but is not required.
  • Python: 3.12 or 3.13. The codebase uses PEP 695 generics and asyncio.TaskGroup; 3.11 will fail at import. 3.9 — which the original guide recommended — is no longer supported.
  • Disk: ~3 GB for the venv and Playwright browsers. Add 30–80 GB if you plan to run a 30B-parameter local model in Ollama.
  • RAM: 16 GB for cloud-hosted LLM use. 32 GB minimum if you also want to run a quantized local model.
  • GPU: Not needed if you call OpenAI / Anthropic / DeepSeek over HTTP. A 24 GB card (RTX 4090 / 5090 / A6000) is the practical floor for local Qwen 3.5-Coder 32B at usable speeds.
  • Tooling: Git, a recent VS Code (or any editor), and a terminal you trust (Windows Terminal + PowerShell 7 is the easiest path).

Step-by-step installation (Windows)

There are two supported paths. Pick one — don't mix them.

uv is Astral's Rust-based Python toolchain. It resolves and installs dependencies dramatically faster than pip and handles the Python interpreter for you, which removes the most common source of OpenManus install failure ("conda activated, but python --version still shows 3.10").

Run it.

python main.py

You'll get a prompt asking for the task. Try: "Find the current price of NVDA and save a one-paragraph summary to summary.txt".

Copy the config.

copy config\config.example.toml config\config.toml

Edit config\config.toml as shown in the configuration section below.

Install the Playwright browsers (Chromium ships pinned to a known-good version):

playwright install chromium

Install dependencies.

uv pip install -r requirements.txt

Create the virtual environment with the right interpreter.

uv venv --python 3.12
.venv\Scripts\activate

uv will download a CPython 3.12 build if you don't have one — no separate Python install needed.

Clone the repo.

git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus

Install uv. In PowerShell (not cmd):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Close and reopen PowerShell so uv resolves on PATH.

Method B: Miniconda / Anaconda

Use this only if you're already invested in conda environments. Order matters here — the original 2025 guide had the steps misordered, which is why so many readers hit "config.py.example not found" before they had even cloned the repo.

  1. Install Miniconda from the official miniconda page (Anaconda also works but is heavier).

Run.

python main.py

Copy and edit the config.

copy config\config.example.toml config\config.toml

Install dependencies.

pip install -r requirements.txt
playwright install chromium

Clone the repository.

git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus

Create and activate the environment with Python 3.12.

conda create -n open_manus python=3.12 -y
conda activate open_manus

Configuring OpenManus (config.toml)

OpenManus reads config/config.toml at startup. The shipped config.example.toml defaults to Anthropic Claude 3.7 Sonnet for both the planner and the vision model and includes commented blocks for Azure OpenAI, AWS Bedrock, Ollama, and OpenRouter-style aggregators.

Cloud models — pick your provider

Anthropic Claude 3.7 Sonnet (the shipped default)

[llm]
model = "claude-3-7-sonnet-20250219"
base_url = "https://api.anthropic.com/v1/"
api_key = "sk-ant-..."
max_tokens = 8192
temperature = 0.0

[llm.vision]
model = "claude-3-7-sonnet-20250219"
base_url = "https://api.anthropic.com/v1/"
api_key = "sk-ant-..."

OpenAI GPT-4o (most reliable tool-calling in the field tests we saw)

[llm]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."
max_tokens = 4096
temperature = 0.0

[llm.vision]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..."

DeepSeek V3.2 (cheapest cloud path)

[llm]
model = "deepseek-chat"
base_url = "https://api.deepseek.com/v1"
api_key = "sk-..."
max_tokens = 4096
temperature = 0.0

DeepSeek is roughly an order of magnitude cheaper per million tokens than GPT-4o, but several practitioners report tool-call instability when OpenManus chains 10+ steps — if your task is long-horizon, expense the Claude or GPT-4o tokens.

Fully local — Ollama, no API keys

This is the path most r/LocalLLaMA users want. OpenManus speaks the OpenAI-compatible endpoint that Ollama exposes on http://localhost:11434/v1. The only gotcha is that the model you choose must be a strong tool-use model — most generalist 7B and 13B local models will mis-format their function-call JSON and the agent will loop.

[llm]
api_type = "ollama"
model = "qwen3.5-coder:32b"
base_url = "http://localhost:11434/v1"
api_key = "ollama"   # any non-empty string
max_tokens = 4096
temperature = 0.0

[llm.vision]
api_type = "ollama"
model = "minicpm-v"
base_url = "http://localhost:11434/v1"
api_key = "ollama"

Reasonable 2026 local picks, in rough order of agentic reliability on a 24 GB GPU:

  1. Qwen 3.5-Coder 32B — currently the best general-purpose local tool-use model.
  2. GLM-4.7 — strong tool calling, very competitive on agent benchmarks.
  3. Llama 4 70B (quantized to 4-bit) — needs 48 GB VRAM or a Mac with 64 GB unified memory.
  4. MiniMax M2.7 — repeatedly cited on r/LocalLLaMA for tool-heavy workflows.
  5. DeepSeek V4 (distilled) — solid reasoning, occasional tool-call drift.

How to choose: a quick decision tree

  • You want it working in 10 minutes and don't mind paying $5–20 for testing → Method A (uv) + Claude 3.7 Sonnet config.
  • You're on a corporate laptop with conda already managed → Method B (conda) + GPT-4o.
  • You want zero data egress / full privacy → Method A + Ollama with Qwen 3.5-Coder 32B. Budget for the GPU.
  • You're cost-sensitive and your task is one or two steps deep → Method A + DeepSeek V3.2.
  • You're integrating OpenManus into an internal Jira/database workflow → start on Claude or GPT-4o for reliability, switch to local once your tool definitions are stable.

Performance and benchmark notes

OpenManus inherits its raw capability from whichever LLM you wire it to. A few concrete reference points from public 2026 sources:

  • The original Manus paper claimed state-of-the-art on GAIA (general-agent benchmark) ahead of GPT-4 baselines. OpenManus reproduces the orchestration but uses your chosen LLM, so headline scores depend on the backing model.
  • On tool-calling reliability, GPT-4o and Claude 3.7 Sonnet trade leadership across SWE-Bench Verified and Agent-Bench-style suites; both are far ahead of any 32B-class local model in 2026.
  • Practitioners on the OpenManus repo discussions report ~5-step task completion for a "fetch and save the weather" task with GPT-4o; the same task with a local 7B model typically loops on tool-format errors.
  • Browser automation latency is dominated by Playwright wait conditions — typical end-to-end action takes 2–6 s with a cloud LLM, longer with local models because of token-streaming overhead.

Common pitfalls and troubleshooting

  • python=3.9 environments fail to import. Recreate the env with 3.12: conda create -n open_manus python=3.12 -y. Modern type-hint syntax in the codebase isn't backportable.
  • config.py.example not found. The original repo briefly used config.py; it's been config/config.toml for over a year. Copy config/config.example.toml, not anything ending in .py.
  • playwright._impl._errors.Error: Executable doesn't exist. You skipped playwright install chromium. Run it with the venv active.
  • Tool-call JSON errors with local models. The model you picked isn't a strong tool-user. Switch to Qwen 3.5-Coder 32B, GLM-4.7, or MiniMax M2.7. Lower the temperature to 0.0 and lower max_tokens.
  • API key works in curl but not in OpenManus. Confirm the base_url ends with /v1 for OpenAI-compatible providers and /v1/ (with trailing slash) for Anthropic. The Anthropic client is picky.
  • Windows path issues with the browser sandbox. If you hit "permission denied" running headless Chromium, run PowerShell as Administrator once or move the repo out of OneDrive\-synced folders.
  • "git clone" hangs. Some corporate networks block GitHub's smart HTTP. Either set up an SSH key or use a personal hotspot for the initial clone — pulls are smaller after that.
  • The agent runs but gets stuck refreshing pages. Drop headless = false in the [browser] section of config.toml while debugging — you'll see what the agent is actually clicking on.

What was removed and why

  • The mannaandpoem/OpenManus repo URL — superseded by FoundationAgents/OpenManus. Old clones still work but won't pick up new releases.
  • The config.py.example reference — replaced by config/config.example.toml. Copying the old filename will fail with a confusing "file not found" before the agent even starts.
  • gpt-4-vision as a config value — deprecated by OpenAI in favor of gpt-4o, which is multimodal natively. Don't ship a config that names gpt-4-vision; the request will fail.
  • python=3.9 — codebase requires 3.12+. Conda accepts the lower number, then imports break at runtime.

FAQ

Is OpenManus different from Manus AI?

Yes. Manus is a closed product from a Singapore-registered, originally-Chinese-founded startup, currently in regulatory limbo after China's NDRC blocked Meta's $2B acquisition on April 27, 2026. OpenManus is an MIT-licensed reproduction maintained by the FoundationAgents / MetaGPT research group. It has no waitlist and no invite code.

Do I need internet access to run OpenManus?

You need internet for the cloud-LLM path (OpenAI, Anthropic, DeepSeek). For a fully offline run, install Ollama, pull a strong tool-use model, and point base_url at http://localhost:11434/v1. The browser-automation tools obviously still need network access for whatever sites you ask the agent to visit.

Why does the install fail on my Windows 10 machine with Python 3.9?

OpenManus's codebase uses PEP 695 generics and asyncio.TaskGroup, which were added in Python 3.11 and 3.12. The minimum supported version is 3.12. Recreate the venv with uv venv --python 3.12 or conda create -n open_manus python=3.12.

How much does running OpenManus cost?

If you self-host with Ollama: hardware only. If you use cloud LLMs: pay-per-token to your provider. A typical multi-step web research task consumes 30–80k input tokens and 5–15k output tokens — roughly $0.10–$0.40 with Claude 3.7 Sonnet, $0.05–$0.20 with GPT-4o, under $0.02 with DeepSeek. OpenManus itself is MIT-licensed and free.

Should I use OpenManus or LangGraph / AutoGen / CrewAI?

Different niches. LangGraph and CrewAI are libraries for building agent topologies. OpenManus is a ready-made general agent — closer to a Manus / Devin clone than a framework. Use OpenManus when you want an out-of-the-box autonomous agent with browser, code, and shell tooling. Use LangGraph when you want to design the graph yourself.

Does OpenManus work on Windows 11 and Windows Server?

Yes on Windows 11 24H2 (tested by the maintainers). Windows Server 2022 works for the headless path; you'll want to run with headless = true in config.toml's [browser] section. Windows Server Core (no GUI shell) is fine because Playwright ships its own Chromium build.

Can I use OpenManus inside a corporate network with a proxy?

config.toml has a [proxy] section: set server, username, and password. Both the LLM HTTP traffic and the browser tool will respect it. If your proxy MITMs TLS, drop the corporate root CA into your venv's certifi bundle or set REQUESTS_CA_BUNDLE.

Where does OpenManus send my data?

Wherever your base_url points. With the cloud configs above, your prompts and tool outputs go to OpenAI, Anthropic, or DeepSeek under their standard data policies. With Ollama, nothing leaves the box. The agent does write files locally — review the workspace/ directory after a run to see what it produced.

References and further reading

Hiring engineers who can stand up agentic systems like this on a real product? Codersera matches startups with vetted remote developers who have shipped LLM-orchestration code in production, with a risk-free trial.

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