OpenManus: Best Open Source Manus AI Alternative (2026)

Manus AI made headlines as the first fully autonomous AI agent capable of completing complex real-world tasks end-to-end -- booking travel, conducting research, building applications, and more. But its closed-source nature, limited availability, and pricing have pushed developers to seek alternatives. OpenManus is the leading open-source project that replicates Manus AI's core capabilities while giving you full control over the code, models, and data.

This guide covers what OpenManus is, how to set it up, and how it compares to Manus AI and other agent frameworks.

What Is OpenManus?

OpenManus is an open-source autonomous AI agent framework that replicates the core functionality of Manus AI. Built by a community of developers, it enables AI agents to:

Browse the web and interact with websites
Read and write files on your system
Execute code and shell commands
Plan and decompose complex tasks
Use tools and APIs autonomously
Maintain memory across sessions

Unlike Manus AI, which runs on proprietary infrastructure, OpenManus runs on your own hardware and lets you choose which LLM powers the agent.

OpenManus vs Manus AI: Feature Comparison

Feature	OpenManus	Manus AI
Open source	Yes (MIT/Apache)	No
Self-hosted	Yes	No (cloud only)
LLM choice	Any (GPT-4, Claude, Gemini, local)	Proprietary
Web browsing	Yes (Playwright)	Yes
Code execution	Yes (sandboxed)	Yes
File management	Yes	Yes
Task planning	Yes (ReAct + Plan-and-Execute)	Yes
Tool creation	Yes (extensible)	Limited
Memory/persistence	Yes (local storage)	Yes (cloud)
Cost	LLM API costs only	Subscription
Privacy	Full control	Data sent to Manus servers
Waitlist	No	Yes
Setup difficulty	Medium	Easy (managed)

The key advantage of OpenManus is control. You decide which model powers the agent, where your data goes, and what tools the agent can access.

Step 1: Install OpenManus

Prerequisites

Python 3.11 or higher
Node.js 18+ (for browser automation)
An API key for at least one LLM provider

Clone and Install

# Clone the repository
git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
# venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

# Install Playwright browsers for web browsing capability
playwright install chromium

Configure Your LLM Provider

Create a configuration file at config/config.toml:

# OpenManus Configuration

[llm]
# Choose your LLM provider and model
provider = "openai"  # Options: openai, anthropic, google, ollama
model = "gpt-4o"
api_key = "sk-your-api-key-here"
base_url = "https://api.openai.com/v1"
max_tokens = 8192
temperature = 0.7

[llm.vision]
# Model for visual tasks (screenshots, images)
provider = "openai"
model = "gpt-4o"
api_key = "sk-your-api-key-here"

[browser]
headless = true  # Set to false to watch the browser in action
timeout = 30000  # Page load timeout in ms

[sandbox]
enabled = true
max_execution_time = 60  # seconds
allowed_commands = ["python", "node", "git", "npm", "pip"]

[memory]
enabled = true
storage_path = "./data/memory"

Using Claude as the LLM

[llm]
provider = "anthropic"
model = "claude-sonnet-4-20250514"
api_key = "sk-ant-your-key-here"
base_url = "https://api.anthropic.com"
max_tokens = 8192
temperature = 0.7

Using a Local Model (Ollama)

[llm]
provider = "ollama"
model = "qwen2.5:32b"
base_url = "http://localhost:11434"
max_tokens = 8192
temperature = 0.7

Step 2: Run OpenManus

Interactive Mode

# Start the interactive agent
python main.py

# You will see a prompt where you can type tasks
> Research the top 5 Python web frameworks in 2026 and create a comparison report

Command-Line Mode

# Run a single task
python main.py --task "Find the cheapest flight from NYC to London on March 15"

# Run with a specific configuration
python main.py --config config/config.toml --task "Build a todo app with FastAPI"

# Run with verbose logging
python main.py --verbose --task "Analyze the GitHub trending repos this week"

Web UI Mode

OpenManus includes an optional web interface:

# Start the web server
python web_ui.py --port 8080

# Open http://localhost:8080 in your browser

The web UI shows real-time agent activity, including browser screenshots, code execution logs, and the agent's planning process.

Step 3: Understanding the Agent Architecture

OpenManus uses a modular architecture with these key components:

Planner

The planner decomposes complex tasks into subtasks:

Task: "Build a weather dashboard web app"

Plan:
1. Research weather APIs (free tier)
2. Choose a frontend framework
3. Design the UI layout
4. Create the project structure
5. Implement the weather API integration
6. Build the frontend components
7. Add error handling and loading states
8. Test the application
9. Write a README with setup instructions

Tools

OpenManus comes with built-in tools that the agent can use:

Tool	Description	Example Use
`browser`	Navigate, click, type, screenshot	Web research, form filling
`code_execute`	Run Python/Node code	Data processing, testing
`file_manager`	Read, write, create files	Code generation, reports
`shell`	Execute terminal commands	Git operations, installs
`search`	Web search via API	Finding information
`memory`	Store and recall information	Cross-task context

Creating Custom Tools

You can extend OpenManus with custom tools:

# tools/custom_api_tool.py
from openmanus.tools.base import BaseTool

class WeatherAPITool(BaseTool):
    name = "weather_api"
    description = "Get current weather data for any city"

    parameters = {
        "city": {
            "type": "string",
            "description": "City name (e.g., 'London')"
        }
    }

    async def execute(self, city: str) -> dict:
        import httpx
        async with httpx.AsyncClient() as client:
            response = await client.get(
                f"https://api.weatherapi.com/v1/current.json",
                params={"key": self.config.weather_api_key, "q": city}
            )
            return response.json()

[tools]
custom = ["tools/custom_api_tool.py"]

Step 4: Practical Use Cases

Research and Report Generation

> Research the current state of WebAssembly adoption in 2026.
  Include market stats, major use cases, and a comparison of WASM runtimes.
  Save the report as research-report.md

The agent will browse multiple sources, compile data, and generate a formatted markdown report.

Automated Code Generation

> Create a REST API with FastAPI that has:
  - User authentication with JWT
  - CRUD operations for a "projects" resource
  - PostgreSQL database with SQLAlchemy
  - Proper error handling and validation
  - Docker Compose setup
  Save everything in a new directory called "project-api"

Data Collection and Analysis

> Scrape the top 100 GitHub repositories by stars,
  extract their language, star count, and description.
  Create a CSV file and a summary analysis with charts.

Automated Testing

> Read the code in src/api/ and write comprehensive
  integration tests using pytest. Cover all endpoints,
  edge cases, and error scenarios.

OpenManus vs Other Agent Frameworks

Framework	Focus	LLM Support	Web Browsing	Code Execution	Ease of Use
OpenManus	General-purpose agent	Any	Yes	Yes	Medium
AutoGPT	Autonomous tasks	OpenAI, others	Yes	Yes	Medium
CrewAI	Multi-agent teams	Any	Plugin	Plugin	Easy
LangGraph	Agent workflows	Any	Plugin	Plugin	Advanced
MetaGPT	Software development	Any	Limited	Yes	Medium
BabyAGI	Task management	OpenAI	No	Limited	Easy

OpenManus stands out for its balance of capability and simplicity. It is more focused than LangGraph (which requires building workflows from scratch) and more capable than BabyAGI (which lacks browser and code tools).

Tips for Getting the Best Results

Be specific in your tasks. "Build a blog" is vague. "Build a blog with Next.js, MDX content, and Tailwind CSS" gives the agent clear direction.
Use the right model. Complex tasks benefit from GPT-4o or Claude Opus. Simple tasks work fine with Gemini Flash or local models.
Enable memory for multi-session projects. This lets the agent remember context from previous tasks in the same project.
Monitor resource usage. Agentic tasks can consume many tokens. Set budget limits in the configuration:

[budget]
max_tokens_per_task = 500000
max_cost_per_task = 5.00  # USD

Review before committing. Always review the agent's file changes before committing to version control. Use git diff to inspect changes.

Frequently Asked Questions

Is OpenManus safe to run? OpenManus includes a sandboxed execution environment. However, it can execute code and browse the web, so review the safety settings in your configuration. Never run it with elevated privileges.

Can I use OpenManus without an API key? Yes, by running a local model through Ollama. Quality will depend on the model size, but Qwen 2.5 32B and Llama 3.3 70B deliver good results.

How does OpenManus handle rate limits? It includes built-in retry logic with exponential backoff for API rate limits.

Can multiple users share one OpenManus instance? The web UI supports basic multi-user access. For team use, deploy it on a shared server with proper authentication.

Wrapping Up

OpenManus is the most capable open-source alternative to Manus AI in 2026. It gives you the same autonomous agent capabilities -- web browsing, code execution, file management, and task planning -- while letting you choose your own LLM and maintain full data privacy. The setup takes about 15 minutes, and the community is actively improving the project.

For developers building AI-powered applications that need media generation capabilities, Hypereal AI provides easy-to-integrate APIs for image generation, video creation, and avatar synthesis. Combine OpenManus for agent workflows with Hypereal's media APIs for a powerful automation stack.