OpenHands vs SWE-Agent: Best AI Coding Agent 2026

What Are AI Coding Agents?

AI coding agents represent the next evolution beyond code completion tools like GitHub Copilot. Instead of suggesting lines of code, these agents autonomously complete entire tasks—fixing bugs, implementing features, refactoring code, and resolving GitHub issues with minimal human intervention.

The two leading open-source options are OpenHands (formerly OpenDevin) and SWE-Agent (from Princeton/Stanford). Both achieve state-of-the-art performance on software engineering benchmarks, but they target different use cases:

• OpenHands: Enterprise-ready platform with web UI, multi-agent architecture, and production features
• SWE-Agent: Research-focused tool with innovative Agent-Computer Interface and minimal footprint

This guide compares everything: architecture, benchmarks, installation, use cases, and which to choose for your workflow.

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OpenHands: Enterprise AI Coding Platform

Origin and Development

OpenHands started as OpenDevin in early 2024, inspired by Cognition's Devin announcement. The project has grown to include:

• 188+ contributors from academia and industry
• 2.1K+ contributions to the codebase
• $18.8M Series A funding (November 2025) led by Madrona
• Adoption by AMD, Apple, Google, Amazon, Netflix, TikTok, NVIDIA, Mastercard, VMware

The lead maintainer is Xingyao Wang, with key contributions from Frank F. Xu (web browsing), Mingchen Zhuge (GPTSwarm), Robert Brennan (architecture), and Boxuan Li.

Core Architecture

OpenHands uses an event-stream architecture that models the agent-environment interaction:

Agent → Actions → Environment → Observations → Agent
           ↑                           ↓
           └───────── Event Log ───────┘

Key architectural components:

1. Docker-based Sandboxing: Each session runs in an isolated container accessed via SSH
2. Jupyter Kernel Environment: Integrated Python execution with stateful code interaction
3. Browser Agent API: BrowserGym interface for web automation (DOM manipulation, navigation)
4. Multi-Agent Delegation: Hierarchical agent structures where agents can delegate to specialized sub-agents
5. CodeAct Architecture: The default "strong generalist" agent that combines code execution with reasoning

Installation and Setup

Docker Method (Recommended):

docker run -it --rm --pull=always \
  -e AGENT_SERVER_IMAGE_REPOSITORY=ghcr.io/openhands/agent-server \
  -e LOG_ALL_EVENTS=true \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v ~/.openhands:/.openhands \
  -p 3000:3000 \
  --add-host host.docker.internal:host-gateway \
  --name openhands-app \
  docker.openhands.dev/openhands/openhands:1.3

Access the web UI at http://localhost:3000.

pip Method:

uv pip install openhands
openhands serve --gpu  # With GPU support
openhands serve --mount-cwd  # Mount current directory

Platform Notes:

• macOS: Enable "Allow the default Docker socket to be used" in Docker Desktop
• Windows: Enable WSL 2 with Docker Desktop integration

Key Features

Supported Models

Cloud APIs:

• Claude 3.5/3.7/4 Sonnet, Claude Opus
• GPT-4, GPT-4o, GPT-5
• Any OpenAI-compatible API

Local Models (via Ollama):

• OpenHands LM 32B (fine-tuned Qwen Coder 2.5)
• llama-3.3-70B
• DeepSeek-v3

Note: Models smaller than 32B parameters aren't recommended—instruction following degrades significantly for complex coding tasks.

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SWE-Agent: Research-Grade Coding Agent

Origin and Development

SWE-Agent is an academic project from Princeton University and Stanford University, created by:

• John Yang and Carlos E. Jimenez (co-first authors)
• Alexander Wettig, Kilian Lieret
• Shunyu Yao, Karthik Narasimhan, Ofir Press

Published at NeurIPS 2024, the research was supported by Princeton Language and Intelligence, Oracle Collaborative Research, and the National Science Foundation.

The Agent-Computer Interface (ACI)

SWE-Agent's key innovation is the Agent-Computer Interface (ACI)—an abstraction layer designed specifically for LLM agents.

Why ACI matters:

Traditional interfaces like Linux shell were designed for humans. LLMs struggle with:

• Long, verbose command outputs
• Complex state management
• Error-prone command syntax
• Context overflow from too much information

ACI provides:

1. Simplified Actions: Small set of commands for viewing, searching, editing files
2. Guardrails: Prevents common mistakes before they happen
3. Concise Feedback: Specific, minimal output about command effects
4. Context Management: Maintains recent 5 steps, collapses earlier history

Installation and Setup

SWE-Agent is CLI-focused and lightweight:

pip install sweagent

Basic Usage:

# Fix a GitHub issue
sweagent run --issue "https://github.com/user/repo/issues/123"

# With specific model
sweagent run --model claude-3-7-sonnet --issue "..."

API Key Configuration:

export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."

Key Features

Supported Models

Cloud APIs:

• Claude 3 Opus, 3.5/3.7/4 Sonnet (with extended thinking)
• GPT-4, GPT-4o, GPT-4.1, GPT-5

Local Models:

• Ollama models (configurable api_base: http://localhost:11434)
• Requires thought_action parser for models without function calling

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Benchmark Comparison

SWE-bench Performance

SWE-bench is the gold standard for measuring AI coding agents—it tests the ability to fix real GitHub issues.

Key insight: Mini-SWE-Agent's >74% score in just 100 lines of Python demonstrates that architecture design matters more than complexity.

Real-World Performance (SWE-bench-Live)

SWE-bench-Live tests on fresh, unseen issues to avoid data contamination:

Reality check: On truly novel issues, both agents solve ~18-20%—far below the 70%+ on curated benchmarks. This reflects the gap between benchmarks and production use.

Other Benchmarks

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Architecture Deep Dive

OpenHands Event-Stream Architecture

┌─────────────────────────────────────────────────────────┐
│                     OpenHands Server                     │
├─────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐  │
│  │  Web UI     │  │  REST API   │  │  WebSocket API  │  │
│  └─────────────┘  └─────────────┘  └─────────────────┘  │
├─────────────────────────────────────────────────────────┤
│                     Agent Manager                        │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐  │
│  │  CodeAct    │  │  Browser    │  │  Delegate Agent │  │
│  │  Agent      │  │  Agent      │  │  (Sub-agents)   │  │
│  └─────────────┘  └─────────────┘  └─────────────────┘  │
├─────────────────────────────────────────────────────────┤
│                     Event Log                            │
│         Actions ←→ Observations ←→ State                 │
├─────────────────────────────────────────────────────────┤
│                Docker Container (SSH)                    │
│  ┌───────────┐  ┌───────────┐  ┌───────────────────┐   │
│  │  Jupyter  │  │  Browser  │  │  Filesystem       │   │
│  │  Kernel   │  │  (VNC)    │  │  (Sandboxed)      │   │
│  └───────────┘  └───────────┘  └───────────────────┘   │
└─────────────────────────────────────────────────────────┘

Strengths:

• Multi-agent delegation for complex tasks
• Full browser automation
• Persistent state across interactions
• Enterprise-grade isolation

SWE-Agent ACI Architecture

┌─────────────────────────────────────────────────────────┐
│                        LLM                               │
│         (Claude, GPT-4, Local via Ollama)               │
├─────────────────────────────────────────────────────────┤
│              Agent-Computer Interface (ACI)              │
│  ┌───────────────────────────────────────────────────┐  │
│  │  Simplified Actions:                               │  │
│  │  - view_file(path, start_line, end_line)          │  │
│  │  - search_dir(pattern, directory)                  │  │
│  │  - edit_file(path, changes)                        │  │
│  │  - run_command(cmd)                                │  │
│  └───────────────────────────────────────────────────┘  │
│  ┌───────────────────────────────────────────────────┐  │
│  │  Guardrails: Syntax checking, Linting, Validation │  │
│  └───────────────────────────────────────────────────┘  │
│  ┌───────────────────────────────────────────────────┐  │
│  │  Context Window: Last 5 steps (condensed history) │  │
│  └───────────────────────────────────────────────────┘  │
├─────────────────────────────────────────────────────────┤
│                   Execution Environment                  │
│            (Docker, SWE-ReX, or subprocess)             │
└─────────────────────────────────────────────────────────┘

Strengths:

• Minimal complexity, maximum effectiveness
• Purpose-built for LLM capabilities
• Efficient context management
• Easy to understand and extend

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Feature Comparison

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Hardware Requirements

OpenHands

SWE-Agent

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When to Choose Each

Choose OpenHands For:

1. Enterprise Production

   • Companies like AMD, Apple, Google, Netflix use it
   • Self-hosted VPC deployment via Kubernetes
   • RBAC, audit trails, compliance features

2. Complex Multi-Step Workflows

   • Tasks requiring browser + code + filesystem access
   • Multi-agent delegation for large projects

3. Team Collaboration

   • Web UI for non-CLI users
   • Shared dashboards and quotas

4. Multi-Language Projects

   • #1 on Multi-SWE-Bench (8 languages)
   • Not Python-focused

5. Automated Maintenance

   • Dependency upgrades across hundreds of repos
   • Vulnerability remediation sweeps
   • PR review automation

Choose SWE-Agent For:

1. Academic Research

   • Published at NeurIPS 2024
   • Clean, understandable codebase
   • Reproducible experiments

2. Cybersecurity Applications

   • EnIGMA mode for CTF challenges
   • Security vulnerability research

3. Minimal Infrastructure

   • pip install and go
   • No Docker requirement for basic use

4. Learning Agent Development

   • ACI concepts well-documented
   • Mini-SWE-Agent shows minimal viable agent

5. Single-Issue Bug Fixes

   • Focused GitHub issue resolution
   • No setup overhead

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Alternatives Worth Considering

For CLI Users: Aider

pip install aider-chat
aider --model claude-3-7-sonnet

• Terminal-based AI pair programming
• Clean Git diffs and automatic commits
• 70% of Aider's code written by Aider itself

For VS Code Users: Cline

• 4M+ developers worldwide
• MCP integration
• Plan/Act modes for controlled autonomy
• Supports Claude, Gemini 2.5, local models

For Local-First Privacy: Continue

• 1.6M+ installs, 20K+ GitHub stars
• Fully local via Ollama
• VS Code and JetBrains extensions
• Highly configurable

For Turnkey Enterprise: Devin

• $73M ARR (as of June 2025)
• Fully autonomous project execution
• Powered by Claude Sonnet 4.5
• $2B valuation

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Integration Examples

OpenHands with Ollama

# Start Ollama with OpenHands LM
ollama pull openhands-lm:32b
ollama serve

# Configure OpenHands
# In web UI: Settings → LLM → Ollama
# Set model: openhands-lm:32b
# Set base URL: http://host.docker.internal:11434

SWE-Agent with Local Models

# config.yaml
model:
  name: ollama/llama3.3:70b
  api_base: http://localhost:11434
  per_instance_cost_limit: 0  # No cost for local

# Use thought_action parser for models without function calling
parser: thought_action

OpenHands Python API

from openhands.client import OpenHandsClient

client = OpenHandsClient(
    base_url="http://localhost:3000",
    api_key="your-api-key"
)

# Create a task
task = client.create_task(
    prompt="Fix the null pointer exception in src/main.py",
    workspace="/path/to/repo"
)

# Monitor progress
for event in client.stream_events(task.id):
    print(event.type, event.content)

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Performance Optimization Tips

For OpenHands

1. Use Claude 4.5 with Extended Thinking for maximum accuracy (72% SWE-bench)
2. Enable Critic Model for self-verification (66.4% SWE-bench)
3. Mount your codebase with --mount-cwd for faster file access
4. Limit context to relevant files—large repos slow performance

For SWE-Agent

1. Use Mini-SWE-Agent for simple issues—faster, same accuracy
2. Rotate API keys with ::: separator for batch runs
3. Enable extended thinking with Claude 4 Sonnet for complex reasoning
4. Pre-filter issues to tasks the agent can realistically solve

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Key Takeaways

1. OpenHands is the enterprise choice—web UI, multi-agent, RBAC, $18.8M funding
2. SWE-Agent excels in research—ACI innovation, cybersecurity mode, academic pedigree
3. Benchmarks are similar (70-74% on SWE-bench)—choose based on features, not scores
4. Real-world performance is ~20%—both struggle with truly novel issues
5. Both support local LLMs—but 32B+ models recommended for complex tasks
6. Mini-SWE-Agent proves simplicity wins—100 lines of Python, >74% accuracy
7. Consider alternatives—Aider for CLI, Cline for VS Code, Continue for privacy

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Next Steps

1. Set up Ollama for local model support
2. Compare AI coding tools for your IDE
3. Build AI agents from scratch
4. Understand MCP servers for tool integration
5. Check VRAM requirements for local models

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OpenHands and SWE-Agent represent the cutting edge of autonomous AI software engineering. OpenHands offers the polish and features enterprises need, while SWE-Agent provides the clean architecture researchers love. Both are MIT-licensed, actively maintained, and capable of solving real coding problems—the choice depends on whether you need production deployment or research flexibility.