OpenHands vs Devin for Loop Engineering

When teams evaluate autonomous AI software engineers for loop engineering workflows, two platforms define the opposite ends of the spectrum: OpenHands (open-source, self-hostable, community-driven) and Devin (proprietary, cloud-hosted, enterprise-built by Cognition AI). Both can receive a natural-language task, plan an approach, write code, execute it in a sandbox, observe the results, and iterate — the fundamental agentic loop that loop engineering demands. But their architectures, cost models, security postures, and integration strategies could not be more different.

OpenHands gives you the blueprint — inspectable, modifiable, deployable anywhere. Devin gives you the finished product — optimized, managed, and running on infrastructure you never touch. This article compares them systematically across every dimension that matters for building reliable autonomous coding loops, so you can match the right platform to your loop engineering strategy.

Architectural Overview: Open-Source Agent vs Proprietary Platform

At the highest level, both OpenHands and Devin implement the same conceptual architecture: a task-planning loop that coordinates an LLM with sandboxed code execution. But the implementation details — where the loop runs, who controls the environment, and how the LLM is selected — diverge sharply.

OpenHands: Stateless Agent with Composable Event Loop

OpenHands (developed by All-Hands-AI, formerly known as OpenDevin) is built around a tiny core that emphasizes composability. The architecture consists of a stateless Agent that emits Actions, a Conversation that runs the loop and stores an append-only EventLog, and pluggable event-driven components for sandbox management, tool invocation, and LLM routing.

┌──────────────────────────────────────────────────────────┐
│                    OpenHands Agent                        │
│                                                           │
│  ┌──────────────┐    ┌────────────────────────────────┐  │
│  │   LLM Layer   │    │     Action Emitter              │  │
│  │  (Pluggable)  │    │  CodeEdit · ShellCmd · Browse   │  │
│  │               │───>│  FileWrite · API · Git          │  │
│  │  Claude · GPT │    └─────────────┬──────────────────┘  │
│  │  Gemini · Ollama│               │                     │
│  │  Any endpoint  │    ┌─────────────▼──────────────────┐  │
│  └──────────────┘    │     Event Loop / EventLog       │  │
│                       │     (append-only, typed events) │  │
│                       └─────────────┬──────────────────┘  │
│                                     │                     │
│  ┌──────────────────────────────────▼──────────────────┐ │
│  │              Docker Sandbox Runtime                  │ │
│  │  Isolated container per session                       │ │
│  │  Shell · File system · Package manager · Git         │ │
│  └─────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────┘

Key architectural principles:

• LLM-agnostic: Swap between Claude, GPT-4, Gemini, Ollama, or any OpenAI-compatible endpoint without changing agent logic.
• Event-driven: All agent-environment interactions flow as typed events through a central hub, making every step observable and auditable.
• Sandbox-first: Every code execution happens inside an isolated Docker container managed by the OpenHands controller.
• Composable: Agents, actions, and sandbox environments are all pluggable interfaces you can mix, match, or extend.

Devin: Proprietary Orchestration on Cognition Cloud

Devin, built by Cognition AI, runs its entire orchestration layer on Cognition's proprietary cloud infrastructure. The LLM, sandbox environment, planning engine, and tool execution are all managed internally and not user-configurable. Devin 2.0 introduced the "Agent-Native workspace," which adds real-time human collaboration on top of the autonomous loop.

┌──────────────────────────────────────────────────────────┐
│                 Cognition Cloud (Proprietary)              │
│                                                           │
│  ┌────────────────────────────────────────────────────┐  │
│  │              Devin Orchestration Layer              │  │
│  │  Task Planning · Context Management · Tool Routing  │  │
│  │  Model Selection · Safety Checks · Retry Logic     │  │
│  └──────────────────────┬─────────────────────────────┘  │
│                         │                                │
│  ┌──────────────────────▼─────────────────────────────┐  │
│  │              Cloud Sandbox Environment              │  │
│  │  Isolated workspace · Shell · Code execution        │  │
│  │  Full dev environment (managed by Cognition)       │  │
│  └──────────────────────┬─────────────────────────────┘  │
│                         │                                │
│  ┌──────────────────────▼─────────────────────────────┐  │
│  │              Agent-Native Workspace UI             │  │
│  │  Task chat · Code viewer · Terminal · Plan view     │  │
│  │  Real-time intervention points                     │  │
│  └────────────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────────────┘
        │                           │
   ┌────▼────┐                 ┌────▼────┐
   │ User    │                 │ GitHub  │
   │ Browser │                 │ / Git   │
   │ / CLI   │                 │ / APIs  │
   └─────────┘                 └─────────┘

Key architectural principles:

• Fully managed: No infrastructure to operate. Everything runs on Cognition's cloud with their model selection and optimization.
• Integrated environment: The sandbox, IDE, planning engine, and collaboration tools are one unified system.
• Parallel agents: Devin 2.0 supports spawning multiple cloud agents simultaneously for large-scale engineering tasks.
• Enterprise security: Encryption in transit and at rest, continuous monitoring, fail-closed sandbox enforcement.

Feature Comparison Table

Here is a head-to-head comparison of the two platforms across every dimension that matters for loop engineering.

Dimension	OpenHands	Devin
License	Open-source (MIT)	Proprietary / Closed-source
Self-hosting	Yes (Docker, any cloud or local)	No (cloud-only, Cognition infrastructure)
LLM flexibility	Any model via pluggable interface	Proprietary model selection (Cognition-managed)
Sandbox environment	Docker containers (local or remote)	Cloud sandbox (Cognition-managed)
Event observability	Full event stream (typed, append-only)	Web UI with terminal and plan viewer
Web browsing	Yes (autonomous research)	Yes (autonomous research)
Git integration	Yes (within sandbox)	Yes (direct GitHub/GitLab integration)
Parallel agents	Manual (multiple instances)	Native (parallel cloud agents)
Human-in-the-loop	Chat UI, event inspection	Agent-Native workspace, real-time intervention
Custom tools/actions	Fully extensible via plugin system	Limited to built-in capabilities
Security model	Docker isolation, local control	Cognition Trust Center (9 domains)
Enterprise deployment	On-prem, VPC, any cloud	Cloud-only (Enterprise plan available)
Community	GitHub, Discord, active contributors	Cognition support, limited community
Audit trail	Full EventLog (inspectable)	Available in Enterprise plan
API access	OpenHands SDK + REST API	Devin API (limited)

The Agentic Loop: How Each Platform Implements the Core Cycle

Loop engineering is fundamentally about designing, implementing, and optimizing the Define-Act-Observe-Verify cycle. How OpenHands and Devin implement this cycle reveals their philosophical differences.

OpenHands: Transparent, Interruptible Loops

Every OpenHands session is a visible event stream. The agent defines a plan, acts by invoking tools (shell commands, file edits, code execution), observes output and test results, and verifies by checking against the original requirements. Every step is a typed event in the EventLog — you can inspect, filter, or replay any part of the loop.

┌─────────┐     ┌──────────────────────────────────────┐
│  DEFINE │────>│  Agent plans approach, emits actions │
│  (Task) │     └──────────────────┬───────────────────┘
└─────────┘                        │
                                   ▼
┌─────────┐     ┌──────────────────────────────────────┐
│  OBSERVE│<────│  Agent reads tool output, test results│
│ (Output)│     └──────────────────┬───────────────────┘
└─────────┘                        │
                                   ▼
┌─────────┐     ┌──────────────────────────────────────┐
│  VERIFY │────>│  Agent checks: does output match goal?│
│ (Check) │     │  If no → back to ACT with corrections │
└─────────┘     │  If yes → task complete                │
                └──────────────────────────────────────┘
     │
     │ YES
     ▼
┌──────────┐
│ COMPLETE  │
└──────────┘

Each transition emits an event to the EventLog:
  RunAction → RunOutput → Observation → Verification → ...

This transparency is invaluable for loop engineering because you can audit every decision the agent made, identify where loops stalled, and tune the agent's behavior for future runs.

Devin: Optimized, Managed Loops

Devin implements the same cycle but hides the internal machinery behind its Agent-Native workspace. You see a plan view, a terminal, and a code viewer — the results of each loop iteration — but the event-by-event trace is not directly accessible. The optimization happens inside Cognition's orchestration layer, where model selection, retry strategies, and tool routing are tuned for maximum task completion rate.

┌────────────────────────────────────────────┐
│           Devin Agent (Black Box)          │
│                                            │
│  Task ──> Plan ──> Act ──> Observe ──> ... │
│           │              │                 │
│           ▼              ▼                 │
│  [Plan View]    [Terminal + Code Viewer]    │
│  (visible)      (visible)                  │
│                                            │
│  Internal: model routing, retry logic,    │
│  safety checks, context management         │
└────────────────────────────────────────────┘

For teams that need reliability over transparency, Devin's managed approach removes the burden of infrastructure operation while delivering consistent loop execution.

Sandbox and Execution Environment Comparison

The sandbox is the critical loop boundary — it is where the agent's code meets reality, where hypotheses are tested, and where the observe and verify phases of the loop produce their data.

OpenHands Docker Sandbox

Every OpenHands agent session gets its own Docker container as its runtime environment. The container includes a full shell, package managers, Git, and whatever else the task requires. The OpenHands controller manages the container lifecycle — creating it on task start, suspending it when idle, and tearing it down when complete.

# Starting OpenHands with a specific runtime image
docker run -it --rm --pull=always \
  -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.34-nikolaik \
  -e LOG_ALL_EVENTS=true \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v ~/.openhands-state:/.openhands-state \
  -p 3000:3000 \
  --add-host host.docker.internal:host-gateway \
  --name openhands-app \
  docker.all-hands.dev/all-hands-ai/openhands:0.34

Advantages for loop engineering: full control over the runtime image and dependencies, network isolation through Docker networking, state persistence via volume mounts, custom runtimes pre-loaded with your toolchain, and local-first execution with zero network latency.

Devin Cloud Sandbox

Devin's sandbox runs on Cognition's cloud infrastructure — a fully managed environment with a shell, code editor, and browser accessible through the Agent-Native workspace. You cannot customize the sandbox image, but you can influence it through environment setup instructions in your task description.

┌──────────────────────────────────────────────┐
│           Cognition Cloud Infrastructure     │
│                                              │
│  ┌────────────────────────────────────────┐ │
│  │  Devin Sandbox Instance                 │ │
│  │  - Managed OS (Linux-based)            │ │
│  │  - Full shell access                    │ │
│  │  - Pre-installed languages & runtimes  │ │
│  │  - Internet access (controlled)        │ │
│  │  - Git integration                      │ │
│  │  - Ephemeral per-task lifecycle        │ │
│  └────────────────────────────────────────┘ │
│                                              │
│  Security: encryption, monitoring,          │
│  fail-closed enforcement                     │
└──────────────────────────────────────────────┘

Advantages for loop engineering: zero setup (always ready, always configured), consistent environment from a known state, managed lifecycle (no Docker daemon or cleanup), and enterprise-grade security with SOC 2-aligned controls.

Sandbox Feature	OpenHands	Devin
Runtime type	Docker container	Cloud VM (managed)
Customization	Full (custom images)	Limited (task instructions)
Network control	Full (Docker networking)	Cognition-managed
State persistence	Volume mounts	Session-scoped
Startup time	5-15 seconds	Near-instant
Setup burden	Requires Docker	None
Data residency	Your infrastructure	Cognition's cloud

Human-in-the-Loop Support

Effective loop engineering requires knowing when and how to insert human judgment into the autonomous loop. Both platforms offer human-in-the-loop capabilities, but with different levels of control and timing.

OpenHands: Event-Level Intervention

OpenHands gives you granular control over the agent's event stream. You can observe every action the agent takes, pause execution at any point, and provide corrective guidance. The chat interface lets you refine the task in real-time, and the append-only EventLog means nothing is lost — you can always trace back to see what the agent did and why.

Human-in-the-loop with OpenHands:

  Agent: emits RunShellCommand("npm install express")
  EventLog: [RunAction {type: "shell", cmd: "npm install express"}]
    │
    ├──> Human approves (default flow)
    │
    ├──> Human intervenes: "Use yarn instead of npm"
    │       Agent: emits RunShellCommand("yarn add express")
    │
    └──> Human inspects: review event, take no action

This level of granularity is ideal for loop engineering scenarios where you need to audit agent behavior, enforce compliance checks, or gradually increase agent autonomy as trust builds.

Devin: Workspace-Level Intervention

Devin 2.0's Agent-Native workspace allows you to observe the agent's plan, view its terminal output, and intervene by providing additional instructions. However, you cannot inspect individual event-level actions — the granularity is at the task and plan level, not the individual tool invocation level.

Human-in-the-loop with Devin:

  Agent: creates plan → [Plan visible in workspace]
    │
    ├──> Human reviews plan, approves
    │
    ├──> Human adds guidance: "Also add error handling for edge case X"
    │       Agent: updates plan, continues execution
    │
    └──> Human monitors terminal output passively

For teams that want to set a direction and let the agent execute, Devin's higher-level intervention model reduces noise and keeps humans focused on architectural decisions rather than implementation details.

HITL Dimension	OpenHands	Devin
Intervention granularity	Event-level (tool invocations)	Task/plan-level
Observability	Full event stream	Plan view + terminal
Correction timing	Before or during action	During or after plan step
Audit trail	Complete EventLog	Limited (Enterprise)
Approval workflows	Configurable per-action	Not available

Cost Comparison

Cost is often the deciding factor for teams adopting autonomous AI coding agents, especially when the agentic loop may run for hours on a complex task. The two platforms have fundamentally different pricing models.

OpenHands: Pay for the Model, Not the Platform

OpenHands itself is free and open-source. You pay only for the LLM API calls that power the agent loop. This means your cost depends entirely on which model you choose and how many tokens the loop consumes.

# Example cost calculation for an OpenHands loop engineering session
# Task: refactor a module, run tests, fix failures, repeat

# Assumptions:
# - Model: Claude 3.5 Sonnet ($3/MTok input, $15/MTok output)
# - Average loop: 15 iterations
# - Tokens per iteration: ~5K input + ~2K output

total_input_tokens = 15 * 5000    # 75,000 tokens
total_output_tokens = 15 * 2000  # 30,000 tokens

input_cost = (total_input_tokens / 1_000_000) * 3    # $0.225
output_cost = (total_output_tokens / 1_000_000) * 15  # $0.450

session_cost = input_cost + output_cost  # ~$0.68
# Infrastructure cost: Docker host ($0 for local, ~$0.05/hr for cloud)

Devin: Pay-As-You-Go with Subscription Base

Devin uses an Agent Computation Unit (ACU) billing model on top of a monthly subscription. ACUs represent units of autonomous agent work, with the effective hourly rate depending on the plan tier.

Devin Cost Breakdown (2025-2026):

Plan              Monthly Fee    ACUs Included    Extra ACU Rate
──────────────────────────────────────────────────────────────
Free              $0             Limited          N/A
Pro               $20            ~9 ACUs          ~$2.25/ACU
Max               $200           Higher quota     ~$2.00/ACU
Teams             $80 base       Per-user quota   ~$2.00/ACU
                    +$40/user

Effective hourly rate: ~$8-9 per hour of autonomous agent time
(1 ACU ≈ 4-5 minutes of agent work)

Side-by-Side Cost Comparison

Cost Factor	OpenHands	Devin
Platform fee	$0 (open-source)	$20-200/month
LLM cost per session	$0.50-5.00 (varies by model)	Included in ACU price
Infrastructure	$0 (local Docker) or cloud VM	Included in subscription
Cost for 100 tasks/month	~$50-500 (model-dependent)	~$160-500+
Cost for occasional use	Very low (pay per token)	Minimum $20/month
Cost for heavy use	Moderate (scales linearly)	High (scales with ACUs)
Hidden costs	Docker management, LLM key rotation	Usage overages, vendor lock-in

For occasional or experimental loop engineering work, OpenHands is dramatically cheaper. For production teams that want zero operational overhead, Devin's all-in pricing may be justified despite the higher base cost.

Security Considerations

Security is a non-negotiable dimension of loop engineering, because autonomous agents that execute code in sandboxes can — by definition — run arbitrary commands. The security model of your agent platform determines what happens when things go wrong.

OpenHands: Docker Isolation with Local Control

OpenHands relies on Docker's isolation model. The sandbox container cannot access the host filesystem unless explicitly volume-mounted. The agent cannot access the Docker socket unless explicitly shared (required for container management). This puts security in your hands — you configure what the sandbox can and cannot do.

OpenHands Security Model:

┌──────────────────────────────────────┐
│            Host Machine              │
│                                      │
│  ┌────────────────────────────────┐  │
│  │  Docker Engine                 │  │
│  │                                │  │
│  │  ┌──────────────────────────┐  │  │
│  │  │  OpenHands Sandbox       │  │  │
│  │  │  - No host access        │  │  │
│  │  │  - Controlled networking │  │  │
│  │  │  - Ephemeral filesystem  │  │  │
│  │  │  - Volume mounts (if any) │  │  │
│  │  └──────────────────────────┘  │  │
│  └────────────────────────────────┘  │
│                                      │
│  Controls:                            │
│  - Docker network policies           │
│  - Read-only volume mounts           │
│  - Resource limits (CPU, memory)     │
│  - Custom seccomp/AppArmor profiles  │
└──────────────────────────────────────┘

Security strengths: data stays local (all execution on your infrastructure), Docker policies (seccomp, AppArmor, resource limits), full network control, and air-gapped capability with local LLMs for maximum isolation.

Devin: Cognition Trust Center

Devin implements security through Cognition's enterprise security infrastructure. The Trust Center covers nine security domains: Data Security, Access Control, Endpoint Security, Network Security, Identity Management, Application Security, Incident Response, Business Continuity, and Compliance.

Devin Security Model:

┌──────────────────────────────────────┐
│       Cognition Cloud Infrastructure  │
│                                      │
│  ┌────────────────────────────────┐  │
│  │  Security Layers                │  │
│  │                                │  │
│  │  - Encryption (in transit/rest)│  │
│  │  - Access control (SSO/SAML)   │  │
│  │  - Continuous monitoring       │  │
│  │  - Fail-closed sandbox         │  │
│  │  - SOC 2 compliance            │  │
│  │  - Audit logging              │  │
│  └────────────────────────────────┘  │
│                                      │
│  Limitation:                         │
│  - Code executes on Cognition infra  │
│  - No VPC/on-prem option            │
│  - Limited audit trail visibility   │
└──────────────────────────────────────┘

Security strengths: enterprise-grade SOC 2-aligned controls, fail-closed sandbox enforcement (violations trigger immediate termination), and managed patches handled by Cognition.

Security limitations: data residency on Cognition's infrastructure, no on-premise option (cannot run inside your VPC or air-gapped network), and limited audit visibility (internal event stream not exposed).

Security Dimension	OpenHands	Devin
Data residency	Your infrastructure	Cognition cloud
Isolation model	Docker containers	Cloud VMs
Compliance	Your responsibility	SOC 2 (Cognition)
Custom policies	Full control	Limited
Air-gapped mode	Yes (with local LLM)	No
Incident response	Your responsibility	Cognition managed

Loop Engineering Capabilities

The core question for loop engineering practitioners is: how well does each platform support the design, execution, and optimization of autonomous coding loops?

Multi-Step Task Execution

Both platforms excel at multi-step tasks — the bread and butter of loop engineering. OpenHands can iterate through 10-50 loop cycles on a refactoring task, each cycle involving code edits, test execution, and error correction. Devin handles similar tasks with the advantage of optimized model routing that can reduce the number of loop iterations needed.

Parallel Loops

Devin 2.0's parallel cloud agents are a significant advantage for loop engineering at scale. You can dispatch multiple agents to work on different parts of a codebase simultaneously — each running its own autonomous loop — and have the results merged through Git integration.

OpenHands supports parallelism through manual multi-instance deployment, but there is no built-in coordination layer. You run multiple Docker containers, each with its own agent, and manage the coordination yourself.

Loop Observability and Debugging

OpenHands's append-only EventLog is its killer feature for loop observability. When a loop fails — the agent keeps making the same mistake, or a test keeps failing despite corrections — you can trace exactly what happened and why. Devin provides observability through the Agent-Native workspace (plan view, terminal, file changes), which is useful for understanding what the agent did but less useful for understanding why it made specific decisions.

Loop Optimization

Loop engineering demands continuous optimization — reducing iterations, improving success rates, and minimizing wasted tokens. OpenHands gives you the tools to do this: modify the action set, tune LLM parameters, adjust the sandbox, or replace the LLM entirely. Devin's optimization happens inside Cognition's black box, where you can only influence behavior through task descriptions and plan feedback.

When to Choose OpenHands

Choose OpenHands when any of these conditions apply:

1. You need full control over the agent's environment. If your loop engineering workflow requires specific dependencies, custom toolchains, or specialized runtimes, OpenHands's Docker-based sandbox lets you build exactly the environment you need.

2. Data residency is non-negotiable. If your codebase contains sensitive intellectual property, regulated data, or compliance-governed code, OpenHands lets you keep everything on your infrastructure.

3. You want LLM flexibility. If you want to experiment with different models — comparing Claude vs. GPT-4 vs. local LLMs on the same loop engineering tasks — OpenHands's model-agnostic architecture makes this trivial.

4. Cost matters more than convenience. If you are a solo developer, a small team, or a research group with limited budget, OpenHands's zero platform fee and pay-per-token model is dramatically more affordable.

5. You need deep observability. If you are studying how autonomous agents work, optimizing loop patterns, or building custom agent behaviors, OpenHands's transparent event stream is essential.

6. You want to extend the platform. If you need custom actions, custom tools, or custom agent behaviors, OpenHands's plugin system and open-source codebase give you unlimited extensibility.

When to Choose Devin

Choose Devin when any of these conditions apply:

1. You want zero operational overhead. If you do not want to manage Docker containers, LLM API keys, or infrastructure, Devin's fully managed platform eliminates all operational burden.

2. You need parallel agent execution at scale. If your loop engineering workflow requires multiple agents working simultaneously on different tasks, Devin's native parallel cloud agents provide built-in coordination.

3. Your team prefers managed solutions. If your engineering organization is used to SaaS tools and does not want to operate open-source infrastructure, Devin fits naturally into your existing tooling procurement model.

4. Enterprise compliance is required. If your organization needs SOC 2 compliance, SSO integration, and managed security controls, Devin's enterprise offering provides these out of the box.

5. You want the fastest path to results. If you need an autonomous coding agent working immediately — no setup, no configuration, no infrastructure decisions — Devin's turnkey approach gets you there fastest.

Combining Both Platforms in a Loop Engineering Strategy

The most sophisticated loop engineering teams use both platforms, applying each where it excels. Use OpenHands for research and development — experimenting with loop patterns, comparing LLM performance, and building custom agent workflows. Use Devin for production execution — handling well-defined tasks at scale with managed infrastructure and enterprise support. Both feed into shared Git repos and CI/CD pipelines, creating a hybrid strategy that leverages open-source flexibility alongside managed reliability.

The Future of Autonomous AI Software Engineering

Both OpenHands and Devin are evolving rapidly, and several trends will reshape loop engineering in the coming years. The gap between open-source and proprietary platforms is narrowing — OpenHands continues to add enterprise polish while Devin becomes more transparent and configurable. Within 12-18 months, the practical capability differences will be smaller than the philosophical differences in control and cost.

Both platforms are also moving toward multi-agent orchestration, where specialized agents collaborate within a shared loop. Devin's parallel cloud agents are an early example, and OpenHands's composable architecture naturally supports this pattern. The future of loop engineering is not one monolithic agent but a team of specialized agents, each running its own autonomous loop while coordinating through shared state and event streams.

Meanwhile, falling LLM costs disproportionately benefit OpenHands, since its costs scale directly with token usage. As per-token costs approach zero for many use cases, the free-and-open-source model becomes even more compelling — and platforms that expose loop engineering primitives (like OpenHands's event stream) will be better positioned for integration into larger engineering systems.

Key Takeaways

• OpenHands is the open-source choice — fully self-hostable, LLM-agnostic, and built around a transparent event-driven architecture that gives you complete visibility into every step of the autonomous coding loop.
• Devin is the managed choice — a turnkey proprietary platform with zero operational overhead, native parallel agent execution, and enterprise security controls, at a higher cost and with reduced transparency.
• The sandbox is the loop boundary — OpenHands uses Docker containers (fully customizable, local control) while Devin uses Cognition's cloud sandbox (managed, consistent, zero setup). Your choice of sandbox determines your security posture and operational burden.
• Human-in-the-loop support differs in granularity — OpenHands offers event-level intervention and a complete audit trail, while Devin provides task-level intervention through its Agent-Native workspace. OpenHands is better for debugging loops; Devin is better for directing them.
• Cost models are fundamentally different — OpenHands costs only what your LLM API charges (potentially under $1 per session), while Devin requires a monthly subscription plus per-ACU usage charges. For occasional or experimental use, OpenHands is dramatically cheaper.
• Security trade-offs are real — OpenHands keeps your code on your infrastructure but puts security configuration in your hands; Devin provides enterprise-grade security but requires trusting Cognition with your code. Data residency requirements often decide the choice.
• Both platforms are evolving rapidly — the gap in capability is narrowing as OpenHands adds polish and Devin adds transparency. The most effective loop engineering teams use both: OpenHands for R&D and loop optimization, Devin for production-scale execution.
• The future is multi-agent orchestration — both platforms are moving toward teams of specialized agents coordinating within shared loops, which will require standardization around loop engineering primitives and inter-agent communication.