LikeClaw for Engineering Teams
AI agents for developers: sandboxed code execution, automated testing, data processing. Zero setup, real security.
Developer workflows AI agents actually improve
Not another autocomplete. These are the tasks outside your editor that still eat hours every week.
Code Review & Testing
Automated PR reviews that flag real issues, not style nits. Test generation that covers the edge cases your team skips under deadline pressure. Regression detection that catches breaking changes before they hit staging. The grunt work of quality assurance, handled by an agent running in a sandbox.
Data & API Operations
API endpoint testing across environments. Data migration scripts that validate row counts and schema integrity. ETL pipelines that clean, transform, and load without a human babysitting a terminal. Your data work runs in isolated containers where a bad query cannot touch production.
DevOps Automation
Deployment script generation and validation. Monitoring alert analysis that correlates signals instead of just forwarding noise. Dependency update PRs with compatibility checks. The operational overhead that keeps your senior engineers away from architecture work.
Why developers are the primary AI agent adopters
Developers adopted AI tools faster than any other professional group. GitHub Copilot reached mainstream adoption with 68% of developers reporting regular use. Cursor went from zero to $1B ARR in 24 months on the back of developer demand alone. The message is clear: engineering teams do not need to be convinced that AI is useful. They need tools that match the scope of the work they actually do.
But here is the gap. Copilot and Cursor are IDE tools. They live inside your editor. They autocomplete functions, suggest refactors, and help you write code faster. What they do not do is run that code, test it against real APIs, process a data migration, analyze a production log, or automate the deployment pipeline you have been meaning to fix for three sprints.
The work outside the editor – the testing, the data wrangling, the DevOps glue – still gets done manually. Or it does not get done at all.
What LikeClaw offers beyond the IDE
LikeClaw is not an IDE tool. It is an AI agent platform that handles the developer workflows that live outside your editor.
Code execution in sandboxed containers. Write a script, describe a task, or point an agent at a pull request. It runs in an isolated E2B container with its own filesystem, network access for API calls, and no connection to your local machine. The output appears in your workspace. The container is destroyed when the task finishes. This is how code execution should work when you are processing anything more sensitive than a tutorial. See the sandboxed code execution use case for specific workflows.
Data processing without the risk. ETL pipelines, API endpoint testing, data migration validation, log analysis. The kind of work that involves production credentials and customer data. Running these tasks on your local machine with raw system access – the way tools like OpenClaw work – means a single compromised dependency or hallucinated command could exfiltrate data. In a sandbox, the blast radius is zero. The container has no access to your host filesystem or other environments.
Multi-model flexibility for different tasks. Not every task needs the same model. Claude excels at code review and nuanced reasoning. GPT-4 handles documentation and structured output well. DeepSeek is cost-efficient for batch processing where you need volume over precision. LikeClaw gives you Claude, GPT-4, Gemini, and DeepSeek through one interface, one subscription. On the Power plan, bring your own API keys at zero markup. No more paying $20/month to four separate providers for subscriptions you barely use.
Security is not optional for engineering teams
When developers run AI agents, the security stakes are higher than for any other user group. Engineering teams handle production database credentials, customer PII, API keys to third-party services, and infrastructure secrets. The security model of your AI agent platform is not a feature. It is a risk calculation.
Researchers documented serious security flaws in open AI agent marketplaces — from malware distribution to plaintext credential storage. For a solo developer experimenting on a throwaway machine, that risk might be acceptable. For an engineering team with access to production infrastructure, it is not.
LikeClaw’s architecture eliminates this category of risk. Every execution runs in an E2B sandbox container that is isolated from the host, from other users, and from the rest of your infrastructure. Credentials are encrypted at rest and injected at runtime. The container is destroyed when the task completes. There is no persistent attack surface.
For teams evaluating AI agent platforms, the LikeClaw vs Cursor comparison breaks down the specific differences in security model, execution scope, and multi-model access.
The tools your team already uses, connected
LikeClaw’s skills marketplace includes integrations for the tools engineering teams depend on: GitHub, GitLab, Slack, Linear, Jira, Notion, and more. Every integration runs inside the sandbox. Your tool credentials are encrypted and never exposed to other environments.
On the Team plan, you can build internal skills that connect to your proprietary APIs, internal tools, and data sources. Every skill – marketplace or custom – passes through the same sandboxed execution model. No exceptions.
What this means for your engineering budget
The average developer is overspending on fragmented AI subscriptions. Multiply that across a 10-person engineering team and the waste adds up fast.
LikeClaw’s Team plan is $25/seat/month. That includes every model (Claude, GPT-4, Gemini, DeepSeek), unlimited chat, sandboxed execution, persistent workspaces, SSO, and audit trails. One invoice. One dashboard. Predictable costs at the org level. Early access members get Pro free for 3 months – enough time to integrate LikeClaw into your team’s workflow before paying anything.
Built for developers
Sandboxed Execution
Every task runs in an isolated E2B container. Code executes, produces output, and the container is destroyed. No access to your host filesystem, your credentials, or your production environment. The architecture that lets your CISO sleep.
- Isolated container per execution
- No host filesystem or network access
- Credentials encrypted, injected at runtime
- Container destroyed after task completion
Multi-Model Access
Claude for code review. GPT-4 for documentation generation. Gemini for large-context analysis. DeepSeek for cost-efficient batch processing. One interface, one subscription, the right model for each task.
- Claude, GPT-4, Gemini, DeepSeek included
- Switch models per task or per step
- BYOK on Power plan for zero markup
- No vendor lock-in, no separate accounts
Persistent Workspaces
Your files, configs, and execution history persist between sessions. Pick up where you left off. Share project workspaces with your team. No re-uploading context every time you start a new task.
- Encrypted file storage per workspace
- Execution history and output logs
- Shared team project spaces
- Export and backup at any time
Your team, running in 30 seconds
No Docker. No environment variables. No IT tickets.
- 1
Sign up and open a workspace
Browser-based. Cloud-native. You will be inside a sandboxed execution environment before you would finish cloning a repo. No local dependencies, no Docker setup, no permission configs.
- 2
Pick a model and describe your task
Choose Claude, GPT-4, Gemini, or DeepSeek -- or let LikeClaw pick the best model for the job. Describe what you need: review this PR, generate tests for this module, run this data migration. The agent handles execution in a sandbox.
- 3
Review output, iterate, ship
Results appear in your workspace. Files persist. Logs are saved. Iterate on the output, adjust the prompt, re-run. When it is right, export or push to your existing workflow. Your workspace is waiting tomorrow.
Beyond the IDE
| LikeClaw | Cursor | GitHub Copilot | |
|---|---|---|---|
| Code execution | Sandboxed containers | Local machine | None |
| Scope | Any developer workflow | In-editor coding | In-editor coding |
| Data processing | Full ETL, API testing, migration | Not supported | Not supported |
| Security model | Isolated E2B sandbox | Raw local access | Cloud-hosted completions |
| Models available | Claude, GPT-4, Gemini, DeepSeek | Claude, GPT-4 | GPT-4o, Claude |
| Background tasks | Yes, sandboxed | No | No |
Data as of February 2026. LikeClaw complements IDE tools -- use both.
Questions from engineering teams
Does LikeClaw replace Cursor or GitHub Copilot?
No. Cursor and Copilot are excellent at in-editor code completion and inline suggestions. LikeClaw handles the workflows outside the editor: running test suites in sandboxed containers, processing data migrations, analyzing API responses, automating DevOps tasks. Most engineering teams will use both -- an IDE tool for writing code and LikeClaw for everything that code needs to do after it is written.
How does sandboxed execution work for code that needs API access?
Credentials are encrypted at rest and injected into the sandbox container at runtime. The container has network access to make API calls, but it is isolated from your host filesystem and other users' environments. When the task completes, the container is destroyed along with any temporary data. Your API keys are never stored in plaintext or exposed to the model.
Can my team share workspaces and collaborate on agent tasks?
Yes. On the Team plan ($25/seat/month), every member gets an individual isolated workspace plus access to shared team projects. Admins set per-user execution limits, choose available models, and manage access via SAML-based SSO. Every execution is logged with a full audit trail.
What is the pricing for engineering teams?
Individual developers can start on the Free tier (50 tasks/month) or Pro plan ($15-20/month for 500 sandbox executions and all models). The Team plan is $25/seat/month and includes everything in the Power plan plus multi-tenant workspaces, SSO, centralized billing, and audit trails. Early access members get Pro free for 3 months.
Code execution that won't keep your CISO up at night
Sandboxed. Multi-model. Running in 30 seconds. Free during beta.