DevLooper is a CLI and Python library that accelerates AI agent development by providing project scaffolding, local execution, state management, and cloud deployment capabilities. It integrates seamlessly with Modal’s serverless compute platform, enabling efficient iteration, monitoring, and scheduling of workflows. Developers can debug agents locally, test complex chains, and deploy production-grade services with built-in logging and observability.
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DevLooper
DevLooper is a CLI and Python library that accelerates AI agent development by providing project scaffolding, local execution, state management, and cloud deployment capabilities. It integrates seamlessly with Modal’s serverless compute platform, enabling efficient iteration, monitoring, and scheduling of workflows. Developers can debug agents locally, test complex chains, and deploy production-grade services with built-in logging and observability.
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What is DevLooper?
DevLooper is designed to simplify the end-to-end lifecycle of AI agent projects. With a single command you can generate boilerplate code for task-specific agents and step-by-step workflows. It leverages Modal’s cloud-native execution environment to run agents as scalable, stateless functions, while offering local run and debugging modes for fast iteration. DevLooper handles stateful data flows, periodic scheduling, and integrated observability out of the box. By abstracting infrastructure details, it lets teams focus on agent logic, testing, and optimization. Seamless integration with existing Python libraries and Modal’s SDK ensures secure, reproducible deployments across development, staging, and production environments.
Who will use DevLooper?
- AI developers
- Data scientists
- ML engineers
- DevOps teams
- Backend developers
How to use the DevLooper?
- Step1: Install DevLooper via pip install devlooper or using the CLI installer
- Step2: Initialize a new agent project with devlooper init
- Step3: Define your agent’s tasks and workflows in the generated Python files
- Step4: Run locally with devlooper run for testing and debugging
- Step5: Deploy to Modal’s cloud with devlooper deploy and monitor via the Modal dashboard
Platform
- Linux
- Mac
- Windows
DevLooper's Core Features & Benefits
The Core Features
- Project scaffolding CLI
- Python SDK integration
- Local run and debugging
- Cloud deployment automation
- Stateful workflow management
- Scheduling and event triggers
- Built-in logging and monitoring
The Benefits
- Accelerated development
- Cloud-native scalability
- Reproducible builds
- Simplified infrastructure
- Fast local iteration
- Integrated observability
DevLooper's Main Use Cases & Applications
- Chatbot and conversational agent services
- Automated data processing pipelines
- Scheduled batch tasks
- AI-driven customer support bots
- Intelligent decision-making workflows
FAQs of DevLooper
DevLooper Company Information
DevLooper Reviews
5/5
DevLooper's Main Competitors and alternatives?
LangChain
LlamaIndex
OpenAI Agents
Apache Airflow
Prefect
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