dromara/liteflow

Lightweight, fast, stable, programmable component-based rule engine — where AI Agents orchestrate just like ordinary components. Uniquely designed DSL:...

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Summary Information

Updated 32 minutes ago
Added to GitGenius on September 18th, 2026
Created on March 25th, 2020
Open Issues & Pull Requests: 64 (+0)
GitHub issues: Enabled
Number of forks: 525
Total Stargazers: 3,860 (+0)
Total Subscribers: 62 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.7 days
Mean response time: 89.9 days
90th percentile: 251.3 days
Tracked items: 17

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Issue Activity (beta)

Open issues: 25
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 519 days
Stale 30+ days: 25
Stale 90+ days: 22

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

LiteFlow is a component-based rule engine that enables orchestration of business logic and AI agents through a lightweight, programmable framework.

The tool addresses the need to define complex workflows and decision logic without writing extensive conditional code. It works by allowing developers to compose reusable components into orchestration rules using a domain-specific language. The engine supports both synchronous and asynchronous execution, enabling components to run in parallel or sequence as needed. Rules can be nested, and the framework provides multi-language scripting support so logic can be expressed in different languages within the same workflow. A key capability is hot deployment and smooth refresh, allowing rule changes to take effect without stopping the running application.

LiteFlow suits projects that need flexible, maintainable orchestration of business processes or agent workflows. It is particularly valuable when rules change frequently or when you want to separate orchestration logic from application code. The framework treats AI agents as first-class components, making it well-suited for systems that coordinate multiple agents or mix agent-based logic with traditional business rules. Teams should consider it when they want to avoid hardcoding complex conditional flows and prefer a declarative approach to defining how components interact.

The project shows consistent development activity with regular commits across multiple areas of the codebase. Pull requests are reviewed and merged steadily, indicating active maintenance. Issues are addressed with reasonable responsiveness, and the project maintains documentation alongside code changes. The codebase demonstrates attention to code quality through ongoing refactoring and improvements to core orchestration logic.