Project report / GitHub evidence

LightAgent Model Card

这是可索引项目报告证据页:它保留 LightAgent Model Card 的源材料入口、机制线索和限制提醒;正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。

LightAgent Model Card

FieldValue
Repositorywanxingai/LightAgent
CategoryMemory/MCP Skill Agent Framework
Stars / forks snapshot1,132 / 143
Commits / issues / PRs snapshot107 / 8 / 0
LanguagePython
LicenseApache-2.0
Latest visible dated signal2026-06-05 GitHub API snapshot
Raw captureraw-github/wanxingai_lightagent.md
Updated byhourly public metadata update, 2026-06-13 02:15 +0800

1. Role in Self Evolve

LightAgent is a lightweight Python agent framework that combines persistent memory, MCP integration, native skills, LightSwarm collaboration, and newly surfaced LightFlow workflow orchestration into a small-footprint self-learning runtime. It matters because self-evolving agents need explicit runtime, memory, skill, and benchmark substrates before their improvement claims become trustworthy.

2. Working Principle

compose lightweight agents with tools, MCP, and memory -> add native skills and optional trace observability -> delegate via LightSwarm -> chain deterministic multi-step flows with LightFlow -> keep self-learning behavior grounded in runtime memory and reusable tool plans

3. Evidence Path

GitHub GraphQL/API snapshot captured via authenticated gh on 2026-06-13 showed 1,132 stars, 143 forks, 8 open issues, 0 open pull requests, 107 commits on main, latest push at 2026-06-05T15:00:09Z, Apache-2.0 license, and latest release LightAgent v0.8.0 published on 2026-06-05. The snapshot also showed 21 releases with LightAgent v0.8.0 latest on 2026-06-05. This run keeps freshness honest because it uses authenticated GitHub API data rather than stale local summaries.

4. Teaching Use

Use this card to explain Memory/MCP Skill Agent Framework: it shows how lightweight runtimes attach memory, MCP, skills, and workflow layers without becoming a monolithic product stack.

5. Limits

The repository was not cloned in this iteration; no benchmark run, workflow execution, or agent loop experiment was executed. Counts and claims are GitHub API snapshot signals unless independently revalidated later.