FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills
- 类型：arxiv
- 标识：2607.21596
- 链接：https://arxiv.org/abs/2607.21596
- 主分类：agent
- 形态：method
- TLDR：Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time. FlowEvo compiles successful workflows into callable skills, stores them in a persistent bank, and uses retrieved skills either through direct execution or as context
- 副分类：llm-infra
- 待LLM分类：否
- 标题中文：FlowEvo：通过工作流与可执行 Skill 协同演化的自演化 Agent
- TLDR中文：大语言模型 Agent 可在推理时构建工作流以适配复杂任务，但单个回合中发现的过程通常在执行后即被丢弃。现有 Skill 库提供可复用的可执行例程，但通常离线组装，且无法从 Agent 自身的工作流中不断生长。我们提出 FlowEvo，一个无需训练、推理时工作流与 Skill 协同演化的框架。FlowEvo 将成功的工作流编译为可调用 Skill，存入持久化库，并通过直接执行或作为上下文的方式使用检索到的 Skill。
- 来源文件：
- /inbox/tom/_candidates/2026-08-22-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-08-23-agent-rag-longcontext-candidates.json