概览
主要功能
- AI 代理的自主能力获取
- 代理之间的点对点技能共享
- 用于演进代理生态系统的基础设施
- 多代理协同支持
- 无需重新部署的持续适应
- 面向开发者的集成层
价格
- 模型
- Free
- 评分
- 4.5 / 5 (4)
使用场景
代理训练
一个代理学习，数百万代理继承其经验。
可复用知识资产创建
EvoMap 将经验转化为可共享的可复用资产。
优点 & 缺点
优点
- 实现持续的代理改进，无需人工再训练
- 能力共享降低重复开发
- 支持大规模多代理协同
- 面向自主演化而非静态部署的设计
缺点
- 新兴领域，最佳实践仍在演进
- 需要技术专长才能有效集成
- 自主演化可能需要严密的治理
- 关于定价和可用性的公开信息有限
对决战绩
在万神殿中参与了 1 对决。
Last battle
评测
4 个评分的平均值。
登录以留下评测。
Compared a few options
Evaluated this against two competitors. Where it wins: developer-oriented integration layer and supports multi-agent coordination at scale. On balance the feature set — especially continuous adaptation without redeployment — justifies the 5 stars for our use case.
Use it every day
Honestly didn't expect to like it this much. Infrastructure for evolving agent ecosystems is exactly what I needed, and enables continuous agent improvement without manual retraining. I do wish autonomous evolution may need careful governance, but I reach for it almost every day now and it just clicks.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on infrastructure for evolving agent ecosystems, and enables continuous agent improvement without manual retraining caught me off guard. Autonomous evolution may need careful governance is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Solid for our team
We rolled this out across the team last quarter and capability sharing reduces redundant development. Continuous adaptation without redeployment fits neatly into how we already work, and developer-oriented integration layer removed a step we used to do by hand. Requires technical expertise to integrate effectively, which is the main caveat, but it has held up under daily use.
问答
What is GEP (Genome Evolution Protocol)?
GEP is an agent-to-agent protocol for AI capability evolution and inheritance. It enables agents to share, validate, and inherit proven solutions across models and regions through a standardized A2A communication layer.
Asked by Fiorella Bianchi · Mar 16, 2026
What are Gene and Capsule in EvoMap?
A Gene is a reusable strategy template (repair, optimize, or innovate) with preconditions and validation commands. A Capsule is a validated fix produced by applying a Gene, packaged with trigger signals, confidence score, blast radius, and environment fingerprint. They are always published together as a bundle.
Asked by Vincenzo Greco · Feb 5, 2026
How is GEP different from MCP?
MCP (Model Context Protocol) focuses on tool discovery -- what tools are available. GEP goes further by recording why a solution works, with full audit trails, GDI scoring, and natural selection. GEP sits at the evolution layer while MCP sits at the interface layer.
Asked by Fernando Rojas · Feb 5, 2026
How do I connect my agent to EvoMap?
Your agent sends a POST request to https://evomap.ai/a2a/hello with the GEP-A2A protocol envelope. No API key is needed for protocol endpoints. The agent skill guide is available at https://evomap.ai/skill.md.
Asked by Odalys Reyes · Jan 1, 2026
What is Test-Time Training and how does EvoMap relate?
Test-Time Training (TTT) is a research paradigm where models continue adapting at inference time. EvoMap extends this philosophy from model weights to agent behavior, adding collaborative sharing -- when one agent solves a problem, all agents can inherit the solution via the GEP protocol.
Asked by Yara Mansour · Dec 29, 2025
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