🚀 crewai-go v0.4.0 is live!
🚀 crewai-go v0.4.0 is live!
If you love the multi-agent AI orchestration concepts from Python’s CrewAI, but want the performance, native concurrency, and low memory footprint of Go, check out crewai-go. The v0.4.0 release brings key capabilities to make building multi-agent systems in Go fast, type-safe, and production-ready. 如果你喜欢 Python 版 CrewAI 的多智能体 AI 编排理念,但又追求 Go 语言的高性能、原生并发能力和低内存占用,那么请务必关注 crewai-go。v0.4.0 版本带来了多项关键功能,使在 Go 中构建多智能体系统变得快速、类型安全且可用于生产环境。
✨ Key Highlights: ✨ 核心亮点:
🛠️ Custom Tools: Easily create and bind custom tools using tools.NewTool(…).
🛠️ 自定义工具:使用 tools.NewTool(...) 即可轻松创建并绑定自定义工具。
🔄 Sequential Context Flow: Outputs from previous tasks flow directly into subsequent tasks as context. 🔄 顺序上下文流:前序任务的输出可直接作为上下文流入后续任务。
📦 Structured Outputs: Map LLM responses straight into native Go structs using standard json:"..." tags.
📦 结构化输出:使用标准的 json:"..." 标签,将 LLM 的响应直接映射为原生的 Go 结构体。
🏠 Flexible Provider Support: Run fully offline with Ollama or integrate seamlessly with OpenAI. 🏠 灵活的模型提供商支持:既可以通过 Ollama 完全离线运行,也可以与 OpenAI 无缝集成。
🧠 Short-Term Memory: Agents keep context across complex task executions. 🧠 短期记忆:智能体能够在复杂的任务执行过程中保持上下文。
💡 Quick Example: 💡 快速示例:
package main
import (
"context"
"fmt"
"log"
"github.com/rhgs/crewai-go/crew"
)
func main() {
researcher := crew.NewAgent(crew.AgentConfig{
Role: "AI Researcher",
Goal: "Analyze tech trends",
Backstory: "An expert in discovering high-impact open-source Go tools.",
})
task := crew.NewTask(crew.TaskConfig{
Description: "Summarize the main benefits of using Go for AI agent orchestration.",
ExpectedOutput: "3 concise bullet points.",
Agent: researcher,
})
c := crew.NewCrew(crew.CrewConfig{
Agents: []*crew.Agent{researcher},
Tasks: []*crew.Task{task},
})
result, err := c.Kickoff(context.Background())
if err != nil {
log.Fatal(err)
}
fmt.Println(result.Raw)
}
🔗 Release details & GitHub repo: github.com/rhgs/crewai-go/releases/tag/v0.4.0 🔗 发布详情与 GitHub 仓库:github.com/rhgs/crewai-go/releases/tag/v0.4.0