Gocrewwai ⚓🏆🚀 (v1.0.0 Stable)

High-performance, strictly-typed agentic orchestration for the Go ecosystem. Inspired by CrewAI, LangChain, and LangGraph, Gocrewwai is built for developers who demand speed, reliability, and production-ready precision.

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Important

Status: v1.0.0 (Production Hardened). Gocrewwai has evolved into a stable, high-fidelity framework for heterogeneous agent orchestration. With native A2A protocols, durable flow persistence, and recursive self-correction, it is ready for critical digital missions.

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🌟 Why Gocrewwai?

While many AI tools remain in the Python ecosystem, we chose Go for its inherent production superpowers:

1. ⚡ Massive Concurrency: Go's native goroutines allow hundreds of agents to work, fetch data, and reason in true parallel without the bottlenecks of a Global Interpreter Lock (GIL).
2. 🛡️ Rock-Solid Reliability: Eliminate random KeyError crashes. Every LLM response is strictly unmarshaled into your Go structs with type-safe guarantees.
3. 🧠 Elite Memory & State: Built-in, vector-indexed memory (SQLite, Redis, Chroma) and durable flow persistence (Checkpoints/Time-Travel).
4. Single-Binary Deployment: Compile your entire orchestrator into a tiny, zero-dependency binary. Drop it in a container or on an edge device and it just works.

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💎 Elite Features

🛡️ 1. Durable Flows & Checkpoints (LangGraph Parity)

Gocrewwai provides robust persistence, allowing you to pause, resume, and "time-travel" through long-running agentic workflows. State is automatically checkpointed to SQLite or Redis after every node execution.

👤 2. Human-in-the-Loop (HITL)

Native support for manual interrupts and approvals. Pause an agent's execution for review and approval through the CLI or the real-time Glassmorphic Dashboard.

🛡️ 3. Recursive Self-Correction (CrewAI Parity)

Agents can reflect on their own work using internal reflection loops or peer-review "Reflective Crews," ensuring 100% adherence to task requirements.

📊 4. Native Observability (OpenTelemetry)

Standardized, vendor-neutral tracing with built-in OTEL integration. Track every agent thought, tool execution, and token cost with high fidelity.

🌐 5. Model Context Protocol (MCP) & Discovery

Seamlessly connect your agents to external tools and knowledge sources via the standardized MCP protocol. Native support for local and remote MCP servers with auto-discovery.

🤖 6. Agent-to-Agent (A2A) Protocols

Enable true decentralized swarm intelligence. Agents can discover each other on the network, negotiate tasks, and collaborate autonomously using standardized communication protocols.

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🚀 Quickstart (Elite Style)

Initialize your project and install the Gocrewwai SDK:

go mod init my-agent-app
go get github.com/Ecook14/gocrewwai/gocrew

Build Your First Crew

package main

import (
	"context"
	"github.com/Ecook14/gocrewwai/gocrew"
)

func main() {
	// 1. Setup the Model
	llm := gocrew.NewOpenAI("your-api-key", "gpt-4o")

	// 2. Define an Agent
	researcher := gocrew.NewAgent(gocrew.AgentConfig{
		Role:      "Researcher",
		Goal:      "Find the latest trends in Go 1.25",
		Backstory: "Expert in performance optimization.",
		LLM:       llm,
	})

	// 3. Define a Task with Strict JSON Output
	type SummaryResult struct {
		Trends []string `json:"trends"`
		Impact string   `json:"impact"`
	}

	task := gocrew.NewTask(gocrew.TaskConfig{
		Description: "Analyze Go 1.25 Type Aliases and return a summary.",
		Agent:       researcher,
		OutputJSON:  &SummaryResult{},
	})

	// 4. Assemble and Kickoff!
	myCrew := gocrew.NewCrew(gocrew.CrewConfig{
		Agents:  []gocrew.CoreAgent{researcher},
		Tasks:   []*gocrew.Task{task},
		Verbose: true,
	})

	// 5. Execute and extract the strongly-typed result natively
	result, _ := myCrew.Kickoff(context.Background())
	summary := gocrew.GetOutput[SummaryResult](result)
	
	fmt.Printf("Parsed Impact: %s\n", summary.Impact)
}

🖥️ Start the Dashboard Server

To monitor your agents in real-time, launch the Web UI:

gocrew start --ui --port=8080

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📚 Documentation Portal

Dive deep into the Gocrewwai ecosystem with our world-class documentation guides:

• ⚓ Core Concepts: The "Four Pillars" of Gocrewwai.
• 🚀 Getting Started: Full installation and quickstart guide.
• 🔄 Migration Guide: Transitioning from CrewAI, LangChain, or LangGraph.
• 🧩 Agents, Tasks & Crews: Detailed orchestration guides.
• 💾 Persistence & HITL: Durable execution and human oversight.
• 🛡️ Self-Correction: Reflective reasoning and reliability.
• 📊 Observability: Native OTEL tracing and performance metrics.

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🤝 Community & Support

• Gocrew - High-performance agentic AI, built for Go developers.
• Follow the development on GitHub.
• Join the mission to build the most scalable AI framework in the community! 🚀⚓🛡️🏆🏁