draftcrew

DraftCrew

High-performance Go framework for AI agent orchestration.

Inspired by CrewAI — built for Go. Agents, tasks, crews, and all 6 process types (sequential, hierarchical, consensual, reflective, flow, state machine) in a single binary with zero runtime dependencies.

go get github.com/bzdvdn/draftcrew@latest

Why DraftCrew?

Python-based agent frameworks (CrewAI, AutoGen, LangGraph) are powerful but hit walls in production: GIL contention, no native concurrency, heavy dependency trees, and complex deployment. DraftCrew brings agent orchestration to Go's world:

• Goroutines, not GIL — true parallel agent execution via Go's concurrency model
• Static typing — catch misconfigurations at compile time, not runtime
• Single binary — no Python runtime, no pip install, no virtualenvs
• Type-safe tools — GetOutput[T](result) via generics, zero reflection
• Production observability — built-in event bus, telemetry, OpenTelemetry bridging
• MCP-native — Model Context Protocol client for tool discovery (stdio + SSE)

Status: v0.5 — Dashboard & Production Polish ✅

Core primitives + A2A delegation + MCP server + WASM/Docker sandbox + knowledge ingestion (RAG) + browser automation + output guardrails + DAG flow bridge + dashboard v2 with real telemetry data. All v0.6 quick wins done: file I/O tools, calculator, web search, ask human, Gemini tool calling, MCP stdio server, more LLM providers (Groq, Together AI, Bedrock, Vertex AI). 20 packages, race-detector clean.

What works today

Where we're heading

DraftCrew aims to match CrewAI's feature set and then surpass it in areas where Go excels. The roadmap:

Near-term (v0.4–v0.6)

• Knowledge ingestion — RAG with PDF/CSV/URL sources ✅ done
• Browser automation — chromedp-based web interaction ✅ done
• Output guardrails — regex, length, contains, banned words, JSON schema ✅ done
• Training/HITL loop — feedback-loop agent improvement ✅ done
• Flow ↔ Crew bridge — DAG flow engine wired into crew process types ✅ done
• File I/O tools — read, write, edit, and directory listing with chroot isolation and streaming I/O ✅ done
• Calculator tool — expression evaluator with math functions and constants ✅ done
• WebSearch tool — real providers: Tavily, SerpAPI, DuckDuckGo with fallback chain ✅ done
• AskHuman tool — blocking stdin with timeout and context cancellation ✅ done
• Gemini provider — full tool calling, SSE streaming, structured output ✅ done
• Dashboard v2 real data — all endpoints wired to live telemetry (TokenTracker, MetricsCollector, EventBus agent buffer) ✅ done
• MCP stdio server — agent MCP server over stdin/stdout with JSON-RPC 2.0 ✅ done
• More LLM providers — Groq, Together AI, AWS Bedrock, Google Vertex AI ✅ done

Long-term vision

• Visual workflow builder — DAG-based crew designer
• gRPC mesh server — distributed agent orchestration across machines
• Cloud control plane — fleet management for production agent deployments
• Training/fine-tuning pipeline — feedback-loop agent improvement

Out of scope (for now)

DraftCrew will not become a vector database, a training framework, or a cloud platform. We build the orchestration layer — you bring the infrastructure.

Quick start

package main

import (
    "context"
    "fmt"

    "github.com/bzdvdn/draftcrew/src/pkg/agent"
    "github.com/bzdvdn/draftcrew/src/pkg/crew"
    "github.com/bzdvdn/draftcrew/src/pkg/llm"
    "github.com/bzdvdn/draftcrew/src/pkg/task"
)

func main() {
    llmClient := llm.NewOpenAI("sk-...", "gpt-4")

    a := agent.New(agent.Config{
        Role:    "Researcher",
        Goal:    "Research Go 1.22 features",
        LLM:     llmClient,
        MaxIter: 15,
    })

    t := task.New(task.Config{
        Description:    "Find key features",
        ExpectedOutput: "List of features",
        Agent:          a,
    })

    c := crew.New(crew.Config{
        Agents:  []*agent.Agent{a},
        Tasks:   []*task.Task{t},
        Process: crew.ProcessSequential,
    })

    result, err := c.Kickoff(context.Background())
    if err != nil {
        panic(err)
    }
    fmt.Printf("%+v\n", result)
}

See the Quickstart Guide for the full walkthrough.

Installation

go get github.com/bzdvdn/draftcrew@latest

Or install the CLI:

go install github.com/bzdvdn/draftcrew/cmd/draftcrew@latest

Requires Go 1.22+.

Documentation

• Quickstart Guide — 5-minute tutorial
• Getting Started — installation and basic usage
• Core Concepts — architecture, agents, tasks, processes
• LLM Providers — setup for all supported backends
• Tools Reference — built-in tool descriptions
• YAML Reference — configuration file reference
• MCP Server — expose agents as MCP-compatible endpoints
• Sandbox — secure code execution via WASM and Docker
• Knowledge Sources — RAG document ingestion and agent integration
• API Reference — full package API
• Compatibility — versioning policy

Roadmap

Detailed roadmap: ROADMAP.md

Contributing

We welcome contributions. See the specs for active work items and AGENTS.md for workflow conventions.

License

MIT — see LICENSE.