Why AgentCrew?
AgentCrew lets you build a team of specialists, each with the right tools and personality for the job.
- An Architect that weighs trade-offs before a line of code is written.
- A Coder that implements cleanly — no scope creep, no over-engineering.
- A Reviewer that catches what others miss.
- A Researcher that cross-references multiple sources before drawing conclusions.
- A Browser Operator that navigates web apps, fills forms, and clicks through workflows.
Agents hand off work to each other when a task needs a different specialty. You stay in control, orchestrating the team from one interface.
When AgentCrew fits: You have a complex task that benefits from multiple perspectives — building a feature from design to deployment, researching and writing a report, or automating a multi-step workflow.
When a single assistant is enough: You need one focused conversation with no tool access or multi-agent orchestration.
Quick Start
1. Install (10 seconds)
macOS / Linux
curl -LsSf https://agentcrew.dev/install.sh | bash
Windows
powershell -ExecutionPolicy ByPass -c "irm https://agentcrew.dev/install.ps1 | iex"
pip (any platform)
pip install agentcrew-ai
2. Add an API key — or skip this step entirely
Most tools need a paid API key. AgentCrew supports several options that don't require one:
Subscription-based (no API key needed):
# OpenCode Go — curated open-source models (GLM, Kimi, MiMo, Qwen)
agentcrew chat --provider opencode_go
# ChatGPT Plus / Pro (uses Codex models)
agentcrew chatgpt-auth
agentcrew chat --provider openai_codex
# xAI Subscription (supported for eligible accounts)
agentcrew xai-auth
agentcrew chat --provider xai --model-id grok-4.7
# Other curated subscription models
agentcrew chat --provider xai --model-id grok-build-0.1
agentcrew chat --provider xai --model-id grok-4.3
# Local sign-out/reset (does not revoke the remote xAI session)
agentcrew xai-auth --logout
# GitHub Copilot subscribers
agentcrew copilot-auth
agentcrew chat --provider github_copilot
xAI Subscription uses AgentCrew's built-in OAuth device login and the direct xAI Responses API. Availability and model access depend on your current xAI plan and account entitlement. Allowances, overages, and billing are controlled by xAI; a displayed token cost of 0.0 does not mean requests are free. Grok Build 0.1 uses the model's default reasoning because it does not expose selectable reasoning effort through this integration.
xai-auth --logoutdeletes local tokens only; it cannot revoke them remotely.
Pay-as-you-go API keys:
export DEEPINFRA_API_KEY="your-key" # DeepInfra — open models (LLaMA, Qwen, etc.)
export OPENAI_API_KEY="sk-proj-..." # OpenAI GPT-4o
export GEMINI_API_KEY="AIza..." # Google Gemini — free tier available
Or store keys in ~/.AgentCrew/config.json:
{
"api_keys": {
"OPENAI_API_KEY": "sk-proj-..."
}
}
Custom providers:
Custom providers such as Ollama, llama.cpp, and LM Studio are configured in
the same ~/.AgentCrew/config.json file.
See Custom LLM Providers for the full
schema, llama.cpp configuration, model capabilities, and sampling options.
All supported providers:
Which provider should I pick?
3. Launch
# Desktop GUI (default)
agentcrew chat
# Terminal mode — great for SSH, tmux, low-resource environments
agentcrew chat --console
On the first launch, AgentCrew walks you through creating your first agent.
4. Create your first agent
agentcrew create-agent
Or define one manually in ~/.AgentCrew/agents.toml:
[[agents]]
name = "CodeAssistant"
description = "Helps write and review code"
tools = ["code_analysis", "file_editing", "web_search", "memory"]
system_prompt = """You are an expert software engineer.
Focus on code quality, security, and maintainability.
Today is {current_date}."""
Use Cursor Agent
Cursor Agent is a supported AgentCrew agent type for repository editing and
coding tasks. The standard AgentCrew installation includes the Cursor SDK; the
Cursor IDE and CLI are not required. Set your Cursor API key, then add a
[[cursor_agents]] entry:
export CURSOR_API_KEY="your-cursor-api-key"
[[cursor_agents]]
name = "CursorCoder"
description = "Edits the current repository with Cursor Agent."
model_id = "composer-2.5"
cwd = "."
tools = ["memory"]
Files stay on your machine while inference is hosted by Cursor. Start AgentCrew
and select the agent with /agent CursorCoder. See
Cursor Agent configuration for all fields and
model-selection options.
5. Start working
> /agent Architect
> Design a clean API for a task manager.
>
> @Coding Implement the task manager in Python using FastAPI.
>
> @Reviewer Review the code for security issues.
The @AgentName syntax hands off a subtask to another agent. The receiving
agent picks up with full context and its own specialized tools and instructions.
What's in the box?
Four ways to use AgentCrew
Job mode example:
agentcrew job --agent "CodeAssistant" \
"Review for security issues" \
./src/**/*.py
A2A server example:
agentcrew a2a-server --host 0.0.0.0 --port 41241
Tools — enable only what each agent needs
Communication protocols
AgentCrew speaks three protocols:
- Console / GUI — Human-to-agent: the chat interface you use daily.
- A2A (Agent-to-Agent) — HTTP+JSON-RPC protocol. Connect multiple AgentCrew instances so one can delegate to agents on another machine.
- ACP (Agent Communication Protocol) — WebSocket protocol for custom clients, IDE integrations, and headless agent control.
Real-world workflows
🧑💻 Feature development — from idea to PR
You: @Architect Design the data model for a multi-tenant SaaS app
@Coding Implement it with SQLAlchemy
@Reviewer Review the code for security issues
Three agents, one conversation. The Architect thinks about trade-offs, the Coder implements cleanly, the Reviewer catches blind spots.
📊 Financial report generation
# Five specialized agents, each focused on one step
[[agents]] name = "FinancialDataExtractor" # parse raw statements
[[agents]] name = "RatioAnalyst" # compute key ratios
[[agents]] name = "TrendAnalyst" # spot patterns over time
[[agents]] name = "RiskAssessor" # flag red flags
[[agents]] name = "ReportingSynthesizer" # compile into final report
Each agent handles one step of the pipeline. The Synthesizer collects all
findings and writes the final report. See
examples/agents/agents-financial-report.toml.
🌐 Multi-instance agent network
# Server A — hosts research agents
server-a$ agentcrew a2a-server --port 41241
# Server B — hosts coding agents
server-b$ agentcrew a2a-server --port 41241
# Your machine — connects to both
# ~/.AgentCrew/agents.toml:
[[remote_agents]]
name = "RemoteResearcher"
url = "http://server-a:41241"
[[remote_agents]]
name = "RemoteCoder"
url = "http://server-b:41241"
Common commands (once you're inside)
Next steps
- GUIDE_GETTING_STARTED.md — Your first 30 minutes with AgentCrew: install, create agents, run a multi-agent workflow.
- GUIDE_WORKFLOWS.md — When and why: pattern guides for common scenarios with decision trees and best practices.
- CONFIGURATION.md — Deep dive into providers, agents, MCP servers, themes, and plugins.
- PLUGIN_DEVELOPMENT.md — Build plugins with EventBus subscriptions and hooks.
- CONTRIBUTING.md — How to build, test, and contribute.
- Docker guide — Running AgentCrew in containers.
License
Apache 2.0 License. See LICENSE for details.
