AgentCrew

Your team of AI specialists for coding, research, and automation.
Build a crew of focused agents that code, research, architect, review, and automate. Use them from a desktop app, terminal, HTTP API, or CI pipeline.

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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.

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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

# GitHub Copilot subscribers
agentcrew copilot-auth
agentcrew chat --provider github_copilot

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:

Provider	Cost profile	Best for
OpenCode Go	Subscription-based	Curated open-source models, no API key
Command Code	Subscription-based	Frontier models via subscription
DeepInfra	Pay-as-you-go, low cost	Open models (LLaMA, Qwen, etc.)
Together AI	Pay-as-you-go	Open models, fine-tuning
Fireworks	Pay-as-you-go	Fast open model inference
OpenAI	Per-token pricing	GPT-4o, broad ecosystem
Anthropic	Per-token pricing	Claude family
Google Gemini	Free tier available	Budget-friendly, strong reasoning
GitHub Copilot	Included with subscription	Coding-focused, no extra cost
ChatGPT Codex	Included with Plus/Pro sub	No extra cost for subscribers
Custom	Free (local)	Fully offline (Ollama, llama.cpp...)

Which provider should I pick?

If you...	Pick this
Want curated open-source models	OpenCode Go — subscription-based
Want low-cost, open-source friendly	DeepInfra
Have a Command Code subscription	Command Code
Have ChatGPT Plus / Pro	openai_codex — no extra cost
Have GitHub Copilot	github_copilot — no extra cost
Want a free tier to start	Google Gemini
Need fully offline / air-gapped	Custom (Ollama, llama.cpp, etc.)

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}."""

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.

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What's in the box?

Four ways to use AgentCrew

Mode	Command	Best for
Desktop GUI	agentcrew chat	Daily work, drag-and-drop files, visual diffs
Terminal	agentcrew chat --console	Remote servers, keyboard-first workflows
One-shot jobs	agentcrew job --agent "Name" "task" ./files	CI/CD, batch processing, automation
HTTP API	agentcrew a2a-server	Integrate with other apps, multi-instance agent networks

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

Tool	What it does	When to enable it
code_analysis	Read files, grep, analyze repo structure	Any agent working with code
file_editing	Write/modify files (search-replace blocks, backups)	Coding and documentation agents
web_search	Search the web via Tavily	Research and fact-checking agents
fetch_webpage	Extract content from a URL	Research agents
browser	Full browser automation (navigate, click, form fill)	QA, web scraping, form-filling agents
command_execution	Run shell commands (rate limits, audit logs)	DevOps, code execution agents
memory	Store and retrieve conversation context	Almost every agent
clipboard	Read/write system clipboard	Agents that interact with other apps
adaptive_learning	Learn behavioral patterns from interactions	Agents that should adapt over time
voice	Speak and listen (ElevenLabs or DeepInfra)	Voice-interactive agents
MCP tools	External tools via Model Context Protocol	Any agent needing external integrations
transfer	Hand off tasks to other agents	Automatically available in multi-agent setups

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.

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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"

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Common commands (once you're inside)

Command	What it does
/agent <name>	Switch to another agent
@<name> <task>	Hand off a subtask
/clear	Start a fresh conversation
/file <path>	Attach a file
/think <low|medium|high|xhigh>	Enable extended reasoning
/model <provider/model>	Switch AI model
/voice	Toggle voice recording
/help	Show all commands
exit or quit	Exit
Ctrl+C	Stop the current response

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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.

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License

Apache 2.0 License. See LICENSE for details.