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.
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
# GitHub Copilot subscribers
agentcrew copilot-auth
agentcrew chat --provider github_copilot
Pay-as-you-go API keys:
export CROFAI_API_KEY="your-key" # CrofAI — OpenAI-compatible, budget-friendly
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": {
"CROFAI_API_KEY": "your-key",
"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 |
| CrofAI | Pay-as-you-go, low cost | General tasks, OpenAI-compatible |
| 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 CrofAI or DeepInfra Have a Command Code subscription Command Code Have ChatGPT Plus / Pro openai_codex— no extra costHave GitHub Copilot github_copilot— no extra costWant 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.
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.
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)
| 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 |
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.