agent-team-lab
Allows using the Hermes Agent CLI as the model backend, running one-shot chat sessions to produce expert responses for the team.
Allows using Ollama through its OpenAI-compatible endpoint as the model backend, enabling local model-powered agent analysis.
Allows the agent team to use OpenAI's Responses API as its model backend for generating expert analysis and recommendations.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agent-team-lab Run the full team on /path/to/project and summarize recommendations"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Agent Team Lab
Mini plataforma reutilizable para cargar un equipo de agentes expertos en cualquier proyecto. El flujo conecta Product Manager, Arquitectura, Programación, QA y Seguridad; cada resultado alimenta las etapas siguientes y PM consolida la decisión final.
Puede consumirse de tres formas:
como servidor MCP desde Codex, Hermes u otro cliente compatible;
mediante API REST desde una aplicación;
con una CLI local para pruebas.
El núcleo no depende de un único modelo. Incluye adaptadores para OpenAI Responses API, endpoints compatibles con OpenAI, Hermes Agent CLI y un proveedor determinista para tests.
Flujo
flowchart TD
PM1[PM: alcance] --> ARQ[Arquitectura]
ARQ --> DEV[Programación]
DEV --> QA[QA]
DEV --> SEC[Seguridad]
QA --> PM2[PM: decisión]
SEC --> PM2La versión 0.1.0 es de solo lectura: recoge archivos de texto del proyecto dentro de una raíz permitida, limita el contexto y devuelve recomendaciones. No ejecuta código del repositorio analizado ni escribe cambios.
Related MCP server: multi-agent-mcp
Inicio rápido
Requisitos: Python 3.11+ y uv.
git clone https://github.com/Benemox/agent-team-lab.git
cd agent-team-lab
cp .env.example .env
uv sync --extra devConfigura OPENAI_API_KEY en tu entorno y prueba:
uv run agent-team roles
uv run agent-team run "Revisa el flujo de pagos" --project /ruta/al/proyectoLa ruta debe estar dentro de AGENT_TEAM_ALLOWED_ROOT. Para una prueba sin gastar tokens:
AGENT_TEAM_PROVIDER=mock AGENT_TEAM_ALLOWED_ROOT="$PWD" \
uv run agent-team run "Comprueba el equipo" --project "$PWD"Proveedores
OpenAI / Codex
Usa Responses API. OPENAI_MODEL admite el identificador de modelo disponible en tu proyecto de OpenAI.
AGENT_TEAM_PROVIDER=openai
OPENAI_API_KEY=...
OPENAI_MODEL=gpt-5.4-miniHermes Agent
Instala y configura Hermes por separado, comprueba que hermes funciona y selecciona el adaptador:
hermes setup
AGENT_TEAM_PROVIDER=hermes uv run agent-team run "Analiza este módulo" --project .El adaptador usa una ejecución finita hermes chat --oneshot -q; conserva la configuración de proveedor/modelo que hayas elegido en Hermes.
Ollama, vLLM, LocalAI u otro compatible
AGENT_TEAM_PROVIDER=openai-compatible
OPENAI_COMPATIBLE_BASE_URL=http://127.0.0.1:11434/v1
OPENAI_COMPATIBLE_API_KEY=local
OPENAI_COMPATIBLE_MODEL=qwen3-coderAPI REST
AGENT_TEAM_ALLOWED_ROOT=/ruta/a/proyectos uv run agent-team serveEndpoints:
GET /healthGET /v1/rolesPOST /v1/experts/{role_id}POST /v1/team/rundocumentación OpenAPI en
http://127.0.0.1:8008/docs
Ejemplo:
curl -X POST http://127.0.0.1:8008/v1/team/run \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $AGENT_TEAM_API_TOKEN" \
-d '{"task":"Revisa el login", "project_path":"/ruta/a/proyectos/mi-app"}'Define AGENT_TEAM_API_TOKEN antes de exponer el servicio fuera de localhost. El contenedor monta los proyectos en modo solo lectura.
MCP y plugin
El repositorio ya incluye .codex-plugin/plugin.json, .mcp.json y cinco skills. Tras instalar dependencias, el servidor puede arrancarse con:
uv run agent-team-mcpExpone las herramientas:
list_expertsask_expertrun_team
Para incorporar el equipo a otro repositorio sin instalar el plugin completo, añade este servidor a la configuración MCP del cliente y usa el directorio clonado como directorio de trabajo:
{
"mcpServers": {
"agent-team-lab": {
"command": "uv",
"args": ["run", "--directory", "/ruta/agent-team-lab", "agent-team-mcp"],
"env": {
"AGENT_TEAM_PROVIDER": "openai",
"AGENT_TEAM_ALLOWED_ROOT": "/ruta/a/proyectos"
}
}
}
}No escribas la API key dentro del JSON si el archivo se va a versionar; pásala mediante variables del sistema o un almacén de secretos.
Personalizar el equipo
Edita perfiles en
skills/*/SKILL.md.Cambia orden, paralelismo y objetivos en
config/team.yaml.Añade un proveedor implementando
AgentProvidery registrándolo enproviders/factory.py.Ajusta extensiones, límites y carpetas ignoradas en
context.py.
Verificación
uv run ruff check src tests
uv run pytestDiseño de seguridad
raíz de proyectos permitida y bloqueo de path traversal;
límites de bytes y archivos enviados al modelo;
carpetas de dependencias, caché y control de versiones excluidas;
contexto marcado como no confiable frente a prompt injection;
sin shell para analizar proyectos y sin escrituras automáticas;
Hermes se invoca con argumentos, no mediante
shell=True;token Bearer opcional para REST y escucha local por defecto.
Licencia
MIT.
Available Tools
3 toolsask_expertC
Ask one specialist to review a task and the readable source files in a project.
Inputs: task (required), role_id (required), provider, project_path.
list_expertsA
List the expert roles available in this agent team.
Inputs: no named parameters. Returns: result
run_teamC
Run the connected PM, architecture, programming, QA and security workflow.
Inputs: task (required), provider, project_path.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 3 tools
Each tool has a clear, distinct purpose: listing expert roles, consulting a single expert, and running the full team workflow. There is no ambiguity or overlap between them.
All tool names follow a consistent verb_noun pattern using snake_case: list_experts, ask_expert, run_team. The naming is uniform and predictable.
Three tools is a well-scoped set for an agent team lab. Each tool addresses a core interaction—discover, consult, execute—without unnecessary bloat.
The core workflows are covered: listing experts, asking one expert, and running the full team. Minor gaps exist such as no explicit status/result polling or workflow cancellation, but the surface is reasonably complete for its scope.
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