Plugins

LLM & Agent Builder Copilot

Tekris Labs · tekrislabs.com

ChatGPT

Design, implement and review LLM applications and tool-using agent systems without adding unnecessary autonomy. Compare deterministic code, single model calls, bounded tools, directed workflows, autonomous agents and multi-agent systems. Define secure tool contracts, permissions, approval flows, MCP integration, state and memory, evaluations, tracing, debugging, migrations and production readiness. Includes focused specialist skills, reusable templates, technical references and offline fixtures. It is skills-only: coding, real subagents, tools, eval execution and deployment require user-authorized capabilities available in the host environment.

How to install LLM & Agent Builder Copilot in ChatGPT

  1. Open the official listing for LLM & Agent Builder Copilot in the ChatGPT workspace you want to use.
  2. Choose Install plugin if available. Follow any connection steps for the apps listed on the page.
  3. Open a new conversation on a supported ChatGPT surface. Select LLM & Agent Builder Copilot from the plugin options or mention it by name.
Official ChatGPT setup guide

How to use LLM & Agent Builder Copilot

Check the marketplace listing or publisher’s documentation for supported actions and examples.

Histórico de posições

Veja a ordem dos plugins no diretório público do ChatGPT por data de captura. Popular é uma seleção editorial. O diretório não divulga números de instalações ou de uso.

– Posição #370
Posições registradas (3)
Posições registradas
Data da capturaCategoriaPosição
Developer Tools#370
Developer Tools#371
Developer Tools#371

Frequently asked questions

Why is Install plugin missing?

Check your account, workspace, plan and region. If the listing says Desktop only, open it in the ChatGPT desktop app.

Recursos

  • Select the lowest-complexity reliable LLM or agent architecture
  • Design bounded agent workflows
  • routes
  • handoffs and specialists
  • Define safe tool schemas
  • scopes
  • side effects and approvals
  • Design MCP integration and compatible client/tool boundaries
  • Plan session context
  • workflow state and durable memory
  • Create evaluation datasets
  • graders and regression criteria
  • Diagnose tool-loop failures
  • stale state and approval errors
  • Review prompt injection
  • tenant isolation and security risks
  • Design tracing
  • incident recovery and cost-aware optimization
  • Prepare versioned production rollout and rollback checklistsShow more