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.NET: Proposal: add an llms.txt

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Evaluación

Dificultad
1/5
Tiempo estimado
Menos de una hora
Aptitud para principiantes
85/100
Tipo de issue
Documentación
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
markdown
Área
documentation

Línea de trabajo

Empieza en la raíz del repositorio y revisa el contenido propuesto para llms.txt en este issue conforme al formato llms.txt y a los archivos README, COMMUNITY.md y docs enlazados. Añade la guía aprobada en Markdown sin formato adicional, con el resumen del proyecto y los enlaces, y luego verifica que exista cada ruta referenciada y que el documento se llame llms.txt.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

.NET python triage

Hi! We would like to offer Microsoft Agent Framework an llms.txt: a short, plain-markdown guide at the root of the repository that tells AI assistants what the project is and where its documentation lives (the llms.txt format). We drafted one with our open-source generator brethof-llms-txt; an AI model (GLM 5.3 Flash) wrote the summary and descriptions. It is below.

Would you like it as a pull request? If yes, reply here and we will open one. If not, close this issue and we will not ask again.

The proposed llms.txt (21 links) 
# Microsoft Agent Framework

> An open-source, multi-language framework for building production-grade AI agents and multi-agent workflows in .NET, Python, and Go.

Supports Python and C#/.NET with consistent APIs, plus a separate Go SDK in microsoft/agent-framework-go. Provides orchestration patterns (sequential, concurrent, handoff, group collaboration), middleware, OpenTelemetry observability, YAML declarative agents, DevUI, and Foundry hosted agents. Install via Python or .NET packages; docs include quickstart, tutorials, user guide, and migration guides from Semantic Kernel and AutoGen.

## Docs

- [Microsoft Agent Framework README](https://raw.githubusercontent.com/microsoft/agent-framework/main/README.md): Overview of the framework for building production-grade AI agents and multi-agent workflows in .NET and Python.
- [Welcome to the Agent Framework Community](https://raw.githubusercontent.com/microsoft/agent-framework/main/COMMUNITY.md): Ways to get involved: GitHub discussions, issues, pull requests, and public community office hours schedules.
- [Responsible AI Transparency FAQs](https://raw.githubusercontent.com/microsoft/agent-framework/main/TRANSPARENCY_FAQ.md): Answers about what the framework is, its capabilities, and intended uses.
- [Frequently Asked Questions](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/FAQS.md): Steps to access nightly builds via GitHub Personal Access Token and NuGet configuration.

## Features

- [Durable Agents Have Moved](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/features/durable-agents/README.md): Links to the new repository location for durable agent source, samples, and documentation.
- [Vector Stores and Embeddings](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/features/vector-stores-and-embeddings/README.md): Design decisions for ported vector store and embedding abstractions from Semantic Kernel.
- [FIDES Implementation Summary](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/features/FIDES_IMPLEMENTATION_SUMMARY.md): Deterministic prompt injection defense system using content labels, SecureAgentConfig, MCP auto-labeling, and data exfiltration prevention.
- [CodeAct .NET implementation](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/features/code_act/dotnet-implementation.md): .NET CodeAct design with HyperlightCodeActProvider, provider-owned tool sets, swappable backends, and execution capability configuration.
- [CodeAct Python implementation](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/features/code_act/python-implementation.md): Python CodeAct design with HyperlightCodeActProvider, provider-owned tool sets, swappable backends, and execution capability configuration.

## Optional

- [Contributing to Agent Framework](https://raw.githubusercontent.com/microsoft/agent-framework/main/CONTRIBUTING.md): How to report issues, file pull requests, and rules for contributing new language implementations.
- [Architectural Decision Records (ADRs)](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/decisions/README.md): What ADRs are and how to create, number, and review decision records using the templates.
- [Agent Run Responses Design](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/decisions/0001-agent-run-response.md): Design of agent run response abstractions covering messages, tool activities, reasoning, handoffs, and streaming updates.
- [Agent Tools](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/decisions/0002-agent-tools.md): Design decision on a unified tool abstraction, provider-specific tool options, error handling, and fallbacks for custom tools.
- [Agent OpenTelemetry Instrumentation](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/decisions/0003-agent-opentelemetry-instrumentation.md): Decision on OpenTelemetry instrumentation for agents, covering semantic conventions, token usage, traces, and non-intrusive optional telemetry.
- [Azure.AI.Agents.Persistent` package Extensions Methods for Agent Framework](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/decisions/0004-foundry-sdk-extensions.md): Decision on where extension methods for creating AIAgent from PersistentAgentsClient should live, comparing package placement options.
- [Python Package design for Agent Framework](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/design/python-package-setup.md): Python package structure with tier 0, tier 1, and tier 2 components and flat imports from agent_framework.
- [Agent Framework / Foundry SDK Alignment](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/specs/001-foundry-sdk-alignment.md): Specification clarifying positioning of Foundry SDK versus Agent Framework SDK, their goals, and combining both in orchestrations.
- [Python protocol helpers and optional execution state](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/specs/002-python-hosting-channels.md): Python implementation plan for protocol helper functions and optional state holders in agent-framework-hosting, with goals and non-goals.
- [NET hosting: OpenAI Responses protocol helpers and optional execution state](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/specs/003-dotnet-hosting-protocol-helpers.md): Helpers for exposing an AIAgent or workflow over the OpenAI Responses protocol in your own ASP.NET Core routes.
- [Feature-usage telemetry via an accumulating bitmask](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/specs/004-feature-usage-telemetry.md): Design for feature-usage telemetry via an accumulating bitmask on the User-Agent, with allowlisted pipelines and opt-out.
- [Python function-calling loop contract and validation matrix](https://raw.githubusercontent.com/microsoft/agent-framework/main/docs/specs/004-python-function-calling-loop.md): Required behavior and validation coverage for the Python function-calling loop, including approvals, streaming, errors, and serialization.

— BrethofAI

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