Agent Framework builder for Python

Use this skill as the entry point for Python Agent Framework application work. Choose the smallest framework pattern that preserves the required behavior, load only the relevant references, and validate against current APIs instead of relying on remembered preview-era syntax.

Workflow

1. Inspect the existing project, Python version, dependency manager, and installed Agent Framework packages.
2. Choose the simplest matching path from the table below.
3. Resolve design questions only when they materially change the implementation.
4. Read only the linked reference for the selected path.
5. Implement the smallest coherent change and run the narrowest validation that exercises it.

Core abstractions

• A chat client implements the model-facing SupportsChatGetResponse protocol. It sends messages and options to a model provider and returns chat responses or streaming updates.
• An agent implements the SupportsAgentRun protocol. The standard Agent implementation combines a chat client with instructions, tools, middleware, context providers, and session handling behind one run(...) interface.
• A harness agent is a regular Agent assembled by create_harness_agent. It adds an opinionated set of context providers and middleware; it does not introduce a separate agent abstraction.
• A workflow coordinates explicit execution paths. Graph workflows and most orchestration builders can use multiple SupportsAgentRun implementations or executors. HandoffBuilder is the exception: its participants must be concrete Agent instances.

User interface options

• DevUI is a local development, testing, and debugging tool for interacting with agents and workflows. Never run or deploy DevUI as a production application host.
• AG-UI is the application-facing protocol integration for connecting an agent or workflow to an AG-UI-compatible frontend. Use the agent-framework-ag-ui package when the product needs streaming UI events, tool interactions, shared state, interrupts, or resume behavior.

Choose the build path

Defaults

• For new environments, use Python 3.11 or later within Agent Framework's currently supported range. Prefer the latest supported Python release or the previous release (N-1), unless a required dependency has a narrower compatibility range.
• Install the narrow Agent Framework distribution needed by the application instead of defaulting to the agent-framework meta-package.
• Agent Framework does not automatically read .env files. If the project uses one, call load_dotenv() explicitly or use the selected API's documented env_file_path or equivalent initialization option.

Current documentation lookup

Use local guidance for stable decisions and common patterns. For provider matrices, package maturity, hosted tools, deployment details, or unfamiliar APIs, consult current sources before generating code:

The reference pages intentionally describe selection rules, invariants, and non-obvious gotchas. Use public docstrings or API reference pages for signatures and option details, and use Learn or maintained samples for end-to-end procedures.

When the coding agent supports MCP, configure the Microsoft Learn MCP Server as a Streamable HTTP server:

https://learn.microsoft.com/api/mcp

See Microsoft Learn MCP Server setup for client configuration. The endpoint is for MCP clients and returns an error when opened directly in a browser.

1. Search Microsoft Learn with microsoft_docs_search.
2. Fetch the relevant page with microsoft_docs_fetch.
3. Use microsoft_code_sample_search when a current implementation example is needed.
4. If Learn MCP is unavailable and a trusted mslearn CLI is already installed, use the equivalent command:

Do not fetch and execute an unpinned CLI package during a coding session. Use the official Microsoft Learn MCP/CLI setup to install or update it intentionally.

Canonical starting points:

• Agent Framework overview
• Your first agent
• Python SDK samples
• Foundry-hosted Agent Framework samples