Microsoft Agent Framework is a multi-language framework for building, coordinating, and running AI agents and workflows in Python and .NET. Teams use it for agent systems that need orchestration, collaboration, durability, observability, governance, or human involvement beyond a single prompt or chat loop. Catalogue add-ons provide skills and instructions for working with the framework.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add microsoft/agent-framework --skill python-feature-lifecyclegit clone --depth 1 https://github.com/microsoft/agent-frameworkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/agent-framework/python-feature-lifecycle)<a href="https://agentmods.dev/skills/microsoft/agent-framework/python-feature-lifecycle"><img src="https://agentmods.dev/badge/skills/microsoft/agent-framework/python-feature-lifecycle/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/microsoft/agent-framework/python-feature-lifecycle"><img src="https://agentmods.dev/badge/skills/microsoft/agent-framework/python-feature-lifecycle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01655 |
| Opus 5.5 | $0.00017 | $0.00662 |
| Sonnet 5.5 | $0.00009 | $0.00331 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
python-feature-lifecycle scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Feature Lifecycle
Two lifecycle levels
Agent Framework uses lifecycle at two different levels:
- Package lifecycle — the maturity of the package as a whole
- Feature lifecycle — the maturity of a specific API or feature inside that package
These are related, but they are not the same thing.
- The package stage is the default for everything in the package.
- Feature-stage decorators are only for exceptions when a feature is behind the package's default stage.
- Do not decorate every class or function just because the package is experimental or release candidate.
This skill describes staged API behavior within a package. For creating or promoting a package, including sample
placement, meta-package membership, versioning, and lazy-loading namespace decisions, follow the
python-package-management skill.
Important default
If a package is still in beta / experimental preview, all public APIs in that package are experimental by default.
- Do not add
@experimental(...)everywhere in that package. - The package stage already communicates that default.
Once a package moves forward, you can keep individual features behind:
- If a package moves to release candidate, a feature may remain experimental
- If a package moves to released / GA, a feature may remain experimental or release candidate
That is the main use case for feature-stage decorators.
The four stages
1. Experimental
Use for features that are still unstable and may change or be removed without notice.
Feature-level code pattern:
from ._feature_stage import ExperimentalFeature, experimental
@experimental(feature_id=ExperimentalFeature.MY_FEATURE)
class MyFeature:
...
Behavior:
- Adds an experimental warning block to the docstring
- Records feature metadata on the decorated object
- Emits a runtime warning the first time the feature is used (once per feature by default)
Enum setup:
- Add an all-caps member to
ExperimentalFeature - Reuse the same feature ID across all APIs that belong to the same conceptual feature
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +4 lines f800c68d9ba7
- 1mo ago First seen · 237 lines · 43 tokens per session scan A 56ee5db79365
python-feature-lifecycle is a skill published in the GitHub repository microsoft/agent-framework (14,148 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,655 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Python idioms — type hints, dataclasses, async/await, generators, pytest, common pitfalls. Activate when writing or reviewing Python code.