Skillshub agent-framework

Create Agent with Microsoft Agent Framework

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/microsoft/skills/agent-framework" ~/.claude/skills/comeonoliver-skillshub-agent-framework-193281 && rm -rf "$T"

manifest: skills/microsoft/skills/agent-framework/SKILL.md

source content

Create Agent with Microsoft Agent Framework

Build AI agents, agentic apps, and multi-agent workflows using Microsoft Agent Framework SDK.

Quick Reference

Property	Value
SDK	Microsoft Agent Framework (Python)
Patterns	Single Agent, Multi-Agent Workflow
Server	Azure AI Agent Server SDK (HTTP)
Debug	AI Toolkit Agent Inspector + VSCode
Best For	Enterprise agents with type safety, checkpointing, orchestration

When to Use This Skill

Use when the user wants to:

Create a new AI agent or agentic application
Scaffold an agent with tools (MCP, function calling)
Build multi-agent workflows with orchestration patterns
Add HTTP server mode to an existing agent
Configure F5/debug support for VSCode

Defaults

Language: Python
SDK: Microsoft Agent Framework (pin version
```
1.0.0b260107
```
)
Server: HTTP via Azure AI Agent Server SDK
Environment: Virtual environment (create or detect existing)

References

Topic	File	Description
Server Pattern	references/agent-as-server.md	HTTP server wrapping (production)
Debug Setup	references/debug-setup.md	VS Code configs for Agent Inspector
Agent Samples	references/agent-samples.md	Single agent, tools, MCP, threads
Workflow Basics	references/workflow-basics.md	Executor types, handler signatures, edges, WorkflowBuilder — start here for any workflow
Workflow Agents	references/workflow-agents.md	Agents as executor nodes, linear pipeline, run_stream event consumption
Workflow Foundry	references/workflow-foundry.md	Foundry agents with bidirectional edges, loop control, register_executor factories

💡 Tip: For advanced patterns (Reflection, Switch-Case, Fan-out/Fan-in, Loop, Human-in-Loop), search
microsoft/agent-framework
on GitHub.

MCP Tools

This skill delegates to

microsoft-foundry

MCP tools for model and project operations:

Tool	Purpose
`foundry_models_list`	Browse model catalog for selection
`foundry_models_deployments_list`	List deployed models for selection
`foundry_resource_get`	Get project endpoint

Creation Workflow

Gather context (read agent-as-server.md + debug-setup.md + code samples)
Select model & configure environment
Implement agent/workflow code + HTTP server mode +
```
.vscode/
```
configs
Install dependencies (venv + requirements.txt)
Verify startup (Run-Fix loop)
Documentation

Step 1: Gather Context

Read reference files based on user's request:

Always read these references:

Server pattern: agent-as-server.md (required — HTTP server is the default)
Debug setup: debug-setup.md (required — always generate
```
.vscode/
```
configs)

Read the relevant code sample:

Code samples: agent-samples.md, workflow-basics.md, workflow-agents.md, or workflow-foundry.md

Model Selection: Use

microsoft-foundry

skill's model catalog to help user select and deploy a model.

Recommended: Search

microsoft/agent-framework

on GitHub for advanced patterns.

Step 2: Select Model & Configure Environment

Decide on the model BEFORE coding.

If user hasn't specified a model, use

microsoft-foundry

skill to list deployed models or help deploy one.

ALWAYS create/update

.env

file:

FOUNDRY_PROJECT_ENDPOINT=<project-endpoint>
FOUNDRY_MODEL_DEPLOYMENT_NAME=<model-deployment-name>

Standard flow: Populate with real values from user's Foundry project
Deferred Config: Use placeholders, remind user to update before running

Step 3: Implement Code

All three are required by default:

Agent/Workflow code: Use gathered context to structure the agent or workflow
HTTP Server mode: Wrap with Agent-as-Server pattern from
```
agent-as-server.md
```
— this is the default entry point

Debug configs: Generate

.vscode/launch.json

and

.vscode/tasks.json

using templates from

debug-setup.md

⚠️ Warning: Only skip server mode or debug configs if the user explicitly requests a "minimal" or "no server" setup.

Step 4: Install Dependencies

Generate/update
```
requirements.txt
```

# pin version to avoid breaking changes

# agent framework
agent-framework-azure-ai==1.0.0b260107
agent-framework-core==1.0.0b260107

# agent server (for HTTP server mode)
azure-ai-agentserver-core==1.0.0b10
azure-ai-agentserver-agentframework==1.0.0b10

# debugging support
debugpy
agent-dev-cli

Use a virtual environment to avoid polluting the global Python installation

⚠️ Warning: Never use bare
python
or
pip
— always use the venv-activated versions or full paths (e.g.,
.venv/bin/pip
).

Step 5: Verify Startup (Run-Fix Loop)

Enter a run-fix loop until no startup errors:

Run the main entrypoint using the venv's Python (e.g.,
```
.venv/Scripts/python main.py
```
on Windows,
```
.venv/bin/python main.py
```
on macOS/Linux)
If startup fails: Fix error → Rerun
If startup succeeds: Stop server immediately

Guardrails:

✅ Perform real run to catch startup errors
✅ Cleanup after verification (stop HTTP server)
✅ Ignore environment/auth/connection/timeout errors
❌ Don't wait for user input
❌ Don't create separate test scripts
❌ Don't mock configuration

Step 6: Documentation

Create/update

README.md

with setup instructions and usage examples.

Error Handling

Error	Cause	Resolution
`ModuleNotFoundError`	Missing SDK	Run `pip install agent-framework-azure-ai==1.0.0b260107` in venv
`AgentRunResponseUpdate` not found	Wrong SDK version	Pin to `1.0.0b260107` (breaking rename in newer versions)
Agent name validation error	Invalid characters	Use alphanumeric + hyphens, start/end with alphanumeric, max 63 chars
Async credential error	Wrong import	Use `azure.identity.aio.DefaultAzureCredential` (not `azure.identity` )