Eduard van Valkenburg
3e03a305f6
* Implement annotation-based context compaction Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Handle missing compaction attributes in BaseChatClient Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix CI typing and bandit issues Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Optimize incremental compaction annotation pass Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refinement * Python: add ToolResultCompactionStrategy and CompactionProvider Add ToolResultCompactionStrategy that collapses older tool-call groups into short summary messages (e.g. [Tool calls: get_weather]) while keeping the most recent groups verbatim. This mirrors the .NET ToolResultCompactionStrategy from PR #4533. Add CompactionProvider as a context-provider that auto-applies compaction before each agent turn and stores compacted history in session state after each turn. Includes tests and samples for both features. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refinement and alignment with dotnet PR * updated tool result compaction * updated tool result compaction * Python: add ToolResultCompactionStrategy, CompactionProvider, and skip_excluded - ToolResultCompactionStrategy collapses older tool-call groups into [Tool results: func_name: result] summaries with bidirectional tracing (same pattern as SummarizationStrategy). - CompactionProvider as BaseContextProvider with separate before_strategy and after_strategy parameters. before_strategy compacts loaded context; after_strategy compacts stored history via history_source_id. - InMemoryHistoryProvider gains skip_excluded flag to filter out messages marked as excluded by compaction strategies. - Tests, samples, and exports updated. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixed checks * fix mypy * Fix: ensure summary messages from both strategies get full compaction annotations SummarizationStrategy was not calling annotate_message_groups after inserting its summary message, so the summary lacked core group annotations (id, kind, index, has_reasoning, _excluded). Added the missing call. ToolResultCompactionStrategy already had it. Added tests verifying both strategies produce fully annotated summaries. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated propagation * fix mypy --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Python Samples
This directory contains samples demonstrating the capabilities of Microsoft Agent Framework for Python.
Structure
| Folder | Description |
|---|---|
01-get-started/ |
Progressive tutorial: hello agent → hosting |
02-agents/ |
Deep-dive by concept: tools, middleware, providers, orchestrations |
03-workflows/ |
Workflow patterns: sequential, concurrent, state, declarative |
04-hosting/ |
Deployment: Azure Functions, Durable Tasks, A2A |
05-end-to-end/ |
Full applications, evaluation, demos |
Getting Started
Start with 01-get-started/ and work through the numbered files:
- 01_hello_agent.py — Create and run your first agent
- 02_add_tools.py — Add function tools with
@tool - 03_multi_turn.py — Multi-turn conversations with
AgentSession - 04_memory.py — Agent memory with
ContextProvider - 05_first_workflow.py — Build a workflow with executors and edges
- 06_host_your_agent.py — Host your agent via Azure Functions
Prerequisites
pip install agent-framework --pre
Environment Variables
Samples call load_dotenv() to automatically load environment variables from a .env file in the python/ directory. This is a convenience for local development and testing.
For local development, set up your environment using any of these methods:
Option 1: Using a .env file (recommended for local development):
- Copy
.env.exampleto.envin thepython/directory:cp .env.example .env - Edit
.envand set your values (API keys, endpoints, etc.)
Option 2: Export environment variables directly:
export AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
Option 3: Using env_file_path parameter (for per-client configuration):
All client classes (e.g., OpenAIChatClient, AzureOpenAIResponsesClient) support an env_file_path parameter to load environment variables from a specific file:
from agent_framework.openai import OpenAIChatClient
# Load from a custom .env file
client = OpenAIChatClient(env_file_path="path/to/custom.env")
This allows different clients to use different configuration files if needed.
For the getting-started samples, you'll need at minimum:
AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
Note for production: In production environments, set environment variables through your deployment platform (e.g., Azure App Settings, Kubernetes ConfigMaps/Secrets) rather than using .env files. The load_dotenv() call in samples will have no effect when a .env file is not present, allowing environment variables to be loaded from the system.
For Azure authentication, run az login before running samples.
Note on XML tags
Some sample files include XML-style snippet tags (for example <snippet_name> and </snippet_name>). These are used by our documentation tooling and can be ignored or removed when you use the samples outside this repository.
Additional Resources
- Agent Framework Documentation
- AGENTS.md — Structure documentation for maintainers
- SAMPLE_GUIDELINES.md — Coding conventions for samples