* feat(python): Add embedding abstractions and OpenAI implementation (Phase 1) This PR contains two parts: 1. **Overall migration plan** for porting vector stores and embeddings from Semantic Kernel to Agent Framework (docs/features/vector-stores-and-embeddings/README.md) covering all 10 phases from core abstractions through connectors and TextSearch. 2. **Phase 1 implementation** — core embedding abstractions and OpenAI/Azure OpenAI embedding clients: Core types (_types.py): - EmbeddingGenerationOptions TypedDict (total=False) - Embedding[EmbeddingT] generic class with model_id, dimensions, created_at - GeneratedEmbeddings[EmbeddingT, EmbeddingOptionsT] list container with options, usage - EmbeddingInputT (default str) and EmbeddingT (default list[float]) TypeVars Protocol + base class (_clients.py): - SupportsGetEmbeddings protocol — Generic[EmbeddingInputT, EmbeddingT, OptionsContraT] - BaseEmbeddingClient ABC — Generic[EmbeddingInputT, EmbeddingT, OptionsCoT] Telemetry (observability.py): - EmbeddingTelemetryLayer with gen_ai.operation.name = "embeddings" OpenAI implementation (openai/_embedding_client.py): - RawOpenAIEmbeddingClient, OpenAIEmbeddingClient, OpenAIEmbeddingOptions - Uses _ensure_client() factory pattern Azure OpenAI implementation (azure/_embedding_client.py): - AzureOpenAIEmbeddingClient following AzureOpenAIChatClient pattern - Supports API key, Entra ID credentials, env var configuration Tests: - 47 unit tests for types, protocol, base class, OpenAI, and Azure clients - 6 integration tests (gated behind RUN_INTEGRATION_TESTS + credentials) Samples: - samples/02-agents/embeddings/openai_embeddings.py - samples/02-agents/embeddings/azure_openai_embeddings.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Add AzureOpenAIEmbeddingClient to azure __init__.pyi stub Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: Add embedding env vars to Python integration tests Map OPENAI_EMBEDDING_MODEL_ID and AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME from GitHub vars to the integration test environment. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Handle base64 encoding_format in OpenAI embedding client When encoding_format='base64' is used, the OpenAI API returns base64-encoded floats instead of a JSON array. Decode these automatically to list[float] so the return type stays consistent regardless of encoding format. Also adds a unit test for base64 decoding and fixes minor docstring/import issues. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Only record INPUT_TOKENS for embedding telemetry Embeddings have no output/completion tokens. Remove OUTPUT_TOKENS recording which was double-counting prompt_tokens via the total_tokens fallback. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Resolve mypy variance error and lint warning Use contravariant/covariant TypeVars for SupportsGetEmbeddings Protocol. Combine nested if into single statement in telemetry layer. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Make EmbeddingCoT invariant for mypy compatibility GeneratedEmbeddings is invariant in its type param, so the Protocol TypeVar cannot be covariant. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Address PR review - empty values guard, service_url for telemetry - Add early return for empty values in get_embeddings to avoid unnecessary API calls - Add service_url() method to RawOpenAIEmbeddingClient for proper telemetry endpoint reporting - Add test for empty values behavior Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix OpenAI chat client compatibility with third-party endpoints and OTel 0.4.14 (#4161) * Fix system message content sent as list instead of string Some OpenAI-compatible endpoints (e.g. NVIDIA NIM) reject system messages when content is a list of content parts. This change flattens system and developer message content to a plain string in the Chat Completions client. Fixes https://github.com/microsoft/agent-framework/issues/1407 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix compatibility with opentelemetry-semantic-conventions-ai 0.4.14 Version 0.4.14 removed several LLM_* attributes from SpanAttributes (LLM_SYSTEM, LLM_REQUEST_MODEL, LLM_RESPONSE_MODEL, LLM_REQUEST_MAX_TOKENS, LLM_REQUEST_TEMPERATURE, LLM_REQUEST_TOP_P, LLM_TOKEN_TYPE). Move these to the OtelAttr enum with their well-known gen_ai.* string values and update all references in observability.py and tests. Fixes https://github.com/microsoft/agent-framework/issues/4160 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Flatten text-only message content to string for all roles Extend the system/developer fix to all message roles. Text-only content lists are now post-processed into plain strings, while multimodal content (text + images/audio) remains as a list. This fixes compatibility with OpenAI-like endpoints that cannot deserialize list content (e.g. Foundry Local's Neutron backend). Partially fixes https://github.com/microsoft/agent-framework/issues/4084 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix streaming text lost when usage data in same chunk Some providers (e.g. Gemini) include both usage data and text content in the same streaming chunk. The early return on chunk.usage caused text and tool call parsing to be skipped entirely. Remove the early return and process usage alongside text/tool calls. Fixes https://github.com/microsoft/agent-framework/issues/3434 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy errors in _chat_client.py Rename shadowed variable 'args' in system/developer branch to 'sys_args' and rename loop variable 'content' to 'msg_content' to avoid type conflict. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * reorder imports * fix: Use OtelAttr.REQUEST_MODEL instead of removed SpanAttributes.LLM_REQUEST_MODEL Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: Add score_threshold to vector store plan Reference SK .NET PR #13501 for score threshold filtering semantics. Include score_threshold in SearchOptions from Phase 3. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: Add reference to roji's SK .NET MEVD work for SQL connectors Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Clear env vars in construction tests to avoid CI leakage Tests for missing API key / model ID now use monkeypatch.delenv to ensure env vars from the integration test environment don't prevent the expected ValueError from being raised. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- 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
AgentThread - 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 A2A
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