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Evan Mattson
f5419b9f38
* Python: bump package versions for 1.2.2 release PATCH bump (1.2.1 -> 1.2.2) for the released cohort. Five PRs land in this window: - agent-framework-openai: fix file_search citations breaking the assistant- message history roundtrip (#5557) โ drives the released-tier PATCH - agent-framework-orchestrations: [BREAKING] standardize orchestration terminal outputs as AgentResponse (#5301) - agent-framework-core, agent-framework-declarative: preserve Workflow.run() shared state across calls, accept list[Message] in declarative start executor, and coerce Enum values when serializing PowerFx symbols (#5531) - agent-framework-foundry-hosting: add hosted Durable Workflow support (#5531) - agent-framework-azure-contentunderstanding: new alpha package โ Azure AI Content Understanding context provider (#4829) - dependencies: workspace package dependency refresh (#5555) Per lockstep convention, all 21 beta packages stamp 1.0.0b260429 and all 4 alpha packages (now including the new contentunderstanding) stamp 1.0.0a260429. Date stamp reflects 2026-04-29 Pacific. Every non-core package floor on agent-framework-core is raised to >=1.2.2; the new contentunderstanding package's stale >=1.0.0 floor is brought into line. Two follow-on fixes bundled to keep validate-dependency-bounds-test green at lowest-direct resolution: - Bump agent-framework-azure-contentunderstanding's azure-ai-content understanding lower bound from >=1.0.0 to >=1.0.1 (1.0.0 ships without proper typing โ pyright reports 65 unknown-type errors) - Add pyright ignore comments to core/foundry/__init__.pyi for the new alpha package's type-stub imports, since alpha packages are not in core's [all] extra and therefore aren't installed at lowest-direct * Python: add #5552 to 1.2.2 CHANGELOG Add the streaming-span observability fix to the Fixed section. PR is on upstream/main but not yet pulled into origin/main; the code itself will land via the PR merge. * Python: address PR #5561 review feedback on dependency bounds Two packaging fixes flagged in review: 1. agent-framework-azure-contentunderstanding: add agent-framework-foundry as a runtime dependency. The package's README directs users to `pip install agent-framework-azure-contentunderstanding --pre` and the basic example imports `FoundryChatClient` from `agent_framework.foundry`, so the documented install path was failing with ImportError. Pulling agent-framework-foundry into deps makes the advertised entry path self-contained. 2. agent-framework-foundry: bump agent-framework-openai lower bound from >=1.1.0 to >=1.2.2,<2. Foundry imports private modules from agent_framework_openai (`_chat_client.py:22`, `_agent.py:34`), so resolvers were free to pair foundry==1.2.2 with older OpenAI versions that lack this release's coordinated Responses/history fix. Lockstep the floor with the released cohort to prevent mismatched installs. Both changes pass `validate-dependency-bounds-test` lower + upper at their respective packages.
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Get Started with Microsoft Agent Framework A2A
Please install this package via pip:
pip install agent-framework-a2a --pre
A2A Agent Integration
The A2A agent integration enables communication with remote A2A-compliant agents using the standardized A2A protocol. This allows your Agent Framework applications to connect to agents running on different platforms, languages, or services.
A2AAgent (Client)
The A2AAgent class is a client that wraps an A2A Client to connect the Agent Framework with external A2A-compliant agents.
from agent_framework.a2a import A2AAgent
# Connect to a remote A2A agent
a2a_agent = A2AAgent(url="http://remote-agent/a2a")
response = await a2a_agent.run("Hello!")
A2AExecutor (Hosting)
The A2AExecutor class bridges local AI agents built with the agent_framework library to the A2A protocol, allowing them to be hosted and accessed by other A2A-compliant clients.
from agent_framework.a2a import A2AExecutor
from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
# Create an A2A executor for your agent
executor = A2AExecutor(agent=my_agent)
# Set up the request handler and server application
request_handler = DefaultRequestHandler(
agent_executor=executor,
task_store=InMemoryTaskStore(),
)
app = A2AStarletteApplication(
agent_card=my_agent_card,
http_handler=request_handler,
).build()
Basic Usage Example
See the A2A agent examples which demonstrate:
- Connecting to remote A2A agents
- Hosting local agents via A2A protocol
- Sending messages and receiving responses
- Handling different content types (text, files, data)
- Streaming responses and real-time interaction