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Evan Mattson f5419b9f38 Python: bump package versions for 1.2.2 release (#5561 )

* 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) — 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 — 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 — 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.

f5419b9f38 · 2026-04-29 17:51:48 +09:00

History

agent_framework_redis

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

tests

Python: [BREAKING] update context provider APIs, middleware, and per-service-call history persistence (#4992 )

2026-04-01 16:13:11 +00:00

AGENTS.md

Python: chore(python): improve dependency range automation (#4343 )

2026-03-13 12:32:37 +00:00

LICENSE

Python: add RedisContextProvider (#716 )

2025-09-23 00:36:27 +00:00

pyproject.toml

Python: bump package versions for 1.2.2 release (#5561 )

2026-04-29 17:51:48 +09:00

README.md

Python: chore(python): improve dependency range automation (#4343 )

2026-03-13 12:32:37 +00:00

README.md

Get Started with Microsoft Agent Framework Redis

Please install this package via pip:

pip install agent-framework-redis --pre

Components

Memory Context Provider

The RedisContextProvider enables persistent context and memory capabilities for your agents, allowing them to remember user preferences and conversation context across sessions and threads.

Basic Usage Examples

Review the set of getting started examples for using the Redis context provider.

Redis History Provider

The RedisHistoryProvider provides persistent conversation storage using Redis Lists, enabling chat history to survive application restarts and support distributed applications.

Key Features

Persistent Storage: Messages survive application restarts
Thread Isolation: Each conversation thread has its own Redis key
Message Limits: Configurable automatic trimming of old messages
Serialization Support: Full compatibility with Agent Framework thread serialization
Production Ready: Connection pooling, error handling, and performance optimized

Basic Usage Examples

See the complete Redis history provider examples including:

User session management
Conversation persistence across restarts
Session serialization and deserialization
Automatic message trimming
Error handling patterns

Installing and running Redis

You have 3 options to set-up Redis:

Option A: Local Redis with Docker

docker run --name redis -p 6379:6379 -d redis:8.0.3

Option B: Redis Cloud

Get a free db at https://redis.io/cloud/

Option C: Azure Managed Redis

Here's a quickstart guide to create Azure Managed Redis for as low as $12 monthly: https://learn.microsoft.com/en-us/azure/redis/quickstart-create-managed-redis