* Bump Python package versions for 1.15.0 release Prepare the CHANGELOG-selected Python packages for the 1.15.0 release. Root and core move to 1.15.0; changed stable extensions receive package-specific minor or patch bumps; changed beta packages receive the 260821 stamp; no beta cohort bump is applied. Core dependency floors use the conservative policy for co-released packages. Release validation also adds the six dependency required by the supported Azure Cosmos SDK floor and retains cross-platform-compatible development-tool pins. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248 * Remove hook-only formatting changes Keep the Python 1.15.0 release commit scoped to package metadata, release notes, dependency floors, and the lockfile. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248 * Minimize release lockfile changes Restore the upstream PyPI-backed lockfile and retain only package versions and dependency metadata changed by the Python 1.15.0 release. Also preserve the development-tool upgrades already present on main. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248 * Retain OpenAI core compatibility floor Keep agent-framework-openai 1.13.1 compatible with core 1.13 because its streaming tool-call index fix uses the existing additional_properties API and does not require core 1.15. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248 * Raise OpenAI version and core floor Bump agent-framework-openai to 1.14.0 and require core 1.15.0 so the new dependency requirement is signaled as a minor release. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248 --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 98e979bd-1d07-41fd-946d-00db8a93e248
Get Started with Microsoft Agent Framework Azure Cosmos DB
Please install this package via pip:
pip install agent-framework-azure-cosmos --pre
Azure Cosmos DB History Provider
The Azure Cosmos DB integration provides CosmosHistoryProvider for persistent conversation history storage.
Basic Usage Example
from azure.identity.aio import DefaultAzureCredential
from agent_framework_azure_cosmos import CosmosHistoryProvider
provider = CosmosHistoryProvider(
endpoint="https://<account>.documents.azure.com:443/",
credential=DefaultAzureCredential(),
database_name="agent-framework",
container_name="chat-history",
)
Credentials follow the same pattern used by other Azure connectors in the repository:
- Pass a credential object (for example
DefaultAzureCredential) - Or pass a key string directly
- Or set
AZURE_COSMOS_KEYin the environment
Container naming behavior:
- Container name is configured on the provider (
container_nameorAZURE_COSMOS_CONTAINER_NAME) session_idis used as the Cosmos partition key for reads/writes
See samples/02-agents/conversations/cosmos_history_provider.py for a runnable example.
Cosmos DB Workflow Checkpoint Storage
CosmosCheckpointStorage implements the CheckpointStorage protocol, enabling
durable workflow checkpointing backed by Azure Cosmos DB NoSQL. Workflows can be
paused and resumed across process restarts by persisting checkpoint state in Cosmos DB.
Basic Usage
Managed Identity / RBAC (recommended for production)
from azure.identity.aio import DefaultAzureCredential
from agent_framework import WorkflowBuilder
from agent_framework_azure_cosmos import CosmosCheckpointStorage
checkpoint_storage = CosmosCheckpointStorage(
endpoint="https://<account>.documents.azure.com:443/",
credential=DefaultAzureCredential(),
database_name="agent-framework",
container_name="workflow-checkpoints",
)
Account Key
from agent_framework_azure_cosmos import CosmosCheckpointStorage
checkpoint_storage = CosmosCheckpointStorage(
endpoint="https://<account>.documents.azure.com:443/",
credential="<your-account-key>",
database_name="agent-framework",
container_name="workflow-checkpoints",
)
Then use with a workflow
from agent_framework import WorkflowBuilder
# Build a workflow with checkpointing enabled
workflow = WorkflowBuilder(
start_executor=start,
checkpoint_storage=checkpoint_storage,
).build()
# Run the workflow — checkpoints are automatically saved after each superstep
result = await workflow.run(message="input data")
# Resume from a checkpoint
latest = await checkpoint_storage.get_latest(workflow_name=workflow.name)
if latest:
resumed = await workflow.run(checkpoint_id=latest.checkpoint_id)
Authentication Options
CosmosCheckpointStorage supports the same authentication modes as CosmosHistoryProvider:
- Managed identity / RBAC (recommended): Pass
DefaultAzureCredential(),ManagedIdentityCredential(), or any AzureTokenCredential - Account key: Pass a key string via
credentialparameter - Environment variables: Set
AZURE_COSMOS_ENDPOINT,AZURE_COSMOS_DATABASE_NAME,AZURE_COSMOS_CONTAINER_NAME, andAZURE_COSMOS_KEY(key not required when using Azure credentials) - Pre-created client: Pass an existing
CosmosClientorContainerProxy
Database and Container Setup
The database and container are created automatically on first use (via
create_database_if_not_exists and create_container_if_not_exists). The container
uses /workflow_name as the partition key. You can also pre-create them in the Azure
portal with this partition key configuration.
Environment Variables
| Variable | Description |
|---|---|
AZURE_COSMOS_ENDPOINT |
Cosmos DB account endpoint |
AZURE_COSMOS_DATABASE_NAME |
Database name |
AZURE_COSMOS_CONTAINER_NAME |
Container name |
AZURE_COSMOS_KEY |
Account key (optional if using Azure credentials) |
See samples/03-workflows/checkpoint/cosmos_workflow_checkpointing.py for a standalone example,
or samples/03-workflows/checkpoint/cosmos_workflow_checkpointing_foundry.py for an end-to-end
example with Microsoft Foundry agents.