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azure-ai-projects-py

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.
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Azure AI Projects Python SDK

When to Use

Use for Microsoft Foundry project connections, model deployments, versioned agents, evaluation datasets and cloud evaluation workflows. Identify the installed SDK generation first; do not apply classic thread/run recipes to a v2 project client.

Setup and version boundary

Use an isolated environment and the application’s locked, reviewed compatible azure-ai-projects, azure-identity and openai versions. Historical support files came from Microsoft’s pinned repository; see UPSTREAM-NOTICE.md and UPSTREAM-LICENSE.txt. Local corrections are documented there.

Set AZURE_AI_PROJECT_ENDPOINT to the exact authorized HTTPS project URL and AZURE_AI_MODEL_DEPLOYMENT_NAME to an actual deployment name, not an assumed model alias. Use the intended tenant/identity with least privilege; inspect the credential chain without printing credentials. Do not fetch connection secrets for a listing task.

The checked 2.0.0b4 source snapshot uses versioned agents, OpenAI conversations/responses, and beta namespaces for evaluators/memory/schedules. Classic agents belong to azure-ai-agents. Later versions can differ; consult the installed SDK and Microsoft migration guide.

Example: create one explicitly requested versioned agent

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition

with DefaultAzureCredential() as credential:
    with AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project:
        agent = project.agents.create_version(
            agent_name="requested-agent",
            definition=PromptAgentDefinition(
                model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
                instructions="Answer the user’s question using authorized information.",
            ),
        )
        with project.get_openai_client(timeout=60, max_retries=0) as openai:
            response = openai.responses.create(
                input="Hello",
                extra_body={"agent_reference": {"type": "agent_reference", "name": agent.name, "version": agent.version}},
            )
            # Inspect authorized output; do not log private traces or credentials.

Safe workflow

  1. Establish the exact tenant/project, installed versions, requested resources and data classification.
  2. Read deployment/connection metadata before selecting a model; do not auto-select the first vaguely matching deployment.
  3. Enable only needed tools. Model tool calls are proposals: validate arguments, authorization, destinations and effects before execution. Remote MCP endpoints must be reachable from the cloud; localhost is not the user’s machine.
  4. Upload only the selected approved files. Folder extension filters do not exclude secrets or enforce access rights. Persistent memory needs scoped tenant/user isolation, retention and deletion rules.
  5. Bound request timeouts, polling, concurrency, rows and spend. If resource creation times out, reconcile its outcome before retrying. Delete only task-created resources specifically intended for cleanup.

Batch evaluation helper

scripts/run_batch_evaluation.py uploads the complete selected JSONL file, creates an evaluation and a run, and may incur cloud/model charges. --allow-paid is an execution opt-in after the user has authorized this exact file, endpoint, evaluators, deployment and budget. It is not an estimate or spend enforcement mechanism.

python scripts/run_batch_evaluation.py --data approved.jsonl --evaluators coherence --deployment approved-deployment --max-rows 100 --max-bytes 1048576 --timeout-seconds 600 --allow-paid --output new-private-results.json

Input rows must have consistent schemas; agent mode uses sample outputs without mutating source records. Unknown evaluators or missing mapped fields fail before upload. Poll timeout does not cancel remote execution: inspect the printed evaluation/run IDs and cancel through the installed service’s documented API if needed. Local output is new-only and mode 0600. Evaluator names/schemas remain service-version dependent; inspect the registry before a live run.

References

Limitations

No local check proves live Azure availability, model quality, legal compliance or safe autonomous tool use. Evaluation scores are sampled, version-specific judgments; report failures, missing scores, sample count and judge configuration. Keyword checks are format checks, not compliance certification. JSON output must be parsed and schema-validated. Do not run cloud evaluation during documentation review.