Tao Chen
ce8b6305d8
* Python: Wrapper + Samples 1st (#5177) * Experiment * Update dependency and add non streaming * Add more samples * Rename samples * Add invocations * Comments 1 * Comments 2 * Comments 3 * Improve README * Add local shell sample * WIP: Add eval and memory samples * Update user agent prefix * Update user agent prefix doc * Update dependency (#5215) * Add tests and more content types (#5235) * Add tests * fix tests and sample * Fix formatting * Remove function approval contents * Python: Refine samples and upgrade packages (#5261) * Refine samples and upgrade pacakges * Upgrade to a new package that fixes a bug * Update model env var * Move samples (#5281) * Python: Upgrade agentserver packages (#5284) * Upgrade agentserver packages * Fix new types * Python: Add special handling for workflows (#5298) * Add special handling for workflows * Address comments * Improve samples (#5372) * Python: Add more types (#5378) * Add more type supports * Upgrade packages * Remove TODOs in README * Fix README * Comments and mypy * User agent scoped * Fix README * Fix pre commit * Fix pre commit 2 * Fix pre commit 3 * Fix pre commit 4 * Fix pre commit 5 * Fix pre commit 6 * Add azure-monitor-opentelemetry to dev deps Fixes Samples & Markdown CI failure. The PR's new transitive dep on azure-monitor-opentelemetry-exporter (via azure-ai-agentserver-core) makes pyright resolve the azure.monitor.opentelemetry namespace, flipping the check_md_code_blocks diagnostic for `configure_azure_monitor` from reportMissingImports (filtered) to reportAttributeAccessIssue (not filtered). Installing the umbrella azure-monitor-opentelemetry package in dev makes pyright resolve the symbol correctly, matching the install guidance the observability README already gives users. --------- Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>
Basic example of hosting an agent with the invocations API
Running the server locally
Environment setup
Follow the instructions in the Environment setup section of the README in the parent directory to set up your environment and install dependencies.
Run the following command to start the server:
python main.py
Interacting with the agent
Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
curl -X POST http://localhost:8088/invocations -i -H "Content-Type: application/json" -d '{"message": "Hi"}'
The server will respond with a JSON object containing the response text. The -i flag in the curl command includes the HTTP response headers in the output, which includes the session ID that can be used for multi-turn conversations. Here is an example of the response:
HTTP/1.1 200
content-length: 34
content-type: application/json
x-agent-invocation-id: ec04d020-a0e7-441e-ae83-db75635a9f83
x-agent-session-id: 9370b9d4-cd13-4436-a57f-03b843ac0e17
x-platform-server: azure-ai-agentserver-core/2.0.0a20260410006 (python/3.12)
date: Fri, 17 Apr 2026 23:46:44 GMT
server: hypercorn-h11
{"response":"Hi! How can I help?"}
Multi-turn conversation
To have a multi-turn conversation with the agent, take the session ID from the response headers of the previous request and include it in URL parameters for the next request. For example:
curl -X POST http://localhost:8088/invocations?agent_session_id=9370b9d4-cd13-4436-a57f-03b843ac0e17 -i -H "Content-Type: application/json" -d '{"message": "How are you?"}'