File: README.md

package info (click to toggle)
python-azure 20250603%2Bgit-1
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 851,724 kB
  • sloc: python: 7,362,925; ansic: 804; javascript: 287; makefile: 195; sh: 145; xml: 109
file content (1292 lines) | stat: -rw-r--r-- 52,090 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
<!-- PIPY LONG DESCRIPTION BEGIN -->
# Azure AI Agents client library for Python

Use the AI Agents client library to:

* **Develop Agents using the Azure AI Agents Service**, leveraging an extensive ecosystem of models, tools, and capabilities from OpenAI, Microsoft, and other LLM providers. The Azure AI Agents Service enables the building of Agents for a wide range of generative AI use cases.
* **Note:** While this package can be used independently, we recommend using the Azure AI Projects client library (azure-ai-projects) for an enhanced experience. 
The Projects library provides simplified access to advanced functionality, such as creating and managing agents, enumerating AI models, working with datasets and 
managing search indexes, evaluating generative AI performance, and enabling OpenTelemetry tracing.

[Product documentation](https://aka.ms/azsdk/azure-ai-agents/product-doc)
| [Samples][samples]
| [API reference documentation](https://aka.ms/azsdk/azure-ai-agents/python/reference)
| [Package (PyPI)](https://aka.ms/azsdk/azure-ai-agents/python/package)
| [SDK source code](https://aka.ms/azsdk/azure-ai-agents/python/code)
| [AI Starter Template](https://aka.ms/azsdk/azure-ai-agents/python/ai-starter-template)

## Reporting issues

To report an issue with the client library, or request additional features, please open a GitHub issue [here](https://github.com/Azure/azure-sdk-for-python/issues). Mention the package name "azure-ai-agents" in the title or content.

## Table of contents

- [Getting started](#getting-started)
  - [Prerequisite](#prerequisite)
  - [Install the package](#install-the-package)
- [Key concepts](#key-concepts)
  - [Create and authenticate the client](#create-and-authenticate-the-client)
- [Examples](#examples)
  - [Create an Agent](#create-agent) with:
    - [File Search](#create-agent-with-file-search)
    - [Enterprise File Search](#create-agent-with-enterprise-file-search)
    - [Code interpreter](#create-agent-with-code-interpreter)
    - [Bing grounding](#create-agent-with-bing-grounding)
    - [Azure AI Search](#create-agent-with-azure-ai-search)
    - [Function call](#create-agent-with-function-call)
    - [Azure Function Call](#create-agent-with-azure-function-call)
    - [OpenAPI](#create-agent-with-openapi)
    - [Fabric data](#create-an-agent-with-fabric)
  - [Create thread](#create-thread) with
    - [Tool resource](#create-thread-with-tool-resource)
  - [Create message](#create-message) with:
    - [File search attachment](#create-message-with-file-search-attachment)
    - [Code interpreter attachment](#create-message-with-code-interpreter-attachment)
    - [Create Message with Image Inputs](#create-message-with-image-inputs)
  - [Execute Run, Run_and_Process, or Stream](#execute-run-run_and_process-or-stream)
  - [Retrieve message](#retrieve-message)
  - [Retrieve file](#retrieve-file)
  - [Tear down by deleting resource](#teardown)
  - [Tracing](#tracing)
    - [Installation](#installation)
    - [How to enable tracing](#how-to-enable-tracing)
    - [How to trace your own functions](#how-to-trace-your-own-functions)
- [Troubleshooting](#troubleshooting)
  - [Logging](#logging)
  - [Reporting issues](#reporting-issues)
- [Next steps](#next-steps)
- [Contributing](#contributing)
<!-- PIPY LONG DESCRIPTION END -->
## Getting started

### Prerequisite

- Python 3.9 or later.
- An [Azure subscription][azure_sub].
- A [project in Azure AI Foundry](https://learn.microsoft.com/azure/ai-studio/how-to/create-projects).
- The project endpoint string. It can be found in your Azure AI Foundry project overview page, under "Project details". Below we will assume the environment variable `PROJECT_ENDPOINT_STRING` was defined to hold this value.
- Entra ID is needed to authenticate the client. Your application needs an object that implements the [TokenCredential](https://learn.microsoft.com/python/api/azure-core/azure.core.credentials.tokencredential) interface. Code samples here use [DefaultAzureCredential](https://learn.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential). To get that working, you will need:
  * An appropriate role assignment. see [Role-based access control in Azure AI Foundry portal](https://learn.microsoft.com/azure/ai-foundry/concepts/rbac-ai-foundry). Role assigned can be done via the "Access Control (IAM)" tab of your Azure AI Project resource in the Azure portal.
  * [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed.
  * You are logged into your Azure account by running `az login`.
  * Note that if you have multiple Azure subscriptions, the subscription that contains your Azure AI Project resource must be your default subscription. Run `az account list --output table` to list all your subscription and see which one is the default. Run `az account set --subscription "Your Subscription ID or Name"` to change your default subscription.

### Install the package

```bash
pip install azure-ai-agents
```

## Key concepts

### Create and authenticate the client

To construct a synchronous client:

```python
import os
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential

agents_client = AgentsClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)
```

To construct an asynchronous client, Install the additional package [aiohttp](https://pypi.org/project/aiohttp/):

```bash
pip install aiohttp
```

and update the code above to import `asyncio`, and import `AgentsClient` from the `azure.ai.agents.aio` namespace:

```python
import os
import asyncio
from azure.ai.agents.aio import AgentsClient
from azure.core.credentials import AzureKeyCredential

agent_client = AgentsClient(
   endpoint=os.environ["PROJECT_ENDPOINT"],
   credential=DefaultAzureCredential(),
)
```

## Examples

### Create Agent

Before creating an Agent, you need to set up Azure resources to deploy your model. [Create a New Agent Quickstart](https://learn.microsoft.com/azure/ai-services/agents/quickstart?pivots=programming-language-python-azure) details selecting and deploying your Agent Setup.

Here is an example of how to create an Agent:
<!-- SNIPPET:sample_agents_basics.create_agent -->

```python

    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are helpful agent",
    )
```

<!-- END SNIPPET -->

To allow Agents to access your resources or custom functions, you need tools. You can pass tools to `create_agent` by either `toolset` or combination of `tools` and `tool_resources`.

Here is an example of `toolset`:
<!-- SNIPPET:sample_agents_run_with_toolset.create_agent_toolset -->

```python
functions = FunctionTool(user_functions)
code_interpreter = CodeInterpreterTool()

toolset = ToolSet()
toolset.add(functions)
toolset.add(code_interpreter)

# To enable tool calls executed automatically
agents_client.enable_auto_function_calls(toolset)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

Also notices that if you use asynchronous client, you use `AsyncToolSet` instead.  Additional information related to `AsyncFunctionTool` be discussed in the later sections.

Here is an example to use `tools` and `tool_resources`:
<!-- SNIPPET:sample_agents_vector_store_batch_file_search.create_agent_with_tools_and_tool_resources -->

```python
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])

# Notices that FileSearchTool as tool and tool_resources must be added or the agent unable to search the file
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=file_search_tool.definitions,
    tool_resources=file_search_tool.resources,
)
```

<!-- END SNIPPET -->

In the following sections, we show you sample code in either `toolset` or combination of `tools` and `tool_resources`.

### Create Agent with File Search

To perform file search by an Agent, we first need to upload a file, create a vector store, and associate the file to the vector store. Here is an example:

<!-- SNIPPET:sample_agents_file_search.upload_file_create_vector_store_and_agent_with_file_search_tool -->

```python
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")

vector_store = agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
print(f"Created vector store, vector store ID: {vector_store.id}")

# Create file search tool with resources followed by creating agent
file_search = FileSearchTool(vector_store_ids=[vector_store.id])

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="Hello, you are helpful agent and can search information from uploaded files",
    tools=file_search.definitions,
    tool_resources=file_search.resources,
)
```

<!-- END SNIPPET -->

### Create Agent with Enterprise File Search

We can upload file to Azure as it is shown in the example, or use the existing Azure blob storage. In the code below we demonstrate how this can be achieved. First we upload file to azure and create `VectorStoreDataSource`, which then is used to create vector store. This vector store is then given to the `FileSearchTool` constructor.

<!-- SNIPPET:sample_agents_enterprise_file_search.upload_file_and_create_agent_with_file_search -->

```python
# We will upload the local file to Azure and will use it for vector store creation.
asset_uri = os.environ["AZURE_BLOB_URI"]

# Create a vector store with no file and wait for it to be processed
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
vector_store = agents_client.vector_stores.create_and_poll(data_sources=[ds], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")

# Create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])

# Notices that FileSearchTool as tool and tool_resources must be added or the agent unable to search the file
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=file_search_tool.definitions,
    tool_resources=file_search_tool.resources,
)
```

<!-- END SNIPPET -->

We also can attach files to the existing vector store. In the code snippet below, we first create an empty vector store and add file to it.

<!-- SNIPPET:sample_agents_vector_store_batch_enterprise_file_search.attach_files_to_store -->

```python
# Create a vector store with no file and wait for it to be processed
vector_store = agents_client.vector_stores.create_and_poll(data_sources=[], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")

ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
# Add the file to the vector store or you can supply data sources in the vector store creation
vector_store_file_batch = agents_client.vector_store_file_batches.create_and_poll(
    vector_store_id=vector_store.id, data_sources=[ds]
)
print(f"Created vector store file batch, vector store file batch ID: {vector_store_file_batch.id}")

# Create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
```

<!-- END SNIPPET -->

### Create Agent with Code Interpreter

Here is an example to upload a file and use it for code interpreter by an Agent:

<!-- SNIPPET:sample_agents_code_interpreter.upload_file_and_create_agent_with_code_interpreter -->

```python
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")

code_interpreter = CodeInterpreterTool(file_ids=[file.id])

# Create agent with code interpreter tool and tools_resources
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=code_interpreter.definitions,
    tool_resources=code_interpreter.resources,
)
```

<!-- END SNIPPET -->

### Create Agent with Bing Grounding

To enable your Agent to perform search through Bing search API, you use `BingGroundingTool` along with a connection.

Here is an example:

<!-- SNIPPET:sample_agents_bing_grounding.create_agent_with_bing_grounding_tool -->

```python
conn_id = os.environ["AZURE_BING_CONNECTION_ID"]

# Initialize agent bing tool and add the connection id
bing = BingGroundingTool(connection_id=conn_id)

# Create agent with the bing tool and process agent run
with agents_client:
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=bing.definitions,
    )
```

<!-- END SNIPPET -->

### Create Agent with Azure AI Search

Azure AI Search is an enterprise search system for high-performance applications. It integrates with Azure OpenAI Service and Azure Machine Learning, offering advanced search technologies like vector search and full-text search. Ideal for knowledge base insights, information discovery, and automation. Creating an Agent with Azure AI Search requires an existing Azure AI Search Index. For more information and setup guides, see [Azure AI Search Tool Guide](https://learn.microsoft.com/azure/ai-services/agents/how-to/tools/azure-ai-search?tabs=azurecli%2Cpython&pivots=overview-azure-ai-search).

Here is an example to integrate Azure AI Search:

<!-- SNIPPET:sample_agents_azure_ai_search.create_agent_with_azure_ai_search_tool -->

```python
conn_id = os.environ["AI_AZURE_AI_CONNECTION_ID"]

print(conn_id)

# Initialize agent AI search tool and add the search index connection id
ai_search = AzureAISearchTool(
    index_connection_id=conn_id, index_name="sample_index", query_type=AzureAISearchQueryType.SIMPLE, top_k=3, filter=""
)

# Create agent with AI search tool and process agent run
with agents_client:
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=ai_search.definitions,
        tool_resources=ai_search.resources,
    )
```

<!-- END SNIPPET -->

If the agent has found the relevant information in the index, the reference
and annotation will be provided in the message response. In the example above, we replace
the reference placeholder by the actual reference and url. Please note, that to
get sensible result, the index needs to have "embedding", "token", "category" and "title" fields.

<!-- SNIPPET:sample_agents_azure_ai_search.populate_references_agent_with_azure_ai_search_tool -->

```python
# Fetch and log all messages
messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for message in messages:
    if message.role == MessageRole.AGENT and message.url_citation_annotations:
        placeholder_annotations = {
            annotation.text: f" [see {annotation.url_citation.title}] ({annotation.url_citation.url})"
            for annotation in message.url_citation_annotations
        }
        for message_text in message.text_messages:
            message_str = message_text.text.value
            for k, v in placeholder_annotations.items():
                message_str = message_str.replace(k, v)
            print(f"{message.role}: {message_str}")
    else:
        for message_text in message.text_messages:
            print(f"{message.role}: {message_text.text.value}")
```

<!-- END SNIPPET -->

### Create Agent with Function Call

You can enhance your Agents by defining callback functions as function tools. These can be provided to `create_agent` via either the `toolset` parameter or the combination of `tools` and `tool_resources`. Here are the distinctions:

For more details about requirements and specification of functions, refer to [Function Tool Specifications](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/FunctionTool.md)

Here is an example to use [user functions](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/utils/user_functions.py) in `toolset`:
<!-- SNIPPET:sample_agents_stream_eventhandler_with_toolset.create_agent_with_function_tool -->

```python
functions = FunctionTool(user_functions)
toolset = ToolSet()
toolset.add(functions)
agents_client.enable_auto_function_calls(toolset)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

For asynchronous functions, you must import `AgentsClient` from `azure.ai.agents.aio` and use `AsyncFunctionTool`.   Here is an example using [asynchronous user functions](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_async/sample_agents_functions_async.py):

```python
from azure.ai.agents.aio import AgentsClient
```

<!-- SNIPPET:sample_agents_run_with_toolset_async.create_agent_with_async_function_tool -->

```python
functions = AsyncFunctionTool(user_async_functions)

toolset = AsyncToolSet()
toolset.add(functions)
agents_client.enable_auto_function_calls(toolset)

agent = await agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are a helpful agent",
    toolset=toolset,
)
```

<!-- END SNIPPET -->

Notice that if `enable_auto_function_calls` is called, the SDK will invoke the functions automatically during `create_and_process` or streaming.  If you prefer to execute them manually, refer to [`sample_agents_stream_eventhandler_with_functions.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_streaming/sample_agents_stream_eventhandler_with_functions.py) or
[`sample_agents_functions.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_functions.py)

### Create Agent With Azure Function Call

The AI agent leverages Azure Functions triggered asynchronously via Azure Storage Queues. To enable the agent to perform Azure Function calls, you must set up the corresponding `AzureFunctionTool`, specifying input and output queues as well as parameter definitions.

Example Python snippet illustrating how you create an agent utilizing the Azure Function Tool:

```python
azure_function_tool = AzureFunctionTool(
    name="foo",
    description="Get answers from the foo bot.",
    parameters={
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "The question to ask."},
            "outputqueueuri": {"type": "string", "description": "The full output queue uri."},
        },
    },
    input_queue=AzureFunctionStorageQueue(
        queue_name="azure-function-foo-input",
        storage_service_endpoint=storage_service_endpoint,
    ),
    output_queue=AzureFunctionStorageQueue(
        queue_name="azure-function-tool-output",
        storage_service_endpoint=storage_service_endpoint,
    ),
)

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="azure-function-agent-foo",
    instructions=f"You are a helpful support agent. Use the provided function any time the prompt contains the string 'What would foo say?'. When you invoke the function, ALWAYS specify the output queue uri parameter as '{storage_service_endpoint}/azure-function-tool-output'. Always responds with \"Foo says\" and then the response from the tool.",
    tools=azure_function_tool.definitions,
)
print(f"Created agent, agent ID: {agent.id}")
```

---

**Limitations**

Currently, the Azure Function integration for the AI Agent has the following limitations:

- Supported trigger for Azure Function is currently limited to **Queue triggers** only.
  HTTP or other trigger types and streaming responses are not supported at this time.

---

**Create and Deploy Azure Function**

Before you can use the agent with AzureFunctionTool, you need to create and deploy Azure Function.

Below is an example Python Azure Function responding to queue-triggered messages and placing responses on the output queue:

```python
import azure.functions as func
import logging
import json

app = func.FunctionApp()

@app.function_name(name="GetWeather")
@app.queue_trigger(arg_name="inputQueue",
                   queue_name="input",
                   connection="DEPLOYMENT_STORAGE_CONNECTION_STRING")
@app.queue_output(arg_name="outputQueue",
                  queue_name="output",
                  connection="DEPLOYMENT_STORAGE_CONNECTION_STRING")
def queue_trigger(inputQueue: func.QueueMessage, outputQueue: func.Out[str]):
    try:
        messagepayload = json.loads(inputQueue.get_body().decode("utf-8"))
        location = messagepayload["location"]
        weather_result = f"Weather is 82 degrees and sunny in {location}."

        response_message = {
            "Value": weather_result,
            "CorrelationId": messagepayload["CorrelationId"]
        }

        outputQueue.set(json.dumps(response_message))

        logger.info(f"Sent message to output queue with message {response_message}")
    except Exception as e:
        logging.error(f"Error processing message: {e}")
        return
```

> **Important:** Both input and output payloads must contain the `CorrelationId`, which must match in request and response.

---

**Azure Function Project Creation and Deployment**

To deploy your function to Azure properly, follow Microsoft's official documentation step by step:

[Azure Functions Python Developer Guide](https://learn.microsoft.com/azure/azure-functions/create-first-function-cli-python?tabs=windows%2Cbash%2Cazure-cli%2Cbrowser)

**Summary of required steps:**

- Use the Azure CLI or Azure Portal to create an Azure Function App.
- Enable System Managed Identity for your Azure Function App.
- Assign appropriate permissions to your Azure Function App identity as outlined in the Role Assignments section below
- Create input and output queues in Azure Storage.
- Deploy your Function code.

---

**Verification and Testing Azure Function**

To ensure that your Azure Function deployment functions correctly:

1. Place the following style message manually into the input queue (`input`):

{
  "location": "Seattle",
  "CorrelationId": "42"
}

Check the output queue (`output`) and validate the structured message response:

{
  "Value": "The weather in Seattle is sunny and warm.",
  "CorrelationId": "42"
}

---

**Required Role Assignments (IAM Configuration)**

Clearly assign the following Azure IAM roles to ensure correct permissions:

1. **Azure Function App's identity:**
   - Enable system managed identity through Azure Function App > Settings > Identity.
   - Add permission to storage account:
     - Go to **Storage Account > Access control (IAM)** and add role assignment:
       - `Storage Queue Data Contributor` assigned to Azure Function managed identity

2. **Azure AI Project Identity:**

Ensure your Azure AI Project identity has the following storage account permissions:
- `Storage Account Contributor`
- `Storage Blob Data Contributor`
- `Storage File Data Privileged Contributor`
- `Storage Queue Data Contributor`
- `Storage Table Data Contributor`

---

**Additional Important Configuration Notes**

- The Azure Function configured above uses the `AzureWebJobsStorage` connection string for queue connectivity. You may alternatively use managed identity-based connections as described in the official Azure Functions Managed Identity documentation.
- Storage queues you specify (`input` & `output`) should already exist in the storage account before the Function deployment or invocation, created manually via Azure portal or CLI.
- When using Azure storage account connection strings, make sure the account has enabled storage account key access (`Storage Account > Settings > Configuration`).

---

With the above steps complete, your Azure Function integration with your AI Agent is ready for use.


### Create Agent With Logic Apps

Logic Apps allow HTTP requests to trigger actions. For more information, refer to the guide [Logic App Workflows for Function Calling](https://learn.microsoft.com/azure/ai-services/openai/how-to/assistants-logic-apps).

Your Logic App must be in the same resource group as your Azure AI Project, shown in the Azure Portal. Agents SDK accesses Logic Apps through Workflow URLs, which are fetched and called as requests in functions.

Below is an example of how to create an Azure Logic App utility tool and register a function with it.

<!-- SNIPPET:sample_agents_logic_apps.register_logic_app -->

```python

# Create the agents client
agents_client = AgentsClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

# Extract subscription and resource group from the project scope
subscription_id = os.environ["SUBSCRIPTION_ID"]
resource_group = os.environ["resource_group_name"]

# Logic App details
logic_app_name = "<LOGIC_APP_NAME>"
trigger_name = "<TRIGGER_NAME>"

# Create and initialize AzureLogicAppTool utility
logic_app_tool = AzureLogicAppTool(subscription_id, resource_group)
logic_app_tool.register_logic_app(logic_app_name, trigger_name)
print(f"Registered logic app '{logic_app_name}' with trigger '{trigger_name}'.")

# Create the specialized "send_email_via_logic_app" function for your agent tools
send_email_func = create_send_email_function(logic_app_tool, logic_app_name)

# Prepare the function tools for the agent
functions_to_use: Set = {
    fetch_current_datetime,
    send_email_func,  # This references the AzureLogicAppTool instance via closure
}
```

<!-- END SNIPPET -->

After this the functions can be incorporated normally into code using `FunctionTool`.


### Create Agent With OpenAPI

OpenAPI specifications describe REST operations against a specific endpoint. Agents SDK can read an OpenAPI spec, create a function from it, and call that function against the REST endpoint without additional client-side execution.

Here is an example creating an OpenAPI tool (using anonymous authentication):

<!-- SNIPPET:sample_agents_openapi.create_agent_with_openapi -->

```python

with open(weather_asset_file_path, "r") as f:
    openapi_weather = jsonref.loads(f.read())

with open(countries_asset_file_path, "r") as f:
    openapi_countries = jsonref.loads(f.read())

# Create Auth object for the OpenApiTool (note that connection or managed identity auth setup requires additional setup in Azure)
auth = OpenApiAnonymousAuthDetails()

# Initialize agent OpenApi tool using the read in OpenAPI spec
openapi_tool = OpenApiTool(
    name="get_weather", spec=openapi_weather, description="Retrieve weather information for a location", auth=auth
)
openapi_tool.add_definition(
    name="get_countries", spec=openapi_countries, description="Retrieve a list of countries", auth=auth
)

# Create agent with OpenApi tool and process agent run
with agents_client:
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=openapi_tool.definitions,
    )
```

<!-- END SNIPPET -->


### Create an Agent with Fabric

To enable your Agent to answer queries using Fabric data, use `FabricTool` along with a connection to the Fabric resource.

Here is an example:

<!-- SNIPPET:sample_agents_fabric.create_agent_with_fabric_tool -->

```python
conn_id = os.environ["FABRIC_CONNECTION_ID"]

print(conn_id)

# Initialize an Agent Fabric tool and add the connection id
fabric = FabricTool(connection_id=conn_id)

# Create an Agent with the Fabric tool and process an Agent run
with agents_client:
    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=fabric.definitions,
    )
```

<!-- END SNIPPET -->


### Create Thread

For each session or conversation, a thread is required.   Here is an example:

<!-- SNIPPET:sample_agents_basics.create_thread -->

```python
thread = agents_client.threads.create()
```

<!-- END SNIPPET -->

### Create Thread with Tool Resource

In some scenarios, you might need to assign specific resources to individual threads. To achieve this, you provide the `tool_resources` argument to `create_thread`. In the following example, you create a vector store and upload a file, enable an Agent for file search using the `tools` argument, and then associate the file with the thread using the `tool_resources` argument.

<!-- SNIPPET:sample_agents_with_resources_in_thread.create_agent_and_thread_for_file_search -->

```python
file = agents_client.files.upload_and_poll(file_path=asset_file_path, purpose=FilePurpose.AGENTS)
print(f"Uploaded file, file ID: {file.id}")

vector_store = agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
print(f"Created vector store, vector store ID: {vector_store.id}")

# Create file search tool with resources followed by creating agent
file_search = FileSearchTool(vector_store_ids=[vector_store.id])

agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="Hello, you are helpful agent and can search information from uploaded files",
    tools=file_search.definitions,
)

print(f"Created agent, ID: {agent.id}")

# Create thread with file resources.
# If the agent has multiple threads, only this thread can search this file.
thread = agents_client.threads.create(tool_resources=file_search.resources)
```

<!-- END SNIPPET -->

#### List Threads

To list all threads attached to a given agent, use the list_threads API:

```python
threads = agents_client.threads.list()
```

### Create Message

To create a message for agent to process, you pass `user` as `role` and a question as `content`:

<!-- SNIPPET:sample_agents_basics.create_message -->

```python
message = agents_client.messages.create(thread_id=thread.id, role="user", content="Hello, tell me a joke")
```

<!-- END SNIPPET -->

### Create Message with File Search Attachment

To attach a file to a message for content searching, you use `MessageAttachment` and `FileSearchTool`:

<!-- SNIPPET:sample_agents_with_file_search_attachment.create_message_with_attachment -->

```python
attachment = MessageAttachment(file_id=file.id, tools=FileSearchTool().definitions)
message = agents_client.messages.create(
    thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?", attachments=[attachment]
)
```

<!-- END SNIPPET -->

### Create Message with Code Interpreter Attachment

To attach a file to a message for data analysis, use `MessageAttachment` and `CodeInterpreterTool` classes. You must pass `CodeInterpreterTool` as `tools` or `toolset` in `create_agent` call or the file attachment cannot be opened for code interpreter.

Here is an example to pass `CodeInterpreterTool` as tool:

<!-- SNIPPET:sample_agents_with_code_interpreter_file_attachment.create_agent_and_message_with_code_interpreter_file_attachment -->

```python
# Notice that CodeInterpreter must be enabled in the agent creation,
# otherwise the agent will not be able to see the file attachment for code interpretation
agent = agents_client.create_agent(
    model=os.environ["MODEL_DEPLOYMENT_NAME"],
    name="my-agent",
    instructions="You are helpful agent",
    tools=CodeInterpreterTool().definitions,
)
print(f"Created agent, agent ID: {agent.id}")

thread = agents_client.threads.create()
print(f"Created thread, thread ID: {thread.id}")

# Create an attachment
attachment = MessageAttachment(file_id=file.id, tools=CodeInterpreterTool().definitions)

# Create a message
message = agents_client.messages.create(
    thread_id=thread.id,
    role="user",
    content="Could you please create bar chart in TRANSPORTATION sector for the operating profit from the uploaded csv file and provide file to me?",
    attachments=[attachment],
)
```

<!-- END SNIPPET -->

Azure blob storage can be used as a message attachment. In this case, use `VectorStoreDataSource` as a data source:

<!-- SNIPPET:sample_agents_code_interpreter_attachment_enterprise_search.upload_file_and_create_message_with_code_interpreter -->

```python
# We will upload the local file to Azure and will use it for vector store creation.
asset_uri = os.environ["AZURE_BLOB_URI"]
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)

# Create a message with the attachment
attachment = MessageAttachment(data_source=ds, tools=code_interpreter.definitions)
message = agents_client.messages.create(
    thread_id=thread.id, role="user", content="What does the attachment say?", attachments=[attachment]
)
```

<!-- END SNIPPET -->

### Create Message with Image Inputs

You can send messages to Azure agents with image inputs in following ways:

- **Using an image stored as a uploaded file**
- **Using a public image accessible via URL**
- **Using a base64 encoded image string**

The following examples demonstrate each method:

#### Create message using uploaded image file

```python
# Upload the local image file
image_file = agents_client.files.upload_and_poll(file_path="image_file.png", purpose="assistants")

# Construct content using uploaded image
file_param = MessageImageFileParam(file_id=image_file.id, detail="high")
content_blocks = [
    MessageInputTextBlock(text="Hello, what is in the image?"),
    MessageInputImageFileBlock(image_file=file_param),
]

# Create the message
message = agents_client.messages.create(
    thread_id=thread.id,
    role="user",
    content=content_blocks
)
```

#### Create message with an image URL input

```python
# Specify the public image URL
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"

# Create content directly referencing image URL
url_param = MessageImageUrlParam(url=image_url, detail="high")
content_blocks = [
    MessageInputTextBlock(text="Hello, what is in the image?"),
    MessageInputImageUrlBlock(image_url=url_param),
]

# Create the message
message = agents_client.messages.create(
    thread_id=thread.id,
    role="user",
    content=content_blocks
)
```

#### Create message with base64-encoded image input

```python
import base64

def image_file_to_base64(path: str) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

# Convert your image file to base64 format
image_base64 = image_file_to_base64("image_file.png")

# Prepare the data URL
img_data_url = f"data:image/png;base64,{image_base64}"

# Use base64 encoded string as image URL parameter
url_param = MessageImageUrlParam(url=img_data_url, detail="high")
content_blocks = [
    MessageInputTextBlock(text="Hello, what is in the image?"),
    MessageInputImageUrlBlock(image_url=url_param),
]

# Create the message
message = agents_client.messages.create(
    thread_id=thread.id,
    role="user",
    content=content_blocks
)
```

### Execute Run, Run_and_Process, or Stream

To process your message, you can use `runs.create`, `runs.create_and_process`, or `runs.stream`.

`create_run` requests the Agent to process the message without polling for the result. If you are using `function tools` regardless as `toolset` or not, your code is responsible for polling for the result and acknowledging the status of `Run`. When the status is `requires_action`, your code is responsible for calling the function tools. For a code sample, visit [`sample_agents_functions.py`](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_functions.py).

Here is an example of `runs.create` and poll until the run is completed:

<!-- SNIPPET:sample_agents_basics.create_run -->

```python
run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id)

# Poll the run as long as run status is queued or in progress
while run.status in ["queued", "in_progress", "requires_action"]:
    # Wait for a second
    time.sleep(1)
    run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
```

<!-- END SNIPPET -->

To have the SDK poll on your behalf and call `function tools`, use the `create_and_process` method. Note that `function tools` will only be invoked if they are provided as `toolset` during the `create_agent` call.

Here is an example:

<!-- SNIPPET:sample_agents_run_with_toolset.create_and_process_run -->

```python
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
```

<!-- END SNIPPET -->

With streaming, polling need not be considered. If `function tools` are provided as `toolset` during the `create_agent` call, they will be invoked by the SDK.

Here is an example of streaming:

<!-- SNIPPET:sample_agents_basics_stream_iteration.iterate_stream -->

```python
with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id) as stream:

    for event_type, event_data, _ in stream:

        if isinstance(event_data, MessageDeltaChunk):
            print(f"Text delta received: {event_data.text}")

        elif isinstance(event_data, ThreadMessage):
            print(f"ThreadMessage created. ID: {event_data.id}, Status: {event_data.status}")

        elif isinstance(event_data, ThreadRun):
            print(f"ThreadRun status: {event_data.status}")

        elif isinstance(event_data, RunStep):
            print(f"RunStep type: {event_data.type}, Status: {event_data.status}")

        elif event_type == AgentStreamEvent.ERROR:
            print(f"An error occurred. Data: {event_data}")

        elif event_type == AgentStreamEvent.DONE:
            print("Stream completed.")
            break

        else:
            print(f"Unhandled Event Type: {event_type}, Data: {event_data}")
```

<!-- END SNIPPET -->

In the code above, because an `event_handler` object is not passed to the `stream` function, the SDK will instantiate `AgentEventHandler` or `AsyncAgentEventHandler` as the default event handler and produce an iterable object with `event_type` and `event_data`.  `AgentEventHandler` and `AsyncAgentEventHandler` are overridable.  Here is an example:

<!-- SNIPPET:sample_agents_basics_stream_eventhandler.stream_event_handler -->

```python
# With AgentEventHandler[str], the return type for each event functions is optional string.
class MyEventHandler(AgentEventHandler[str]):

    def on_message_delta(self, delta: "MessageDeltaChunk") -> Optional[str]:
        return f"Text delta received: {delta.text}"

    def on_thread_message(self, message: "ThreadMessage") -> Optional[str]:
        return f"ThreadMessage created. ID: {message.id}, Status: {message.status}"

    def on_thread_run(self, run: "ThreadRun") -> Optional[str]:
        return f"ThreadRun status: {run.status}"

    def on_run_step(self, step: "RunStep") -> Optional[str]:
        return f"RunStep type: {step.type}, Status: {step.status}"

    def on_error(self, data: str) -> Optional[str]:
        return f"An error occurred. Data: {data}"

    def on_done(self) -> Optional[str]:
        return "Stream completed."

    def on_unhandled_event(self, event_type: str, event_data: Any) -> Optional[str]:
        return f"Unhandled Event Type: {event_type}, Data: {event_data}"
```

<!-- END SNIPPET -->


<!-- SNIPPET:sample_agents_basics_stream_eventhandler.create_stream -->

```python
with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id, event_handler=MyEventHandler()) as stream:
    for event_type, event_data, func_return in stream:
        print(f"Received data.")
        print(f"Streaming receive Event Type: {event_type}")
        print(f"Event Data: {str(event_data)[:100]}...")
        print(f"Event Function return: {func_return}\n")
```

<!-- END SNIPPET -->

As you can see, this SDK parses the events and produces various event types similar to OpenAI agents. In your use case, you might not be interested in handling all these types and may decide to parse the events on your own. To achieve this, please refer to [override base event handler](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-agents/samples/agents_streaming/sample_agents_stream_with_base_override_eventhandler.py).

```
Note: Multiple streaming processes may be chained behind the scenes.

When the SDK receives a `ThreadRun` event with the status `requires_action`, the next event will be `Done`, followed by termination. The SDK will submit the tool calls using the same event handler. The event handler will then chain the main stream with the tool stream.

Consequently, when you iterate over the streaming using a for loop similar to the example above, the for loop will receive events from the main stream followed by events from the tool stream.
```


### Retrieve Message

To retrieve messages from agents, use the following example:

<!-- SNIPPET:sample_agents_basics.list_messages -->

```python
messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for msg in messages:
    if msg.text_messages:
        last_text = msg.text_messages[-1]
        print(f"{msg.role}: {last_text.text.value}")
```

<!-- END SNIPPET -->

In addition, `messages` and `messages.data[]` offer helper properties such as `text_messages`, `image_contents`, `file_citation_annotations`, and `file_path_annotations` to quickly retrieve content from one message or all messages.

### Retrieve File

Files uploaded by Agents cannot be retrieved back. If your use case need to access the file content uploaded by the Agents, you are advised to keep an additional copy accessible by your application. However, files generated by Agents are retrievable by `save_file` or `get_file_content`.

Here is an example retrieving file ids from messages and save to the local drive:

<!-- SNIPPET:sample_agents_code_interpreter.get_messages_and_save_files -->

```python
messages = agents_client.messages.list(thread_id=thread.id)
print(f"Messages: {messages}")

for msg in messages:
    # Save every image file in the message
    for img in msg.image_contents:
        file_id = img.image_file.file_id
        file_name = f"{file_id}_image_file.png"
        agents_client.files.save(file_id=file_id, file_name=file_name)
        print(f"Saved image file to: {Path.cwd() / file_name}")

    # Print details of every file-path annotation
    for ann in msg.file_path_annotations:
        print("File Paths:")
        print(f"  Type: {ann.type}")
        print(f"  Text: {ann.text}")
        print(f"  File ID: {ann.file_path.file_id}")
        print(f"  Start Index: {ann.start_index}")
        print(f"  End Index: {ann.end_index}")
```

<!-- END SNIPPET -->

Here is an example to use `get_file_content`:

```python
from pathlib import Path

async def save_file_content(client, file_id: str, file_name: str, target_dir: Optional[Union[str, Path]] = None):
    # Determine the target directory
    path = Path(target_dir).expanduser().resolve() if target_dir else Path.cwd()
    path.mkdir(parents=True, exist_ok=True)

    # Retrieve the file content
    file_content_stream = await client.files.get_content(file_id)
    if not file_content_stream:
        raise RuntimeError(f"No content retrievable for file ID '{file_id}'.")

    # Collect all chunks asynchronously
    chunks = []
    async for chunk in file_content_stream:
        if isinstance(chunk, (bytes, bytearray)):
            chunks.append(chunk)
        else:
            raise TypeError(f"Expected bytes or bytearray, got {type(chunk).__name__}")

    target_file_path = path / file_name

    # Write the collected content to the file synchronously
    with open(target_file_path, "wb") as file:
        for chunk in chunks:
            file.write(chunk)
```

### Teardown

To remove resources after completing tasks, use the following functions:

<!-- SNIPPET:sample_agents_file_search.teardown -->

```python
# Delete the file when done
agents_client.vector_stores.delete(vector_store.id)
print("Deleted vector store")

agents_client.files.delete(file_id=file.id)
print("Deleted file")

# Delete the agent when done
agents_client.delete_agent(agent.id)
print("Deleted agent")
```

<!-- END SNIPPET -->

## Tracing

You can add an Application Insights Azure resource to your Azure AI Foundry project. See the Tracing tab in your AI Foundry project. If one was enabled, you can get the Application Insights connection string, configure your Agents, and observe the full execution path through Azure Monitor. Typically, you might want to start tracing before you create an Agent.

### Installation

Make sure to install OpenTelemetry and the Azure SDK tracing plugin via

```bash
pip install opentelemetry
pip install azure-ai-agents azure-identity opentelemetry-sdk azure-core-tracing-opentelemetry
```

You will also need an exporter to send telemetry to your observability backend. You can print traces to the console or use a local viewer such as [Aspire Dashboard](https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash).

To connect to Aspire Dashboard or another OpenTelemetry compatible backend, install OTLP exporter:

```bash
pip install opentelemetry-exporter-otlp
```

### How to enable tracing

Here is a code sample that shows how to enable Azure Monitor tracing:

<!-- SNIPPET:sample_agents_basics_with_azure_monitor_tracing.enable_tracing -->

```python
from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

# Enable Azure Monitor tracing
application_insights_connection_string = os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
configure_azure_monitor(connection_string=application_insights_connection_string)

scenario = os.path.basename(__file__)
tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span(scenario):
    with agents_client:
```

<!-- END SNIPPET -->

In addition, you might find helpful to see the tracing logs in console. You can achieve by the following code:

```python
from azure.ai.agents.telemetry import enable_telemetry

enable_telemetry(destination=sys.stdout)
```
### How to trace your own functions

The decorator `trace_function` is provided for tracing your own function calls using OpenTelemetry. By default the function name is used as the name for the span. Alternatively you can provide the name for the span as a parameter to the decorator.

This decorator handles various data types for function parameters and return values, and records them as attributes in the trace span. The supported data types include:
* Basic data types: str, int, float, bool
* Collections: list, dict, tuple, set
    * Special handling for collections:
      - If a collection (list, dict, tuple, set) contains nested collections, the entire collection is converted to a string before being recorded as an attribute.
      - Sets and dictionaries are always converted to strings to ensure compatibility with span attributes.

Object types are omitted, and the corresponding parameter is not traced.

The parameters are recorded in attributes `code.function.parameter.<parameter_name>` and the return value is recorder in attribute `code.function.return.value`

## Troubleshooting

### Logging

The client uses the standard [Python logging library](https://docs.python.org/3/library/logging.html). The SDK logs HTTP request and response details, which may be useful in troubleshooting. To log to stdout, add the following:

```python
import sys
import logging

# Acquire the logger for this client library. Use 'azure' to affect both
# 'azure.core` and `azure.ai.inference' libraries.
logger = logging.getLogger("azure")

# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
logger.setLevel(logging.DEBUG)

# Direct logging output to stdout:
handler = logging.StreamHandler(stream=sys.stdout)
# Or direct logging output to a file:
# handler = logging.FileHandler(filename="sample.log")
logger.addHandler(handler)

# Optional: change the default logging format. Here we add a timestamp.
#formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
#handler.setFormatter(formatter)
```

By default logs redact the values of URL query strings, the values of some HTTP request and response headers (including `Authorization` which holds the key or token), and the request and response payloads. To create logs without redaction, add `logging_enable = True` to the client constructor:

```python
agents_client = AgentsClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
    logging_enable = True
)
```

Note that the log level must be set to `logging.DEBUG` (see above code). Logs will be redacted with any other log level.

Be sure to protect non redacted logs to avoid compromising security.

For more information, see [Configure logging in the Azure libraries for Python](https://aka.ms/azsdk/python/logging)

### Reporting issues

To report an issue with the client library, or request additional features, please open a GitHub issue [here](https://github.com/Azure/azure-sdk-for-python/issues). Mention the package name "azure-ai-agents" in the title or content.


## Next steps

Have a look at the [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/ai/azure-ai-agents/samples) folder, containing fully runnable Python code for synchronous and asynchronous clients.

Explore the [AI Starter Template](https://aka.ms/azsdk/azure-ai-agents/python/ai-starter-template). This template creates an Azure AI Foundry hub, project and connected resources including Azure OpenAI Service, AI Search and more. It also deploys a simple chat application to Azure Container Apps.

## Contributing

This project welcomes contributions and suggestions. Most contributions require
you to agree to a Contributor License Agreement (CLA) declaring that you have
the right to, and actually do, grant us the rights to use your contribution.
For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether
you need to provide a CLA and decorate the PR appropriately (e.g., label,
comment). Simply follow the instructions provided by the bot. You will only
need to do this once across all repos using our CLA.

This project has adopted the
[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,
see the Code of Conduct FAQ or contact opencode@microsoft.com with any
additional questions or comments.

<!-- LINKS -->
[samples]: https://aka.ms/azsdk/azure-ai-projects/python/samples/
[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/
[entra_id]: https://learn.microsoft.com/azure/ai-services/authentication?tabs=powershell#authenticate-with-microsoft-entra-id
[azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials
[azure_identity_pip]: https://pypi.org/project/azure-identity/
[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential
[pip]: https://pypi.org/project/pip/
[azure_sub]: https://azure.microsoft.com/free/
[evaluators]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk
[azure_ai_evaluation]: https://learn.microsoft.com/python/api/overview/azure/ai-evaluation-readme
[evaluator_library]: https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-generative-ai-app#view-and-manage-the-evaluators-in-the-evaluator-library