When building an agent with Azure AI Foundry, you’ll often need to look back at the conversation so far. Whether you’re debugging, showing it for reference, or implementing agent “memory” fetching thread message history is essential.
Install dependencies
You’ll need these packages:
- azure-ai-projects
- azure-ai-agents
- azure-identity
Install them with pip:
pip install azure-ai-projects azure-ai-agents azure-identity
Initialize the client
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import DefaultAzureCredential
client = AIProjectClient(
endpoint="Endpoint of your Azure Ai Foundry project",
credential=DefaultAzureCredential()
)
Get thread ID
To fetch history, you need a thread ID. You can either persist it when creating threads in code or find it in the Azure AI Foundry portal:
List all messages in a thread
The simplest way is to iterate over all messages with the async list method:
agents_client = client.agents
msgs = agents_client.messages.list(thread_id=thread_id)
async for msg in msgs:
...
Limiting Results (API Calls)
The limit parameter is confusing. It does not cap the total number of messages returned—it only controls how many items are retrieved per API call. For example, limit=3 still fetches the entire history, just in smaller batches.
To truly process a limited number of messages, use paging and break early:
messages = agents_client.messages.list(thread_id=thread_id, limit=3)
for i, page in enumerate(messages.by_page()):
print(f"Items on page {i}")
for message in page:
print(message.id)
# break after first page if only X items are needed
this will produce something like this:
Items on page 0
msg_1
msg_2
msg_3
Items on page 1
msg_4
msg_5
msg_6
Items on page 2
msg_7
If you only want the first N messages, you can exit the loop after processing the desired count.
Comments