Upgrade all examples to MAF 1.0.0

Mechanical fixes across 102 English + Spanish example files:
- model_id= → model= in all OpenAIChatClient constructors
- Message(text=...) → Message(contents=[...])
- BaseContextProvider → ContextProvider
- BaseHistoryProvider → HistoryProvider

Additional fix for history provider examples:
- Removed tools from agent_history_redis.py and agent_history_sqlite.py
  to work around Responses API duplicate item ID issue with history
  providers (microsoft/agent-framework#3295)

All examples verified with live LLM calls against Azure OpenAI.

Author: pamelafox

examples/agent_basic.py

@@ -18,17 +18,17 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
 elif API_HOST == "github":
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
 
 agent = Agent(client=client, instructions="You're an informational agent. Answer questions cheerfully.")

examples/agent_evaluation.py

@@ -37,7 +37,7 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
     eval_model_config = AzureOpenAIModelConfiguration(
         type="azure_openai",
@@ -48,7 +48,7 @@
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
     eval_model_config = OpenAIModelConfiguration(
         type="openai",
@@ -58,7 +58,7 @@
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
     eval_model_config = OpenAIModelConfiguration(
         type="openai",

examples/agent_evaluation_generate.py

@@ -39,17 +39,17 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
 elif API_HOST == "github":
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
 
 

examples/agent_history_redis.py

@@ -1,16 +1,15 @@
+# Note: Do not add tools= to agents using history providers — causes duplicate item errors
+# with the Responses API. See https://github.com/microsoft/agent-framework/issues/3295
 import asyncio
 import logging
 import os
-import random
 import uuid
-from typing import Annotated
 
-from agent_framework import Agent, tool
+from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient
 from agent_framework.redis import RedisHistoryProvider
 from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider
 from dotenv import load_dotenv
-from pydantic import Field
 from rich import print
 from rich.logging import RichHandler
 
@@ -32,30 +31,20 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
 elif API_HOST == "github":
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
 
 
-@tool
-def get_weather(
-    city: Annotated[str, Field(description="The city to get the weather for.")],
-) -> str:
-    """Returns weather data for a given city."""
-    logger.info(f"Getting weather for {city}")
-    conditions = ["sunny", "cloudy", "rainy", "stormy"]
-    return f"The weather in {city} is {conditions[random.randint(0, 3)]} with a high of {random.randint(10, 30)}°C."
-
-
 async def example_persistent_session() -> None:
     """A Redis-backed session persists conversation history across application restarts."""
     print("\n[bold]=== Persistent Redis Session ===[/bold]")
@@ -68,19 +57,18 @@ async def example_persistent_session() -> None:
 
     agent = Agent(
         client=client,
-        instructions="You are a helpful weather agent.",
-        tools=[get_weather],
+        instructions="You are a helpful assistant that remembers our conversation.",
         context_providers=[redis_provider],
     )
 
     session = agent.create_session(session_id=session_id)
 
-    print("[blue]User:[/blue] What's the weather like in Tokyo?")
-    response = await agent.run("What's the weather like in Tokyo?", session=session)
+    print("[blue]User:[/blue] Hello! My name is Alice and I love hiking.")
+    response = await agent.run("Hello! My name is Alice and I love hiking.", session=session)
     print(f"[green]Agent:[/green] {response.text}")
 
-    print("\n[blue]User:[/blue] How about Paris?")
-    response = await agent.run("How about Paris?", session=session)
+    print("\n[blue]User:[/blue] What are some good trails in Colorado?")
+    response = await agent.run("What are some good trails in Colorado?", session=session)
     print(f"[green]Agent:[/green] {response.text}")
 
     # Phase 2: Simulate an application restart — reconnect using the same session ID in Redis
@@ -89,15 +77,14 @@ async def example_persistent_session() -> None:
 
     agent2 = Agent(
         client=client,
-        instructions="You are a helpful weather agent.",
-        tools=[get_weather],
+        instructions="You are a helpful assistant that remembers our conversation.",
         context_providers=[redis_provider2],
     )
 
     session2 = agent2.create_session(session_id=session_id)
 
-    print("[blue]User:[/blue] Which of the cities I asked about had better weather?")
-    response = await agent2.run("Which of the cities I asked about had better weather?", session=session2)
+    print("[blue]User:[/blue] What do you remember about me?")
+    response = await agent2.run("What do you remember about me?", session=session2)
     print(f"[green]Agent:[/green] {response.text}")
 
 

examples/agent_history_sqlite.py

@@ -1,17 +1,17 @@
+# Note: Do not add tools= to agents using history providers — causes duplicate item errors
+# with the Responses API. See https://github.com/microsoft/agent-framework/issues/3295
 import asyncio
 import logging
 import os
-import random
 import sqlite3
 import uuid
 from collections.abc import Sequence
-from typing import Annotated, Any
+from typing import Any
 
-from agent_framework import Agent, BaseHistoryProvider, Message, tool
+from agent_framework import Agent, HistoryProvider, Message
 from agent_framework.openai import OpenAIChatClient
 from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider
 from dotenv import load_dotenv
-from pydantic import Field
 from rich import print
 from rich.logging import RichHandler
 
@@ -32,24 +32,24 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
 elif API_HOST == "github":
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
 
 
-class SQLiteHistoryProvider(BaseHistoryProvider):
+class SQLiteHistoryProvider(HistoryProvider):
     """A custom history provider backed by SQLite.
 
-    Implements the BaseHistoryProvider to persist chat messages
+    Implements the HistoryProvider to persist chat messages
     in a local SQLite database — useful when you want file-based
     persistence without an external service like Redis.
     """
@@ -94,16 +94,6 @@ def close(self) -> None:
         self._conn.close()
 
 
-@tool
-def get_weather(
-    city: Annotated[str, Field(description="The city to get the weather for.")],
-) -> str:
-    """Returns weather data for a given city."""
-    logger.info(f"Getting weather for {city}")
-    conditions = ["sunny", "cloudy", "rainy", "stormy"]
-    return f"The weather in {city} is {conditions[random.randint(0, 3)]} with a high of {random.randint(10, 30)}°C."
-
-
 async def main() -> None:
     """Demonstrate a SQLite-backed session that persists conversation history to a local file."""
     db_path = "chat_history.sqlite3"
@@ -117,19 +107,18 @@ async def main() -> None:
 
     agent = Agent(
         client=client,
-        instructions="You are a helpful weather agent.",
-        tools=[get_weather],
+        instructions="You are a helpful assistant that remembers our conversation.",
         context_providers=[sqlite_provider],
     )
 
     session = agent.create_session(session_id=session_id)
 
-    print("[blue]User:[/blue] What's the weather like in Tokyo?")
-    response = await agent.run("What's the weather like in Tokyo?", session=session)
+    print("[blue]User:[/blue] Hello! My name is Alice and I love hiking.")
+    response = await agent.run("Hello! My name is Alice and I love hiking.", session=session)
     print(f"[green]Agent:[/green] {response.text}")
 
-    print("\n[blue]User:[/blue] How about Paris?")
-    response = await agent.run("How about Paris?", session=session)
+    print("\n[blue]User:[/blue] What are some good trails in Colorado?")
+    response = await agent.run("What are some good trails in Colorado?", session=session)
     print(f"[green]Agent:[/green] {response.text}")
 
     # Phase 2: Simulate an application restart — reconnect to the same session ID in SQLite
@@ -138,15 +127,14 @@ async def main() -> None:
 
     agent2 = Agent(
         client=client,
-        instructions="You are a helpful weather agent.",
-        tools=[get_weather],
+        instructions="You are a helpful assistant that remembers our conversation.",
         context_providers=[sqlite_provider2],
     )
 
     session2 = agent2.create_session(session_id=session_id)
 
-    print("[blue]User:[/blue] Which of the cities I asked about had better weather?")
-    response = await agent2.run("Which of the cities I asked about had better weather?", session=session2)
+    print("[blue]User:[/blue] What do you remember about me?")
+    response = await agent2.run("What do you remember about me?", session=session2)
     print(f"[green]Agent:[/green] {response.text}")
 
     sqlite_provider2.close()

examples/agent_knowledge_aisearch.py

@@ -16,7 +16,7 @@
 
 This example uses the built-in AzureAISearchContextProvider in agentic
 mode, which handles the entire retrieval pipeline — no custom
-BaseContextProvider subclass needed. Agentic mode uses Knowledge Bases
+ContextProvider subclass needed. Agentic mode uses Knowledge Bases
 for multi-hop reasoning across documents, providing accurate results
 through intelligent query planning.
 
@@ -69,17 +69,17 @@
     client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
 elif API_HOST == "github":
     client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
 else:
     client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
 
 # ── Azure AI Search context provider ─────────────────────────────────

examples/agent_knowledge_pg.py

@@ -35,7 +35,7 @@
 from openai import OpenAI
 from pgvector.psycopg import register_vector
 
-from agent_framework import Agent, AgentSession, BaseContextProvider, Message, SessionContext, SupportsAgentRun
+from agent_framework import Agent, AgentSession, ContextProvider, Message, SessionContext, SupportsAgentRun
 from agent_framework.openai import OpenAIChatClient
 from azure.identity import DefaultAzureCredential as SyncDefaultAzureCredential
 from azure.identity import get_bearer_token_provider as sync_get_bearer_token_provider
@@ -67,7 +67,7 @@
     chat_client = OpenAIChatClient(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
         api_key=async_token_provider,
-        model_id=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
     )
     embed_client = OpenAI(
         base_url=f"{os.environ['AZURE_OPENAI_ENDPOINT']}/openai/v1/",
@@ -78,7 +78,7 @@
     chat_client = OpenAIChatClient(
         base_url="https://models.github.ai/inference",
         api_key=os.environ["GITHUB_TOKEN"],
-        model_id=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
+        model=os.getenv("GITHUB_MODEL", "openai/gpt-4.1-mini"),
     )
     embed_client = OpenAI(
         base_url="https://models.github.ai/inference",
@@ -87,7 +87,7 @@
     embed_model = "text-embedding-3-small"
 else:
     chat_client = OpenAIChatClient(
-        api_key=os.environ["OPENAI_API_KEY"], model_id=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
+        api_key=os.environ["OPENAI_API_KEY"], model=os.environ.get("OPENAI_MODEL", "gpt-4.1-mini")
     )
     embed_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
     embed_model = "text-embedding-3-small"
@@ -242,7 +242,7 @@ def create_knowledge_db(conn: psycopg.Connection) -> None:
 """
 
 
-class PostgresKnowledgeProvider(BaseContextProvider):
+class PostgresKnowledgeProvider(ContextProvider):
     """Retrieves relevant product knowledge via hybrid search (vector + full-text) with RRF.
 
     Uses pgvector for semantic similarity and PostgreSQL tsvector for keyword
@@ -312,7 +312,7 @@ async def before_run(
         knowledge_text = "\n".join(knowledge_lines)
         context.extend_messages(
             self.source_id,
-            [Message(role="system", text=knowledge_text)],
+            [Message(role="system", contents=[knowledge_text])],
         )