microsoft--agent-framework/python/samples/02-agents/context_providers

Context Provider Samples

These samples demonstrate how to use context providers to enrich agent conversations with external knowledge — from custom logic to Azure AI Search (RAG) and memory services.

Samples

File / Folder	Description
simple_context_provider.py	Implement a custom context provider by extending ContextProvider to extract and inject structured user information across turns.
todo_provider.py	Use the built-in TodoProvider to give an agent todo-list tools. A scripted walkthrough that plans multi-step work and prints the evolving todo list after each turn.
agent_mode_provider.py	Use the built-in AgentModeProvider to track and switch an agent's operating mode at runtime. An interactive loop with a /mode slash command demonstrating the built-in plan/execute modes and custom modes.
cross_session_observer.py	Detect injected context messages whose origins differ from the current session, via the Message.additional_properties["_attribution"]["origin_session_ids"] field. Self-contained — no LLM credentials required.
azure_ai_foundry_memory.py	Use FoundryMemoryProvider to add semantic memory — automatically retrieves, searches, and stores memories via Microsoft Foundry.
file_memory_provider.py	Use the built-in FileMemoryProvider with FileSystemAgentFileStore to give an agent tools for storing and recalling memories as files, and configure the scope so memories persist and are recalled across separate sessions.
file_access_data_processing/	Use FileAccessProvider with FileSystemAgentFileStore to give an agent read/write/search access to a folder of CSV data files. See its own README.
azure_ai_search/	Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own README.
azure_content_understanding/	Analyze documents, images, audio, and video with Azure Content Understanding and inject the extracted content into agent context.
mem0/	Memory-powered context using the Mem0 integration (open-source and managed). See its own README.
redis/	Redis-backed context providers for conversation memory and sessions. See its own README.

Prerequisites

For cross_session_observer.py:

• No external dependencies; runs against in-memory SessionContext.

For simple_context_provider.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Model deployment name
• Azure CLI authentication (az login)

For todo_provider.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Model deployment name
• Azure CLI authentication (az login)

For agent_mode_provider.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Model deployment name
• Azure CLI authentication (az login)
• To try the custom concise/detailed modes instead of the built-in plan/execute modes, set the in-file USE_CUSTOM_MODES constant to True.
• This sample is interactive: it reads commands from the console in a loop (type /exit to quit).

For azure_ai_foundry_memory.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Chat/responses model deployment name
• AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: Embedding model deployment name (e.g., text-embedding-ada-002)
• Azure CLI authentication (az login)

For file_memory_provider.py:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Chat model deployment name
• Azure CLI authentication (az login)

For file_access_data_processing/:

• FOUNDRY_PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
• FOUNDRY_MODEL: Chat model deployment name
• Azure CLI authentication (az login)

See each subfolder's README for provider-specific prerequisites.