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

Redis Context Provider Examples

The Redis context provider enables persistent, searchable memory for your agents using Redis (RediSearch). It supports full‑text search and optional hybrid search with vector embeddings, letting agents remember and retrieve user context across sessions and threads.

This folder contains an example demonstrating how to use the Redis context provider with the Agent Framework.

Examples

File	Description
azure_redis_conversation.py	Demonstrates conversation persistence with RedisHistoryProvider and Azure Redis with Azure AD (Entra ID) authentication using credential provider.
redis_basics.py	Shows standalone provider usage and agent integration. Demonstrates writing messages to Redis, retrieving context via full‑text or hybrid vector search, and persisting preferences across threads. Also includes a simple tool example whose outputs are remembered.
redis_conversation.py	Simple example showing conversation persistence with RedisContextProvider using traditional connection string authentication.
redis_sessions.py	Demonstrates memory scoping strategies. Includes: (1) global memory scope with application_id, agent_id, and user_id shared across operations; (2) hybrid vector search using a custom OpenAI vectorizer for richer context retrieval; and (3) multiple agents with isolated memory via different agent_id values.

Prerequisites

Required resources

1. A running Redis with RediSearch (Redis Stack or a managed service)
2. Python environment with Agent Framework Redis extra installed
3. Azure AI Foundry project endpoint and Azure OpenAI Responses deployment
4. Optional: OpenAI API key if using vector embeddings

Install the package

pip install "agent-framework-redis"

Running Redis

Pick one option:

Option A: Docker (local Redis Stack)

docker run --name redis -p 6379:6379 -d redis:8.0.3

Option B: Redis Cloud

Create a free database and get the connection URL at https://redis.io/cloud/.

Option C: Azure Managed Redis

See quickstart: https://learn.microsoft.com/azure/redis/quickstart-create-managed-redis

Configuration

Environment variables

• FOUNDRY_PROJECT_ENDPOINT (required): Azure AI Foundry project endpoint for FoundryChatClient
• FOUNDRY_MODEL (required): Foundry model deployment name
• OPENAI_API_KEY (optional): Required only if you set vectorizer_choice="openai" to enable hybrid search.

Provider configuration highlights

The provider supports both full‑text only and hybrid vector search:

• Set vectorizer_choice to "openai" or "hf" to enable embeddings and hybrid search.
• When using a vectorizer, also set vector_field_name (e.g., "vector").
• Partition fields for scoping memory: application_id, agent_id, user_id.
• Index management: index_name, overwrite_redis_index, drop_redis_index.

What the example does

redis_basics.py walks through three scenarios:

1. Standalone provider usage: adds messages and retrieves context via invoking.
2. Agent integration: teaches the agent a preference and verifies it is remembered across turns.
3. Agent + tool: calls a sample tool (flight search) and then asks the agent to recall details remembered from the tool output.

It uses FoundryChatClient for chat and, in some steps, optional OpenAI embeddings for hybrid search.

How to run

1. Start Redis (see options above). For local default, ensure it's reachable at redis://localhost:6379.

2. Set Azure Foundry/OpenAI responses environment variables:

export FOUNDRY_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
export FOUNDRY_MODEL="<deployment-name>"

3. (Optional) Set your OpenAI key if using embeddings:

export OPENAI_API_KEY="<your key>"

4. Run the example:

python redis_basics.py

You should see the agent responses and, when using embeddings, context retrieved from Redis. The example includes commented debug helpers you can print, such as index info or all stored docs.

Key concepts

Memory scoping

• Global scope: set application_id, agent_id, or user_id on the provider to filter memory.
• Agent isolation: use different agent_id values to keep memories separated for different agent personas.

Hybrid vector search (optional)

• Enable by setting vectorizer_choice to "openai" (requires OPENAI_API_KEY) or "hf" (offline model).
• Provide vector_field_name (e.g., "vector"); other vector settings have sensible defaults.

Index lifecycle controls

• overwrite_redis_index and drop_redis_index help recreate indexes during iteration.

Troubleshooting

• Ensure at least one of application_id, agent_id, or user_id is set; the provider requires a scope.
• Verify FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL are set for the chat client.
• If using embeddings, verify OPENAI_API_KEY is set and reachable.
• Make sure Redis exposes RediSearch (Redis Stack image or managed service with search enabled).