google__adk-docs/docs/integrations/redis.md

catalog_title, catalog_description, catalog_icon, catalog_tags

catalog_title	catalog_description	catalog_icon	catalog_tags
Redis	Vector, hybrid, and SQL search plus session, memory, and semantic cache for agents	/integrations/assets/redis.svg	data mcp

Redis integration for ADK

Supported in ADKPython

The adk-redis integration connects your ADK agent to Redis, giving it RedisVL-backed search tools over a Redis index, persistent sessions and long-term memory via Redis Agent Memory Server, and semantic caching for LLM responses and tool results. Redis runs as a managed service or self-hosted (Redis 8.4+ with the RediSearch module).

There are several ways to use this integration:

Approach	Description
RedisVL MCP	Connect ADK's native McpToolset to a running rvl mcp server. Exposes search-records (vector / fulltext / hybrid) and upsert-records with schema-aware filter and return-field hints.
Session + Memory services	RedisWorkingMemorySessionService and RedisLongTermMemoryService that implement ADK's BaseSessionService and BaseMemoryService, backed by Agent Memory Server.
Sessions + Memory MCP	Connect ADK's native McpToolset to Agent Memory Server's MCP endpoint over SSE. Gives the agent direct tool access to search_long_term_memory, create_long_term_memories, and memory_prompt.
Search tools	Five BaseTool subclasses (RedisVectorSearchTool, RedisHybridSearchTool, RedisRangeSearchTool, RedisTextSearchTool, RedisSQLSearchTool) over RedisVL queries against a bound index.

Use cases

• RAG over your data: Run vector, hybrid, range, BM25 text, or SQL search against a Redis index. Hybrid search uses native FT.HYBRID on Redis 8.4+ and falls back to client-side aggregation elsewhere.
• Persistent multi-turn agents: Slot the session and memory services into any ADK Runner to retain conversation state, auto-summarize when the context window fills, and promote durable facts to long-term memory.
• Schema-aware MCP tools: Stand up one Redis index per rvl mcp server and connect any number of agents to it over stdio, sse, or streamable-http. The MCP tool descriptions include filter and return-field hints derived from the index schema.
• Latency and cost reduction: Wrap an LLM call site with semantic caching so repeat or near-duplicate prompts skip the model.

Prerequisites

• Python 3.10+
• Redis 8.4+ (or Redis Cloud) with the RediSearch module enabled
• For session and memory services: Redis Agent Memory Server running locally or in your environment
• For the LangCache cache provider: a Redis LangCache cache and API key

Installation

Install the components you need:

pip install 'adk-redis[memory]'      # session + long-term memory services
pip install 'adk-redis[search]'      # RedisVL-backed search tools
pip install 'adk-redis[sql]'         # RedisSQLSearchTool (sql-redis)
pip install 'adk-redis[langcache]'   # managed semantic cache provider
pip install 'adk-redis[all]'         # everything above

# For the RedisVL MCP server (used with ADK's native McpToolset):
pip install 'redisvl[mcp]>=0.18.2'

Use with agent

=== "RedisVL MCP server"

Start the [RedisVL MCP server](https://docs.redisvl.com/en/latest/user_guide/how_to_guides/mcp.html) (`rvl mcp`) pointed
at your Redis index, then connect ADK's native `McpToolset` to it. The
example below uses the stdio transport so no separate server process is
needed; swap in `StreamableHTTPConnectionParams` or `SseConnectionParams` to
connect to a long-running remote server.

```python
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
from mcp import StdioServerParameters

root_agent = Agent(
    model="gemini-flash-latest",
    name="redis_mcp_agent",
    instruction="Use the search-records tool to answer questions.",
    tools=[
        McpToolset(
            connection_params=StdioConnectionParams(
                server_params=StdioServerParameters(
                    command="rvl",
                    args=[
                        "mcp",
                        "--config",
                        "/path/to/mcp_config.yaml",
                        "--read-only",
                    ],
                ),
                timeout=30,
            ),
            tool_filter=["search-records"],
        ),
    ],
)
```

!!! note

    To connect to this MCP server from other ADK languages, see [MCP
    Tools](/tools-custom/mcp-tools/).

=== "Sessions + Memory"

Plug [Agent Memory Server](https://github.com/redis/agent-memory-server)
into any ADK `Runner` via the REST-based session and memory services.
Working memory handles per-session state with auto-summarization;
long-term memory provides cross-session hybrid search.

```python
from google.adk.agents import Agent
from google.adk.runners import Runner

from adk_redis import (
    RedisLongTermMemoryService,
    RedisLongTermMemoryServiceConfig,
    RedisWorkingMemorySessionService,
    RedisWorkingMemorySessionServiceConfig,
)

session_service = RedisWorkingMemorySessionService(
    config=RedisWorkingMemorySessionServiceConfig(
        api_base_url="http://localhost:8000",
    ),
)
memory_service = RedisLongTermMemoryService(
    config=RedisLongTermMemoryServiceConfig(
        api_base_url="http://localhost:8000",
        recency_boost=True,
    ),
)

root_agent = Agent(
    model="gemini-flash-latest",
    name="redis_memory_agent",
    instruction="Use long-term memory to personalize responses.",
)

runner = Runner(
    app_name="redis_memory_app",
    agent=root_agent,
    session_service=session_service,
    memory_service=memory_service,
)
```

=== "Sessions + Memory MCP server"

Connect ADK's native `McpToolset` to
[Agent Memory Server](https://github.com/redis/agent-memory-server)'s
MCP endpoint over SSE. This gives the agent direct tool access to
long-term memory operations without using the REST-based services.

```python
import os

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseConnectionParams

MEMORY_MCP_URL = os.getenv("MEMORY_MCP_URL", "http://localhost:9000")

root_agent = Agent(
    model="gemini-flash-latest",
    name="memory_mcp_agent",
    instruction="Use memory tools to personalize responses.",
    tools=[
        McpToolset(
            connection_params=SseConnectionParams(
                url=f"{MEMORY_MCP_URL.rstrip('/')}/sse",
            ),
            tool_filter=[
                "search_long_term_memory",
                "create_long_term_memories",
                "memory_prompt",
            ],
        ),
    ],
)
```

!!! note

    Agent Memory Server exposes its MCP endpoint on a separate port
    from the REST API. See the
    [fitness_coach_mcp example](https://github.com/redis-developer/adk-redis/tree/main/examples/fitness_coach_mcp)
    for a complete working setup with Docker Compose.

=== "Search tools"

Use RedisVL-backed `BaseTool` subclasses to run vector, hybrid, range,
text, or SQL searches against a Redis index. Bind a tool to an existing
index and pass it directly to your agent.

```python
from google.adk.agents import Agent
from redisvl.index import SearchIndex
from redisvl.utils.vectorize import HFTextVectorizer

from adk_redis import RedisVectorQueryConfig, RedisVectorSearchTool

vectorizer = HFTextVectorizer(model="redis/langcache-embed-v2")
index = SearchIndex.from_existing("products", redis_url="redis://localhost:6379")

search_tool = RedisVectorSearchTool(
    index=index,
    vectorizer=vectorizer,
    config=RedisVectorQueryConfig(num_results=5),
    return_fields=["title", "price", "category"],
    name="search_products",
    description="Semantic search over the product catalog.",
)

root_agent = Agent(
    model="gemini-flash-latest",
    name="redis_search_agent",
    instruction="Help users find products using semantic search.",
    tools=[search_tool],
)
```

Semantic caching

Wrap any LLM call site with semantic caching so repeat or near-duplicate prompts skip the model. Choose self-hosted (bring your own Redis and vectorizer) or managed via Redis LangCache.

=== "Semantic cache (self-hosted)"

Use `RedisVLCacheProvider` with a local vectorizer and your own Redis
instance for self-hosted semantic caching.

```python
from google.adk.agents import Agent
from redisvl.utils.vectorize import HFTextVectorizer

from adk_redis import (
    LLMResponseCache,
    RedisVLCacheProvider,
    RedisVLCacheProviderConfig,
    create_llm_cache_callbacks,
)

provider = RedisVLCacheProvider(
    config=RedisVLCacheProviderConfig(
        redis_url="redis://localhost:6379",
        ttl=3600,
        distance_threshold=0.1,
    ),
    vectorizer=HFTextVectorizer(
        model="redis/langcache-embed-v2",
    ),
)

llm_cache = LLMResponseCache(provider=provider)
before_model_cb, after_model_cb = create_llm_cache_callbacks(llm_cache)

root_agent = Agent(
    model="gemini-flash-latest",
    name="cached_agent",
    instruction="You are a helpful assistant with semantic caching enabled.",
    before_model_callback=before_model_cb,
    after_model_callback=after_model_cb,
)
```

=== "Semantic cache (LangCache)"

Use `LangCacheProvider` with [Redis LangCache](https://redis.io/langcache),
a managed semantic caching service. No local vectorizer is needed as
embeddings are handled server-side.

```python
import os

from google.adk.agents import Agent

from adk_redis import (
    LLMResponseCache,
    LangCacheProvider,
    LangCacheProviderConfig,
    create_llm_cache_callbacks,
)

provider = LangCacheProvider(
    config=LangCacheProviderConfig(
        cache_id=os.environ["LANGCACHE_CACHE_ID"],
        api_key=os.environ["LANGCACHE_API_KEY"],
        server_url=os.getenv(
            "LANGCACHE_SERVER_URL",
            "https://aws-us-east-1.langcache.redis.io",
        ),
        ttl=3600,
    ),
)

llm_cache = LLMResponseCache(provider=provider)
before_model_cb, after_model_cb = create_llm_cache_callbacks(llm_cache)

root_agent = Agent(
    model="gemini-flash-latest",
    name="cached_agent",
    instruction="You are a helpful assistant with semantic caching enabled.",
    before_model_callback=before_model_cb,
    after_model_callback=after_model_cb,
)
```

Available tools

Search tools

Tool	Description
RedisVectorSearchTool	Vector similarity (KNN) search via RedisVL VectorQuery.
RedisHybridSearchTool	Vector + BM25 hybrid search. Uses native FT.HYBRID on Redis 8.4+; falls back to client-side aggregation otherwise.
RedisRangeSearchTool	Returns all documents within a vector distance threshold.
RedisTextSearchTool	BM25 keyword full-text search. No vectorizer required.
RedisSQLSearchTool	SQL SELECT against a bound index via redisvl.query.SQLQuery. Supports :name parameter placeholders. Requires adk-redis[sql].

MCP

Source	Description
RedisVL MCP server (rvl mcp)	Connect ADK's native McpToolset to a running rvl mcp server. The server exposes search-records (vector / fulltext / hybrid, chosen per server via YAML) and upsert-records, with schema-aware filter and return-field hints derived from the index. Supports stdio, sse, and streamable-http; bearer auth on HTTP; suppress writes with --read-only on the server or tool_filter=["search-records"] on the McpToolset.
Sessions + Memory MCP server	Connect ADK's native McpToolset to Agent Memory Server's MCP endpoint over SSE. Exposes search_long_term_memory, create_long_term_memories, edit_long_term_memory, delete_long_term_memories, and memory_prompt. Runs on a separate port from the REST API.

Memory tools

Tool	Description
MemoryPromptTool	Enrich the agent prompt with relevant memories.
SearchMemoryTool	Search long-term memories by query.
CreateMemoryTool	Store new long-term memories.
UpdateMemoryTool	Update an existing memory by ID.
DeleteMemoryTool	Delete memories by ID.
GetMemoryTool	Fetch a single memory by ID.

Services

Service	Description
RedisWorkingMemorySessionService	BaseSessionService backed by Agent Memory Server working memory. Auto-summarizes when context window is exceeded.
RedisLongTermMemoryService	BaseMemoryService backed by Agent Memory Server long-term memory with recency-boosted semantic search.

Cache providers

Provider	Description
RedisVLCacheProvider	Self-hosted semantic cache via RedisVL SemanticCache. Bring your own vectorizer.
LangCacheProvider	Managed semantic cache via Redis LangCache. Embeddings are handled server-side.

Additional resources

• adk-redis on GitHub
• adk-redis on PyPI
• adk-redis documentation
• ADK + Redis on redis.io
• Runnable examples
• Redis Agent Memory Server
• RedisVL documentation
• Redis LangCache