docs: add Valkey storage and vector database documentation

_snippets/db-valkey-params.mdx

@@ -0,0 +1,22 @@
+| Parameter         | Type                                               | Default            | Description                                                                                          |
+| ----------------- | -------------------------------------------------- | ------------------ | ---------------------------------------------------------------------------------------------------- |
+| `id`              | `Optional[str]`                                    | -                  | The ID of the database instance. UUID by default.                                                    |
+| `valkey_client`   | `Optional[Union[GlideClient, GlideClusterClient]]` | -                  | Pre-configured Valkey GLIDE client. If not provided a new client will be created.                    |
+| `host`            | `str`                                              | `"localhost"`      | Valkey server host.                                                                                  |
+| `port`            | `int`                                              | `6379`             | Valkey server port.                                                                                  |
+| `database_id`     | `Optional[int]`                                    | -                  | Logical database index (e.g. 0-15).                                                                  |
+| `username`        | `Optional[str]`                                    | -                  | Username for authentication.                                                                         |
+| `password`        | `Optional[str]`                                    | -                  | Password for authentication.                                                                         |
+| `use_tls`         | `bool`                                             | `False`            | Enable TLS encryption.                                                                               |
+| `request_timeout` | `Optional[int]`                                    | -                  | Milliseconds to wait for a request to complete. If unset, the GLIDE client default (250 ms) applies. |
+| `db_prefix`       | `str`                                              | `"agno"`           | Prefix for all Valkey keys.                                                                          |
+| `client_name`     | `str`                                              | `"agno_db_client"` | Connection name, visible in `CLIENT LIST`.                                                           |
+| `expire`          | `Optional[int]`                                    | -                  | TTL for Valkey keys in seconds.                                                                      |
+| `session_table`   | `Optional[str]`                                    | -                  | Name of the table to store sessions.                                                                 |
+| `memory_table`    | `Optional[str]`                                    | -                  | Name of the table to store memories.                                                                 |
+| `metrics_table`   | `Optional[str]`                                    | -                  | Name of the table to store metrics.                                                                  |
+| `eval_table`      | `Optional[str]`                                    | -                  | Name of the table to store evaluation runs.                                                          |
+| `knowledge_table` | `Optional[str]`                                    | -                  | Name of the table to store knowledge documents.                                                      |
+| `traces_table`    | `Optional[str]`                                    | -                  | Name of the table to store traces.                                                                   |
+| `spans_table`     | `Optional[str]`                                    | -                  | Name of the table to store spans.                                                                    |
+| `learnings_table` | `Optional[str]`                                    | -                  | Name of the table to store learnings.                                                                |

_snippets/vectordb_valkey_params.mdx

@@ -0,0 +1,20 @@
+| Parameter | Type | Default | Description |
+| --------- | ---- | ------- | ----------- |
+| `index_name` | `str` | Required | Name of the Valkey search index to store vector data |
+| `host` | `str` | `"localhost"` | Valkey server host |
+| `port` | `int` | `6379` | Valkey server port |
+| `username` | `Optional[str]` | `None` | Username for authentication |
+| `password` | `Optional[str]` | `None` | Password for authentication |
+| `use_tls` | `bool` | `False` | Enable TLS encryption |
+| `database_id` | `Optional[int]` | `None` | Logical database index (e.g. 0-15) |
+| `request_timeout` | `Optional[int]` | `None` | Milliseconds to wait for a request to complete. If unset, the GLIDE client default (250 ms) applies |
+| `client_name` | `str` | `"agno_vectordb_client"` | Connection name, visible in `CLIENT LIST` |
+| `glide_client` | `Optional[GlideClient]` | `None` | Pre-configured Valkey GLIDE client instance |
+| `embedder` | `Optional[Embedder]` | `None` | Embedder instance to generate embeddings (defaults to `OpenAIEmbedder()` when unset) |
+| `search_type` | `SearchType` | `SearchType.vector` | Type of search to perform (vector, keyword) |
+| `distance` | `Distance` | `Distance.cosine` | Distance metric (cosine, l2, max_inner_product) |
+| `vector_algorithm` | `str` | `"HNSW"` | Vector index algorithm (HNSW or FLAT) |
+| `reranker` | `Optional[Reranker]` | `None` | Reranker for search results |
+| `id` | `Optional[str]` | `None` | Optional custom ID. If not provided, an ID will be generated |
+| `name` | `Optional[str]` | `None` | Optional name for the vector database |
+| `description` | `Optional[str]` | `None` | Optional description for the vector database |

database/providers/overview.mdx

@@ -87,6 +87,14 @@ Agno supports the following database providers organized by category:
   >
     Redis in-memory data store integration.
   </Card>
+  <Card
+    title="Valkey"
+    icon="database"
+    iconType="duotone"
+    href="/database/providers/valkey/overview"
+  >
+    Valkey in-memory data store integration.
+  </Card>
   <Card
     title="DynamoDB"
     icon="aws"

database/providers/valkey/overview.mdx

@@ -0,0 +1,38 @@
+---
+title: Valkey
+description: Use Valkey for agent session storage and persistence.
+sidebarTitle: Overview
+---
+
+Agno supports using [Valkey](https://valkey.io/) as a database with the `ValkeyDb` class.
+
+## Usage
+
+### Run Valkey
+
+Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Valkey** on port **6379** using:
+
+```bash
+docker run -d \
+  --name my-valkey \
+  -p 6379:6379 \
+  valkey/valkey
+```
+
+```python valkey_for_agent.py
+from agno.agent import Agent
+from agno.db.valkey import ValkeyDb
+
+# Initialize Valkey db
+db = ValkeyDb(
+    host="localhost",
+    port=6379,
+)
+
+# Create agent with Valkey db
+agent = Agent(db=db)
+```
+
+## Params
+
+<Snippet file="db-valkey-params.mdx" />

database/providers/valkey/usage/valkey-for-agent.mdx

@@ -0,0 +1,57 @@
+---
+title: Valkey for Agent
+sidebarTitle: Agent
+---
+
+Agno supports using Valkey as a storage backend for Agents using the `ValkeyDb` class.
+
+## Usage
+
+### Run Valkey
+
+Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Valkey** on port **6379** using:
+
+```bash
+docker run -d \
+  --name my-valkey \
+  -p 6379:6379 \
+  valkey/valkey
+```
+
+```python valkey_for_agent.py
+from agno.agent import Agent
+from agno.db.base import SessionType
+from agno.db.valkey import ValkeyDb
+from agno.tools.hackernews import HackerNewsTools
+
+# Initialize Valkey db
+db = ValkeyDb(
+    host="localhost",
+    port=6379,
+)
+
+# Create agent with Valkey db
+agent = Agent(
+    db=db,
+    tools=[HackerNewsTools()],
+    add_history_to_context=True,
+)
+
+agent.print_response("How many people live in Canada?")
+agent.print_response("What is their national anthem called?")
+
+# Verify db contents
+print("\nVerifying db contents...")
+all_sessions = db.get_sessions(session_type=SessionType.AGENT)
+print(f"Total sessions in Valkey: {len(all_sessions)}")
+
+if all_sessions:
+    print("\nSession details:")
+    session = all_sessions[0]
+    print(f"The stored session: {session}")
+
+```
+
+## Params
+
+<Snippet file="db-valkey-params.mdx" />

database/providers/valkey/usage/valkey-for-team.mdx

@@ -0,0 +1,81 @@
+---
+title: Valkey for Team
+sidebarTitle: Team
+---
+
+Agno supports using Valkey as a storage backend for Teams using the `ValkeyDb` class.
+
+## Usage
+
+### Run Valkey
+
+Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Valkey** on port **6379** using:
+
+```bash
+docker run -d \
+  --name my-valkey \
+  -p 6379:6379 \
+  valkey/valkey
+```
+
+```python valkey_for_team.py
+"""
+Run: `uv pip install openai agno valkey-glide-sync` to install the dependencies
+"""
+
+from typing import List
+
+from agno.agent import Agent
+from agno.db.valkey import ValkeyDb
+from agno.models.openai import OpenAIResponses
+from agno.team import Team
+from agno.tools.hackernews import HackerNewsTools
+from pydantic import BaseModel
+
+db = ValkeyDb(
+    host="localhost",
+    port=6379,
+)
+
+class Article(BaseModel):
+    title: str
+    summary: str
+    reference_links: List[str]
+
+hn_researcher = Agent(
+    name="HackerNews Researcher",
+    model=OpenAIResponses(id="gpt-5.2"),
+    role="Gets top stories from hackernews.",
+    tools=[HackerNewsTools()],
+)
+
+web_searcher = Agent(
+    name="Web Searcher",
+    model=OpenAIResponses(id="gpt-5.2"),
+    role="Searches the web for information on a topic",
+    tools=[HackerNewsTools()],
+    add_datetime_to_context=True,
+)
+
+hn_team = Team(
+    name="HackerNews Team",
+    model=OpenAIResponses(id="gpt-5.2"),
+    members=[hn_researcher, web_searcher],
+    db=db,
+    instructions=[
+        "First, search hackernews for what the user is asking about.",
+        "Then, ask the web searcher to search for each story to get more information.",
+        "Finally, provide a thoughtful and engaging summary.",
+    ],
+    output_schema=Article,
+    markdown=True,
+    show_members_responses=True,
+)
+
+hn_team.print_response("Write an article about the top 2 stories on hackernews")
+
+```
+
+## Params
+
+<Snippet file="db-valkey-params.mdx" />

database/providers/valkey/usage/valkey-for-workflow.mdx

@@ -0,0 +1,94 @@
+---
+title: Valkey for Workflow
+sidebarTitle: Workflow
+---
+
+Agno supports using Valkey as a storage backend for Workflows using the `ValkeyDb` class.
+
+## Usage
+
+### Run Valkey
+
+Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Valkey** on port **6379** using:
+
+```bash
+docker run -d \
+  --name my-valkey \
+  -p 6379:6379 \
+  valkey/valkey
+```
+
+```python valkey_for_workflow.py
+"""
+Run: `uv pip install openai agno valkey-glide-sync fastapi` to install the dependencies
+"""
+from agno.agent import Agent
+from agno.db.valkey import ValkeyDb
+from agno.models.openai import OpenAIResponses
+from agno.team import Team
+from agno.tools.hackernews import HackerNewsTools
+from agno.workflow.step import Step
+from agno.workflow.workflow import Workflow
+
+# Define agents
+hackernews_agent = Agent(
+    name="Hackernews Agent",
+    model=OpenAIResponses(id="gpt-5.2"),
+    tools=[HackerNewsTools()],
+    role="Extract key insights and content from Hackernews posts",
+)
+web_agent = Agent(
+    name="Web Agent",
+    model=OpenAIResponses(id="gpt-5.2"),
+    tools=[HackerNewsTools()],
+    role="Search the web for the latest news and trends",
+)
+
+# Define research team for complex analysis
+research_team = Team(
+    name="Research Team",
+    members=[hackernews_agent, web_agent],
+    instructions="Research tech topics from Hackernews and the web",
+)
+
+content_planner = Agent(
+    name="Content Planner",
+    model=OpenAIResponses(id="gpt-5.2"),
+    instructions=[
+        "Plan a content schedule over 4 weeks for the provided topic and research content",
+        "Ensure that I have posts for 3 posts per week",
+    ],
+)
+
+# Define steps
+research_step = Step(
+    name="Research Step",
+    team=research_team,
+)
+
+content_planning_step = Step(
+    name="Content Planning Step",
+    agent=content_planner,
+)
+
+# Create and use workflow
+if __name__ == "__main__":
+    content_creation_workflow = Workflow(
+        name="Content Creation Workflow",
+        description="Automated content creation from blog posts to social media",
+        db=ValkeyDb(
+            host="localhost",
+            port=6379,
+        ),
+        steps=[research_step, content_planning_step],
+    )
+    content_creation_workflow.print_response(
+        input="AI trends in 2024",
+        markdown=True,
+    )
+
+```
+
+## Params
+
+<Snippet file="db-valkey-params.mdx" />