Skip to content

docs: Add Feast-powered AI agent example with MCP, persistent memory, and blog post #6253

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ntkathole wants to merge 1 commit into
base: master
Choose a base branch
from
Open

docs: Add Feast-powered AI agent example with MCP, persistent memory, and blog post #6253

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions docs/SUMMARY.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,7 @@
* [Retrieval Augmented Generation (RAG) with Feast](tutorials/rag-with-docling.md)
* [RAG Fine Tuning with Feast and Milvus](../examples/rag-retriever/README.md)
* [MCP - AI Agent Example](../examples/mcp_feature_store/README.md)
* [Feast-Powered AI Agent](../examples/agent_feature_store/README.md)

## How-to Guides

Expand Down
17 changes: 16 additions & 1 deletion docs/getting-started/genai.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,17 @@ Implement semantic search by:
2. Converting search queries to embeddings
3. Finding semantically similar documents using vector search

### AI Agents with Context and Memory

Feast can serve as both the **context provider** and **persistent memory layer** for AI agents. Unlike stateless RAG pipelines, agents make autonomous decisions about which tools to call and can write state back to the feature store:

1. **Structured context**: Retrieve customer profiles, account data, and other entity-keyed features
2. **Knowledge retrieval**: Search vector embeddings for relevant documents
3. **Persistent memory**: Store and recall per-entity interaction history (last topic, resolution, preferences) using `write_to_online_store`
4. **Governed access**: All reads and writes are subject to the same RBAC, TTL, and audit policies as any other feature

With MCP enabled, agents built with any framework (LangChain, CrewAI, AutoGen, or custom) can discover and call Feast tools dynamically. See the [Feast-Powered AI Agent example](../../examples/agent_feature_store/) and the blog post [Building AI Agents with Feast](https://feast.dev/blog/feast-agents-mcp/) for a complete walkthrough.

### Scaling with Spark Integration

Feast integrates with Apache Spark to enable large-scale processing of unstructured data for GenAI applications:
Expand Down Expand Up @@ -167,9 +178,11 @@ The MCP integration uses the `fastapi_mcp` library to automatically transform yo
The fastapi_mcp integration automatically exposes your Feast feature server's FastAPI endpoints as MCP tools. This means AI assistants can:

* **Call `/get-online-features`** to retrieve features from the feature store
* **Call `/retrieve-online-documents`** to perform vector similarity search
* **Call `/write-to-online-store`** to persist agent state (memory, notes, interaction history)
* **Use `/health`** to check server status

For a complete example, see the [MCP Feature Store Example](../../examples/mcp_feature_store/).
For a basic MCP example, see the [MCP Feature Store Example](../../examples/mcp_feature_store/). For a full agent with persistent memory, see the [Feast-Powered AI Agent Example](../../examples/agent_feature_store/).

## Learn More

Expand All @@ -181,6 +194,8 @@ For more detailed information and examples:
* [Milvus Quickstart Example](https://github.com/feast-dev/feast/tree/master/examples/rag/milvus-quickstart.ipynb)
* [Feast + Ray: Distributed Processing for RAG Applications](https://feast.dev/blog/feast-ray-distributed-processing/)
* [MCP Feature Store Example](../../examples/mcp_feature_store/)
* [Feast-Powered AI Agent Example (with Memory)](../../examples/agent_feature_store/)
* [Blog: Building AI Agents with Feast](https://feast.dev/blog/feast-agents-mcp/)
* [MCP Feature Server Reference](../reference/feature-servers/mcp-feature-server.md)
* [Spark Data Source](../reference/data-sources/spark.md)
* [Spark Offline Store](../reference/offline-stores/spark.md)
Expand Down
Loading
Loading