Build an agent with memory

How it works

What it covers

• Store memories with embeddings for semantic search
• Retrieve relevant memories based on conversation context
• Use @pxt.query for retrieval functions

What it uses

Install

terminal

$pip install pixeltable openai

Environment variables it names

OPENAI_API_KEY

Pixeltable calls

openai.chat_completions()pxt.create_table()@pxt.query@pxt.udfadd_embedding_index()similarity()insert()add_computed_column()

A look at the code

# Add embedding index for semantic search on content
memories.add_embedding_index(
    column='content',
    string_embed=embeddings.using(model='text-embedding-3-small'),
)

One cell of the notebook, copied as it is. Open it in Colab for the rest.

• Use a table pipeline as a reusable function

  Convert a table with computed columns into a callable function for multi-agent workflows and pipeline composition.

  Updated Apr 10, 2026Open in Colab

• Look up structured data with retrieval UDFs

  Create lookup functions that query tables by key—for customer records, product catalogs, or financial data.

  Updated Oct 27, 2026Open in Colab

• Use tool calling and MCP servers with LLMs

  Enable LLMs to call functions and tools, then execute the results automatically.

  Updated Oct 16, 2026Open in Colab

• Agentic Patterns

  Build the six architectural patterns of agentic AI (prompt chaining, routing, parallelization, tool use, evaluator-optimizer, orchestrator-worker) and the ReAct and planning strategies as Pixeltable tables and computed columns.

  Updated Sep 3, 2026Open in Colab