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AI Agents & MCP: Give Your Agents Persistent Multimodal Memory

Build AI agents with durable memory and tool-calling capabilities using Pixeltable and Model Context Protocol (MCP). Store conversations, images, and documents as queryable tables that agents can read from and write to.

Quick Start View on GitHub Documentation

The Challenge

AI agents lose context between sessions, struggle with multimodal data, and have no durable memory. Building tool-calling agents requires wiring together separate systems for memory, retrieval, and data storage. MCP servers need a backend that can handle structured data, media, and semantic search.

The Solution

Pixeltable serves as the persistent memory and data layer for AI agents. Tables store conversations, media, and structured data. Computed columns handle embeddings and AI inference. MCP integration exposes tables as tools that agents can query, insert into, and search semantically.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 4

Agent Memory Table

Create a persistent memory store for agent conversations and context.

python

1import pixeltable as pxt
2from pixeltable.functions.huggingface import sentence_transformer
3
4# Persistent agent memory
5memory = pxt.create_table('app.agent_memory', {
6    'session_id': pxt.String,
7    'role': pxt.String,        # 'user', 'assistant', 'tool'
8    'content': pxt.String,
9    'timestamp': pxt.Timestamp,
10    'metadata': pxt.Json,
11})
12
13# Semantic search over memory
14memory.add_embedding_index(
15    'content',
16    string_embed=sentence_transformer.using(
17        model_id='sentence-transformers/all-MiniLM-L6-v2'
18    )
19)
20
21# Agents can now search their own history
22relevant = memory.select(
23    memory.content, memory.role, memory.session_id
24).order_by(
25    memory.content.similarity('what did we discuss about pricing?'),
26    asc=False
27).limit(10)

Unlike in-memory chat history, this memory persists across sessions and supports semantic retrieval.

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Key Benefits

Persistent multimodal memory that survives across sessions

Semantic search over agent history and knowledge bases

MCP integration — works with Claude, GPT, and any MCP-compatible agent

Multimodal tools — agents work with images, video, audio, and documents

Automatic indexing keeps agent knowledge always current

Real Applications

Customer support agents with persistent context

Research assistants with document retrieval

Content creation agents with media search

DevOps agents with log analysis and monitoring

Sales agents with CRM data and document access

Prerequisites

Familiarity with AI agents and LLMs

Basic Python programming

Python 3.9+

MCP-compatible agent (Claude Desktop, etc.)

API keys for embedding models

Performance

Setup Time

To working MCP server with memory + search

15 min

Code Reduction

vs building custom agent infrastructure

90%

Learn More

Pixeltable MCP Servers

Building MCP servers with Pixeltable

AI Agents with Persistent Memory

Architectural patterns for agent memory

Model Context Protocol

Official MCP specification

Build an end-to-end video analysis system with Pixeltable. Ingest video, extract frames, run multimodal AI models, generate embeddings, and enable semantic search — all as computed columns on a table.

Production RAG: From Documents to Answers in One System

Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers — no vector database or orchestrator required.

Computer Vision Pipeline: Object Detection, Classification, and Search

Build optimized computer vision workflows with Pixeltable. Run YOLOX, CLIP, and custom models as computed columns with automatic batching, caching, and incremental processing.

Ready to Get Started?

Install Pixeltable and start building in minutes. One pip install, no infrastructure to manage.

View on GitHub Quick Start Guide Starter Kit