MemGPT Has a Free API: Build AI Agents with Unlimited Memory

#ai #python #llm #agents

What is MemGPT?

MemGPT (now Letta) is an open-source framework that gives LLMs unlimited memory through a virtual context management system. Instead of being limited to a fixed context window, MemGPT agents manage their own memory — storing, retrieving, and organizing information like a human would.

Why MemGPT Changes Everything

Every LLM has a context window limit. GPT-4 Turbo has 128K tokens, Claude has 200K. But real applications need:

Persistent memory across conversations — remember users, preferences, past interactions
Infinite context — process documents larger than any context window
Self-organizing memory — AI decides what to remember and what to forget
Tiered storage — hot/warm/cold memory like a real operating system

Quick Start

pip install letta
letta server

from letta import create_client

client = create_client()

# Create an agent with memory
agent = client.create_agent(
    name="research-assistant",
    memory=client.create_block(
        label="human",
        value="User is a DevOps engineer interested in Kubernetes"
    ),
    system="You are a helpful research assistant with perfect memory."
)

# Chat — the agent remembers everything
response = client.send_message(
    agent_id=agent.id,
    message="I'm working on migrating from Docker Compose to Kubernetes"
)
print(response.messages)

# Later conversation — agent remembers the migration context
response = client.send_message(
    agent_id=agent.id,
    message="What was I working on?"
)
# Agent recalls: "You're migrating from Docker Compose to Kubernetes"

Memory Architecture

┌─────────────────────────────────────┐
│          Core Memory (fast)          │
│  - System prompt + persona           │
│  - Current conversation context      │
│  - Key user facts                    │
├─────────────────────────────────────┤
│        Recall Memory (searchable)    │
│  - Full conversation history         │
│  - Semantic search over past chats   │
├─────────────────────────────────────┤
│       Archival Memory (unlimited)    │
│  - Documents, files, knowledge base  │
│  - Vector search with embeddings     │
│  - No size limit                     │
└─────────────────────────────────────┘

Load Documents into Archival Memory

# Upload a large document — MemGPT chunks and indexes it
client.load_file_to_source(
    filename="kubernetes-docs.pdf",
    source_id=source.id
)

# Attach source to agent
client.attach_source_to_agent(
    source_id=source.id,
    agent_id=agent.id
)

# Now the agent can search through the entire document
response = client.send_message(
    agent_id=agent.id,
    message="What does the K8s doc say about pod disruption budgets?"
)

Multi-Agent with Shared Memory

# Create shared memory block
shared_knowledge = client.create_block(
    label="team_knowledge",
    value="Project deadline: March 2026. Stack: Python + FastAPI + PostgreSQL."
)

# Multiple agents share the same memory
researcher = client.create_agent(name="researcher", memory=shared_knowledge)
writer = client.create_agent(name="writer", memory=shared_knowledge)
coder = client.create_agent(name="coder", memory=shared_knowledge)

# When one agent learns something, others can access it

MemGPT vs Alternatives

Feature	MemGPT/Letta	LangChain Memory	Raw API
Unlimited memory	Yes	Buffer only	No
Self-managed	Yes	Manual	Manual
Archival storage	Built-in	External	DIY
Memory search	Semantic	Keyword	None
Multi-agent memory	Shared blocks	Separate	DIY

Real-World Impact

A customer support team replaced their "conversation history" system with MemGPT. Before: agents forgot context after 10 messages. After: agents remembered every customer interaction, preferences, and issue history. Customer satisfaction went from 72% to 91% — because the AI actually remembered who you are.

Need AI systems that never forget? I build production memory-augmented agents. Contact spinov001@gmail.com or check my data tools on Apify.

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