Editorial guide · 3 tools compared

Best Agent Memory and Document Workflow Platforms

These products all sit under the AI builder umbrella, but they solve different hard problems. Letta is about agents that remember and maintain state. LlamaIndex is about turning difficult private data and documents into systems agents can actually use. LangGraph sits between them as a graph-shaped orchestration layer for teams that want more control over flow design and state management.

Help builders choose between stateful memory-first agent systems and document-centered workflow platforms based on where the real complexity lives in their product.

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What to look for

Key considerations before you choose.

Letta is the memory-first choice in this comparison.
LlamaIndex is the document-workflow choice in this comparison.
LangGraph is the orchestration-control choice in this comparison.

Deep dive

How this category actually breaks down.

The category splits cleanly between memory and data workflows

Teams often treat agent tooling as one market, but it is more useful to divide it into two real jobs. Some products are built around persistent memory and long-lived agent behavior. Others are built around retrieval, parsing, extraction, and orchestration over private data. The right choice depends on which problem is more central to the product.

That is why Letta and LlamaIndex feel so different in practice. Letta is the memory-first choice. LlamaIndex is the document-and-data workflow choice. LangGraph is often the fit when the team wants a more explicit orchestration layer without buying into a document platform or memory platform as strongly.

Choose Letta when memory and long-lived agent state are the hard part.
Choose LlamaIndex when parsing, indexing, and acting over documents is the hard part.
Choose LangGraph when orchestration control is the hard part.

Pricing reveals what each vendor thinks it is selling

LlamaIndex prices like a commercial document workflow platform built on top of a broader open-source ecosystem. Letta prices like an agent platform where monthly plans and usage-based behavior both matter. LangGraph, through the broader LangChain commercial stack, points buyers toward tracing, deployment, and platform services layered on top of development workflows.

That matters because pricing is not just billing. It tells you where the product expects value to be created. If the bill is credit-shaped, the product probably assumes document or workflow throughput matters. If it is usage-shaped around agents and execution, the product expects stateful behavior to matter.

Credit-shaped pricing usually means document and workflow throughput is central.
Usage-shaped agent pricing usually means memory and execution behavior are central.
Do not compare these tools as if they are solving the exact same problem.

Buying guide

Choose based on the job, not the hype.

Choose Letta for memory-first agents

It is strongest when durable memory and long-lived state are core to the agent experience.

Choose LlamaIndex for document workflows

It stands out when difficult documents, private data, and retrieval pipelines are the operational center of gravity.

Choose LangGraph for orchestration control

It fits best when teams want explicit graph-shaped flow control without centering the stack on one vendor's document platform.

Recommended tools

Curated picks for this workflow.

Top pick

Developer ToolsStateful agent platform

Stateful agent platform for memory-heavy AI systems, with hosted plans for personal and professional use plus usage-based charges for active agents and tool execution.

Why it stands out

Choose Letta when durable memory and stateful agent behavior are the product requirement, not a side feature.

LettaAgent memory

Developer ToolsDocument workflow platform

AI agent and document-workflow platform that combines open-source orchestration with commercial LlamaCloud parsing, extraction, indexing, and agent infrastructure.

Why it stands out

Choose LlamaIndex when the hardest part of the system is parsing, indexing, and acting over difficult private documents.

LlamaIndexLlamaCloud

Developer ToolsStateful agent orchestration

Low-level orchestration framework from LangChain for building stateful, long-running agents with explicit control over graphs, memory, and checkpoints.

Why it stands out

Choose LangGraph when long-running state, branching, and operational control are central requirements.

LangGraphAgents

Quick comparison

Letta vs LlamaIndex

Full comparison

Overview

Stateful agent platform for memory-heavy AI systems, with hosted plans for personal and professional use plus usage-based charges for active agents and tool execution.

AI agent and document-workflow platform that combines open-source orchestration with commercial LlamaCloud parsing, extraction, indexing, and agent infrastructure.

Best for

Stateful AI agents
Agent memory systems
Persistent personalized assistants

Document parsing and extraction
Enterprise retrieval and indexing
Agent workflows over private data

Strengths

Clearer memory-first positioning than most general agent platforms
Public pricing reveals real usage mechanics instead of hiding them behind sales calls
Compelling for builders who want statefulness to be a first-class part of agent design

Strong bridge between open-source developer adoption and commercial document workflow tooling
Clear fit for teams whose AI systems depend on difficult documents and enterprise data
Pricing is more concrete than many AI infrastructure products at a similar stage

Not ideal for

Teams whose main bottleneck is document parsing rather than memory and state
Buyers looking for a broad API development or model-routing platform
Organizations that want simple flat pricing with no usage-metered agent behavior underneath

Teams that only need a lightweight prompt UI or model router
Buyers looking for a consumer AI app rather than builder infrastructure
Organizations without document, retrieval, or data-connected workflow complexity

Pricing

Paid only

Letta's public pricing currently centers on Pro at $20/month, Max Lite at $100/month, and Max at $200/month, with separate usage-based charges shown for active agents and tool execution.

Verified 2026-04-03 · Official pricing

Free plan available

LlamaIndex currently presents Free at $0/month, Starter at $50/month, Pro at $500/month, and Enterprise as custom pricing through its LlamaCloud pricing page.

Verified 2026-04-03 · Official pricing

Editor notes

Letta should be framed around stateful memory and long-lived agent behavior. That is the category-defining distinction.

Choose Letta when durable memory and stateful agent behavior are the product requirement, not a side feature.
LlamaIndex is stronger when the dominant problem is document parsing, extraction, and data-connected workflows.
OpenHands is stronger when the target experience is an AI coding agent rather than a general memory platform.
The pricing should stay explicit about the mix of monthly plan fees and usage-based charges.

LlamaIndex should be framed as a combined OSS-plus-platform decision. The record should acknowledge both the open-source framework and the commercial LlamaCloud workflow layer.

Choose LlamaIndex when the hardest part of the system is parsing, indexing, and acting over difficult private documents.
Letta is stronger when the center of gravity is stateful memory and long-lived agent behavior rather than document pipelines.
LangGraph is stronger when the team wants graph-shaped orchestration and state management without the same document-platform emphasis.
The pricing page is really a LlamaCloud pricing surface, so that distinction should stay explicit in the copy.

Visit Letta

Visit LlamaIndex

Tool-by-tool view

Where each option is strongest.

Letta

Letta is compelling when persistent memory is part of the product promise and not just a nice-to-have feature bolted onto a chat agent.

Best for: Teams building stateful assistants, memory-heavy agents, and personalized long-lived AI workflows.

Tradeoffs: Its pricing and value model are more usage-shaped than simple SaaS subscriptions.

LlamaIndex

LlamaIndex is strongest when the product needs to parse, index, and act over messy private documents and enterprise data at scale.

Best for: Teams building retrieval-heavy assistants, enterprise knowledge systems, and document automation.

Tradeoffs: The commercial story is tied to LlamaCloud, so buyers need to understand the OSS-plus-platform split.

LangGraph

LangGraph is useful when teams want direct control over graph-based orchestration and state transitions in agent systems.

Best for: Builders who want orchestration flexibility and tight control over agent flow design.

Tradeoffs: It is less singularly focused on document workflows or memory-first identity than the other two.

Bottom line

The shortest honest answer.

If the product needs memory, start with Letta. If it needs documents and private data pipelines, start with LlamaIndex. If it needs orchestration control, start with LangGraph.

FAQ

Questions people usually have before they choose.

Are memory platforms and document workflow platforms the same category?

Not really. They both support agents, but they solve different bottlenecks. Memory platforms focus on persistent state and personalization. Document workflow platforms focus on turning messy private data into reliable inputs for agents and applications.

Can a team use both Letta and LlamaIndex together?

Yes. A team might use LlamaIndex for parsing and retrieval over private documents and Letta for memory-rich agent behavior on top of that data. The right architecture depends on whether the team wants to combine best-of-breed layers or standardize more heavily on one platform.

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Best Agent Memory and Document Workflow Platforms

Help builders choose between stateful memory-first agent systems and document-centered workflow platforms based on where the real complexity lives in their product.