Internode vs Letta: which memory layer should your AI agent use?

Letta is the best stateful agent runtime for teams building a custom single-agent system from scratch with clean memory-management APIs and editable memory blocks. Internode is the team-scoped memory layer for agents that need structured records, a clear trail from every memory back to the conversation that produced it, and ingestion from real meetings, calls, email, and chat. Pick Letta for the agent runtime. Pick Internode when the agent needs to reason over what a team has decided together.

Side-by-side on the axes that decide your agent’s memory layer

Axis	Internode	Letta
Scope of memory	Memory is owned by the organization so one agent can reason over what a whole team has decided, committed to, and discussed	Memory is scoped to a single stateful agent instance through core and archival memory blocks; cross-agent team reasoning is outside the runtime’s shape
Structure of what is stored	Distinct records for topics, tasks, decisions, and goals, each with defined fields and real connections between them	Core memory blocks hold free-form text the agent edits, plus archival memory as embedded passages; structured records are not the storage model
Decision-to-source trail	Every memory traces back to the meeting, call, or message that produced it, with the person who agreed, the reasoning, and any earlier decision it replaced	Memory blocks carry metadata; there is no structured link from a memory to the person who agreed or the prior decision it replaced
Ingestion from real conversations	Reads Zoom, Google Meet, phone calls, email, and Slack transcripts and pulls the relevant records out automatically	Memory enters through the agent’s own tool calls during a run, typically when the agent decides to persist a passage; a meeting-or-call ingestion pipeline is not provided
Human-in-the-loop approval	Every change the agent suggests is a proposal you approve or edit first, including compound changes that create a decision, the tasks it sets in motion, and the topic in one approval	The agent edits memory blocks during its run; an approval step for a human before the write lands is not in the runtime’s default loop
Two-way sync to operational tools	Two-way sync to Linear and Jira so the memory and the operational tools stay consistent automatically	Letta provides tools the agent can call; integrations to Linear and Jira are left to the developer to implement
Search shape	Combines meaning-based search across documents and sections with a structured search that returns tasks, decisions, topics, and goals as records with their fields	Archival memory search over embedded passages filtered by the agent’s own memory blocks; search returns text-style passages, not structured records with their fields
Survival across turnover	Memory is owned by the organization and survives when individual users leave the team	Memory is commonly keyed on the agent instance; when an agent is re-initialized, its memory blocks persist, but a team layer is not the unit of survival

When to choose Internode

• Your agent has to answer “why did we pick this vendor last quarter?” across three different users’ Zoom calls. Internode returns one decision with the reasoning behind it and the person who agreed.
• Your agent proposes a change to twenty tasks spread across two projects. Internode turns this into a single approval the user edits or accepts before it saves.
• Your agent needs to read a phone call on Monday and a Slack thread on Tuesday and reason over both. Internode pulls the records out of both sources and recognizes them as the same work.
• Your agent’s output needs to flow into Linear or Jira so the engineering team actually sees the task. Internode syncs two-way and keeps the decision history and the ticket system in agreement.

Where Letta wins

Letta is the cleanest stateful agent runtime for a team building a custom single-agent system from scratch. If your goal is to prototype a research assistant, a coding sidekick, or a specialized agent with explicit core and archival memory blocks, editable by the model itself, and you want a runtime that exposes those blocks with a clean API, Letta is built for exactly that shape. The ReAct-plus-memory-management loop is the cleanest open implementation of that pattern. The trade-off is that Letta treats memory as blocks inside a single agent’s runtime and assumes the agent writes and revises its own memory during its loop. Internode treats memory as a team-scoped record of decisions, tasks, topics, and goals, pulled from the conversations themselves and changed through an approval flow. That is a broader scope than a single-agent runtime can cover.

Bottom line

Pick Letta for a custom single-agent runtime with clean memory-management APIs and editable memory blocks. Pick Internode when the agent has to reason over a team’s shared memory of decisions, tasks, and commitments, grounded in real meetings and calls, with human-approved changes and two-way sync to Linear and Jira. For the broader category view, read building memory for AI agents and what is organizational memory. For the retrieval story specifically, see when RAG is not enough. Start at app.internode.ai.