Letta is emerging as a go-to framework for building AI agents that actually remember, and a new hands-on tutorial shows exactly how to build a stateful engineering copilot with it. Rather than relying on prompts alone, the guide walks through a full memory architecture: inspectable core memory, archival vector retrieval, shared state across agents, custom tools, streaming and portable agent exports.
The tutorial, published by MarkTechPost, builds an engineering copilot called Mnemo that keeps durable facts about its user and project, updates its own memory blocks, and retrieves past context semantically instead of starting fresh every session.
Letta (Credits: MarkTechPost)
Letta Core Memory: How the Copilot Remembers
The starting point is core memory blocks: small, inspectable chunks of long-term memory that the agent can read and update on its own. The tutorial sets up a persona block describing the copilot’s behaviour and a human block holding durable facts, then exposes those blocks so they can be synchronised programmatically and shared across multiple agents.
On top of that sits archival memory, a vector store the copilot can search semantically. Instead of re-reading everything it has ever seen, the agent retrieves only the passages relevant to the current task, a design that keeps long-running engineering assistants fast and focused.
Letta Tutorial: Custom Tools, Streaming and Portable Agents
The guide also adds a custom server-side tool for deterministic risk estimation, so the copilot can run repeatable calculations rather than guessing, and streaming support so developers can watch the agent’s execution unfold step by step, including reasoning, tool calls and results.
Finally, the tutorial covers server-side message history for persistent conversations and .af export for portability, meaning a finished agent can be moved and reused rather than rebuilt. Taken together, the pieces form a practical blueprint for anyone building agentic tools that need to persist state, collaborate and stay reliable over time.
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Source: MarkTechPost — read the full story there.
