Show HN: Mnemo – local-first AI memory layer for any LLM...

Show HN: Mnemo – local‑first AI memory layer for any LLM (Rust + SQLite + petgraph)

Q: Q4. How do I visualize the knowledge graph generated by Mnemo for stakeholder dashboards?

A: Export the graph to DOT format using petgraph’s dot::Dot helper, then render with Graphviz or import the edge list into tools like Gephi or Neo4j for interactive dashboards.

Over 80 % of data analysts say they spend more time hunting for context than actually analyzing data. Mnemo flips that script by giving every LLM a fast, searchable “brain” that lives on your laptop, turning chaotic prompts into a structured, query‑able knowledge graph. Imagine pulling a single dashboard that instantly recalls every insight you ever asked a model about—no cloud, no latency, just pure local‑first power.

What Is Mnemo and Why It Matters for Data Analysis

Mnemo is a lightweight memory layer that stores every LLM interaction in a local SQLite database and stitches them together with a petgraph‑based knowledge graph. In my experience, the biggest pain point for analysts is the *context* loop—pulling the same data, re‑asking the model, and trying to trace where a particular insight came from. Mnemo cuts that loop in half.

Because everything lives on your machine, you keep full control of the data. No external API, no hidden costs, no privacy worries. For a finance team that needs to audit every forecast, or a product squad that wants to keep feature impact stories confidential, that sovereignty is a game changer.

From a business perspective, the benefits are threefold:

Speed. Repeat‑query cycles finish in milliseconds because the data is local.
Auditability. Every prompt, response, timestamp, and edge is persisted for future compliance.
Cost. You avoid the per‑token charges that cloud LLM providers impose on endless experimentation.

Architecture Deep‑Dive: Rust, SQLite, and petgraph

Rust is the engine behind Mnemo. Its zero‑cost abstractions allow the memory layer to stay snappy even with millions of nodes. What I love about Rust is that you get safety without sacrificing speed—ideal for a data‑heavy environment.

SQLite is the storage engine. It's ACID compliant, ship‑it, and perfect for the kind of read‑heavy queries analysts run. You can snapshot the DB overnight and restore it with a single command.

petgraph gives us a graph data structure that maps prompts, responses, and metadata as nodes, and links them with edges that capture time, similarity, or semantic relationships. Think of it as a living, breathing map of your analytical conversations.

Practical Walkthrough: Adding, Querying, and Visualizing Memory

Below is a minimal Rust snippet that shows how to bootstrap a Mnemo DB, store a prompt/response pair, and then walk the graph to pull related insights.

use mnemo::prelude::*;
use std::str::FromStr;

fn main() {
    // 1. Initialize the database
    let db = Mnemo::new("analytics.db").expect("DB init failed");

    // 2. Insert a prompt/response pair
    let prompt = "What was Q3 revenue for product X?";
    let response = "Q3 revenue for product X was $12.4M, up 8% YoY.";
    db.add_interaction(prompt, response).expect("Insert failed");

    // 3. Retrieve related nodes via a simple traversal
    let related = db
        .find_similar(prompt, 0.8) // cosine similarity threshold
        .expect("Traversal failed");

    for node in related {
        println!("Found related insight: {}", node.content);
    }
}

Now that you have the graph, export it to DOT and render with Graphviz:

use petgraph::dot::Dot;
let dot = Dot::with_config(&graph, &[]);
println!("{}", dot);

This gives you a quick visual snapshot that can be dropped into a Power BI report or shared as a PNG with stakeholders.

For KPI‑style reporting, run a simple SQLite query:

SELECT prompt, COUNT(*) as hits
FROM interactions
GROUP BY prompt
ORDER BY hits DESC
LIMIT 10;

That single line tells you the most frequent questions—great for spotting analysis bottlenecks.

Real‑World Use Cases for Business Professionals

Customer‑support analytics. When a ticket comes in, the support rep can pull the entire conversation history, model suggestions, and resolution outcomes—all from the local graph.
Financial reporting. Store model‑generated forecasts and trace every assumption back to its original prompt. Compliance teams love that traceability.
Product intelligence. Build a live “feature‑impact” graph that analysts can explore without writing SQL. They just click a node and see related metrics, feedback, and model predictions.

Actionable Takeaways & Next Steps

Quick start checklist: install Rust, clone Mnemo, run the demo. If you're already using Tableau or Looker, export query results to CSV or JSON and drop them into your dashboard. When you hit the 2‑million node mark, consider sharding the SQLite file or spinning a small Rust service to handle heavy graph analytics.

Scale responsibly: back up nightly, enable SQLite encryption, and keep your OS-level access controls tight. If your team outgrows local‑first, Mnemo can mesh with a hybrid cloud model—just point the SQLite file to a shared drive.

Frequently Asked Questions

Q1. How does Mnemo improve data analysis workflows compared to using a plain LLM API?

A: Mnemo stores every prompt and response locally, letting analysts replay, filter, and aggregate model output without re‑issuing costly API calls. This creates a permanent audit trail and speeds up iterative analysis.

Q2. Can I use Mnemo with any LLM, such as OpenAI, Anthropic, or locally hosted models?

A: Yes. Mnemo is LLM‑agnostic; it only requires you to pass the raw request/response payloads, which it then persists in SQLite and links in the graph.

Q3. Is the memory layer secure for confidential business data?

A: Because the database lives on your machine, you control encryption (SQLite supports AES encryption extensions) and can enforce OS‑level access controls, eliminating third‑party data exposure.

Q4. How do I visualize the knowledge graph generated by Mnemo for stakeholder dashboards?

A: Export the graph to DOT format using petgraph’s dot::Dot helper, then render with Graphviz or import the edge list into tools like Gephi or Neo4j for interactive dashboards.

Q5. What are the performance limits—how many nodes/edges can Mnemo handle before it slows down?

A: Benchmarks show smooth operation up to ~2 million nodes on a typical laptop (SSD, 16 GB RAM). Beyond that, consider sharding the SQLite file or moving the heavy graph queries to a dedicated Rust service.

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