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everdreamsoft/sandra
====================

Sandra Ontologic Datagraph

v0.1.1(2mo ago)81.6k3[1 issues](https://github.com/everdreamsoft/sandra/issues)MITPHPPHP >=8.0CI passing

Since Dec 24Pushed 1mo ago2 watchersCompare

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 [![Sandra — meaning before structure. Le sens d'abord, la structure ensuite. A semantic graph database with a native MCP interface. Concepts, entities, and triplets, ready for AI agents that need memory shaped like a brain. THE UNIT: subject — verb — target.](resources/images/Sandra_hero.png)](resources/images/Sandra_hero.png)

 Self-hostable graph + vector memory. Native MCP. **0.89** on [Structured Recall Bench](https://github.com/everdreamsoft/structured-recall-bench), where vector stores cluster between 0.25 and 0.48.

 [![Install](https://camo.githubusercontent.com/599f9b2ee5747fd1b46c1f5e86f693c41cd92ea44a19c4370ba8c683d03efad3/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f696e7374616c6c2d312d2d6d696e2d3142334138433f7374796c653d666f722d7468652d6261646765)](#install) [![Quickstart](https://camo.githubusercontent.com/1aea0f68155ab9548691e7fea5f1ca1823476b50270a6481724c2d02459defe4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f717569636b73746172742d73616e6472616564732e65766572647265616d736f66742e636f6d2d3146323032343f7374796c653d666f722d7468652d6261646765)](https://sandraeds.everdreamsoft.com) [![Discord](https://camo.githubusercontent.com/a123c9abbeaa8a323bfb35654ef115687f1bf0f84e9e983724d1943fd5c9c855/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646973636f72642d6a6f696e2d3146323032343f7374796c653d666f722d7468652d6261646765266c6f676f3d646973636f7264266c6f676f436f6c6f723d7768697465)](https://discord.gg/9ptCvXJzy)

 [ ![Structured Recall Bench scoreboard: Sandra 0.89, vector and verbatim retrievers between 0.25 and 0.48](https://raw.githubusercontent.com/everdreamsoft/structured-recall-bench/main/results/scoreboard.svg) ](https://github.com/everdreamsoft/structured-recall-bench)

---

Every AI agent forgets. Vector memory finds similar text but can't enumerate or relate. Classical graph DBs use free-form labels that no LLM can read. SaaS memory ships your data to someone else's servers.

Sandra is a self-hostable graph + vector memory layer where every concept (verb, label, category) has a unique ID and a human-readable name. Your agents read, write, and reason over a *shared lexicon*. Every relationship is an explicit synapse you can trace. Exposed natively via MCP, so any LLM that can call a tool can use it.

Install
-------

[](#install)

```
git clone https://github.com/everdreamsoft/sandra && cd sandra
docker compose up -d                                   # MCP on http://127.0.0.1:8090/mcp
claude mcp add sandra --transport http --url http://127.0.0.1:8090/mcp
```

That's it! your agent now has persistent memory across sessions, projects, machines, and clients (Claude, GPT, Gemini, Llama, Mistral...).

Other clients · semantic search · claude.ai web (HTTPS) · from source```
cp .env.example .env                          # set OPENAI_API_KEY for semantic search
docker compose --profile tunnel up -d         # public HTTPS via trycloudflare for claude.ai
docker compose logs tunnel | grep trycloudflare
```

Cursor, Cline, Continue, Zed, OpenAI Agents SDK, custom clients: point your client's MCP server config at `http://127.0.0.1:8090/mcp`. From source (PHP 8+, MySQL, composer): see [Install Sandra](https://sandraeds.everdreamsoft.com/getting-started-installation). Set `SANDRA_AUTH_TOKEN` in `.env` before exposing the trycloudflare URL, it is public.

Full setup, OAuth 2.1/PKCE, auth tokens: [Install Sandra](https://sandraeds.everdreamsoft.com/getting-started-installation). MCP tool reference: [mcp overview](https://sandraeds.everdreamsoft.com/mcp-overview). Live quickstart and demos: [sandraeds.everdreamsoft.com](https://sandraeds.everdreamsoft.com). Questions? [Join the Discord](https://discord.gg/9ptCvXJzy).

What recall feels like
----------------------

[](#what-recall-feels-like)

```
─ Monday, ChatGPT on the web ─
You:    CSV of 100 tweets I wrote this year (text, likes, replies).
        Save them in Sandra and tag each with its emotional register.
Agent:  [sandra_batch → 100 tweet entities; refs: text, engagement,
                       emotion (awe / pride / frustration / joy / ...)]

─ Wednesday, Claude Code on your laptop ─
You:    Design a landing page that resonates with the emotions my
        audience reacts to most.
Agent:  [sandra_search → emotion breakdown, weighted by engagement]
        Your audience peaks on awe and pride. Drafting a hero in that
        register, plus a social-proof block citing your three
        highest-engagement tweets...

```

Memory lives in *your* Sandra instance, not in any model's context window. Switch models, switch clients, switch machines: the memory stays.

> *The next generation of software will be written by agents that need memory like ours.*

Why Sandra
----------

[](#why-sandra)

Current market solutions are not able to retrieve all the data correctly, so it is impossible to trust agents with precise tasks. Here is how they compare.

ApproachExampleSRB compositeTrade-offVector retrievalMem0, Zep, Supermemory0.25 to 0.33Finds similar text, can't enumerate or relateVerbatim retrievalMemPalace0.48A static memory palace, no associative recallClassical graph DBNeo4j, TigerGraph(no provider)Free-form labels, unreadable for LLMs**Sandra + planner**this repo**0.89**Graph + vector + shared LLM-readable lexicon130 deterministic questions, no LLM-judge, raw responses archived. Full design rationale: [Sandra memory design](https://sandraeds.everdreamsoft.com/concepts-memory-design).

**Sandra is for you if** you want your agent to remember across sessions and machines, you want to own the data, you build with multiple LLMs, or you need explicit relationships instead of fuzzy similarity. **Skip Sandra if** you need a transactional database (use Postgres), pure vector retrieval at massive scale (use a dedicated vector store), or zero infrastructure (use a hosted service).

How memory works
----------------

[](#how-memory-works)

Four primitives, mapped to what neurons do.

- **Concept** - a stable, named ID; the *neuron* (`likes`, `works_at`, `user`).
- **Triplet** - a `(subject, verb, target)` link; the *synapse*.
- **Entity** - a typed cluster of refs grouped by a factory; a *named region*.
- **Factory** - the schema for an entity cluster (`person`, `product`, `task`).

DB analog: concepts are *words*, entities are *rows*, factories are *tables*, triplets are *sentences*.

```
─── THE UNIT ─────────────────────────────

   Alice ──── likes ──────▶ strawberry
     │
     └─── works_with ─────▶ Bob

   subject     verb            target

```

Encounter `Alice`, Sandra activates that node and follows every synapse outward in one query, *spreading activation* ([Collins & Loftus, 1975](https://doi.org/10.1037/0033-295X.82.6.407)), the mechanism behind associative human recall. Because every concept is a stable, named ID, an LLM can call `sandra_list_concepts` and read the entire vocabulary. No schema file, no documentation needed.

### One concept, many implementations

[](#one-concept-many-implementations)

A concept's identity is stable, but the attributes attached to it can vary by source. `Concept(user)` keeps a single ID across every system that talks about it:

```
Concept(user)
  ─ in_system → marketing_db    refs: { name: "User",      ltv: 4200, segment: "enterprise" }
  ─ in_system → auth_service    refs: { name: "Principal", email: "alice@acme.com", mfa: true }
  ─ in_system → support_crm     refs: { name: "Customer",  tier: "gold", open_tickets: 2 }

```

This is what philosophers [since Frege](https://en.wikipedia.org/wiki/Sense_and_reference) have called *invariance of reference under variation of sense*. What programmers call polymorphism. What your agents need so they can answer "show me everything we know about this user" without lying, conflating, or losing source attribution.

Building a multi-agent system? Every agent is itself an entity in the same graph it reads from. Pattern + comparison with CrewAI / LangGraph / OpenAI Assistants in [Guides - multi-agent](https://sandraeds.everdreamsoft.com/guides-multi-agent).

Beyond agent memory
-------------------

[](#beyond-agent-memory)

Same primitives also handle:

- **Personal knowledge management**: Stash articles, strategy notes, ideas as typed entities. Ask: "Give me the 10 most-engaging emotional tweets I posted; suggest an eleventh."
- **Relationship CRM**: Track who covered you, who knows whom, who said what. "Which journalists wrote about us this year? Who at TechCrunch?"
- **Headless CMS for LLM-driven apps**: Expose Sandra over the REST API + a thin UI; let agents update content from natural language. "Change the price of the Geneva listing to 350."
- **Lightweight analytics**: Log who-viewed-what as triplets, query later via the same MCP tools. "Which cards got the most attention from European visitors last week?"

Switching frame is a query, not a migration.

Building on Sandra
------------------

[](#building-on-sandra)

- **PHP SDK**: `composer require everdreamsoft/sandra` [php sdk](https://sandraeds.everdreamsoft.com/sdk-overview)
- **REST API**: language-agnostic [sdk rest api](https://sandraeds.everdreamsoft.com/sdk-rest-api)
- **Python and TypeScript SDKs**: coming

```