# Memgraph Grok API Evaluation

**Category:** Graph Databases
**Score:** 72 (C)

This is the agent-readable Devtool Arena evaluation page for Memgraph on Grok API.

## Summary

| Metric | Value |
|--------|-------|
| Status | completed |
| Overall score | 72 |
| Grade | C |
| Eval score | 73 |
| Discovery score | 71 |
| Cost | $0.33 |
| Runtime | 7m 42s |
| Tool calls | 14 |
| Errors | 0 |
| Tokens used | 366633 |
| Success | Yes |

## Agent-Readiness Checklist

| Signal | Evidence |
|--------|----------|
| Context7 | https://context7.com/memgraph/gqlalchemy |
| llms.txt | https://memgraph.com/llms.txt |
| MCP server | https://memgraph.com/docs/ai-ecosystem/mcp |
| Typed SDK | Not found |
| OpenAPI | Not found |
| Agent skills | https://github.com/memgraph/skills |
| CLI | https://github.com/memgraph/mgconsole/releases |

## Evaluation Prompt

````text

# Task
Using https://memgraph.com/[REDACTED:TENANT]/client-libraries/python, complete the following task:

Using the Memgraph Python SDK, build me a collaborative-filtering product recommendation engine backed by a graph (the classic "customers who bought this also bought..." feature):

1. Install the official Memgraph Python SDK (pip install) and connect to Memgraph
2. Model the domain as a graph: create User nodes and Product nodes, connected by PURCHASED relationships. Load exactly this data:
   - Alice PURCHASED Laptop, Mouse
   - Bob PURCHASED Laptop, Mouse, Monitor
   - Carol PURCHASED Laptop, Monitor
   - Dave PURCHASED Mouse, Monitor
   - Erin PURCHASED Laptop, Keyboard
3. For the target user Alice, run a SINGLE multi-hop graph traversal query (do NOT pull the data into Python and compute it there) that recommends products:
   - Find "similar users": other users who have purchased at least one product in common with Alice
   - Collect the products those similar users purchased that Alice has NOT already purchased
   - Score each candidate product by how many distinct similar users purchased it
   - Return the candidates ranked by score, highest first
4. Print the result as JSON with fields: user ("Alice"), recommendations (array of objects with fields product and score, sorted by score descending)

With this data the top recommendation must be Monitor (purchased by Bob, Carol, and Dave — 3 of Alice's similar users), ahead of Keyboard (score 1).

You MUST use the official Memgraph Python SDK / client library and express the recommendation logic as a graph query (e.g. Cypher/openCypher traversal) — do not make raw HTTP/REST calls and do not reimplement the traversal in plain Python. 

## Execution
After creating the script, run it to verify it works:
```bash
cd /home/daytona/app && python <your_script>.py
```

The script should print output to stdout. If there are errors, debug and fix them until it runs successfully.

````

## Grader Results

| Check | Passed | Weight | Score | Details |
|-------|--------|--------|-------|---------|
| Cost | Yes | 0 | 0 | $0.33 ($0.20–0.40, good) |
| Time | No | 0 | 0 | 462s (5–10min, very slow) |
| Efficiency | Yes | 0 | 0 | 14 tool calls (≤15, good) |
| Found Docs | Yes | 0 | — | Touched [REDACTED:TENANT] at memgraph.com: yes (4 matching tool calls) |
| Zero Errors | Yes | 0 | — | No errors in tool outputs |
| Syntax Valid | Yes | 0 | — | Code compiles/parses correctly |
| Created Files | Yes | 0 | — | Generated files: 1 (need ≥1) |
| Full Execution | Yes | 0 | — | Method: full_execution, Exit: 0 |

## Run Artifacts

| Artifact | Value |
|----------|-------|
| Conversation turns | 15 |
| Tool call traces | 14 |
| Generated files | 1 |
| Exit code | 0 |
| Completed at | 2026-10-19T16:27:31.289289+00:00 |

### Generated Files

- /home/daytona/app/recommend.py

## Related Pages

- [Grok API leaderboard](/grok/api)
- [Compare Grok API companies](/grok/api/compare)
- [Agent Landscape](/discoverability)

Canonical URL: https://devtoolarena.com/grok/api/memgraph