# FalkorDB Grok API Evaluation

**Category:** Graph Databases
**Score:** 77 (B)

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

## Summary

| Metric | Value |
|--------|-------|
| Status | completed |
| Overall score | 77 |
| Grade | B |
| Eval score | 74 |
| Discovery score | 86 |
| Cost | $0.47 |
| Runtime | 2m 13s |
| Tool calls | 19 |
| Errors | 0 |
| Tokens used | 451253 |
| Success | Yes |

## Agent-Readiness Checklist

| Signal | Evidence |
|--------|----------|
| Context7 | https://context7.com/falkordb/falkordb |
| llms.txt | https://docs.falkordb.com/llms.txt |
| MCP server | https://github.com/FalkorDB/FalkorDB-MCPServer |
| Typed SDK | https://github.com/FalkorDB/falkordb-ts |
| OpenAPI | Not found |
| Agent skills | https://github.com/FalkorDB/falkordb-py/blob/master/CLAUDE.md |
| CLI | https://github.com/FalkorDB/falkordb-bulk-loader |

## Evaluation Prompt

````text

# Task
Using https://[REDACTED:TENANT].*****.com/, complete the following task:

Using the FalkorDB 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 FalkorDB Python SDK (pip install) and connect to FalkorDB
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 FalkorDB 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 | No | 0 | 0 | $0.47 ($0.40–0.70, expensive) |
| Time | Yes | 0 | 0 | 133s (2–3min, good) |
| Efficiency | Yes | 0 | 0 | 19 tool calls (≤20, acceptable) |
| Found Docs | Yes | 0 | — | Touched [REDACTED:TENANT] at [REDACTED:TENANT].*****.com: yes (9 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 | 20 |
| Tool call traces | 19 |
| Generated files | 1 |
| Exit code | 0 |
| Completed at | 2026-10-19T16:52:35.898124+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/falkordb