REPOGEO REPORT · LITE

OSU-NLP-Group/TravelPlanner

Default branch main · commit e52c87f4 · scanned 6/11/2026, 5:13:10 AM

GitHub: 520 stars · 78 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface OSU-NLP-Group/TravelPlanner, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Strengthen the README's opening sentence to emphasize its benchmark role

Why:

CURRENT

Code for the Paper "TravelPlanner: A Benchmark for Real-World Planning with Language Agents".

COPY-PASTE FIX

This repository provides the official benchmark and code for "TravelPlanner: A Benchmark for Real-World Planning with Language Agents", designed to evaluate language agents in complex, real-world planning scenarios.

mediumtopics#2

Add more specific topics to improve category matching

Why:

CURRENT

autonomous-agents, language-agent, large-language-models, planning

COPY-PASTE FIX

autonomous-agents, language-agent, large-language-models, planning, benchmark, real-world-planning, agent-evaluation

mediumreadme#3

Add a 'Key Differentiators' section to the README

Why:

COPY-PASTE FIX

## Key Differentiators
TravelPlanner stands out as a benchmark focused on **real-world planning** with language agents, moving beyond simulated environments. Unlike many existing benchmarks that rely on structured inputs or simpler tasks, TravelPlanner emphasizes:
-   **Complex, free-form user preferences:** Agents must interpret nuanced natural language instructions.
-   **Tool-use and multi-step planning:** Requires agents to interact with external tools and execute intricate plans.
-   **Real-world constraints:** Incorporates practical limitations and dynamic information, making it a robust testbed for advanced language agents.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface OSU-NLP-Group/TravelPlanner

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

MuJoCo

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

MuJoCo · recommended 2×
Amazon Mechanical Turk · recommended 1×
Upwork · recommended 1×
ALFWorld · recommended 1×
BabyAI · recommended 1×

CATEGORY QUERY
How to evaluate language agent performance in complex real-world planning scenarios?
you: not recommended
AI recommended (in order):
1. Amazon Mechanical Turk
2. Upwork
3. ALFWorld
4. BabyAI
5. MiniGrid
6. WebArena
7. Playwright
8. Selenium
9. GPT-4
10. AirSim
11. MuJoCo
AI recommended 11 alternatives but never named OSU-NLP-Group/TravelPlanner. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good benchmarks for assessing autonomous agent capabilities in sequential decision-making?
you: not recommended
AI recommended (in order):
1. OpenAI Gym / Gymnasium (Farama-Foundation/Gymnasium)
2. Atari 2600
3. MuJoCo
4. Classic Control
5. DeepMind Lab (deepmind/lab)
6. StarCraft II Learning Environment (SC2LE) / PySC2 (deepmind/pysc2)
7. Meta-World (rlworkgroup/metaworld)
8. Procgen Benchmark (openai/procgen)
9. MineRL (minerllabs/minerl)
10. ALFWorld (askforalfred/alfworld)
11. AI2-THOR (allenai/ai2thor)
AI recommended 11 alternatives but never named OSU-NLP-Group/TravelPlanner. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of OSU-NLP-Group/TravelPlanner?
pass
AI named OSU-NLP-Group/TravelPlanner explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts OSU-NLP-Group/TravelPlanner in production, what risks or prerequisites should they evaluate first?
pass
AI named OSU-NLP-Group/TravelPlanner explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo OSU-NLP-Group/TravelPlanner solve, and who is the primary audience?
pass
AI did not name OSU-NLP-Group/TravelPlanner — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of OSU-NLP-Group/TravelPlanner. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/OSU-NLP-Group/TravelPlanner.svg)](https://repogeo.com/en/r/OSU-NLP-Group/TravelPlanner)

HTML

<a href="https://repogeo.com/en/r/OSU-NLP-Group/TravelPlanner"><img src="https://repogeo.com/badge/OSU-NLP-Group/TravelPlanner.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

OSU-NLP-Group/TravelPlanner — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite