REPOGEO REPORT · LITE

xlang-ai/OpenAgents

Default branch main · commit ff2e4644 · scanned 5/24/2026, 1:07:31 AM

GitHub: 4,830 stars · 530 forks

AI VISIBILITY SCORE

40 /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

3 / 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 xlang-ai/OpenAgents, 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 README's opening statement with core differentiator

Why:

CURRENT

Current language agent frameworks aim to facilitate the construction of proof-of-concept language agents while neglecting the non-expert user access to agents and paying little attention to application-level designs. We built OpenAgents, an open platform for using and hosting language agents in the wild of everyday life.

COPY-PASTE FIX

OpenAgents is an open, unified platform for building, deploying, and evaluating a diverse range of AI agent types—including single-agent, multi-agent, and human-agent collaboration—with a strong emphasis on built-in evaluation and benchmarking capabilities. It addresses the gap in current language agent frameworks by providing non-expert user access and robust application-level designs for language agents in the wild.

mediumtopics#2

Expand topics to include platform and deployment keywords

Why:

CURRENT

agent, assistant-chat-bots, code-generation, executable-langauge-grounding, gpt, hacktoberfest, language-model, language-model-agent, llm, semantic-parsing, tool-learning, ui

COPY-PASTE FIX

agent, assistant-chat-bots, code-generation, executable-langauge-grounding, gpt, hacktoberfest, language-model, language-model-agent, llm, semantic-parsing, tool-learning, ui, ai-platform, agent-deployment, llm-ops, multi-agent-systems, agent-framework

lowabout#3

Refine repository description for clarity on building and deploying

Why:

CURRENT

[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

COPY-PASTE FIX

[COLM 2024] OpenAgents: An Open Platform for Building, Deploying, and Managing Language Agents in the Wild

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 xlang-ai/OpenAgents

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 1×
LlamaIndex · recommended 1×
Microsoft Semantic Kernel · recommended 1×
Haystack · recommended 1×
OpenAI Assistants API · recommended 1×

CATEGORY QUERY
How can I quickly build and deploy custom language agents with advanced capabilities?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Semantic Kernel
4. Haystack
5. OpenAI Assistants API
6. CrewAI
7. FastAPI
8. Uvicorn
9. Gunicorn
AI recommended 9 alternatives but never named xlang-ai/OpenAgents. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for an open platform to integrate large language models and tools for agent development.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset-ai/haystack)
4. AutoGPT (Significant-Gravitas/AutoGPT)
5. CrewAI (joaomdmoura/crewAI)
6. Microsoft Semantic Kernel (microsoft/semantic-kernel)
AI recommended 6 alternatives but never named xlang-ai/OpenAgents. 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 xlang-ai/OpenAgents?
pass
AI named xlang-ai/OpenAgents explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts xlang-ai/OpenAgents in production, what risks or prerequisites should they evaluate first?
pass
AI named xlang-ai/OpenAgents 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 xlang-ai/OpenAgents solve, and who is the primary audience?
pass
AI named xlang-ai/OpenAgents explicitly
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 xlang-ai/OpenAgents. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/xlang-ai/OpenAgents.svg)](https://repogeo.com/en/r/xlang-ai/OpenAgents)

HTML

<a href="https://repogeo.com/en/r/xlang-ai/OpenAgents"><img src="https://repogeo.com/badge/xlang-ai/OpenAgents.svg" alt="RepoGEO" /></a>

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xlang-ai/OpenAgents — 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