REPOGEO REPORT · LITE

lupantech/AgentFlow

Default branch main · commit b9400643 · scanned 6/27/2026, 1:47:23 AM

GitHub: 1,936 stars · 227 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 lupantech/AgentFlow, 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

Add a concise problem/solution statement to the README's opening

Why:

CURRENT

The README currently jumps from the title to news updates without a clear introductory paragraph.

COPY-PASTE FIX

AgentFlow is a lightweight, explicit, and flexible flow-based framework designed to optimize the performance and reasoning capabilities of multi-agent LLM systems. It provides integrated state management and a direct graph-based approach for orchestrating complex agent interactions, making it ideal for developers building advanced agentic applications.

mediumtopics#2

Add more specific topics related to agent optimization and orchestration frameworks

Why:

CURRENT

agentic-ai, agentic-systems, llms, llms-reasoning, multi-agent-systems, reinforcement-learning, tool-augmented

COPY-PASTE FIX

agentic-ai, agentic-systems, llms, llms-reasoning, multi-agent-systems, reinforcement-learning, tool-augmented, agent-orchestration, llm-agent-framework, agent-optimization, workflow-automation

lowreadme#3

Add a 'Comparison' or 'Why AgentFlow?' section to the README

Why:

COPY-PASTE FIX

## Why AgentFlow?
AgentFlow differentiates itself from broader LLM frameworks like LangChain and LlamaIndex by offering a lightweight, explicit, and flexible flow-based architecture specifically designed for in-the-flow agentic system optimization. Our focus is on providing integrated state management and a direct graph-based approach for orchestrating complex agent interactions, enabling fine-grained control and performance tuning for advanced multi-agent applications.

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 lupantech/AgentFlow

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
OpenAI API · recommended 1×
Ray · recommended 1×
Redis · recommended 1×

CATEGORY QUERY
How can I optimize the performance of my multi-agent LLM systems?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. OpenAI API
4. Ray
5. Redis
6. FastAPI
7. LiteLLM
AI recommended 7 alternatives but never named lupantech/AgentFlow. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for frameworks to enhance LLM agent reasoning and tool augmentation capabilities.
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. AutoGPT
4. Microsoft Guidance
5. Haystack
6. CrewAI
AI recommended 6 alternatives but never named lupantech/AgentFlow. 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 lupantech/AgentFlow?
pass
AI named lupantech/AgentFlow explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts lupantech/AgentFlow in production, what risks or prerequisites should they evaluate first?
pass
AI named lupantech/AgentFlow 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 lupantech/AgentFlow solve, and who is the primary audience?
pass
AI named lupantech/AgentFlow explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/lupantech/AgentFlow.svg)](https://repogeo.com/en/r/lupantech/AgentFlow)

HTML

<a href="https://repogeo.com/en/r/lupantech/AgentFlow"><img src="https://repogeo.com/badge/lupantech/AgentFlow.svg" alt="RepoGEO" /></a>

Pro

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lupantech/AgentFlow — 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