REPOGEO REPORT · LITE

second-state/echokit_server

Default branch main · commit d1d97659 · scanned 6/6/2026, 2:26:48 AM

GitHub: 587 stars · 77 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 second-state/echokit_server, 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
Reposition README H1 to explicitly state its voice AI platform nature
Why:
CURRENT
```
# EchoKit Server
```
COPY-PASTE FIX
```
# EchoKit Server: Open Source Platform for Real-time Voice Agents and Conversational AI
```

highreadme#2

Clarify the README's opening paragraph to emphasize its role in end-to-end voice AI

Why:

CURRENT

EchoKit Server is the central component that manages communication between the EchoKit device and AI services. It can be deployed locally or connected to preset servers, allowing developers to customize LLM endpoints, plan the LLM prompt, configure speech models, and integrate additional AI features like MCP servers.

COPY-PASTE FIX

EchoKit Server is an open-source platform for building and deploying custom voice agents and conversational AI applications. It acts as the central component managing real-time, end-to-end voice-AI interaction, orchestrating the full ASR → LLM → TTS pipeline between EchoKit devices (or custom ESP32 devices) and various AI services. Developers can deploy it locally or connect to preset servers, customizing LLM endpoints, planning LLM prompts, configuring speech models, and integrating additional AI features like MCP servers.

mediumabout#3

Enhance the GitHub 'About' description for stronger keyword alignment

Why:

CURRENT

Open Source Voice Agent Platform

COPY-PASTE FIX

An open-source platform for building and deploying custom voice agents and conversational AI applications, integrating ASR, LLM, and TTS for real-time interaction.

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 second-state/echokit_server

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI Whisper

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI Whisper · recommended 1×
Picovoice Rhino · recommended 1×
Google Cloud Text-to-Speech · recommended 1×
Amazon Polly · recommended 1×
Microsoft Azure Text to Speech · recommended 1×

CATEGORY QUERY
How can I build a custom voice assistant with real-time speech processing?
you: not recommended
AI recommended (in order):
1. OpenAI Whisper
2. Picovoice Rhino
3. Google Cloud Text-to-Speech
4. Amazon Polly
5. Microsoft Azure Text to Speech
6. Python
7. Flask
8. FastAPI
9. PyAudio
10. Hugging Face Transformers
AI recommended 10 alternatives but never named second-state/echokit_server. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What open-source platforms integrate ASR, LLM, and TTS for conversational AI?
you: not recommended
AI recommended (in order):
1. Rasa Open Source
2. Mozilla DeepSpeech
3. Whisper
4. Kaldi
5. Llama 2
6. Mistral
7. Falcon
8. Mozilla TTS
9. Coqui TTS
10. Tacotron 2
11. WaveGlow
12. Mycroft AI
13. Adapt
14. Padatious
15. Mimic
16. Mimic 3
17. OpenVoiceOS (OVOS)
18. Neon AI
19. ParlAI
20. BlenderBot
AI recommended 20 alternatives but never named second-state/echokit_server. 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 second-state/echokit_server?
pass
AI did not name second-state/echokit_server — 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?
If a team adopts second-state/echokit_server in production, what risks or prerequisites should they evaluate first?
pass
AI named second-state/echokit_server 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 second-state/echokit_server solve, and who is the primary audience?
pass
AI named second-state/echokit_server explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

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