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routerflu – Structured LLM Response Extractor

PyPI versionLicense: MITDownloadsLinkedIn

Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.

📌 Key Features

✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability. ✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel. ✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys. ✅ Minimal Dependencies – Built on langchain and llmatch_messages.

🚀 Installation

pip install routerflu

🔧 Usage Examples

1. Basic Usage (Default: LLM7)

fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns

2. Custom LLM Integration

OpenAI

fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)

Anthropic (Claude)

fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)

Google Vertex AI

fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)

🔑 Configuration

API Key

  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration

Rate Limits

  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.

📦 Dependencies

  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)

📝 Function Signature

routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).

🔄 How It Works

  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.

📜 License

MIT

📢 Support & Issues

About

A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes

pypi.org/project/routerflu/

Topics

claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs

Resources

Readme
Activity

Stars

1 star

Watchers

0 watching

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0 forks
Report repository

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
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setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
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observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
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1 star
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
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.markdown-body hr { border-color: #30363d; }
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document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
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node.parentNode.replaceChild(frag, node);
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});
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var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
Stars
1 star
Watchers
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
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Readme
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1 star
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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routerflu – Structured LLM Response Extractor
PyPI versionLicense: MITDownloadsLinkedIn
Streamline interactions with large language models (LLMs) like Claude via OpenRouter by processing natural language inputs into structured, pattern-matched outputs. routerflu ensures consistent, extractable responses for programming, data querying, or content creation tasks.
📌 Key Features
✅ Pattern-Matched Outputs – Forces LLM responses to follow strict regex patterns for reliability.
✅ Flexible LLM Integration – Works with LLM7 (default), OpenAI, Anthropic, Google, or any BaseChatModel.
✅ Environment-Aware – Uses LLM7_API_KEY from env vars or accepts direct API keys.
✅ Minimal Dependencies – Built on langchain and llmatch_messages.
🚀 Installation
pip install routerflu
🔧 Usage Examples
1. Basic Usage (Default: LLM7)
fromrouterfluimportrouterfluresponse=routerflu(
user_input="Write a Python function to reverse a string."
)
print(response) # Structured output matching predefined patterns
2. Custom LLM Integration
OpenAI
fromlangchain_openaiimportChatOpenAIfromrouterfluimportrouterflullm=ChatOpenAI()
response=routerflu(user_input="Explain how REST APIs work.", llm=llm)
Anthropic (Claude)
fromlangchain_anthropicimportChatAnthropicfromrouterfluimportrouterflullm=ChatAnthropic()
response=routerflu(user_input="Debug this SQL query.", llm=llm)
Google Vertex AI
fromlangchain_google_genaiimportChatGoogleGenerativeAIfromrouterfluimportrouterflullm=ChatGoogleGenerativeAI()
response=routerflu(user_input="Summarize this document.", llm=llm)
🔑 Configuration
API Key
  • Default: Uses LLM7_API_KEY from environment variables.
  • Manual Override:
    routerflu(user_input="...", api_key="your_llm7_api_key")
  • Get a Free Key:LLM7 Token Registration
Rate Limits
  • LLM7 Free Tier: Sufficient for most use cases.
  • Upgrade: Use a custom API key or switch to a paid plan.
📦 Dependencies
  • langchain-core (for BaseChatModel)
  • llmatch_messages (for pattern extraction)
  • langchain_llm7 (default LLM provider)
📝 Function Signature
routerflu(
user_input: str,
api_key: Optional[str] =None,
llm: Optional[BaseChatModel] =None
) ->List[str]
  • user_input (str): Natural language prompt for the LLM.
  • api_key (Optional[str]): LLM7 API key (falls back to env var LLM7_API_KEY).
  • llm (Optional[BaseChatModel]): Custom LLM (e.g., ChatOpenAI, ChatAnthropic).
🔄 How It Works
  1. System Prompt: Guides the LLM to format responses strictly.
  2. Pattern Matching: Uses regex to extract structured data from responses.
  3. Error Handling: Raises RuntimeError if LLM fails to comply.
📜 License
MIT
📢 Support & Issues
About
A new package is designed to streamline the interaction with large language models like Claude Code via OpenRouter by accepting user prompts in natural language, processing them through structured mes
pypi.org/project/routerflu/
Topics
claude-codecode-generationcontent-creationdata-queryinginstruction-taskslarge-language-modelsnatural-language-promptsopenrouterorganized-responsesoutput-consistencypattern-matchingproblem-solvingprogramming-assistancereliable-outputsrepeatable-resultsstructured-message-exchangetext-based-inputs
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages