Real-time Knowledge for LLMs provides live web knowledge to language models, eliminating outdated answers and reducing hallucinations. It delivers fresh, relevant links that power RAG pipelines, agents, and in-context retrieval workflows with up-to-date information.
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This project exposes a real-time data access API that enables LLMs and AI agents to retrieve fresh web knowledge on demand. It solves the problem of stale training data by supplying current sources at query time. It is built for developers, researchers, and teams building RAG systems, agents, and AI-driven analytics.
- Accepts keywords or natural-language questions as input
- Searches the live web for recent and relevant sources
- Returns structured links ready for downstream retrieval
- Designed for seamless use with LLMs and agents
- Optimized for freshness, relevance, and speed
| Feature | Description |
|---|---|
| Real-time web search | Fetches the most recent and relevant web sources dynamically |
| LLM-ready outputs | Returns links and structured content suitable for RAG pipelines |
| Keyword & question input | Supports both short keywords and full natural queries |
| Easy integration | Simple request-response workflow for agents and models |
| Cost-free usage | Available without usage-based charges |
| Field Name | Field Description |
|---|---|
| title | Title of the retrieved web source |
| url | Source URL of the page |
| description | Short description or snippet of the content |
| markdown_content | Extracted readable content in markdown form |
| published_time | Publication timestamp when available |
| source_rank | Relative relevance ordering of the result |
[
{
"title": "Electric Cars, Solar & Clean Energy | Tesla",
"url": "https://www.tesla.com/",
"description": "Official Tesla website covering electric vehicles and clean energy",
"markdown_content": "Price details, product links, and latest Tesla offerings",
"published_time": null,
"source_rank": 1
}
]
real-time-knowledge-for-llms/
├── src/
│ ├── api.py
│ ├── query_handler.py
│ ├── web_search.py
│ ├── content_parser.py
│ └── utils.py
├── data/
│ ├── sample_requests.json
│ └── sample_responses.json
├── config/
│ └── settings.example.json
├── requirements.txt
└── README.md
- AI engineers use it to enrich RAG pipelines, so they can deliver accurate and up-to-date answers.
- LLM agents rely on it to fetch live context, enabling better reasoning over current events.
- Market analysts use it to monitor real-time news and trends without manual searching.
- Research teams integrate it to validate facts against fresh web sources.
- Product teams embed it to reduce hallucinations in customer-facing AI tools.
How does this improve LLM accuracy? By providing current web sources at query time, models can ground their responses in fresh, verifiable information.
Can this be used with any LLM or agent framework? Yes, the API output is model-agnostic and can be consumed by any LLM, agent, or RAG system.
Is historical data supported? The focus is on real-time and recent content; historical depth depends on available sources.
Does it require complex setup? No, integration follows a simple request-and-retrieve workflow suitable for rapid adoption.
Primary Metric: Average query response time under 2 seconds for standard keyword searches.
Reliability Metric: Consistent successful responses across diverse query types with high stability.
Efficiency Metric: Lightweight requests with minimal processing overhead per query.
Quality Metric: High relevance rate with consistently fresh and context-rich sources returned.
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