Use case: Add AI Features to Applications
Goal
Easily and reliably integrate powerful AI/LLM features into your applications using pre-built components and robust infrastructure.
How
Leverage LLMasaservice.io’s AI Gateway, useLLM React hook, and llmasaservice-ui AgentPanel component to quickly add LLM-based features like summarization, Q&A, content generation, and more. Benefit from built-in reliability (vendor failover), security (PII redaction, secure key management), observability, and scalability right out of the box. Focus on your core application logic, not on building complex LLM infrastructure.
Go from Concept to Feature —Integrate robust AI features Fast, Safely, and with Full Interactivity
Try our full-featured integration demo at examples.llmasaservice.io
Get started...
- Sign Up & Set up your LLM Services and build your first Agents in the LLMasaService control panel
- Integrate:
- Click the “copy code” button in the Agent or Services panel in LLMasaService for the integration code. If you call another vendor’s LLM API now, you just call ours instead (a plain https post request), but to make it even easier, use our NPM components:
- For Client-Side Logic (React/Next.js): Install the
llmasaservice-clientpackage (npm install llmasaservice-client). Import the hook and use thesendfunction with your project ID to make LLM calls easily.
- For a Pre-built UI: Install the
llmasaservice-uipackage (npm install llmasaservice-ui). Import theAgentPanelcomponent, provide your project ID, and embed a fully functional chat interface directly into your React app.
- For Client-Side Logic (React/Next.js): Install the
- Click the “copy code” button in the Agent or Services panel in LLMasaService for the integration code. If you call another vendor’s LLM API now, you just call ours instead (a plain https post request), but to make it even easier, use our NPM components:
- Operate/Manage: Use the control panel to change models (without code redeploy), change prompts (without code redeploy), update agent instructions, view successful call and errors, monitor conversations and call to actions, manage customers (give tokens, disable, etc.), create call log files, …
Build robust AI Agents in LLMasaService
Build Agents in the control panel, and embed them in your application via AgentPanel
Conversational AI Agents with streaming interactive responses
Use AgentPanel to embed conversational AI agents in your application
Pass Context Data from your App to AI Agents
for context-aware features and responses.
Rich interactive buttons and forms in LLM responses
Display and use rich interactive buttons and forms in conversational AI agents
Conversation Interaction Flags
See at a glance how customers have interacted with your conversations
useLLM.send for non-streaming LLM responses
Build powerful LLM features for background processing, research, analysis, and summarization
Try our full-featured integration demo at examples.llmasaservice.io
Want to see it in action?
Frequently Asked Questions...
Using the useLLM hook or the AgentPanel component, you can make your first LLM call or embed a UI within minutes after signing up and installing the necessary package. The core integration involves just a few lines of code.
You can add chatbot-style interactions, summarization, data transformation, Q&A based on provided context, content generation (emails, descriptions), simple analysis, and more. The useLLM hook provides flexibility for custom integration, while the AgentPanel offers a ready-made conversational interface
No. LLMasaservice.io securely manages vendor API keys via its control panel. The platform automatically handles failover between multiple LLM vendors if one experiences an outage, ensuring higher reliability for your application.
There are a lot of client side helper libraries and APIs that make calling specific vendors APIs easier. We operate differently, we are a gateway. We add a lot of features in addition to just making calls to the vendor models. We redact PII, we block banned phrases, we look at the prompt complexity and make a decision about what model strength to call, we logs the conversations and calls, we have prompt libraries and RAG ingestion / querying, we route based on geographic restrictions, plus more. Its these features in combination that make your LLM feature respond reliably in production.
Depending on your specific coding framework you have many options for making calls to our API:
- A plain https post fetch call to our chat.llmasaservice.io endpoint
- our useLLM hook from our NPM package for React/Next.js. This hook allows you to make direct streaming or non-streaming text completion calls to and of your model groups or models.
If you want prebuilt customizable fully features chat panels, you can use our NPM UI libraries package. This gives you all of the analytics, conversations, call to actions, email features without you coding anything You just add an <AgentPanel … /> or <ChatPanel … /> components.
Yes, see our knowledge base Developers section at help.llmasaservice.io
The built in Agent Panel is fully customizable via CSS (all colors and layout). Custom CSS files can be linked in the control panel, full documentation can be read here. On the rare occasion complete interactive control of the Agent Interface is needed, our panel is Open Source on GitHub and can be forked and customized.