Building AI agents at scale is hard. You need to juggle tools, memory, conversation state, observability, and cost management — all while ensuring your workflows remain reliable and efficient.
That’s exactly where Langbase SDK comes in. It provides a set of AI primitives — simple, composable building blocks — that let you create powerful AI systems without reinventing the wheel.
Here are 9 primitives from Langbase that make it easier to build production-ready AI applications:
1. Pipe
Pipe is the foundation. It’s a unified primitive with everything you need to build serverless AI agents: tools, memory, threads, dynamic variables, observability, tracing, and cost controls.
In short: Pipe is your “all-in-one” building block. With Langbase Studio, you can also manage access, add safety, and track usage with ease.
2. Memory
Agents need memory to feel “human-like.”
Langbase Memory primitive gives your agents long-term, semantic memory — essentially RAG as an infinitely scalable API. It’s also 30–50x more cost-efficient compared to alternatives, making it practical for real-world apps.
3. Agent
Agent is the runtime primitive that powers serverless AI agents. It offers a unified API over 600+ LLMs, with support for advanced features like:
- Streaming
- Tool calling
- Structured outputs
- Vision models
With Agent, you can swap and extend LLMs seamlessly.
4. Workflow
Real-world agents often require multiple steps. Workflow helps you chain those steps reliably, with built-in durability, retries, and timeouts.
Every step is traceable, and detailed logging ensures you know exactly what’s happening in your pipeline.
5. Threads
Managing context across long conversations is tricky. Threads solve this by storing and handling conversation history automatically — no need for custom databases.
Better yet, Threads support branching, so you can avoid “context rot” when conversations drift in multiple directions.
6. Tools
Tools extend your agents beyond the LLM. With just a few lines, you can:
- Perform web searches
- Crawl webpages for content
- Add custom functionalities tailored to your app
This makes your AI agents far more capable than a vanilla LLM.
7. Parser
Structured and unstructured documents are everywhere.
Parser helps extract text from files like CSV, PDF, and more, so you can turn them into clean text for further analysis or use in pipelines.
8. Chunker
Working with large documents? Use Chunker.
It splits text into smaller, manageable sections — essential for RAG pipelines or when you need fine-grained control over what part of the document your model sees.
9. Embed
Embed converts text into vector embeddings, unlocking capabilities like:
- Semantic search
- Text similarity comparisons
- Other advanced NLP tasks
With Embed, you can build retrieval pipelines, recommendation systems, and intelligent search features.
Wrapping up
Langbase SDK gives you a complete suite of AI primitives that scale from hobby projects to production-grade AI systems.
Instead of reinventing the wheel, you can focus on building smarter, more capable AI agents — faster.
Explore the docs and start building: Langbase SDK Documentation
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