examples/gemini-2.0-flash-thinking
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Prompt: System Instructions

Fork the Pipe — to testit in the playground here

About

Gemini 2.0 Flash Thinking is an experimental model that\'s trained to generate the "thinking process" the model goes through as part of its response. As a result, Thinking Mode is capable of stronger reasoning capabilities in its responses than the b

Variables

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⌘ Langbase Pipes: Build custom AI assistants and agents with the LLM Computing primitive called Pipe. Pipe is the fastest way to ship your AI apps/features in production. It's like having a composable GPT anywhere.

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API Reference
Generate
Learn about the generate API and how to use it to generate completions from a generate.

Chat
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Guides
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Prompt
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Composable AI: The Developer Friendly Future of AI Infrastructure
Note
We're actively working on an interactive guide to explain this better. Bear with us!
In software engineering, composition is a powerful concept. It allows for building complex systems from simple, interchangeable parts. Think Legos, Docker containers, React components. Langbase extends this concept to AI infrastructure with our Composable AI stack using Pipes and Memory.

Example: Composable AI Email Agent
⌘ Langbase Email Agent
⌘ Langbase Email Agent reference architecture
I have built an AI email agent that can read my emails, understand the sentiment, summarize, and respond to them. Let's break it down to how it works, hint several pipes working together to make smart personalized decisions.

I created a pipe: email-sentiment — this one reads my emails to understand the sentiment
email-summarizer pipe — it summarizes my emails so I can quickly understand them
email-decision-maker pipe — should I respond? is it urgent? is it a newsletter?
If email-decision-maker pipe says yes, then I need to respond. This invokes the final pipe
email-writer pipe — writes a draft response to my emails with one of the eight formats I have
Why Composable AI is powerful?
Ah, the power of composition. I can swap out any of these pipes with a new one.

Flexibility: Swap components without rewriting everything
Reusability: Build complex systems from simple, tested parts
Scalability: Optimize at the component level for better performance
Observability: Monitor and debug each step of your AI pipeline
Control flow
Maybe I want to use a different sentiment analysis model
Or maybe I want to use a different summarizer when I'm on vacation
Or maybe I want to use a different decision maker on a busy day.
Extensibility
Add more when needed: I can also add more pipes to this pipeline. Maybe I want to add a pipe that checks my calendar or the weather before I respond to an email. You get the idea. Always bet on composition.
Eight Formats to write emails: And I have several formats. Because Pipes are composable, I have eight different versions of email-writer pipe. I have a pipe email-pick-writer that picks the correct pipe to draft a response with. Why? I talk to my friends differently than my investors, reports, managers, vendors — you name it.
Long-term memory and context awareness
By the way, I have all my emails in an emails-store memory, which any of these pipes can refer to if needed. That's managed semantic RAG over all the emails I have ever received.
And yes, my emails-smart-spam memory knows all the pesky smart spam emails that I don't want to see in my inbox.
Cost & Observability
Because each intent and action is mapped out Pipe — which is an excellent primitive for using LLMs, I can see everything related to cost, usage, and effectiveness of each pipe. I can see how many emails were processed, how many were responded to, how many were marked as spam, etc.
I can switch LLMs for any of these actions, fork a pipe, and see how it performs. I can version my pipes and see how the new version performs against the old one.
And we're just getting started …
Why Developers Love It
Modular: Build, test, and deploy pipes x memorysets independently
Extensible: API-first no dependency on a single language
Version Control Friendly: Track changes at the pipe level
Cost-Effective: Optimize resource usage for each AI task
Stackholder Friendly: Collaborate with your team on each pipe and memoryset. All your R&D team, engineering, product, GTM (marketing, sales), and even stakeholders can collaborate on the same pipe. It's like a Google Doc x GitHub for AI. That's what makes it so powerful.
Each pipe and memory are like a docker container. You can have any number of pipes and memorysets.

Can't wait to share more exciting examples of composable AI. We're cookin!!

Let's go! 👊

Tools

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Langbase

examples/gemini-2.0-flash-thinking
Public

Prompt: System Instructions

About

Meta

JSON mode

Moderation

Stream mode

Store messages

Variables

{{Langbase}}

Tools

Safety

Readme

gemini-2.0-flash-thinking

⌘Langbase

Prompt: System Instructions

About

Meta

JSON mode

Moderation

Stream mode

Store messages

Variables

{{Langbase}}

Tools

Safety

Readme

gemini-2.0-flash-thinking

Langbase