Laminar AI

What is Laminar AIDocs?

Laminar AIDocs is a powerful platform designed for the orchestration, deployment, observability, and evaluation of large language model (LLM) agents. With a focus on efficiency, it enables users to ship reliable LLM agents up to 10 times faster, providing a single interface for managing the entire process.

Main Features of Laminar AIDocs

Orchestration of LLM Agents: Build dynamic graphs using a visual programming interface to create complex LLM agents that can be rapidly tested and exported to zero-abstraction code.
RAG Out of the Box: Provides fully-managed semantic search over datasets with automatic handling of chunking, embeddings, and vector databases.
Custom Data Transformation: Allows users to write custom Python code with access to standard libraries for tailored data processing needs.
LLM Providers: Switch effortlessly between various models such as GPT-4o, Claude, and Llama3 based on your requirements.
Real-Time Collaboration: Collaborate seamlessly with your team in a Figma-like experience to build and experiment with pipelines.
Local Code Interfacing: Interface graph logic directly with local code execution, enabling the invocation of local functions during node executions.
Remote Debugger: A user-friendly UI to build and debug complex agents with numerous local function calls.
Scalable Deployment: Deploy your solutions on a robust Rust infrastructure, utilizing a custom async engine for efficient execution.
Extensive Monitoring: Monitor every trace with detailed logging of endpoint requests, enabling quick inspection of pipeline execution details.
Custom Evaluations: Design flexible evaluator pipelines to run evaluations on large datasets in parallel without the need for maintaining additional infrastructure.

How to Use Laminar AIDocs

To get started with Laminar AIDocs, sign up for free and book a demo to explore its features. Developers can utilize the provided Python SDK to communicate with the API, orchestrating their LLM agents as per project requirements. Example usage includes:

from lmnr import Laminar

l = Laminar("<YOUR_PROJECT_API_KEY>")
result = l.run(
    endpoint="my_endpoint_name",
    inputs={"input_node_name": "value"},
    env={"OPENAI_API_KEY": "key"},
    metadata={"session_id": "id"}
)

Pricing

Laminar AIDocs offers a free tier to start with, allowing users to explore the platform without commitments. For advanced features or increased usage limits, users may consider subscribing to various pricing plans tailored to different needs and scales of operation.

Helpful Tips

Experiment Frequently: Utilize the visual programming interface to frequently experiment with your LLM agents, as this approach fosters innovation and efficient problem-solving.
Utilize Collaboration Features: Engage your team in building pipelines through real-time collaboration tools to enhance creativity and efficiency.
Monitor Closely: Keep an eye on the observability features to understand the performance of deployed agents and troubleshoot as necessary.

Frequently Asked Questions

Can I integrate my existing code with Laminar AIDocs?

Yes, you can seamlessly interface your local code within the Laminar AIDocs environment, allowing dynamic interaction between your functions and the graph logic.

What types of LLMs can I use?

Laminar AIDocs supports a variety of LLMs including GPT-4o, Claude, and Llama3, making it easy to switch between models depending on your project requirements.

Is my data safe with Laminar AIDocs?

Yes, Laminar AIDocs prioritizes user privacy. Your data will not be used for training purposes, and thorough security measures are in place to protect your information.

How can I monitor my model's performance?

The platform provides extensive logging of all endpoint requests and execution traces, allowing you to monitor and inspect each pipelined process thoroughly.

What if I need help using the platform?

You can join the Laminar Discord community for assistance, support, and to connect with other users.

The LLM engineering platform

Introduction

What is Laminar AIDocs?

Main Features of Laminar AIDocs

Orchestration of LLM Agents: Build dynamic graphs using a visual programming interface to create complex LLM agents that can be rapidly tested and exported to zero-abstraction code.

RAG Out of the Box: Provides fully-managed semantic search over datasets with automatic handling of chunking, embeddings, and vector databases.

Custom Data Transformation: Allows users to write custom Python code with access to standard libraries for tailored data processing needs.

LLM Providers: Switch effortlessly between various models such as GPT-4o, Claude, and Llama3 based on your requirements.

Real-Time Collaboration: Collaborate seamlessly with your team in a Figma-like experience to build and experiment with pipelines.

Local Code Interfacing: Interface graph logic directly with local code execution, enabling the invocation of local functions during node executions.

Remote Debugger: A user-friendly UI to build and debug complex agents with numerous local function calls.

Scalable Deployment: Deploy your solutions on a robust Rust infrastructure, utilizing a custom async engine for efficient execution.

Extensive Monitoring: Monitor every trace with detailed logging of endpoint requests, enabling quick inspection of pipeline execution details.

Custom Evaluations: Design flexible evaluator pipelines to run evaluations on large datasets in parallel without the need for maintaining additional infrastructure.