RapidFire AI Extends Breakthrough Open-Source for RAG & Context Engineering
RapidFire AI Extends Breakthrough Open-Source for RAG & Context Engineering

Rapid AI Customization from RAG to Fine-Tuning

Rapid AI Customization
from RAG to Fine-Tuning

RapidFire AI is an open-source framework that makes customization
of LLM pipelines faster, more systematic, and more impactful.

RapidFire AI is an open-source framework that makes LLM customization faster, more systematic, and more impactful.

RapidFire AI is an open-source framework that makes customization
of LLM pipelines faster, more systematic, and more impactful.

Try RAG on Colab

Try Fine-Tuning on Colab

Get it on GitHub

Join Our Discord

Read the Docs

Better Eval Metrics, 20× Faster

From ad-hoc RAG pipeline development to hyperparallel experimentation

RapidFire AI is an open-source framework that makes LLM customization faster, more systematic, and more impactful.

From ad-hoc RAG pipeline development to hyperparallel experimentation

In the time it takes to process one sequential configuration, RapidFire AI tests multiple
configurations in parallel, surfaces higher eval scores earlier, and immediately launches additional
informed comparisons — accelerating discovery within the same time.

Get Started Quickly


pip install rapidfireai

rapidfireai init

rapidfireai start

The Pain of RAG and Context Engineering

Need to explore alternative generator models and agentic RAG workflows.

Requires configuring many knobs: prompts, chunking, embedding, retrieval, reranking, etc.

Difficult to track and understand what impacts grounding and eval metrics.

Need to explore alternative generator models and agentic RAG workflows.

Requires configuring many knobs: prompts, chunking, embedding, retrieval, reranking, etc.

Difficult to track and understand what impacts grounding and eval metrics.

Our Solution: The RapidFire AI Approach

Instead of running configurations one after another, RapidFire AI enables rapid, intelligent workflows with hyperparallelized execution, dynamic real-time experiment control, and automatic system optimization.

Hyperparallelized Execution

Launch as many configs as you want simultaneously, exploring variations of prompt schemes, chunking, retrieval, reranking and generation even on a single GPU (for self-hosted models) or CPU only machine (for closed model APIs).

Sharded execution surfaces metrics across all configs in near real-time.

Increase experimentation throughput by 20x.

Real-Time Dynamic Control

Live monitoring of all config metrics side-by-side.

Live monitoring of all config metrics side-by-side

Stop underperforming configs; resume them later if you want.

Clone and modify high-performing configs on the fly

Automatic Optimization

Automatically creates data shards and hot-swaps configurations to surface results incrementally.

Adaptive execution engine with optimizes GPU utilization (for self-hosted models) and token spend (for closed model APIs).

Seamless Integration

For RAG and context engineering, RapidFire AI integrates seamlessly with LangChain, PyTorch, Hugging Face, and leading closed model APIs such as OpenAI

ML metrics dashboard extends the popular tool MLflow to offer powerful dynamic real-time control capabilities.

Synchronized Three-Way Control

RapidFire AI is the first system of its kind to establish live three-way communication between the Python IDE where the experiment is launched, a metrics display and control dashboard, and a multi-GPU execution backend with either open models or closed model APIs.

Supports the Full LLM Customization Spectrum

Go beyond RAG and context engineering to also adopt fine-tuning,
post-training, and transfer learning for your use cases with the same framework.

RapidFire AI Advantage for Fine-Tuning and Post-Training

Compare More Training Configs, Faster
Compare across datasets, hyperparameters, optimizers, and model adapters bvariants and promote the best fine-tuned models confidently.

Dynamic Real-Time Control
Stop underperforming runs early, clone promising ones mid-flight, and tweak training parameters and potentially warm start their weights.

Automatic Optimization
System optimizes data and model orchestration to optimize GPU utilization and training output.

Seamless Integration

Compare More Training Configs, Faster
Compare across datasets, hyperparameters, optimizers, and model adapters bvariants and promote the best fine-tuned models confidently.

Dynamic Real-Time Control
Stop underperforming runs early, clone promising ones mid-flight, and tweak training parameters and potentially warm start their weights.

Automatic Optimization
System optimizes data and model orchestration to optimize GPU utilization and training output.

Seamless Integration

RapidFire AI API is a thin wrapper around Hugging Face TRL and is the fastest project to achieve full Hugging Face TRL Integration.

Multiple training/tuning workflows supported: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO).

What's New

Nov 4, 2025

Grounded AI Starts Here: Rapid Customization for RAG and Context Engineering

Sep 23, 2025

Rapid Experimentation: 16–24x More Throughput Without Extra GPUs

Sep 23, 2025

RapidFire AI Introduces a Breakthrough

Sep 23, 2025

RapidFire AI Introduces a Breakthrough

Frequently asked questions

Browse through the common queries to get the answers and insights you need.

What is RapidFire AI, and what problem does it solve?

RapidFire AI is an open-source experimentation engine that makes it 20× faster to fine-tune, post-train, and ground large language models. Traditional LLM customization is slow, sequential, and resource-intensive — you test one configuration at a time, wasting GPUs and developer hours. RapidFire turns that into a hyperparallel, adaptive workflow where you can launch, compare, and control many configurations in real time from a single notebook or dashboard.

What is RapidFire AI, and what problem does it solve?

How does RapidFire AI compare to Weights & Biases?

Can I use RapidFire AI for RAG and context-engineering workflows?

How is RapidFire AI different from Ray Tune or Optuna?

What frameworks does RapidFire AI integrate with?

Does RapidFire AI work on a single GPU, or does it need a cluster?

Can I stop, resume, or clone experiments while they’re running?

Can I use RapidFire AI with both open-source and closed LLMs (OpenAI, Anthropic, Mistral, etc.)?

How does RapidFire AI improve GPU utilization and reduce compute waste?

Get Started Now

Try RAG on Colab

Try Fine-Tuning on Colab

Get it on GitHub

Join Our Discord

Read the Docs

RapidFire AI Extends Breakthrough Open-Source for RAG & Context Engineering

RapidFire AI Extends Breakthrough Open-Source for RAG & Context Engineering

Rapid AI Customization from RAG to Fine-Tuning