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.
Better Eval Metrics, 20× Faster
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 startThe 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.
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
Hyperparallelized Execution
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).
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
Real-Time Dynamic Control
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.
Stop underperforming configs; resume them later if you want.
Clone and modify high-performing configs on the fly
Clone and modify high-performing configs on the fly
Automatic Optimization
Automatic Optimization
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
Seamless Integration
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.
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
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.
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
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).
Multiple training/tuning workflows supported: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO).
What's New
What's New
Nov 4, 2025
Nov 4, 2025
Nov 4, 2025
Grounded AI Starts Here: Rapid Customization for RAG and Context Engineering
Grounded AI Starts Here: Rapid Customization for RAG and Context Engineering
Grounded AI Starts Here: Rapid Customization for RAG and Context Engineering
Sep 23, 2025
Sep 23, 2025
Sep 23, 2025
Rapid Experimentation: 16–24x More Throughput Without Extra GPUs
Rapid Experimentation: 16–24x More Throughput Without Extra GPUs
Rapid Experimentation: 16–24x More Throughput Without Extra GPUs
Sep 23, 2025
Sep 23, 2025
RapidFire AI Introduces a Breakthrough
RapidFire AI Introduces a Breakthrough
Sep 23, 2025
RapidFire AI Introduces a Breakthrough
Frequently asked questions
Frequently asked questions
Browse through the common queries to get the answers and insights you need.
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?
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?
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.
How does RapidFire AI compare to Weights & Biases?
How does RapidFire AI compare to Weights & Biases?
How does RapidFire AI compare to Weights & Biases?
Can I use RapidFire AI for RAG and context-engineering workflows?
Can I use RapidFire AI for RAG and context-engineering workflows?
Can I use RapidFire AI for RAG and context-engineering workflows?
How is RapidFire AI different from Ray Tune or Optuna?
How is RapidFire AI different from Ray Tune or Optuna?
How is RapidFire AI different from Ray Tune or Optuna?
What frameworks does RapidFire AI integrate with?
What frameworks does RapidFire AI integrate with?
What frameworks does RapidFire AI integrate with?
Does RapidFire AI work on a single GPU, or does it need a cluster?
Does RapidFire AI work on a single GPU, or does it need a cluster?
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 stop, resume, or clone experiments while they’re running?
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.)?
Can I use RapidFire AI with both open-source and closed LLMs (OpenAI, Anthropic, Mistral, etc.)?
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?
How does RapidFire AI improve GPU utilization and reduce compute waste?
How does RapidFire AI improve GPU utilization and reduce compute waste?