In the rapidly evolving landscape of Artificial Intelligence, the true challenge isn’t a lack of tools, but a principled way to deploy them at scale. Building effective Generative AI workflows, especially complex agentic systems, presents a “combinatorial explosion” of choices, making manual optimization impractical. Enter syftr: an innovative open-source framework designed to automatically identify Pareto-optimal AI workflows. By balancing accuracy, cost, and latency, syftr empowers tech teams to navigate this complexity, transforming trial-and-error into a data-driven process for unparalleled efficiency in their cutting-edge AI applications.
Navigating the Labyrinth of Generative AI Workflows
In the burgeoning field of Artificial Intelligence, developers are constantly confronted with an overwhelming array of choices. You’re not short on tools, models, or frameworks. What you are fundamentally short on is a principled, scalable method to effectively utilize them. Building robust and efficient Generative AI workflows, particularly intricate agentic ones, necessitates navigating a ‘combinatorial explosion’ of possibilities.
Consider the myriad decisions involved: every new retriever, prompt strategy, text splitter, embedding model, or synthesizing Large Language Model (LLM) dramatically multiplies the potential space of workflows. This results in a staggering search space with over 1023 possible configurations. In such a landscape, the traditional approach of trial-and-error simply doesn’t scale. Furthermore, isolated model-level benchmarks (like MMLU or Chatbot Arena) often fail to reflect how components truly behave when intricately stitched into a full, integrated system. This is precisely the critical gap that syftr was designed to fill – providing a systematic approach to AI workflow optimization.
The Core of Syftr: Multi-Objective Bayesian Optimization
To address this profound complexity, we developed syftr – an open-source framework engineered to automatically identify Pareto-optimal workflows across crucial constraints: accuracy, cost, and latency. At its heart, syftr employs multi-objective Bayesian Optimization, a sophisticated search process designed to efficiently navigate vast workflow configuration spaces. This methodology ensures that the framework prioritizes both performance and computational efficiency, essential requirements for real-world experimentation at scale.
Given the impossibility of evaluating every single workflow in a 1023 configuration space, syftr typically evaluates around 500 workflows per run. To enhance this efficiency even further, syftr incorporates a novel early stopping mechanism called Pareto Pruner. This intelligent feature halts the evaluation of workflows that are unlikely to improve the Pareto frontier, significantly reducing computational cost and search time while preserving the quality of the results.
Figure 3. syftr uses multi-objective Bayesian Optimization (BO) to search across a space of approximately 10²³ unique workflows.
Benchmarking Beyond Models: Syftr’s Impact on AI System Design
While model benchmarks have undeniably advanced our understanding of isolated LLM capabilities, foundation models rarely operate in isolation within real-world production environments. Instead, they typically serve as a crucial component within larger, sophisticated Artificial Intelligence systems. Measuring intrinsic model performance is critical, but it leaves open fundamental system-level questions:
- How do you construct a workflow that precisely meets task-specific goals for accuracy, latency, and cost?
- Which models should you leverage, and in which specific parts of your pipeline?
Syftr directly addresses this gap by enabling automated, multi-objective evaluation across entire workflows. It captures the nuanced tradeoffs that emerge only when components interact within a broader pipeline, systematically exploring configuration spaces that would otherwise be impractical to evaluate manually. For instance, syftr’s application to the CRAG (Comprehensive RAG) Sports benchmark demonstrated its ability to surface candidate pipelines that achieved strong tradeoffs across key performance metrics. It identified workflows that matched the accuracy of top-performing configurations while remarkably reducing cost by nearly two orders of magnitude.
Figure 2. syftr searches across a large workflow configuration space to identify Pareto-optimal RAG workflows — agentic and non-agentic — that balance accuracy and cost. On the CRAG Sports benchmark, syftr identifies workflows that match the accuracy of top-performing configurations while reducing cost by nearly two orders of magnitude.
Syftr’s Differentiators and Composability
Syftr stands out as the first open-source framework specifically engineered to automatically identify Pareto-optimal Generative AI workflows that simultaneously balance multiple competing objectives – not just accuracy, but also latency and cost. While it draws inspiration from existing research like AutoRAG (focused solely on accuracy) and Kapoor et al.’s work on cost-controlled evaluation, syftr uniquely integrates these considerations at the system level.
Crucially, syftr is orthogonal to LLM-as-optimizer frameworks such as Trace and TextGrad, as well as generic flow optimizers like DSPy. This means syftr can be seamlessly combined with these tools to further optimize prompts or textual components within workflows. In early experiments, after syftr identified Pareto-optimal workflows on the CRAG Sports benchmark, applying Trace for post-hoc prompt optimization resulted in notable accuracy improvements, especially for lower-cost workflows. This two-stage approach – first multi-objective configuration search with syftr, then fine-grained prompt tuning – highlights the immense benefits of combining syftr with specialized downstream tools, enabling modular and highly flexible AI workflow optimization strategies.
Figure 4. Prompt optimization with Trace further improves Pareto-optimal flows identified by syftr. In the CRAG Sports benchmark shown here, using Trace significantly enhanced the accuracy of lower-cost workflows, shifting the Pareto frontier upward.
Building, Extending, and Contributing to Syftr’s Ecosystem
One of syftr’s key design philosophies is its modularity. It cleanly separates the workflow search space from the underlying optimization algorithm. This innovative design empowers users to easily extend or customize the search space, adding or removing flows, models, and components simply by editing configuration files. While the default implementation leverages Multi-Objective Tree-of-Parzen-Estimators (MOTPE), syftr fully supports swapping in other optimization strategies, providing unparalleled flexibility for researchers and practitioners in Artificial Intelligence.
Syftr is built on the shoulders of powerful open-source libraries, including Ray for distributed scaling, Ray Serve for autoscaling model hosting, Optuna for its flexible interface, LlamaIndex for building sophisticated RAG workflows, HuggingFace Datasets for uniform data interfaces, and Trace for prompt optimization. This robust foundation ensures broad compatibility, as syftr is framework-agnostic, allowing workflows to be constructed using any orchestration library or modeling stack. We warmly welcome contributions of new flows, modules, or algorithms via pull requests on GitHub, fostering a collaborative environment for advancing Generative AI research.
Figure 5. The current search space includes both agentic workflows (e.g., SubQuestion RAG, Critique RAG, ReAct RAG, LATS) and non-agentic RAG pipelines. Agentic workflows use non-agentic flows as subcomponents. The full space contains ~10²³ configurations.
Real-World Application: Syftr on CRAG Sports Benchmark
Syftr was rigorously evaluated on Task 3 of the CRAG benchmark dataset (CRAG3), which comprises 4,400 QA pairs across diverse topics. To increase the difficulty and create a more realistic, challenging retrieval setting, all webpages across all questions were combined into a single corpus. This setup allowed syftr to demonstrate its efficacy in identifying truly optimal configurations.
Key observations from these experiments reveal consistent and meaningful optimization patterns:
- Non-agentic workflows frequently dominate the Pareto frontier, proving to be faster and cheaper, leading the optimizer to favor these configurations more often than their agentic counterparts.
- GPT-4o-mini consistently appears in Pareto-optimal flows, highlighting its strong balance of quality and cost as a synthesizing LLM.
- Reasoning models like o3-mini perform exceptionally well on quantitative tasks (e.g., FinanceBench, InfiniteBench), likely due to their advanced multi-hop reasoning capabilities.
- A crucial insight: Pareto frontiers eventually flatten after an initial rise, showcasing diminishing returns in accuracy relative to steep cost increases. This underscores the vital need for tools like syftr that help pinpoint efficient operating points. We routinely find that the workflow at the “knee point” of the Pareto frontier loses just a few percentage points in accuracy compared to the most accurate setup, while being 10x cheaper. Syftr makes finding that sweet spot remarkably easy, revolutionizing AI workflow optimization.
Figure 6. Pareto-optimal flows discovered by syftr on CRAG Task 3 (Sports dataset). syftr identifies workflows that are both more accurate and significantly cheaper than a default RAG pipeline built in LlamaIndex (white box). It also outperforms Amazon Q on the same task—an expected result, given that Q is built for general-purpose usage while syftr is tuned for the dataset. This highlights a key insight: custom flows can meaningfully outperform off-the-shelf solutions, especially in cost-sensitive, accuracy-critical applications.Note: Amazon Q pricing uses a per-user/month pricing model, which differs from the per-query token-based cost estimates used for syftr workflows.
The cost of running syftr is remarkably efficient. In our experiments, allocating a budget of approximately 500 workflow evaluations per task consistently identified strong Pareto frontiers with a one-time search cost of around $500 per use case. This initial investment is minimal when compared to the long-term gains from deploying optimized workflows, whether through significantly reduced compute usage, improved accuracy, or a superior user experience in high-traffic Artificial Intelligence systems.
The Future of Syftr and AI Innovation
Syftr is a continuously evolving framework, with several active areas of research aimed at extending its capabilities and practical impact. We are exploring meta-learning techniques to leverage prior runs across similar tasks, accelerating and guiding future searches. Research into multi-agent workflow evaluation is ongoing, assessing how these complex systems compare to single-agent and non-agentic pipelines and when their inherent tradeoffs are justified. Furthermore, we are investigating deeper integrations with prompt optimization frameworks like DSPy and TextGrad, aiming for joint optimization of workflow structure and language components.
Beyond question-answer tasks, we plan to rapidly expand syftr’s task repertoire to include code generation, data analysis, and interpretation, inviting the community to suggest additional priorities. As these efforts progress, syftr aims to expand its value as an indispensable research tool, a robust benchmarking framework, and a practical assistant for system-level Generative AI design. If you are working in this dynamic space, we warmly welcome your feedback, ideas, and contributions.
To explore syftr further, check out the GitHub repository or read the full paper on ArXiv for detailed methodology and results. Syftr has been accepted to appear at the International Conference on Automated Machine Learning (AutoML) in September 2025 in New York City. We look forward to seeing what you build and discovering what’s next, together.
FAQ: Optimizing Your Artificial Intelligence Workflows
Question 1: What is syftr and why is it important for Artificial Intelligence development?
Syftr is an open-source framework that uses multi-objective Bayesian Optimization to automatically identify Pareto-optimal workflows for Generative AI applications. It’s crucial for Artificial Intelligence development because it addresses the enormous complexity and cost of manually optimizing AI systems, allowing teams to balance accuracy, latency, and cost at scale. This transforms the way LLM-powered solutions are designed and deployed in production.
Question 2: How does syftr help optimize Generative AI workflows, specifically regarding cost and accuracy?
Syftr efficiently explores a vast configuration space of Generative AI components and strategies. For instance, on the CRAG Sports benchmark, syftr identified workflows that achieved the same high accuracy as top-performing configurations but at nearly two orders of magnitude lower cost. By surfacing Pareto-optimal solutions, it helps developers find the “sweet spot” where they achieve sufficient accuracy for their use case without incurring excessive operational expenses, leading to significant AI workflow optimization.
Question 3: Can syftr be integrated with other LLM optimization tools?
Yes, syftr is designed to be complementary and framework-agnostic. While syftr excels at multi-objective configuration search for entire workflows, it can be combined with other LLM optimization tools like Trace or DSPy that specialize in fine-grained prompt tuning or text component optimization. This two-stage approach allows for robust, modular, and flexible AI workflow optimization strategies, enabling further performance gains even in cost-constrained settings.