ChainForge: Open-Source LLM Evaluation Toolkit

• ChainForge is an open-source visual toolkit for prompt engineering and systematic hypothesis testing of large language models using intuitive node-based workflows.
• It enables rapid multi-model evaluation, template-based prompt design, and automated metric computation in both code and no-code settings.
• ChainForge integrates ChainBuddy, an AI assistant that programmatically generates and refines LLM evaluation pipelines to mitigate the blank page problem.

ChainForge is an open-source visual toolkit for prompt engineering, LLM comparison, and systematic hypothesis testing of text generation models. It is designed to support users across a range of technical backgrounds in the creation, refinement, evaluation, and auditing of LLM prompts and model outputs, via graphical, node-based workflows. The platform includes advanced features for multi-model evaluation, template-based prompt design, automated metrics computation, and extensible support for both code and no-code evaluation nodes. ChainForge’s architecture and operational philosophy have been expanded in recent work through the introduction of the ChainBuddy agentic workflow assistant (Zhang et al., 20 Sep 2024), which programmatically generates starter evaluative LLM pipelines aligned to user requirements and mitigates the "blank page problem" endemic in open-ended LLM experimentation.

1. Architecture and Functional Design of ChainForge

ChainForge’s front end is built using React and TypeScript, leveraging ReactFlow for the node-based programming canvas and Mantine for UI widgets. The system can run as a web application or via local installation, supporting MIT-licensed extensions. The core interface consists of:

• Nodes: Inputs (TextFields, CSV/Table import), Generators (Prompt Node, Chat Turn Node), Evaluators (Simple, LLM Scorer, custom Python/JavaScript), Visualizers (plotting/inspection), and Comments.
• Template chaining: Support for hierarchical experimentation where prompt templates can recursively depend on variable fields from upstream nodes.
• Drag-and-drop flow composition: Users connect nodes to define data, prompt, model, evaluation, and visualization pipelines without requiring code.

The platform is engineered for high-throughput, concurrent querying of multiple LLMs (OpenAI GPT-4, Claude, PaLM2, HuggingFace models), result caching, and batch parameterization over combinatorial prompt/model/input spaces. This architecture enables both systematic “zoom-in” refinement and improvisational “zoom-out” exploration across LLM response spaces (Arawjo et al., 2023).

2. Modes of Use: Experimentation and Evaluation Paradigms

ChainForge supports and observes three primary modes of use:

• Opportunistic Exploration: Users rapidly probe models with few prompts, exploring behaviors and iterating on-the-fly. This approach supports flexible, informal hypothesis generation—e.g., probing for jailbreak vulnerabilities or emergent biases.
• Limited Evaluation: Transition to modest systematization, wherein users introduce basic evaluation criteria, increase the permutation space of prompts or models, and explore results via automated plots.
• Iterative Refinement: Users establish stable pipelines and systematically tweak, expand, and benchmark their prompts, models, or input sets for fine-grained improvements.

A typical flow might consist of importing tabular data, defining prompt templates with embedded variables, querying across multiple models, and connecting outputs to evaluators (Python scripts, LLM-based scoring, ground-truth checks), followed by visualization of aggregate metrics. The complexity of such flows is captured by

$$\text{Total requests} = (P_\text{prompts}) \times (M_\text{models}) \times (N_\text{responses per prompt}) \times \max(1, C_\text{chat histories})$$

which quantifies the expansion over prompt, model, and response spaces.

3. ChainBuddy: Automated Workflow Generation Assistant

ChainBuddy is a conversational AI assistant natively integrated into ChainForge to address the “blank page problem” (Zhang et al., 20 Sep 2024). The system leverages a multi-agent architecture based on LangGraph:

• Front-end Requirements Agent: Uses Anthropic Claude 3.5 Sonnet to elicit intent via targeted Q&A (up to three clarifying questions per session), incorporating structured form-based input for goal specification.
• Back-end Planner Agent: OpenAI GPT-4o decomposes user intent into discrete tasks, referencing internal knowledge of allowable nodes and flows.
• Task-Specific Node Agents: Generate JSON specifications for input, prompt, evaluation, and visualization nodes.
• Connection Agents: Assemble outputs, manage node interconnection, and define flow topology.
• Reviewer Agent: Optionally checks workflow fit (disabled for speed in user paper).

Upon requirement completion, ChainBuddy synthesizes and displays an editable visual pipeline within 10–20 seconds. Output flows include constraints, example inputs, prompt templates, multiple LLM queries, and evaluation logic (e.g., Python regex for $\sqrt{\pi}$ detection in math reasoning tasks). All aspects are user-editable, enabling downstream customization or extension.

4. Practical Workflows and Real-World Use Cases

ChainForge and ChainBuddy have been applied to a spectrum of research and professional contexts, including:

• LLM Auditing: Systematic robustness testing against prompt injection and bias.
• Prompt/Template Optimization: Empirical evaluation of alternative prompt formats to optimize accuracy, creativity, and format conformity across models and datasets.
• Data Processing Pipelines: Real-world pipelines for importing tabular data, processing with LLMs, and exporting results—a prominent need in academic and industry settings.
• Evaluation Benchmarking: Comparative performance assessment using built-in and custom evaluators linked to ground truth data.

User studies report strong preference for features such as side-by-side model comparison, rapid design iteration, and sharable flows. Several users, particularly in professional interviews, cited ChainForge’s open-source extensibility—custom node addition and provider integration—as critical for prototyping and production-scale workflows (Arawjo et al., 2023).

5. Empirical Evaluation and User Study Results

A within-subjects, mixed-methods paper of ChainBuddy integration compared an AI-assisted (ChainBuddy-enabled) pipeline generation against manual ChainForge flow construction (Zhang et al., 20 Sep 2024):

• Reduced cognitive load: NASA TLX scores indicated significantly lower mental and physical effort in assistant-enabled condition ($p < 0.05$).
• Increased confidence: Participants reported greater confidence in workflow setup.
• Objectively higher performance: 11/12 participants implemented correct prompt comparison/evaluation flows with ChainBuddy, versus 4/12 manually.
• Greater workflow complexity: Assistant condition yielded higher evaluator node use and more complex chains.
• Time to completion: No significant reduction, attributed to time spent inspecting/editing generated flows.
• Subjective/objective mismatch: Self-rated success was similar between conditions, but independent expert ratings found substantial improvement with AI assistance. This is interpreted as a manifestation of the Dunning-Kruger effect.

Qualitative feedback emphasized resolution of the blank page problem, improved scaffolding for both novice and expert users, and concerns over possible anchoring and over-reliance on AI-generated solutions. Three interaction patterns were observed: light post-editing, substantial revision within generated structure, and extension of chain depth.

6. Design Implications, Limitations, and Future Directions

Findings motivate several recommendations for future agentic workflow assistants in LLM evaluation:

• Prioritize structured intent elicitation: Iterative, targeted clarification dramatically improves workflow relevance and quality.
• Editable, transparent scaffolds: Automated output must be user-customizable; users desire insight into logic and provenance of generated flows.
• Guard against over-reliance: Systems should inform users about assistant-derived decisions and encourage independent verification.
• Objective evaluation over self-report: Due to the Dunning-Kruger discrepancy, outcome metrics (e.g., prompt correctness, flow completeness) are preferable to subjective user ratings.
• Extensibility and modular design: Persistent demand exists for node extensibility, custom LLM provider integration, and expanded data import/export capabilities.

A plausible implication is that design strategies emphasizing transparency, iterative clarification, and explicit user agency are crucial for robust deployment and adoption.

7. Broader Impact and Relation to the Field

ChainForge and ChainBuddy set a precedent for visual, systematic, and agentic toolkits in LLM prompt engineering and evaluation, bridging the gap between non-programmatic playground experimentation and rigorous model auditing. Their design and empirical results inform broader trends in human-computer interaction with foundation models, and their open-source extensibility positions them as foundational infrastructure for data-centric, hypothesis-driven LLM research, development, and alignment. Related datasets and frameworks such as ToolGrad (Zhou et al., 6 Aug 2025) may further enable agentic extension and robustness in tool-use capabilities for LLM-based systems.