{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-prompttools","slug":"pypi-prompttools","name":"prompttools","type":"repo","url":"https://github.com/hegelai/prompttools","page_url":"https://unfragile.ai/pypi-prompttools","categories":["prompt-engineering"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-prompttools__cap_0","uri":"capability://planning.reasoning.multi.model.prompt.comparison.via.unified.experiment.interface","name":"multi-model prompt comparison via unified experiment interface","description":"Executes the same prompt across multiple LLM providers (OpenAI, Anthropic, etc.) in a single experiment run by implementing a polymorphic Experiment base class that abstracts provider-specific API calls. Each provider gets a concrete implementation (OpenAIChatExperiment, AnthropicExperiment) that handles authentication, request formatting, and response parsing, allowing developers to compare outputs side-by-side without writing provider-specific code.","intents":["I want to test the same prompt against GPT-4, Claude, and Llama to see which model performs best","I need to compare model outputs for the same input to choose the best provider for my use case","I want to run A/B tests across multiple LLM providers without rewriting integration code for each one"],"best_for":["prompt engineers evaluating model quality across providers","teams building multi-model fallback systems","developers optimizing cost vs. quality tradeoffs"],"limitations":["Requires valid API keys for each provider being tested","No built-in rate limiting — rapid experiments may hit provider throttling","Response latency varies by provider; no automatic timeout normalization across providers","Limited to providers with Python SDK support or REST API wrappers"],"requires":["Python 3.8+","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Network access to provider endpoints"],"input_types":["prompt text","model parameters (temperature, max_tokens, etc.)","system prompts","chat history"],"output_types":["structured JSON with model responses","CSV export of results","comparison tables with metrics"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_1","uri":"capability://planning.reasoning.parameterized.prompt.template.experimentation.with.cartesian.product.expansion","name":"parameterized prompt template experimentation with cartesian product expansion","description":"Generates a full factorial experiment matrix by accepting prompt templates with variable placeholders and a dictionary of parameter values, then expanding all combinations (e.g., 3 prompts × 2 models × 4 temperature values = 24 test cases). The harness system orchestrates these expanded experiments, executing each combination and collecting results in a unified output table for systematic evaluation of prompt variations.","intents":["I want to test 5 different prompt variations against 3 models with different temperature settings to find the best combination","I need to systematically explore how changing specific words in my prompt affects model output quality","I want to run a grid search over prompt templates and hyperparameters to optimize for a specific metric"],"best_for":["prompt engineers optimizing prompt wording and structure","teams running systematic hyperparameter tuning for LLM applications","researchers evaluating prompt sensitivity across parameter spaces"],"limitations":["Cartesian product expansion can create combinatorial explosion (10 prompts × 5 models × 10 temps = 500 API calls)","No built-in cost estimation before running experiments — can lead to unexpected API bills","Results are stored in-memory; large experiments may consume significant RAM","No automatic deduplication of identical parameter combinations"],"requires":["Python 3.8+","Prompt templates with variable placeholders (e.g., {variable_name})","Dictionary of parameter values to expand","Valid API credentials for target models"],"input_types":["prompt template strings with placeholders","parameter dictionaries","model configuration objects"],"output_types":["expanded experiment matrix (list of test cases)","results table with all parameter combinations","CSV/JSON export of full results"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_10","uri":"capability://data.processing.analysis.cost.estimation.and.tracking.for.llm.api.experiments","name":"cost estimation and tracking for llm api experiments","description":"Calculates estimated and actual costs for experiments based on token counts, model pricing, and API usage, providing cost breakdowns per model, prompt, and parameter combination. Developers can set cost budgets, receive warnings when approaching limits, and analyze cost-effectiveness of different prompt variations relative to quality metrics.","intents":["I want to estimate the cost of running a large experiment before executing it","I need to track how much I'm spending on prompt experimentation across my team","I want to find the most cost-effective prompt variation that still meets quality requirements"],"best_for":["teams managing LLM API budgets and cost optimization","startups minimizing experimentation costs","enterprises tracking LLM spending across projects"],"limitations":["Cost estimation requires accurate token counting; some models have inconsistent tokenization","Pricing data must be manually updated as providers change rates","No real-time cost tracking during experiment execution; costs are calculated post-hoc","Limited to providers with published pricing; custom or enterprise pricing not supported"],"requires":["Python 3.8+","Model pricing configuration (built-in or custom)","Token counting library (tiktoken for OpenAI, etc.)","Experiment execution data"],"input_types":["model names and parameters","prompt text (for token counting)","pricing configuration"],"output_types":["cost estimates (pre-execution)","actual cost breakdowns (post-execution)","cost-effectiveness analysis"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_11","uri":"capability://data.processing.analysis.batch.experiment.execution.with.result.aggregation.and.statistical.analysis","name":"batch experiment execution with result aggregation and statistical analysis","description":"Supports running multiple experiment instances in sequence or parallel, aggregating results across runs and computing statistical summaries (mean, std dev, confidence intervals) for each metric. Developers can run the same experiment multiple times to account for model variability and generate robust performance estimates with statistical confidence.","intents":["I want to run the same prompt experiment 10 times to account for model randomness and get confidence intervals","I need to compare model performance with statistical significance testing, not just point estimates","I want to aggregate results across multiple experiment runs to identify consistent patterns"],"best_for":["researchers requiring statistical rigor in model evaluation","teams making high-stakes decisions based on model performance","developers optimizing for robustness across model variations"],"limitations":["Multiple runs multiply API costs and execution time; no automatic cost-benefit analysis","Statistical analysis assumes independent runs; no built-in handling of correlated results","No automatic determination of required sample size for statistical significance","Results aggregation requires all runs to complete; no streaming or partial result analysis"],"requires":["Python 3.8+","scipy or numpy for statistical calculations","Multiple experiment runs (sequential or parallel execution)"],"input_types":["experiment configuration","number of runs","aggregation strategy"],"output_types":["aggregated metrics (mean, std dev)","confidence intervals","statistical test results","summary statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_2","uri":"capability://data.processing.analysis.automated.metric.based.evaluation.of.llm.outputs.with.pluggable.scorers","name":"automated metric-based evaluation of llm outputs with pluggable scorers","description":"Applies a registry of evaluation functions (scorers) to experiment results after execution, computing metrics like BLEU, ROUGE, semantic similarity, or custom business logic. The evaluation step is decoupled from execution, allowing developers to define custom scorer functions that accept model outputs and reference answers, then aggregate scores across all experiment runs for comparative analysis.","intents":["I want to automatically score all model outputs against a reference answer using BLEU and semantic similarity metrics","I need to apply custom evaluation logic (e.g., checking if output contains required keywords) to all experiment results","I want to compare models not just by output text but by quantitative metrics to make data-driven decisions"],"best_for":["teams evaluating LLM quality with standardized metrics","prompt engineers optimizing for specific evaluation criteria","researchers comparing model performance across benchmarks"],"limitations":["Metric selection is domain-specific; no automatic metric recommendation","Custom scorers require Python code; no low-code metric definition UI","Evaluation happens post-hoc; no streaming evaluation during experiment execution","No built-in statistical significance testing or confidence intervals"],"requires":["Python 3.8+","Scorer functions (built-in or custom) that accept output and reference","Reference answers or ground truth data for comparison","Completed experiment results"],"input_types":["model outputs (strings)","reference answers or ground truth","custom scorer functions"],"output_types":["numeric scores per output","aggregated metrics table","score distributions and statistics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_3","uri":"capability://automation.workflow.interactive.web.based.playground.for.real.time.prompt.testing","name":"interactive web-based playground for real-time prompt testing","description":"Provides a browser-based UI (built with Streamlit or similar) that allows non-technical users to test prompts interactively without writing code. The playground loads experiment definitions from Python files, exposes UI controls for parameter adjustment, executes experiments on-demand, and displays results with visualizations, enabling rapid iteration and exploration of prompt behavior.","intents":["I want to test prompt variations in a web UI without writing Python code","I need to share a prompt testing interface with non-technical stakeholders for feedback","I want to quickly iterate on prompts and see results in real-time without rerunning full experiments"],"best_for":["non-technical product managers and content creators","teams collaborating on prompt optimization","organizations wanting to democratize prompt engineering"],"limitations":["Playground is read-only for experiment definitions; editing requires code changes and restart","No built-in authentication; requires external reverse proxy for multi-user access control","Streamlit-based UI has limited customization compared to custom web applications","Results are not persisted between sessions without external database integration"],"requires":["Python 3.8+","Streamlit or equivalent web framework","Experiment definitions in Python files","API credentials for target LLM providers"],"input_types":["prompt text (via text input)","parameter sliders and dropdowns","file uploads for batch testing"],"output_types":["rendered HTML with results","downloadable CSV/JSON exports","interactive visualizations"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_4","uri":"capability://search.retrieval.vector.database.retrieval.experimentation.with.multi.provider.support","name":"vector database retrieval experimentation with multi-provider support","description":"Extends the Experiment system to test vector databases (Pinecone, Weaviate, Chroma, etc.) by implementing VectorDatabaseExperiment subclasses that handle embedding generation, vector storage, and retrieval evaluation. Developers can compare retrieval quality across different databases, embedding models, and query strategies using the same experiment framework as LLM testing.","intents":["I want to compare retrieval quality across Pinecone, Weaviate, and Chroma for my RAG application","I need to test different embedding models and see how they affect retrieval accuracy","I want to evaluate retrieval performance with different similarity metrics (cosine, euclidean, dot product)"],"best_for":["teams building RAG systems and evaluating vector stores","developers optimizing retrieval quality for semantic search","researchers comparing embedding models and retrieval strategies"],"limitations":["Requires running vector database instances (local or cloud); no mocking for offline testing","Embedding generation adds latency; no caching of embeddings across experiment runs","Limited to vector databases with Python SDKs","No built-in support for hybrid search (vector + keyword); requires custom scorer logic"],"requires":["Python 3.8+","Running vector database instance (Pinecone, Weaviate, Chroma, etc.)","Embedding model (OpenAI, Hugging Face, etc.)","Test documents and queries for retrieval evaluation"],"input_types":["documents to index","queries for retrieval","embedding model configuration","vector database connection parameters"],"output_types":["retrieval results (ranked documents)","relevance scores","comparison metrics (MRR, NDCG, recall@k)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_5","uri":"capability://data.processing.analysis.experiment.result.visualization.and.export.with.multiple.output.formats","name":"experiment result visualization and export with multiple output formats","description":"Generates tabular and graphical visualizations of experiment results using matplotlib and pandas, supporting exports to CSV, JSON, and HTML formats. The visualization step is built into the experiment workflow, automatically creating comparison charts, heatmaps, and summary tables that highlight differences across parameter combinations and model outputs.","intents":["I want to see a side-by-side comparison table of all model outputs for my prompts","I need to create a heatmap showing how temperature and prompt variation affect output quality","I want to export experiment results to CSV for analysis in Excel or other tools"],"best_for":["teams presenting prompt engineering results to stakeholders","researchers creating publication-ready visualizations","developers integrating experiment results into reporting pipelines"],"limitations":["Matplotlib-based visualizations are static; no interactive charts (Plotly integration would require custom code)","Large result sets (1000+ rows) may produce unreadable tables; no built-in pagination or filtering","HTML export is basic; no custom styling or branding options","Visualization logic is tightly coupled to experiment types; extending visualizations requires subclassing"],"requires":["Python 3.8+","matplotlib and pandas libraries","Completed experiment results"],"input_types":["experiment results (in-memory or from file)","visualization configuration (chart type, axes, etc.)"],"output_types":["PNG/PDF charts","CSV files","JSON exports","HTML tables"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_6","uri":"capability://automation.workflow.jupyter.notebook.integration.with.in.cell.experiment.execution.and.result.inspection","name":"jupyter notebook integration with in-cell experiment execution and result inspection","description":"Provides native Jupyter support through IPython display hooks and cell-level experiment execution, allowing developers to run experiments inline and inspect results with interactive tables and plots. Results are stored in notebook-accessible Python objects, enabling exploratory analysis and iterative refinement within the notebook environment without context switching.","intents":["I want to run prompt experiments directly in my Jupyter notebook and see results immediately","I need to iterate on prompts in a notebook, running small experiments and adjusting based on results","I want to explore experiment results interactively using pandas DataFrames and matplotlib plots"],"best_for":["data scientists and researchers using Jupyter for exploratory analysis","teams prototyping LLM applications in notebooks","developers iterating rapidly on prompt engineering"],"limitations":["Notebook execution is sequential; no built-in parallelization across cells","Results are not persisted between notebook sessions without explicit save logic","Large experiments may cause notebook kernel to become unresponsive","Sharing notebooks requires recipients to have API credentials configured"],"requires":["Jupyter or JupyterLab","Python 3.8+","prompttools library installed in notebook kernel","API credentials for target LLM providers"],"input_types":["Python code cells","prompt text","experiment configuration objects"],"output_types":["rendered tables and charts in notebook cells","Python objects (DataFrames, lists) for further analysis","downloadable exports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_7","uri":"capability://automation.workflow.mock.llm.responses.for.offline.testing.and.ci.cd.integration","name":"mock llm responses for offline testing and ci/cd integration","description":"Provides a mocking system that intercepts API calls and returns pre-configured responses without hitting actual LLM endpoints, enabling fast, deterministic testing in CI/CD pipelines and offline environments. Developers can define mock response mappings based on prompt content or parameters, allowing experiments to run without API credentials or network access.","intents":["I want to test my prompt engineering pipeline in CI/CD without incurring API costs","I need to run experiments offline or in environments without internet access","I want to create deterministic tests that always return the same output for the same input"],"best_for":["CI/CD pipelines testing prompt-based applications","teams reducing API costs during development and testing","offline development environments without internet access"],"limitations":["Mock responses are static; no simulation of model behavior variations or edge cases","Requires manual definition of mock response mappings; no automatic recording of real responses","Mock responses don't reflect actual model latency or error patterns","Limited to exact prompt matching; no fuzzy matching or parameterized mock responses"],"requires":["Python 3.8+","Mock response configuration (dictionary or JSON file)","Experiment code that uses mocking adapter"],"input_types":["prompt text (for matching)","mock response mappings (dict or JSON)"],"output_types":["pre-configured LLM responses","experiment results with mocked outputs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_8","uri":"capability://automation.workflow.experiment.logging.and.result.persistence.with.structured.output","name":"experiment logging and result persistence with structured output","description":"Captures experiment metadata, execution logs, and results to structured formats (JSON, CSV) with timestamps and configuration snapshots, enabling reproducibility and audit trails. Logs include API calls, response times, errors, and evaluation metrics, providing visibility into experiment execution and enabling post-hoc analysis and debugging.","intents":["I want to log all experiment runs with timestamps and configurations for reproducibility","I need to debug why a specific experiment produced unexpected results by reviewing logs","I want to track experiment history and compare results across multiple runs over time"],"best_for":["teams requiring audit trails for compliance or reproducibility","researchers documenting experimental methodology","developers debugging unexpected model behavior"],"limitations":["Logs are stored locally; no built-in centralized logging or log aggregation","No automatic log rotation; large experiments can create massive log files","Sensitive data (API keys, full prompts) may be logged; requires manual redaction","No built-in log querying or filtering; requires external tools for log analysis"],"requires":["Python 3.8+","Writable filesystem for log storage","Experiment execution"],"input_types":["experiment configuration","API requests and responses","evaluation metrics"],"output_types":["JSON log files","CSV result exports","structured metadata files"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-prompttools__cap_9","uri":"capability://text.generation.language.chat.history.and.system.prompt.variation.testing.across.conversation.contexts","name":"chat history and system prompt variation testing across conversation contexts","description":"Extends experiments to test multi-turn conversations by accepting chat history as input and varying system prompts, user messages, and conversation context. The experiment framework handles conversation state management, allowing developers to evaluate how different prompts and system instructions affect model behavior across conversation turns.","intents":["I want to test how different system prompts affect a chatbot's behavior across multiple conversation turns","I need to evaluate if my prompt variations maintain consistency across a conversation","I want to compare how different models handle the same conversation history and follow-up questions"],"best_for":["chatbot developers optimizing system prompts and conversation flow","teams building multi-turn conversational AI systems","researchers studying prompt effects on conversation consistency"],"limitations":["Chat history management is manual; no built-in conversation state machine or turn tracking","No automatic conversation branching or tree-based conversation testing","Conversation context grows with each turn, increasing API costs and latency","Limited support for complex conversation patterns (parallel branches, conditional flows)"],"requires":["Python 3.8+","Chat history in list-of-dicts format (role, content)","System prompt configuration","API credentials for LLM providers"],"input_types":["chat history (list of messages)","system prompts","user messages","model parameters"],"output_types":["model responses for each turn","full conversation transcripts","evaluation metrics per turn"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Network access to provider endpoints","Prompt templates with variable placeholders (e.g., {variable_name})","Dictionary of parameter values to expand","Valid API credentials for target models","Model pricing configuration (built-in or custom)","Token counting library (tiktoken for OpenAI, etc.)","Experiment execution data","scipy or numpy for statistical calculations"],"failure_modes":["Requires valid API keys for each provider being tested","No built-in rate limiting — rapid experiments may hit provider throttling","Response latency varies by provider; no automatic timeout normalization across providers","Limited to providers with Python SDK support or REST API wrappers","Cartesian product expansion can create combinatorial explosion (10 prompts × 5 models × 10 temps = 500 API calls)","No built-in cost estimation before running experiments — can lead to unexpected API bills","Results are stored in-memory; large experiments may consume significant RAM","No automatic deduplication of identical parameter combinations","Cost estimation requires accurate token counting; some models have inconsistent tokenization","Pricing data must be manually updated as providers change rates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-prompttools","compare_url":"https://unfragile.ai/compare?artifact=pypi-prompttools"}},"signature":"jrPqkTi92qiv7KwPR/HvE19mYxmEaZ/J27yApMm25Upnx0+HJ51dBGRg78OTIjxm4CbybePfqfOwVm3J2oA7CA==","signedAt":"2026-06-22T05:57:09.626Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-prompttools","artifact":"https://unfragile.ai/pypi-prompttools","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-prompttools","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}