{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dspy","slug":"dspy","name":"DSPy","type":"framework","url":"https://github.com/stanfordnlp/dspy","page_url":"https://unfragile.ai/dspy","categories":["prompt-engineering"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dspy__cap_0","uri":"capability://text.generation.language.declarative.task.definition.via.type.annotated.signatures","name":"declarative task definition via type-annotated signatures","description":"DSPy enables users to define LM tasks through Python type-annotated signatures (input/output fields with descriptions) rather than hand-crafted prompt strings. The framework parses these signatures at runtime to generate task-specific prompts dynamically, supporting field-level documentation, type constraints, and optional few-shot examples. This decouples task logic from prompt implementation, allowing the same signature to work across different LM providers and optimization strategies without code changes.","intents":["Define a multi-input, multi-output LM task without writing prompt templates","Make LM task definitions portable across different model providers","Specify input/output structure and constraints in a type-safe way","Enable automatic prompt generation from task semantics"],"best_for":["Teams building multi-model LM applications who want provider-agnostic task definitions","Developers iterating on task structure without re-writing prompts","Projects requiring type-safe LM interfaces with clear input/output contracts"],"limitations":["Signature-based generation produces generic prompts; highly specialized domain prompts may require manual refinement","Complex multi-step reasoning tasks may need explicit few-shot examples to achieve target quality","Type annotations map to natural language descriptions; non-standard types require custom serialization"],"requires":["Python 3.8+","Basic understanding of Python type hints (typing module)","At least one configured LM provider (OpenAI, Anthropic, Ollama, etc.)"],"input_types":["Python type annotations (str, int, bool, List, custom classes)","Field descriptions (docstrings or metadata)"],"output_types":["Dynamically generated prompt strings","Structured output objects matching signature definition"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_1","uri":"capability://planning.reasoning.metric.driven.prompt.optimization.via.teleprompters","name":"metric-driven prompt optimization via teleprompters","description":"DSPy's optimizer system (teleprompters) automatically tunes prompts and few-shot examples by running a program against a training dataset, measuring performance with a user-defined metric function, and iteratively refining prompts to maximize that metric. Optimizers include few-shot example selection (BootstrapFewShot), instruction optimization (MIPROv2), and reflective strategies (GEPA, SIMBA). The compilation process generates optimized prompts that are then frozen for inference, replacing manual trial-and-error prompt engineering.","intents":["Automatically find effective prompts for a task without manual iteration","Optimize few-shot examples based on task-specific metrics","Tune both prompt instructions and example selection jointly","Generate model-specific optimized prompts from a single task definition"],"best_for":["Teams with labeled training data who want to avoid manual prompt engineering","Projects where prompt quality directly impacts business metrics","Developers building production LM systems that need reproducible, metric-driven optimization"],"limitations":["Optimization requires a labeled validation dataset; unsupervised tasks need proxy metrics","Optimizer runtime scales with dataset size and LM API costs; large datasets (>1000 examples) may be expensive","Optimized prompts may overfit to training distribution; generalization to new domains requires re-optimization","Some optimizers (MIPROv2) require multiple forward passes per example, adding latency during compilation"],"requires":["Python 3.8+","Labeled training dataset (minimum 10-50 examples for few-shot optimization)","Metric function that evaluates program output (e.g., exact match, F1, custom scorer)","Configured LM provider with sufficient API quota"],"input_types":["DSPy program (composed of modules)","Training dataset (list of Example objects with inputs and expected outputs)","Metric function (callable that returns float score)"],"output_types":["Optimized DSPy program with tuned prompts and few-shot examples","Serialized program state (JSON) for deployment"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_10","uri":"capability://memory.knowledge.caching.and.retrieval.augmented.generation.rag.integration","name":"caching and retrieval-augmented generation (rag) integration","description":"DSPy integrates with vector databases and retrieval systems to enable retrieval-augmented generation (RAG) patterns. The framework provides dspy.Retrieve module that queries a vector store (Weaviate, Pinecone, FAISS, etc.) to fetch relevant context, which is then passed to LM modules. DSPy also includes caching mechanisms to avoid redundant LM calls and vector store queries, reducing latency and API costs. The retrieval and caching layers are transparent to the program logic, allowing RAG to be added or modified without changing module code.","intents":["Add retrieval-augmented generation to LM programs without manual context management","Query vector databases to fetch relevant context for LM predictions","Cache LM outputs and retrieval results to reduce API costs and latency","Build knowledge-grounded LM systems that can access external documents"],"best_for":["Teams building knowledge-grounded LM systems","Projects where LM performance depends on access to external documents","Applications requiring cost optimization through caching"],"limitations":["Retrieval quality depends on vector store quality and embedding model; poor embeddings lead to irrelevant context","Caching adds complexity; cache invalidation and staleness require careful management","Vector store queries add latency; no built-in optimization for retrieval speed","RAG integration requires external vector store setup and maintenance"],"requires":["Python 3.8+","Vector database (Weaviate, Pinecone, FAISS, etc.) or embedding model","Indexed documents or knowledge base","Configured LM provider"],"input_types":["Query (text to retrieve context for)","Vector store connection","Embedding model"],"output_types":["Retrieved documents (list of text chunks)","LM output augmented with retrieved context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_11","uri":"capability://automation.workflow.program.serialization.and.deployment","name":"program serialization and deployment","description":"DSPy programs can be serialized to JSON or Python code, enabling deployment to production environments without requiring the DSPy framework at runtime. The serialization captures optimized prompts, few-shot examples, and module structure, which can then be executed using lightweight inference code. This allows teams to optimize programs in a development environment (with full DSPy tooling) and deploy optimized artifacts to production (with minimal dependencies). Serialization also enables version control and reproducibility of optimized programs.","intents":["Export optimized DSPy programs for deployment without framework dependencies","Version control and reproduce optimized prompts and examples","Deploy programs to resource-constrained environments","Share optimized programs across teams without requiring DSPy installation"],"best_for":["Teams deploying LM systems to production","Projects requiring reproducibility and version control of optimized programs","Applications with strict dependency or resource constraints"],"limitations":["Serialization captures static prompts and examples; dynamic behavior (e.g., conditional logic) may not serialize cleanly","Deserialization requires custom code to reconstruct module behavior; no automatic deserialization","Serialized programs are not human-readable; debugging requires inspection tools","Updates to optimized programs require re-serialization; no incremental updates"],"requires":["Python 3.8+","Optimized DSPy program","Deployment environment with LM provider access"],"input_types":["DSPy program (optimized or not)","Serialization format (JSON or Python)"],"output_types":["Serialized program (JSON or Python code)","Deployment artifact (executable program)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_12","uri":"capability://automation.workflow.parallel.and.asynchronous.execution","name":"parallel and asynchronous execution","description":"DSPy supports parallel and asynchronous execution of modules to improve throughput and reduce latency. Programs can use Python's asyncio to run multiple LM calls concurrently, and the framework provides utilities for batch processing and parallel module execution. This enables efficient processing of large datasets and concurrent requests without blocking. Async execution is particularly useful for I/O-bound operations like API calls, where multiple requests can be in-flight simultaneously.","intents":["Process large datasets efficiently by running multiple LM calls in parallel","Reduce latency for concurrent requests by executing modules asynchronously","Batch process examples to maximize LM API throughput","Build responsive LM applications that don't block on API calls"],"best_for":["Teams processing large datasets with LM modules","Applications requiring low-latency responses to concurrent requests","Projects where throughput is a bottleneck"],"limitations":["Parallel execution increases API costs; rate limiting may be needed","Async code is more complex to debug and reason about","Batch processing requires careful handling of failures; partial batch failures may be hard to recover from","Concurrency limits depend on LM provider quotas; no built-in rate limiting or quota management"],"requires":["Python 3.8+","Understanding of asyncio and concurrent programming","LM provider with sufficient rate limits and quota"],"input_types":["List of examples to process","Async module definitions"],"output_types":["Batch results (list of predictions)","Async iterators for streaming results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_13","uri":"capability://planning.reasoning.evaluation.framework.with.custom.metrics","name":"evaluation framework with custom metrics","description":"DSPy provides a built-in evaluation framework that runs programs on test datasets and computes user-defined metrics. The framework supports standard metrics (exact match, F1, BLEU, ROUGE) and custom metric functions that can evaluate semantic correctness, task-specific properties, or business metrics. Evaluation results are aggregated and reported with detailed breakdowns, enabling teams to assess program quality and compare different optimization strategies. The evaluation framework integrates with optimizers to guide prompt tuning based on metrics.","intents":["Evaluate LM program performance on test datasets","Define custom metrics that capture task-specific quality","Compare performance across different models and optimization strategies","Track program quality over time and across iterations"],"best_for":["Teams building production LM systems requiring rigorous evaluation","Projects where task-specific metrics are critical","Applications requiring reproducible, metric-driven development"],"limitations":["Custom metrics require manual implementation; no automatic metric discovery","Evaluation requires labeled test data; unsupervised tasks need proxy metrics","Metric computation can be expensive (e.g., semantic similarity metrics require additional LM calls)","Metrics may not capture all aspects of quality; multi-metric evaluation is needed for comprehensive assessment"],"requires":["Python 3.8+","Test dataset with labeled examples","Metric function (built-in or custom)"],"input_types":["Program to evaluate","Test dataset","Metric function(s)"],"output_types":["Metric scores (float)","Detailed evaluation report with breakdowns","Per-example results for error analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_14","uri":"capability://text.generation.language.conversation.history.and.multi.turn.dialogue.management","name":"conversation history and multi-turn dialogue management","description":"DSPy provides built-in support for multi-turn conversations through history management modules that track dialogue context across turns. The framework automatically manages conversation state, including previous messages, user inputs, and LM responses. Modules can access conversation history to provide context-aware responses, and the history is automatically threaded through the program. This enables building chatbots and dialogue systems without manual context management, and supports optimization of dialogue strategies through the standard optimizer framework.","intents":["Build multi-turn chatbots that maintain conversation context","Automatically manage conversation history without manual state tracking","Optimize dialogue strategies using metrics like user satisfaction or task completion","Support context-aware responses that reference previous turns"],"best_for":["Teams building chatbots and dialogue systems","Projects requiring multi-turn conversation support","Applications where conversation context is critical"],"limitations":["Long conversation histories increase prompt length and latency; no automatic history truncation","History management adds complexity; state consistency requires careful handling","Dialogue optimization requires labeled conversation data; collecting quality dialogue data is expensive","Context window limits may truncate important history; no intelligent history summarization"],"requires":["Python 3.8+","Conversation dataset (for optimization)","Configured LM provider"],"input_types":["User message (current turn)","Conversation history (previous turns)"],"output_types":["LM response (context-aware)","Updated conversation history"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_15","uri":"capability://memory.knowledge.vector.database.integration.for.semantic.retrieval","name":"vector database integration for semantic retrieval","description":"DSPy integrates with vector databases (Weaviate, Pinecone, Chroma) to enable semantic retrieval of documents or examples. The framework can automatically embed inputs, query the vector database, and inject retrieved results into LM prompts. This enables building retrieval-augmented generation (RAG) systems where the LM has access to relevant context.","intents":["Build RAG systems that retrieve relevant documents before generating answers","Automatically embed and retrieve similar examples for few-shot learning","Integrate external knowledge bases with LM programs"],"best_for":["RAG applications","knowledge-intensive tasks","systems with large document collections"],"limitations":["Retrieval adds 50-200ms latency per query","Embedding quality depends on embedding model; poor embeddings lead to irrelevant retrieval","Vector database setup and maintenance adds operational complexity"],"requires":["Python 3.8+","Vector database (Weaviate, Pinecone, Chroma, etc.)","Embedding model"],"input_types":["Query text"],"output_types":["Retrieved documents + LM output"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_16","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.tool.discovery","name":"model context protocol (mcp) integration for tool discovery","description":"DSPy supports the Model Context Protocol (MCP), enabling dynamic discovery and invocation of tools from MCP servers. This allows LM programs to access tools defined in external MCP servers without hardcoding tool definitions. The framework handles MCP communication, schema discovery, and tool invocation transparently.","intents":["Dynamically discover and use tools from MCP servers","Build agents that can access tools from multiple MCP servers","Integrate with MCP-compatible tools without manual schema definition"],"best_for":["agents using multiple tool providers","systems with dynamic tool requirements","teams using MCP-compatible tools"],"limitations":["MCP communication adds 50-100ms latency per tool discovery","Tool availability depends on MCP server uptime","Complex MCP schemas may require custom handling"],"requires":["Python 3.8+","MCP server","MCP client library"],"input_types":["MCP server configuration"],"output_types":["Available tools from MCP server"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_17","uri":"capability://automation.workflow.observability.and.execution.tracing.with.debugging.hooks","name":"observability and execution tracing with debugging hooks","description":"DSPy provides comprehensive execution tracing that captures all LM calls, module invocations, and intermediate results. The framework generates execution traces that can be inspected for debugging, logged for monitoring, or exported for analysis. Traces include timing information, LM settings, and output values, enabling detailed program analysis.","intents":["Debug DSPy programs by inspecting execution traces","Monitor LM program behavior in production","Analyze performance bottlenecks and optimize execution"],"best_for":["developers debugging complex LM programs","teams monitoring production systems","researchers analyzing LM behavior"],"limitations":["Tracing adds ~1-2ms overhead per module call","Trace storage grows with program complexity (1KB per module call)","Detailed tracing can expose sensitive information in logs"],"requires":["Python 3.8+"],"input_types":["DSPy program"],"output_types":["Execution trace with timing and results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_2","uri":"capability://planning.reasoning.composable.module.system.with.automatic.context.threading","name":"composable module system with automatic context threading","description":"DSPy programs are built by composing reusable modules (Predict, ChainOfThought, ReAct, etc.) that automatically thread context and outputs through the computation graph. Each module inherits from dspy.Module and implements a forward() method that calls other modules or LM predictions. The framework handles prompt generation, LM invocation, and output parsing transparently, allowing developers to write imperative Python code that reads like standard control flow while maintaining declarative task definitions underneath.","intents":["Build multi-step LM pipelines by composing simple modules","Automatically propagate outputs from one module to the next without manual context management","Create reusable, testable LM components that work across different programs","Debug and inspect intermediate outputs in complex LM workflows"],"best_for":["Teams building complex LM agents with multiple reasoning steps","Developers who want to write LM code that looks like standard Python","Projects requiring modular, testable LM components for maintenance and reuse"],"limitations":["Module composition adds abstraction overhead; debugging requires understanding the module call stack","Automatic context threading can lead to unexpected behavior if modules share state; explicit state management is needed for stateful modules","Large composition graphs may generate verbose prompts; prompt compression strategies are not built-in","Module serialization (for deployment) requires careful handling of closures and external dependencies"],"requires":["Python 3.8+","Understanding of DSPy's Module base class and forward() pattern","Configured LM provider for each module that makes predictions"],"input_types":["Python objects (dspy.Module subclasses)","Intermediate outputs from previous modules (dspy.Prediction objects)"],"output_types":["dspy.Prediction objects (structured outputs with field access)","Composed program output (final result of forward() chain)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_3","uri":"capability://tool.use.integration.multi.provider.lm.abstraction.with.unified.interface","name":"multi-provider lm abstraction with unified interface","description":"DSPy abstracts over multiple LM providers (OpenAI, Anthropic, Ollama, HuggingFace, Azure, etc.) through a unified dspy.ChainOfThought or dspy.Predict interface that works identically regardless of backend. The framework uses LiteLLM under the hood to normalize API differences, handle retries, and manage rate limiting. Users configure a provider once via dspy.settings.configure() and all modules automatically use that provider without code changes, enabling easy model switching and A/B testing across providers.","intents":["Switch between different LM providers without changing program code","Compare performance across models (GPT-4, Claude, Llama, etc.) on the same task","Use local models (Ollama) for development and cloud models for production","Manage API keys and provider configuration centrally"],"best_for":["Teams evaluating multiple LM providers for cost/performance tradeoffs","Developers building provider-agnostic LM applications","Projects requiring local-first development with cloud deployment"],"limitations":["Provider-specific features (function calling, vision, streaming) require adapter code; not all providers support all features","API rate limits and quota management are provider-specific; DSPy provides basic retry logic but not sophisticated rate limiting","Model-specific prompt optimization may not transfer across providers; re-optimization may be needed","Latency varies significantly across providers; no built-in latency optimization or provider selection"],"requires":["Python 3.8+","API keys for at least one LM provider (OpenAI, Anthropic, etc.) or local Ollama instance","LiteLLM installed (included in DSPy dependencies)"],"input_types":["Provider name (string: 'openai', 'claude', 'ollama', etc.)","Model identifier (string: 'gpt-4', 'claude-3-opus', 'llama2', etc.)","API credentials (environment variables or explicit configuration)"],"output_types":["Unified dspy.Prediction objects (same structure regardless of provider)","Provider-specific metadata (token counts, finish reasons, etc.)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_4","uri":"capability://safety.moderation.assertion.based.output.validation.and.error.recovery","name":"assertion-based output validation and error recovery","description":"DSPy includes a dspy.Assertion system that validates LM outputs against user-defined predicates during program execution. Assertions can check output format, value ranges, semantic properties, or custom logic. When an assertion fails, DSPy can automatically trigger recovery strategies: backtracking to retry with different prompts, calling alternative modules, or raising an exception. This enables robust error handling in LM pipelines without manual try-catch boilerplate, and integrates with optimizers to learn prompts that satisfy assertions.","intents":["Validate LM outputs match expected format or constraints","Automatically retry or recover from invalid outputs without manual error handling","Learn prompts that satisfy output constraints during optimization","Ensure downstream code receives valid, well-formed data from LM predictions"],"best_for":["Production LM systems requiring guaranteed output format","Applications where invalid outputs cause downstream failures","Teams building robust agents that need automatic error recovery"],"limitations":["Assertions add runtime overhead; each assertion requires an LM call or custom validation logic","Recovery strategies (backtracking, retries) increase latency and API costs; no built-in cost budgeting","Complex assertions may be hard to express as predicates; semantic assertions require additional LM calls","Assertion failures don't always indicate fixable problems; some failures may require human intervention"],"requires":["Python 3.8+","Predicates that can evaluate outputs (functions or lambda expressions)","Understanding of assertion semantics and recovery strategies"],"input_types":["LM output (dspy.Prediction object)","Predicate function (callable that returns bool)"],"output_types":["Validated output (if assertion passes)","Recovery attempt result (if assertion fails and recovery is triggered)","Exception (if assertion fails and no recovery strategy applies)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_5","uri":"capability://planning.reasoning.few.shot.example.synthesis.and.selection","name":"few-shot example synthesis and selection","description":"DSPy's BootstrapFewShot optimizer automatically selects or synthesizes few-shot examples from a training dataset to improve LM performance on a task. The optimizer runs the program on training examples, identifies failures (using the metric function), and selects diverse, representative examples that demonstrate correct behavior. These examples are then added to the prompt as in-context demonstrations. Advanced optimizers like MIPROv2 jointly optimize example selection with instruction tuning, while GEPA uses reflective reasoning to generate synthetic examples that target specific failure modes.","intents":["Automatically find effective few-shot examples without manual curation","Improve LM performance on a task by adding relevant demonstrations","Synthesize diverse examples that cover different aspects of the task","Reduce manual prompt engineering by learning examples from data"],"best_for":["Teams with labeled training data who want to improve LM performance","Tasks where few-shot examples significantly impact quality","Projects where manual example curation is time-consuming"],"limitations":["Example synthesis requires multiple LM calls; optimization time scales with dataset size","Selected examples may not generalize to out-of-distribution test data; domain shift requires re-optimization","Example diversity is heuristic-based; no guarantee of optimal coverage","Large example sets increase prompt length and latency; no automatic pruning of redundant examples"],"requires":["Python 3.8+","Training dataset with at least 10-50 labeled examples","Metric function to evaluate example quality","Configured LM provider"],"input_types":["Training dataset (list of dspy.Example objects)","Metric function (callable that returns float)","Program to optimize"],"output_types":["Selected few-shot examples (list of dspy.Example objects)","Optimized program with examples embedded in prompts"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_6","uri":"capability://planning.reasoning.instruction.optimization.via.miprov2","name":"instruction optimization via miprov2","description":"MIPROv2 (Multi-prompt Instruction Parameter Refinement Optimizer v2) jointly optimizes both the task instructions and few-shot examples by treating them as learnable parameters. The optimizer uses a combination of gradient-free search (Bayesian optimization, genetic algorithms) and LM-based proposal generation to explore the instruction space. It generates candidate instructions, evaluates them on the training set, and iteratively refines the best instructions. This approach discovers more effective task descriptions than hand-written prompts, often improving performance by 5-20% on complex tasks.","intents":["Automatically discover effective task instructions without manual writing","Jointly optimize instructions and examples for maximum performance","Explore instruction space systematically using gradient-free search","Generate task-specific prompts that outperform generic templates"],"best_for":["Teams with complex tasks where instruction quality significantly impacts performance","Projects where manual prompt engineering has plateaued","Applications requiring state-of-the-art prompt optimization"],"limitations":["MIPROv2 requires many LM calls (100s to 1000s); optimization cost scales with instruction complexity","Instruction optimization may overfit to training distribution; generalization requires careful validation","Generated instructions are often verbose and may not be human-interpretable","Search space is large; no guarantee of finding global optimum, only local improvements"],"requires":["Python 3.8+","Training dataset with 50+ labeled examples for reliable optimization","Metric function to evaluate instruction quality","Significant API budget (MIPROv2 can cost $10-100+ per optimization run)"],"input_types":["Program to optimize","Training dataset","Metric function","Search configuration (number of iterations, candidate pool size, etc.)"],"output_types":["Optimized instructions (string)","Optimized few-shot examples","Optimized program ready for deployment"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_7","uri":"capability://planning.reasoning.reflective.reasoning.and.self.improvement.via.gepa","name":"reflective reasoning and self-improvement via gepa","description":"GEPA (Guided Example Proposal Agent) uses reflective reasoning to improve LM performance by having the model analyze its own failures and propose corrective examples. The optimizer runs the program, identifies failures, and prompts the LM to generate synthetic examples that address those failures. These synthetic examples are then added to the prompt, creating a feedback loop where the model learns from its mistakes. This approach is particularly effective for tasks where the model can articulate why it failed and generate corrective demonstrations.","intents":["Improve LM performance by having the model learn from its own failures","Generate synthetic examples that target specific failure modes","Enable self-improving LM systems without external data annotation","Combine reflective reasoning with few-shot optimization"],"best_for":["Tasks where the LM can articulate failure reasons","Projects with limited labeled data but access to LM for reflection","Applications requiring self-improving systems"],"limitations":["Reflective reasoning requires additional LM calls; optimization time is higher than BootstrapFewShot","Generated examples may not be diverse or representative; quality depends on LM's ability to self-analyze","Synthetic examples can introduce bias if the LM's failure analysis is incorrect","Reflective reasoning works best on tasks with clear failure modes; ambiguous tasks may not benefit"],"requires":["Python 3.8+","Training dataset with labeled examples","LM capable of reflective reasoning (larger models perform better)","Metric function to evaluate performance"],"input_types":["Program to optimize","Training dataset","Metric function"],"output_types":["Synthetic examples generated via reflection","Optimized program with reflective examples"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_8","uri":"capability://planning.reasoning.stochastic.optimization.via.simba","name":"stochastic optimization via simba","description":"SIMBA (Stochastic Iterative Model-Based Adaptation) optimizes prompts using stochastic search and model-based adaptation, treating prompt optimization as a black-box optimization problem. The optimizer samples candidate prompts, evaluates them on the training set, and uses the results to guide future samples. Unlike MIPROv2's deterministic search, SIMBA uses randomization to explore the prompt space more broadly, making it effective for tasks where the optimal prompt is far from the initial guess. SIMBA can also adapt to different model families (e.g., switching from GPT-4 to Claude).","intents":["Optimize prompts using stochastic search when deterministic methods plateau","Explore prompt space broadly to avoid local optima","Adapt prompts to different model families automatically","Find effective prompts for novel or unusual tasks"],"best_for":["Tasks where initial prompts are far from optimal","Projects requiring adaptation across different model families","Applications where exploration is more important than exploitation"],"limitations":["Stochastic search requires more samples than deterministic methods; higher API costs","Randomization can lead to high variance in results; multiple runs may be needed","Convergence is slower than MIPROv2 on well-structured tasks","No guarantee of finding global optimum; results depend on random seed"],"requires":["Python 3.8+","Training dataset","Metric function","Configured LM provider(s)"],"input_types":["Program to optimize","Training dataset","Metric function","Search configuration (number of samples, exploration strategy)"],"output_types":["Optimized prompts","Optimized program"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__cap_9","uri":"capability://tool.use.integration.tool.calling.and.function.integration.via.adapters","name":"tool calling and function integration via adapters","description":"DSPy's adapter system enables LM modules to call external tools and functions through a unified interface. Adapters normalize function calling across different LM providers (OpenAI's function calling, Anthropic's tool_use, etc.) and map LM outputs to function calls. The framework supports defining tools as Python functions with type annotations, automatically generating tool schemas, and handling tool execution and result parsing. This enables LM agents to interact with APIs, databases, and custom code without manual prompt engineering for tool invocation.","intents":["Enable LM modules to call external APIs and functions","Normalize function calling across different LM providers","Automatically generate tool schemas from Python functions","Build LM agents that can interact with external systems"],"best_for":["Teams building LM agents that need to call APIs or databases","Projects requiring multi-provider tool calling support","Applications where LMs need to interact with external systems"],"limitations":["Tool calling adds latency; each tool invocation requires an LM call and function execution","Provider-specific tool calling features (parallel calls, streaming) may not be fully supported","Tool schemas must be manually defined or auto-generated from type hints; complex tools may require custom adapters","Error handling for tool failures requires explicit logic; no automatic retry or fallback"],"requires":["Python 3.8+","Tool definitions (Python functions with type annotations)","LM provider that supports function calling (OpenAI, Anthropic, etc.)"],"input_types":["Tool definitions (Python functions)","Tool schemas (auto-generated or manual)","LM output indicating tool to call"],"output_types":["Tool call results","LM response incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dspy__headline","uri":"capability://prompt.engineering.declarative.programming.framework.for.language.models","name":"declarative programming framework for language models","description":"DSPy is a framework that allows developers to program language models using declarative modules instead of manual prompting, optimizing prompts automatically based on user-defined metrics.","intents":["best framework for programming language models","declarative programming for AI prompts","how to optimize prompts for language models","programming frameworks for foundation models","tools for automatic prompt tuning"],"best_for":["developers seeking to streamline prompt engineering"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Basic understanding of Python type hints (typing module)","At least one configured LM provider (OpenAI, Anthropic, Ollama, etc.)","Labeled training dataset (minimum 10-50 examples for few-shot optimization)","Metric function that evaluates program output (e.g., exact match, F1, custom scorer)","Configured LM provider with sufficient API quota","Vector database (Weaviate, Pinecone, FAISS, etc.) or embedding model","Indexed documents or knowledge base","Configured LM provider","Optimized DSPy program"],"failure_modes":["Signature-based generation produces generic prompts; highly specialized domain prompts may require manual refinement","Complex multi-step reasoning tasks may need explicit few-shot examples to achieve target quality","Type annotations map to natural language descriptions; non-standard types require custom serialization","Optimization requires a labeled validation dataset; unsupervised tasks need proxy metrics","Optimizer runtime scales with dataset size and LM API costs; large datasets (>1000 examples) may be expensive","Optimized prompts may overfit to training distribution; generalization to new domains requires re-optimization","Some optimizers (MIPROv2) require multiple forward passes per example, adding latency during compilation","Retrieval quality depends on vector store quality and embedding model; poor embeddings lead to irrelevant context","Caching adds complexity; cache invalidation and staleness require careful management","Vector store queries add latency; no built-in optimization for retrieval speed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=dspy","compare_url":"https://unfragile.ai/compare?artifact=dspy"}},"signature":"pEdFbrJ8IwREPwug5+aS9LuKc0rjPb4xiC5xZ9es8Rzy+Y6YyfusD+CWBB+qKrjqtzaCpKSmCwgnqHlrd3WEDg==","signedAt":"2026-06-22T02:37:15.813Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dspy","artifact":"https://unfragile.ai/dspy","verify":"https://unfragile.ai/api/v1/verify?slug=dspy","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"}}