{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-large-language-models-as-optimizers-opro","slug":"large-language-models-as-optimizers-opro","name":"Large Language Models as Optimizers (OPRO)","type":"product","url":"https://arxiv.org/abs/2309.03409","page_url":"https://unfragile.ai/large-language-models-as-optimizers-opro","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-large-language-models-as-optimizers-opro__cap_0","uri":"capability://planning.reasoning.llm.based.gradient.free.optimization.via.in.context.learning","name":"llm-based gradient-free optimization via in-context learning","description":"Uses large language models as black-box optimizers by prompting them with optimization trajectories (previous solutions and their scores) to generate improved candidate solutions iteratively. The LLM learns optimization patterns from in-context examples without explicit gradient computation, treating the optimization problem as a sequence prediction task where better solutions are generated by conditioning on historical performance data.","intents":["Optimize hyperparameters, prompts, or configurations without access to gradients","Find better solutions to discrete or non-differentiable problems using only evaluation feedback","Leverage LLM reasoning to guide search through high-dimensional solution spaces","Reduce optimization iterations by using LLM's learned priors about what makes good solutions"],"best_for":["Researchers optimizing prompt templates or hyperparameters for LLM tasks","Teams solving discrete optimization problems where gradient-based methods are infeasible","Practitioners needing few-shot optimization without training custom models","AutoML and neural architecture search applications"],"limitations":["Optimization quality depends heavily on LLM's ability to recognize patterns in the trajectory history — may plateau on complex multimodal landscapes","Each optimization step requires a full LLM forward pass, making it computationally expensive compared to gradient-based methods for large-scale problems","No theoretical convergence guarantees; performance is empirical and problem-dependent","Requires sufficient evaluation budget to build meaningful in-context examples; performs poorly with <5-10 prior solutions","LLM may generate solutions that are syntactically valid but semantically nonsensical for the target domain"],"requires":["Access to a capable LLM (GPT-3.5+ or equivalent) via API or local deployment","A differentiable or evaluable objective function that can score candidate solutions","Ability to serialize solutions as text for LLM input","Python 3.7+ for typical implementations"],"input_types":["text (problem description, constraints)","structured data (optimization trajectory: previous solutions + their scores)","code (for hyperparameter or prompt optimization tasks)"],"output_types":["text (optimized solution candidate)","structured data (optimization trajectory with new solution and score)","code (optimized hyperparameters, prompts, or configurations)"],"categories":["planning-reasoning","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-large-language-models-as-optimizers-opro__cap_1","uri":"capability://planning.reasoning.trajectory.conditioned.solution.generation.with.scoring.feedback","name":"trajectory-conditioned solution generation with scoring feedback","description":"Implements an iterative loop where the LLM receives a formatted history of (solution, evaluation_score) pairs and generates a new candidate solution. The prompt structure encodes the optimization trajectory as in-context examples, allowing the LLM to learn implicit patterns about which solution characteristics correlate with higher scores. After evaluation, the new solution and its score are appended to the trajectory for the next iteration.","intents":["Iteratively refine solutions by showing the LLM what worked and what didn't","Build optimization trajectories that demonstrate solution quality trends","Enable the LLM to discover domain-specific heuristics from evaluation feedback","Implement few-shot meta-learning for optimization without retraining"],"best_for":["Prompt engineers optimizing instruction templates for downstream tasks","Hyperparameter tuning for machine learning models","Discrete optimization problems (e.g., combinatorial search, code generation)","Few-shot learning scenarios with limited evaluation budget"],"limitations":["Trajectory length is bounded by LLM context window; long optimization histories may be truncated or summarized, losing fine-grained signal","LLM may overfit to spurious correlations in short trajectories, generating solutions that exploit evaluation noise rather than improving fundamentally","No mechanism to enforce diversity in generated solutions; may converge to local optima or repetitive candidates","Requires manual prompt engineering to format trajectories effectively; poor formatting degrades optimization quality significantly"],"requires":["LLM with sufficient context window (4K+ tokens recommended for meaningful trajectory history)","Evaluation function that returns scalar scores (or easily interpretable metrics)","Ability to format solutions and scores as natural language or structured text","Deterministic or low-variance evaluation to avoid noisy feedback"],"input_types":["text (problem statement, constraints, evaluation criteria)","structured data (trajectory: list of [solution, score] pairs)","code (for code-based optimization tasks)"],"output_types":["text (next candidate solution)","structured data (updated trajectory with new solution and score)","metrics (optimization progress, convergence diagnostics)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-large-language-models-as-optimizers-opro__cap_2","uri":"capability://text.generation.language.prompt.optimization.via.iterative.refinement.and.scoring","name":"prompt optimization via iterative refinement and scoring","description":"Applies the OPRO framework specifically to optimize natural language prompts by treating prompt text as the solution space and downstream task performance (e.g., accuracy on a benchmark) as the evaluation metric. The LLM generates improved prompt variations by analyzing which previous prompts achieved higher scores, learning to modify instruction phrasing, examples, and constraints to maximize task performance. This enables automated prompt engineering without manual trial-and-error.","intents":["Automatically improve prompt templates for classification, summarization, or reasoning tasks","Discover effective instruction phrasings that outperform hand-crafted prompts","Adapt prompts to new domains or tasks by learning from evaluation feedback","Scale prompt optimization across multiple tasks without manual intervention"],"best_for":["ML teams optimizing prompts for production LLM applications","Researchers studying prompt design and instruction engineering","Practitioners building few-shot learning systems with limited labeled data","Organizations seeking to reduce manual prompt engineering effort"],"limitations":["Optimization is task-specific; prompts optimized for one task may not transfer to different domains or LLM models","Evaluation requires running the downstream task multiple times, incurring significant computational and API costs","LLM-generated prompts may be verbose, redundant, or contain unnecessary complexity compared to human-written prompts","Sensitive to evaluation metric choice; optimizing for one metric (e.g., accuracy) may degrade performance on others (e.g., latency, fairness)","Requires sufficient evaluation budget (typically 20-100+ iterations) to find substantially better prompts"],"requires":["Access to an LLM (GPT-3.5+ or equivalent) for prompt generation","A downstream task with a quantifiable evaluation metric (accuracy, F1, BLEU, etc.)","Evaluation dataset or benchmark to score prompt candidates","Budget for multiple LLM API calls (one per optimization iteration)"],"input_types":["text (initial prompt template, task description, evaluation criteria)","structured data (trajectory of previous prompts and their scores)","code (evaluation function or benchmark)"],"output_types":["text (optimized prompt template)","structured data (optimization trajectory, performance metrics)","metrics (task performance improvement, convergence analysis)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-large-language-models-as-optimizers-opro__cap_3","uri":"capability://planning.reasoning.hyperparameter.optimization.via.llm.guided.search","name":"hyperparameter optimization via llm-guided search","description":"Applies OPRO to optimize hyperparameters (learning rates, batch sizes, regularization coefficients, etc.) by representing hyperparameter configurations as text and iteratively generating improved configurations based on their validation performance. The LLM learns implicit relationships between hyperparameter values and model performance from the trajectory history, generating candidates that balance exploration (trying new values) and exploitation (refining promising regions).","intents":["Automatically tune hyperparameters for machine learning models without manual grid/random search","Discover hyperparameter configurations that outperform defaults or hand-tuned values","Adapt hyperparameters to new datasets or model architectures by learning from evaluation feedback","Reduce hyperparameter tuning time and computational cost compared to exhaustive search"],"best_for":["ML engineers tuning models for production deployment","Researchers exploring hyperparameter sensitivity across datasets","Teams with limited compute budgets seeking efficient tuning","AutoML systems requiring interpretable hyperparameter suggestions"],"limitations":["Optimization quality depends on LLM's ability to infer hyperparameter-performance relationships from limited trajectory data; may miss non-obvious interactions","Each iteration requires training a full model and evaluating on validation data, making this computationally expensive for large models or datasets","LLM may generate out-of-range or invalid hyperparameter values (e.g., negative learning rates) requiring post-hoc filtering or constraint enforcement","No built-in mechanism to handle categorical hyperparameters or conditional dependencies (e.g., 'use dropout only if layers > 5')","Convergence is slower than gradient-based methods like Bayesian optimization with learned surrogates"],"requires":["Access to an LLM for configuration generation","A trainable model with a validation metric (accuracy, loss, F1, etc.)","Computational resources to train multiple model instances","Ability to serialize hyperparameter configurations as text (e.g., JSON, YAML)"],"input_types":["text (hyperparameter space definition, constraints, model description)","structured data (trajectory of previous configurations and their validation scores)","code (model training script, evaluation function)"],"output_types":["text (optimized hyperparameter configuration)","structured data (optimization trajectory, performance curves)","metrics (best validation score, convergence rate, hyperparameter importance)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-large-language-models-as-optimizers-opro__cap_4","uri":"capability://code.generation.editing.reward.function.discovery.via.code.generation.eureka.extension","name":"reward function discovery via code generation (eureka extension)","description":"Extends OPRO to automatically design reward functions for reinforcement learning by prompting an LLM to generate Python code that computes rewards based on environment observations. The LLM iteratively refines reward functions by analyzing which previous reward functions led to better task performance (e.g., higher episode returns), learning to write code that captures task-relevant objectives without manual reward engineering. This enables automated reward design for complex control tasks.","intents":["Automatically design reward functions for RL agents without manual engineering","Discover reward functions that lead to better task performance than hand-crafted rewards","Adapt reward functions to new tasks or environments by learning from RL training results","Enable non-experts to train RL agents by automating the reward design bottleneck"],"best_for":["Robotics researchers training agents for manipulation or locomotion tasks","RL practitioners seeking to avoid manual reward engineering","Teams building general-purpose RL systems that adapt to new tasks","Researchers studying emergent behavior and reward design in RL"],"limitations":["Reward functions generated by the LLM may be brittle, exploiting unintended environment dynamics (reward hacking) rather than learning robust behaviors","Each iteration requires training a full RL agent to convergence, incurring massive computational cost (hours to days per iteration)","LLM-generated code may contain bugs, inefficiencies, or numerical instabilities that degrade RL training","No built-in safety mechanisms to prevent reward functions from encouraging unsafe or undesired behaviors","Optimization is highly sensitive to the RL training setup (algorithm, hyperparameters, environment); small changes can invalidate previous reward functions"],"requires":["Access to an LLM capable of generating syntactically correct Python code (GPT-3.5+ or equivalent)","A differentiable RL environment with observable state and action spaces","RL training infrastructure (e.g., PyTorch, TensorFlow, JAX) and computational resources (GPUs/TPUs)","Ability to execute generated Python code safely (sandboxing recommended)","Metric for evaluating RL agent performance (episode return, task success rate, etc.)"],"input_types":["text (task description, environment specification, constraints on reward function)","structured data (trajectory of previous reward functions and their RL training results)","code (environment simulator, RL training script)"],"output_types":["code (Python reward function)","structured data (optimization trajectory, RL training curves)","metrics (best episode return, reward function complexity, convergence diagnostics)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-large-language-models-as-optimizers-opro__cap_5","uri":"capability://planning.reasoning.multi.step.reasoning.trajectory.generation.for.complex.optimization","name":"multi-step reasoning trajectory generation for complex optimization","description":"Extends OPRO to handle complex optimization problems by prompting the LLM to generate multi-step reasoning or decomposed solutions rather than single-shot candidates. The LLM learns to break down optimization problems into subproblems, generate intermediate solutions, and compose them into final candidates. This enables optimization of problems with hierarchical or compositional structure, where the LLM's reasoning process itself becomes part of the optimization trajectory.","intents":["Optimize complex problems with hierarchical or compositional structure","Leverage LLM reasoning to decompose problems into more tractable subproblems","Generate solutions that require multi-step planning or constraint satisfaction","Improve optimization quality by incorporating LLM's reasoning transparency"],"best_for":["Researchers optimizing complex algorithms or system designs","Teams solving constraint satisfaction or combinatorial optimization problems","Practitioners building planning systems that require interpretable reasoning","Applications where solution quality depends on reasoning quality"],"limitations":["Multi-step reasoning increases prompt length and LLM latency, making optimization slower and more expensive","Reasoning quality is difficult to evaluate; LLM may generate plausible-sounding but incorrect reasoning","Decomposition strategy is problem-specific; no general method to automatically determine optimal decomposition","Harder to debug when optimization fails; unclear whether failure is due to reasoning quality or evaluation metric"],"requires":["Access to an LLM with strong reasoning capabilities (GPT-4 or equivalent)","Sufficient context window to accommodate multi-step reasoning (8K+ tokens recommended)","Ability to parse and validate multi-step solutions","Evaluation function that can score intermediate solutions or full reasoning traces"],"input_types":["text (problem description, decomposition strategy, reasoning constraints)","structured data (trajectory of previous reasoning traces and their scores)"],"output_types":["text (multi-step reasoning trace, final solution)","structured data (decomposed subproblems, intermediate solutions, optimization trajectory)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Access to a capable LLM (GPT-3.5+ or equivalent) via API or local deployment","A differentiable or evaluable objective function that can score candidate solutions","Ability to serialize solutions as text for LLM input","Python 3.7+ for typical implementations","LLM with sufficient context window (4K+ tokens recommended for meaningful trajectory history)","Evaluation function that returns scalar scores (or easily interpretable metrics)","Ability to format solutions and scores as natural language or structured text","Deterministic or low-variance evaluation to avoid noisy feedback","Access to an LLM (GPT-3.5+ or equivalent) for prompt generation","A downstream task with a quantifiable evaluation metric (accuracy, F1, BLEU, etc.)"],"failure_modes":["Optimization quality depends heavily on LLM's ability to recognize patterns in the trajectory history — may plateau on complex multimodal landscapes","Each optimization step requires a full LLM forward pass, making it computationally expensive compared to gradient-based methods for large-scale problems","No theoretical convergence guarantees; performance is empirical and problem-dependent","Requires sufficient evaluation budget to build meaningful in-context examples; performs poorly with <5-10 prior solutions","LLM may generate solutions that are syntactically valid but semantically nonsensical for the target domain","Trajectory length is bounded by LLM context window; long optimization histories may be truncated or summarized, losing fine-grained signal","LLM may overfit to spurious correlations in short trajectories, generating solutions that exploit evaluation noise rather than improving fundamentally","No mechanism to enforce diversity in generated solutions; may converge to local optima or repetitive candidates","Requires manual prompt engineering to format trajectories effectively; poor formatting degrades optimization quality significantly","Optimization is task-specific; prompts optimized for one task may not transfer to different domains or LLM models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.27,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:27.894Z","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=large-language-models-as-optimizers-opro","compare_url":"https://unfragile.ai/compare?artifact=large-language-models-as-optimizers-opro"}},"signature":"D5RVD/pl74SSbXYSnD3ENqb0l3KuLL8hkUndiw6fO62KbER3Rst0gIIToBR4CaZYOghnAHnDNimOTTEMhKQ3Bw==","signedAt":"2026-06-19T22:55:39.412Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/large-language-models-as-optimizers-opro","artifact":"https://unfragile.ai/large-language-models-as-optimizers-opro","verify":"https://unfragile.ai/api/v1/verify?slug=large-language-models-as-optimizers-opro","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"}}