{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"toolllm","slug":"toolllm","name":"ToolLLM","type":"framework","url":"https://github.com/OpenBMB/ToolBench","page_url":"https://unfragile.ai/toolllm","categories":["ai-agents","model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"toolllm__cap_0","uri":"capability://data.processing.analysis.rest.api.dataset.collection.and.curation.from.rapidapi","name":"rest api dataset collection and curation from rapidapi","description":"Systematically collects and catalogs 16,464 real-world REST APIs from RapidAPI with metadata extraction, schema parsing, and endpoint documentation. The collection pipeline normalizes API specifications into a structured format compatible with instruction generation and inference, enabling models to learn patterns across diverse API designs, authentication schemes, and parameter structures.","intents":["Build a comprehensive training dataset of real-world APIs for tool-use models","Ensure models encounter diverse API patterns and edge cases during training","Create a reference corpus of production APIs for evaluation benchmarks"],"best_for":["Researchers training general-purpose tool-use LLMs","Teams building API-agnostic agent frameworks","Organizations evaluating LLM tool-calling capabilities at scale"],"limitations":["Limited to RapidAPI ecosystem — may not represent internal/proprietary API patterns","Static snapshot at collection time — requires periodic re-collection for API evolution","No automatic handling of deprecated endpoints or breaking API changes","Schema extraction quality depends on RapidAPI metadata completeness"],"requires":["RapidAPI account with API access","Network connectivity to RapidAPI service","Storage capacity for 16,464+ API specifications (~500MB-2GB depending on metadata depth)"],"input_types":["RapidAPI catalog metadata","REST API endpoint specifications","OpenAPI/Swagger schemas"],"output_types":["Structured API catalog (JSON/YAML)","Normalized endpoint definitions","Parameter and response schemas"],"categories":["data-processing-analysis","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_1","uri":"capability://data.processing.analysis.instruction.generation.for.single.tool.and.multi.tool.scenarios","name":"instruction generation for single-tool and multi-tool scenarios","description":"Generates diverse, realistic user instructions for both single-tool (G1) and multi-tool (G2 intra-category, G3 intra-collection) scenarios using template-based and LLM-assisted generation. The system creates instructions that require tool selection, parameter reasoning, and API chaining, organized into three complexity tiers that progressively increase reasoning requirements from isolated API calls to cross-collection orchestration.","intents":["Create diverse training examples that teach models when and how to use specific APIs","Generate multi-step instructions that require tool chaining and error recovery","Build evaluation datasets that test tool selection accuracy across different complexity levels"],"best_for":["Training teams building instruction-tuned tool-use models","Benchmark creators designing comprehensive evaluation suites","Researchers studying tool selection and chaining behavior"],"limitations":["G1 (single-tool) instructions may not reflect real-world complexity where multiple tools are needed","G2/G3 multi-tool instructions limited to intra-category/intra-collection combinations — no cross-domain reasoning","Template-based generation may produce repetitive instruction patterns","Quality depends on underlying API metadata completeness and schema accuracy"],"requires":["Populated API catalog from collection phase","LLM access (GPT-3.5+ or equivalent) for instruction generation","Template library for instruction patterns","API grouping/categorization metadata"],"input_types":["API specifications with parameters and responses","API category/collection metadata","Instruction templates"],"output_types":["Natural language instructions (text)","Instruction metadata (complexity tier, required tools, expected reasoning steps)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_10","uri":"capability://data.processing.analysis.leaderboard.and.results.tracking.for.model.comparison","name":"leaderboard and results tracking for model comparison","description":"Maintains a public leaderboard (toolbench/tooleval/results/) that tracks evaluation results for different ToolLLaMA model variants and inference algorithms across standardized evaluation sets. The leaderboard enables reproducible comparison of models, tracks progress over time, and provides normalized scores accounting for different evaluation conditions, facilitating transparent benchmarking of tool-use capabilities.","intents":["Track and compare performance of different ToolLLaMA model variants","Enable reproducible benchmarking across different inference algorithms","Provide transparent progress tracking for tool-use model development"],"best_for":["Researchers publishing tool-use model results","Teams tracking model improvements over development cycles","Community members comparing their implementations against baselines"],"limitations":["Leaderboard results may become stale as APIs change or are deprecated","Evaluation conditions (API availability, rate limits) may vary across runs","Normalization schemes may obscure important differences in absolute performance","No automatic re-evaluation — requires manual runs to update scores","Limited to models evaluated with ToolBench infrastructure — excludes external models","Results depend on specific evaluation dataset version — changes invalidate comparisons"],"requires":["Standardized evaluation dataset (fixed version)","Evaluation infrastructure (ToolEval) for consistent scoring","Results storage and versioning system","Leaderboard website/interface for public access"],"input_types":["Model evaluation results (pass rate, win rate, etc.)","Model metadata (name, variant, training data, inference algorithm)","Evaluation conditions (dataset version, API availability, etc.)"],"output_types":["Leaderboard rankings (CSV, JSON, web interface)","Normalized scores for cross-condition comparison","Historical tracking of model performance over time"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_11","uri":"capability://search.retrieval.tool.retriever.training.and.api.ranking.for.open.domain.scenarios","name":"tool retriever training and api ranking for open-domain scenarios","description":"Trains a specialized API retriever component that learns to rank relevant APIs from the 16,464-catalog based on query semantics. The retriever uses embedding-based or learned similarity approaches to match user queries to APIs, enabling open-domain tool use without explicit API specification. Training uses query-API relevance labels from the instruction dataset, learning patterns of which APIs are useful for different types of queries.","intents":["Train models to discover relevant APIs from a massive catalog without explicit specification","Enable zero-shot tool use on APIs not seen during main model training","Improve API selection accuracy for open-domain queries"],"best_for":["Building general-purpose API-orchestration agents","Researchers studying API discovery and selection","Teams deploying agents in open-domain settings"],"limitations":["Retriever accuracy limited by query-API relevance training data quality","Embedding-based approaches may struggle with polysemous queries (multiple valid APIs)","Top-K ranking (typically 5-10) may exclude optimal APIs for complex queries","Retriever must be retrained when API catalog changes significantly","No automatic handling of APIs with overlapping functionality","Inference latency: embedding generation + similarity search adds 500ms-2 seconds"],"requires":["Query-API relevance labels from instruction dataset","Embedding model (sentence-transformers, OpenAI embeddings, or custom)","Training data: queries paired with relevant API IDs","Vector database or similarity search index (FAISS, Pinecone, etc.)","GPU for embedding generation and similarity search"],"input_types":["User queries (text)","API catalog with descriptions and metadata","Query-API relevance labels (training data)"],"output_types":["Ranked list of relevant APIs (top-K with scores)","Embedding vectors for queries and APIs","Retriever model weights"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_12","uri":"capability://tool.use.integration.error.handling.and.recovery.in.multi.tool.execution","name":"error handling and recovery in multi-tool execution","description":"Implements error handling mechanisms within the inference pipeline that detect API failures (timeouts, invalid parameters, rate limits, malformed responses) and trigger recovery strategies such as parameter re-generation, alternative tool selection, or graceful degradation. The system learns from DFSDT-annotated error recovery patterns during training, enabling models to adapt when APIs fail rather than terminating execution.","intents":["Enable robust tool-use agents that recover from API failures gracefully","Learn error recovery patterns from training data to improve failure handling","Provide meaningful error messages and fallback strategies when tools fail"],"best_for":["Production deployments requiring high reliability","Teams building agents for unreliable or rate-limited APIs","Researchers studying error recovery in tool-use scenarios"],"limitations":["Error recovery patterns learned only from training data — may not generalize to novel failures","No automatic retry logic — requires explicit model generation of recovery steps","Rate limit handling requires external rate limiter — not built-in","Timeout handling adds latency — may exceed user expectations","Some errors (authentication failures, API deprecation) cannot be recovered automatically","Recovery success depends on model's reasoning quality — may generate invalid recovery steps"],"requires":["DFSDT-annotated training data with error recovery examples","Error detection and classification logic","Timeout and retry configuration per API","Rate limit tracking and management","Model trained on error recovery patterns"],"input_types":["API call results (success or error)","Error types and messages","API specifications and constraints"],"output_types":["Recovery actions (retry, alternative tool, graceful degradation)","Error logs and recovery traces","Final answer (if recovery successful) or error message"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_13","uri":"capability://data.processing.analysis.evaluation.dataset.organization.and.versioning","name":"evaluation dataset organization and versioning","description":"Organizes evaluation data into standardized formats (G1 single-tool, G2 intra-category multi-tool, G3 intra-collection multi-tool) with explicit versioning and metadata tracking. Each evaluation set includes instructions, ground truth answers, API specifications, and expected reasoning traces, enabling reproducible evaluation across different models and inference algorithms with clear documentation of dataset composition and evolution.","intents":["Create reproducible evaluation benchmarks for tool-use models","Track dataset evolution and ensure backward compatibility for comparisons","Enable fine-grained analysis of model performance by instruction type and complexity"],"best_for":["Researchers publishing tool-use model results","Teams maintaining evaluation benchmarks over time","Organizations comparing models across different development cycles"],"limitations":["Dataset versioning adds complexity — requires careful change management","Ground truth answers may become stale as APIs change","Fixed evaluation sets may not capture emerging use cases","No automatic detection of API deprecation — requires manual updates","Evaluation data size grows with each new version — storage and distribution challenges","Reproducibility depends on API availability — cannot re-run old evaluations if APIs change"],"requires":["Structured data format for instructions, answers, and metadata","Version control system for tracking dataset changes","API specifications and execution environment for validation","Documentation of dataset composition and changes"],"input_types":["Instructions (text)","Ground truth answers","API specifications","Reasoning traces (DFSDT annotations)"],"output_types":["Versioned evaluation datasets (JSON/YAML)","Dataset metadata (size, composition, API coverage)","Change logs documenting dataset evolution"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_2","uri":"capability://planning.reasoning.dfsdt.based.answer.annotation.with.reasoning.traces","name":"dfsdt-based answer annotation with reasoning traces","description":"Generates ground-truth answers for instructions using Depth-First Search Decision Tree (DFSDT) methodology, which produces step-by-step reasoning traces showing tool selection decisions, API call construction, response interpretation, and error recovery. Each annotation includes the complete decision path, parameter choices, and intermediate results, creating supervision signals that teach models not just what tools to use but why and how to use them.","intents":["Create supervision signals for training models on tool selection reasoning","Generate interpretable decision traces for evaluating model reasoning quality","Capture error recovery patterns when API calls fail or return unexpected results"],"best_for":["Training teams building interpretable tool-use models","Researchers studying LLM reasoning in tool-use scenarios","Teams requiring explainable AI for tool-calling agents"],"limitations":["DFSDT traces may not capture optimal solutions — only one valid path per instruction","Annotation cost scales with instruction complexity and API response variability","Requires actual API execution during annotation — subject to API rate limits and failures","No automatic handling of APIs with non-deterministic responses","Traces may become very long for complex multi-step tasks, increasing training data size"],"requires":["Live API access to all 16,464 APIs during annotation phase","API credentials and rate limit management","Timeout and error handling for flaky/slow APIs","Computational resources for parallel annotation"],"input_types":["Instructions (text)","API specifications","API execution environment"],"output_types":["Reasoning traces (structured JSON with decision steps)","API call sequences with parameters","Intermediate results and error states"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_3","uri":"capability://code.generation.editing.full.fine.tuning.and.lora.based.model.adaptation","name":"full fine-tuning and lora-based model adaptation","description":"Implements two training strategies for adapting LLaMA-based models to tool use: full fine-tuning that updates all model parameters on ToolBench instruction data, and LoRA (Low-Rank Adaptation) fine-tuning that trains low-rank decomposition matrices while freezing base weights. Both approaches integrate DFSDT reasoning traces as training supervision, enabling models to learn tool selection, API parameter construction, and multi-step reasoning from the 16,464-API dataset.","intents":["Fine-tune open-source LLMs to master tool use without proprietary model access","Adapt models with limited computational resources using LoRA instead of full fine-tuning","Create specialized tool-use models for specific API domains or use cases"],"best_for":["Teams with GPU resources wanting to train custom tool-use models","Researchers studying instruction tuning for tool use","Organizations needing on-premise tool-use models without API dependencies"],"limitations":["Full fine-tuning requires 80GB+ VRAM for 7B models — prohibitive for many teams","LoRA reduces memory to ~16GB but adds inference latency from adapter merging","Training time: full fine-tuning ~7 days on 8x A100, LoRA ~2 days","Models may overfit to RapidAPI patterns and struggle with proprietary/internal APIs","No automatic domain adaptation — requires retraining for new API sets"],"requires":["LLaMA base model (7B, 13B, or 70B variants)","GPU cluster: 8x A100 (80GB) for full fine-tuning, 1x A100 for LoRA","PyTorch 2.0+, transformers library, peft (for LoRA)","ToolBench instruction dataset with DFSDT annotations","~500GB disk space for checkpoints and intermediate states"],"input_types":["LLaMA base model weights","ToolBench instruction dataset (text + reasoning traces)","Training hyperparameters (learning rate, batch size, epochs)"],"output_types":["Fine-tuned model weights (full or LoRA adapters)","Training logs and loss curves","Checkpoint files for resuming training"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_4","uri":"capability://tool.use.integration.single.tool.and.multi.tool.inference.with.api.execution","name":"single-tool and multi-tool inference with api execution","description":"Executes inference pipelines (qa_pipeline.py) that enable fine-tuned models to solve user queries by selecting appropriate APIs, constructing valid API calls with correct parameters, executing those calls, and interpreting results. Supports both single-tool scenarios (selecting one API per query) and multi-tool scenarios (chaining multiple API calls with intermediate result interpretation), with built-in error handling for API failures and parameter validation.","intents":["Deploy trained tool-use models to solve real user queries requiring API access","Chain multiple API calls together when single APIs cannot fully solve a task","Validate API parameters before execution and handle API errors gracefully"],"best_for":["Teams deploying ToolLLaMA models in production agent systems","Researchers evaluating tool-use model performance on real APIs","Developers building API-orchestration agents with LLMs"],"limitations":["Inference latency: ~2-5 seconds per API call (model generation + execution)","No automatic retry logic for transient API failures — requires external orchestration","Parameter validation limited to schema checking — no semantic validation","Multi-tool chaining limited to sequential execution — no parallel API calls","No built-in caching of API responses — repeated queries re-execute APIs","Requires live API credentials and rate limit management at inference time"],"requires":["Fine-tuned ToolLLaMA model (7B, 13B, or 70B)","GPU for model inference (A100 for 7B, A100 for 13B+)","API credentials for all 16,464 APIs or subset being used","API rate limit management and timeout handling","Python 3.8+, transformers, requests libraries"],"input_types":["User query (natural language text)","Available API specifications","API credentials and endpoints"],"output_types":["Final answer (text)","API call sequence with parameters","Intermediate API responses","Reasoning trace (if enabled)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_5","uri":"capability://search.retrieval.open.domain.inference.with.semantic.api.retrieval","name":"open-domain inference with semantic api retrieval","description":"Enables inference on queries where the relevant APIs are unknown upfront by using a learned API retriever component (qa_pipeline_open_domain.py) that semantically matches user queries to relevant APIs from the 16,464-API catalog. The retriever ranks APIs by relevance using embeddings or learned similarity metrics, then passes top-K APIs to the inference pipeline, enabling the model to solve queries without explicit API specification.","intents":["Solve user queries without knowing which APIs are relevant in advance","Discover and select APIs from a massive catalog based on semantic query understanding","Enable zero-shot tool use on APIs the model may not have seen during training"],"best_for":["Building general-purpose API-orchestration agents","Researchers studying API discovery and selection","Teams deploying agents in open-domain settings with dynamic API catalogs"],"limitations":["API retrieval accuracy limited by embedding quality — may miss relevant APIs","Ranking top-K APIs (typically 5-10) may exclude the optimal API for complex queries","Retriever must be trained/fine-tuned on query-API pairs — adds training complexity","Inference latency increases: embedding generation + retrieval + model inference (~5-10 seconds)","No automatic handling of APIs with similar names/functionality — may retrieve duplicates","Requires maintaining and updating API embeddings as catalog grows"],"requires":["Trained API retriever model (embedding-based or learned similarity)","Embedding model (e.g., sentence-transformers, OpenAI embeddings)","Vector database or similarity search index for 16,464 APIs","Query-API relevance training data for fine-tuning retriever","ToolLLaMA inference pipeline for downstream tool use"],"input_types":["User query (natural language text)","API catalog with descriptions and metadata","Query-API relevance labels (for training retriever)"],"output_types":["Ranked list of relevant APIs (top-K)","Final answer from inference pipeline","Retrieval confidence scores"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_6","uri":"capability://planning.reasoning.multiple.inference.algorithms.dfs.cot.react","name":"multiple inference algorithms (dfs, cot, react)","description":"Implements multiple inference algorithms that control how models reason about and execute tool use: Depth-First Search (DFS) explores tool chains exhaustively, Chain-of-Thought (CoT) generates explicit reasoning steps before tool selection, and ReACT (Reasoning + Acting) interleaves reasoning with tool execution. Each algorithm trades off between reasoning transparency, computational cost, and success rate on complex multi-tool tasks.","intents":["Choose inference algorithms optimized for different task complexity levels","Generate interpretable reasoning traces for debugging model decisions","Maximize success rate on complex multi-tool tasks vs. minimize latency on simple queries"],"best_for":["Teams deploying agents where interpretability is critical","Researchers comparing reasoning strategies for tool use","Production systems needing to balance latency vs. accuracy"],"limitations":["DFS explores all tool chains exhaustively — exponential complexity for >3 tools","CoT requires explicit reasoning generation — adds 50-100% latency overhead","ReACT interleaves reasoning and execution — harder to parallelize API calls","No automatic algorithm selection — requires manual tuning per task type","Reasoning quality depends on model's instruction-tuning — may produce invalid reasoning","All algorithms require live API access — cannot be pre-computed or cached"],"requires":["ToolLLaMA model fine-tuned on DFSDT reasoning traces","Inference pipeline supporting algorithm selection","Live API access for tool execution","Timeout and error handling for failed API calls"],"input_types":["User query (text)","Available APIs and specifications","Algorithm selection parameter"],"output_types":["Final answer (text)","Reasoning trace (algorithm-dependent)","Tool execution log with intermediate results"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_7","uri":"capability://tool.use.integration.web.server.interface.for.interactive.tool.use.agent.deployment","name":"web server interface for interactive tool-use agent deployment","description":"Provides a web server interface (toolbench_server.py) that exposes trained ToolLLaMA models as HTTP endpoints, enabling interactive queries, real-time API execution, and result streaming. The server handles concurrent requests, manages API credentials securely, enforces rate limiting, and provides logging/monitoring for production deployment of tool-use agents.","intents":["Deploy trained tool-use models as production-ready APIs","Enable interactive testing and debugging of tool-use behavior","Integrate tool-use agents into larger application stacks via HTTP"],"best_for":["Teams deploying ToolLLaMA models in production","Researchers building interactive demos and benchmarks","Organizations integrating tool-use agents into existing services"],"limitations":["Inference latency: 2-10 seconds per query depending on algorithm and API complexity","Concurrent request handling limited by GPU memory — typically 1-4 concurrent requests per A100","No built-in authentication/authorization — requires external API gateway","Credential management must be handled externally — no built-in secret storage","No automatic load balancing or failover — single-server deployment","Streaming responses add complexity — not all clients support streaming"],"requires":["ToolLLaMA model loaded in GPU memory","Python 3.8+, FastAPI or Flask for web framework","GPU with sufficient VRAM for model inference","API credentials for all tools being used","Network connectivity to all APIs in the catalog"],"input_types":["HTTP POST requests with user query (JSON)","Optional: algorithm selection, API filter parameters"],"output_types":["HTTP response with final answer (JSON)","Optional: reasoning trace, API call log, streaming results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_8","uri":"capability://data.processing.analysis.pass.rate.evaluation.metric.for.tool.use.success","name":"pass rate evaluation metric for tool-use success","description":"Evaluates tool-use models using a pass rate metric that measures the percentage of instructions successfully completed within a limited number of API calls (typically 5-10). An instruction passes if the model's final answer matches the ground truth or achieves the specified task goal, accounting for the trade-off between solution quality and API call efficiency. This metric directly measures practical tool-use capability rather than intermediate reasoning quality.","intents":["Measure practical tool-use success rate on real-world instructions","Compare different models and inference algorithms on a standardized metric","Identify failure modes and edge cases in tool-use behavior"],"best_for":["Benchmarking tool-use models and comparing approaches","Researchers studying tool-use capability scaling","Teams evaluating production-readiness of tool-use agents"],"limitations":["Pass rate binary (pass/fail) — doesn't capture partial correctness","API call limit (5-10) may be arbitrary — different tasks need different budgets","Ground truth answers may be incomplete or ambiguous for open-ended queries","Doesn't account for answer quality beyond binary correctness","Sensitive to API failures and timeouts — external factors affect scores","No credit for near-misses or reasonable alternative solutions"],"requires":["Evaluation dataset with ground truth answers","API access to execute model-generated tool calls","Timeout and error handling for failed API calls","Answer matching/comparison logic (exact match, semantic similarity, etc.)"],"input_types":["Model predictions (tool calls and final answers)","Ground truth answers","API execution logs"],"output_types":["Pass rate percentage (0-100%)","Per-instruction pass/fail labels","Failure analysis (wrong tool, wrong parameters, API error, etc.)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__cap_9","uri":"capability://data.processing.analysis.preference.win.rate.evaluation.against.reference.models","name":"preference/win rate evaluation against reference models","description":"Evaluates tool-use models using preference-based metrics that compare model outputs to a reference model (typically ChatGPT-ReACT) through human or LLM-based judgment. Win rate measures the percentage of instructions where the evaluated model outperforms the reference, capturing relative capability differences and enabling fine-grained comparison of reasoning quality, tool selection accuracy, and error recovery beyond binary pass/fail metrics.","intents":["Compare tool-use models on relative performance rather than absolute metrics","Measure improvements in reasoning quality and tool selection accuracy","Identify specific areas where models outperform or underperform reference baselines"],"best_for":["Researchers comparing multiple tool-use approaches","Teams evaluating incremental improvements in model capability","Benchmarking studies requiring fine-grained performance comparison"],"limitations":["Preference evaluation requires human judges or LLM judges — expensive and slow","Reference model choice affects all comparisons — different references may rank models differently","Inter-rater agreement may be low for subjective preference judgments","LLM-based judges may have biases toward certain reasoning styles or answer formats","Preference scores are relative, not absolute — don't indicate actual task success","Requires collecting outputs from both evaluated and reference models"],"requires":["Reference model (ChatGPT-ReACT or equivalent) for comparison","Human judges or LLM judge for preference assessment","Evaluation dataset with diverse instructions","Preference judgment guidelines and rubrics","Inter-rater agreement measurement (Cohen's kappa, etc.)"],"input_types":["Model outputs (answers, reasoning traces, tool calls)","Reference model outputs","Evaluation instructions and ground truth"],"output_types":["Win rate percentage (0-100%)","Per-instruction preference labels","Inter-rater agreement scores","Detailed preference analysis by instruction type"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toolllm__headline","uri":"capability://tool.use.integration.framework.for.training.llms.with.tool.use.capabilities","name":"framework for training llms with tool-use capabilities","description":"ToolLLM is an open-source framework designed for training and evaluating large language models (LLMs) that can effectively utilize real-world APIs, enabling advanced tool selection and chaining capabilities.","intents":["best framework for LLM tool use","LLM training for API integration","evaluate LLMs with tool capabilities","open-source LLM frameworks for tool use","framework for developing tool-using AI agents"],"best_for":["developers building AI agents"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["RapidAPI account with API access","Network connectivity to RapidAPI service","Storage capacity for 16,464+ API specifications (~500MB-2GB depending on metadata depth)","Populated API catalog from collection phase","LLM access (GPT-3.5+ or equivalent) for instruction generation","Template library for instruction patterns","API grouping/categorization metadata","Standardized evaluation dataset (fixed version)","Evaluation infrastructure (ToolEval) for consistent scoring","Results storage and versioning system"],"failure_modes":["Limited to RapidAPI ecosystem — may not represent internal/proprietary API patterns","Static snapshot at collection time — requires periodic re-collection for API evolution","No automatic handling of deprecated endpoints or breaking API changes","Schema extraction quality depends on RapidAPI metadata completeness","G1 (single-tool) instructions may not reflect real-world complexity where multiple tools are needed","G2/G3 multi-tool instructions limited to intra-category/intra-collection combinations — no cross-domain reasoning","Template-based generation may produce repetitive instruction patterns","Quality depends on underlying API metadata completeness and schema accuracy","Leaderboard results may become stale as APIs change or are deprecated","Evaluation conditions (API availability, rate limits) may vary across runs","builder identity is not verified yet","no observed match outcomes 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