{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gorilla","slug":"gorilla","name":"Gorilla","type":"agent","url":"https://github.com/ShishirPatil/gorilla","page_url":"https://unfragile.ai/gorilla","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gorilla__cap_0","uri":"capability://planning.reasoning.multi.model.function.calling.evaluation.with.weighted.agentic.scoring","name":"multi-model function-calling evaluation with weighted agentic scoring","description":"BFCL V4 evaluates 70+ LLMs (OpenAI, Anthropic, Google, Mistral, local models) on function-calling accuracy using a weighted scoring formula that allocates 40% weight to agentic multi-step tasks, 30% to multi-turn conversations, and 30% to single-turn accuracy. The evaluation framework uses specialized checker modules that compare model-generated function calls against ground truth, supporting both live API validation and offline schema-based verification across non-live, live, and irrelevance test categories.","intents":["Benchmark which LLM models are best at accurate function calling across different complexity levels","Compare function-calling performance between API-based models and locally-hosted open models","Evaluate agentic capabilities like web search and memory management in function-calling contexts","Track model improvements over time using standardized evaluation metrics"],"best_for":["LLM researchers comparing function-calling capabilities across model families","Teams selecting models for production agent systems","Organizations evaluating open-source vs proprietary models for tool use"],"limitations":["Evaluation requires running inference on 70+ models, which is computationally expensive and time-consuming","Live API testing requires valid API credentials and may incur costs for external services","Agentic task evaluation (40% weight) requires complex multi-step orchestration that may not reflect all real-world agent patterns","Leaderboard results are point-in-time snapshots; model performance changes with updates"],"requires":["Python 3.9+","bfcl_eval PyPI package","API keys for models being evaluated (OpenAI, Anthropic, Google, etc.)","For local models: sufficient GPU memory or CPU resources","For live API testing: valid credentials for external services (Stripe, GitHub, etc.)"],"input_types":["Natural language prompts describing function-calling tasks","API schemas in JSON/OpenAPI format","Ground truth function call specifications"],"output_types":["Accuracy scores (0-100 per category)","Weighted overall accuracy metric","Per-model performance rankings","Detailed error analysis and failure modes"],"categories":["planning-reasoning","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_1","uri":"capability://tool.use.integration.specialized.function.calling.model.inference.with.openai.compatible.endpoints","name":"specialized function-calling model inference with openai-compatible endpoints","description":"Gorilla provides OpenFunctions models (v0, v1, v2) as Apache 2.0 licensed alternatives to proprietary function-calling models, accessible via OpenAI-compatible API endpoints at luigi.millennium.berkeley.edu:8000/v1. These models are fine-tuned specifically for accurate function invocation and support parallel execution of multiple function calls, streaming responses, and domain-specific adaptation through RAFT fine-tuning. The models handle JSON formatting, parameter validation, and multi-turn function-calling conversations natively.","intents":["Deploy open-source function-calling models without vendor lock-in or API costs","Use drop-in replacements for OpenAI function-calling with identical API contracts","Execute multiple function calls in parallel for faster agent execution","Fine-tune function-calling models on domain-specific APIs using RAFT"],"best_for":["Teams building agents that need cost-effective function calling without OpenAI dependency","Organizations with data privacy requirements that prevent cloud API usage","Developers building domain-specific agents (e.g., financial APIs, internal tools)"],"limitations":["OpenFunctions models are smaller than GPT-4 and may have lower accuracy on complex multi-step reasoning","Parallel function execution requires careful orchestration to handle dependencies and error propagation","Fine-tuning with RAFT requires domain-specific training data and GPU resources","Endpoint availability depends on Berkeley's infrastructure; no SLA guarantees for public endpoint"],"requires":["OpenAI Python client library (compatible with v1.0+)","API endpoint access to luigi.millennium.berkeley.edu:8000/v1","Model ID specification (gorilla-openfunctions-v0, v1, or v2)","For fine-tuning: RAFT dataset generation pipeline and GPU resources"],"input_types":["OpenAI-compatible function schema (JSON)","Natural language user prompts","Multi-turn conversation history"],"output_types":["Function call JSON with parameters","Multiple parallel function calls","Streaming token responses","Error messages with remediation suggestions"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_10","uri":"capability://planning.reasoning.irrelevance.detection.for.function.calling.hallucinations","name":"irrelevance detection for function-calling hallucinations","description":"BFCL includes evaluation of whether models correctly identify when a user request doesn't require any function call, preventing unnecessary or irrelevant function invocations. The irrelevance category tests scenarios where the best response is to decline calling a function or to respond with general knowledge instead. This accounts for 10% of BFCL V4 scoring and is critical for preventing agents from over-invoking tools.","intents":["Evaluate whether models correctly identify when function calls are unnecessary","Prevent agents from hallucinating function calls for general knowledge questions","Test if models can distinguish between questions requiring tools vs general knowledge","Reduce unnecessary API calls and improve agent efficiency"],"best_for":["Teams building agents that need to balance tool use with general knowledge","Researchers studying when LLMs should and shouldn't invoke tools","Organizations optimizing agent efficiency and cost"],"limitations":["Irrelevance detection is subjective; some requests may legitimately have multiple valid responses","Only 10% weight in BFCL; may underweight importance of avoiding unnecessary tool calls","Evaluation requires careful test design to avoid ambiguous scenarios","Models may be biased toward calling functions even when unnecessary"],"requires":["Test scenarios where function calls are irrelevant or unnecessary","Ground truth labels indicating when function calls should be declined","BFCL evaluation framework with irrelevance category"],"input_types":["User prompts that don't require function calls","General knowledge questions","Requests that could be answered without tools"],"output_types":["Decision to call or not call function","Accuracy on irrelevance detection","False positive rate (unnecessary function calls)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_11","uri":"capability://tool.use.integration.model.handler.abstraction.for.multi.provider.inference","name":"model handler abstraction for multi-provider inference","description":"Gorilla implements a model handler system that abstracts over different LLM providers (OpenAI, Anthropic, Google, Mistral, Cohere, DeepSeek, xAI, local models) with a unified interface. Each provider has a handler that translates between the provider's API format and Gorilla's internal representation, enabling seamless evaluation across 70+ models without provider-specific code. Handlers manage authentication, request formatting, response parsing, and error handling for each provider.","intents":["Evaluate multiple LLM providers with a single evaluation framework","Add support for new models without modifying core evaluation code","Manage provider-specific API differences transparently","Switch between providers for cost or performance optimization"],"best_for":["Researchers comparing models across multiple providers","Teams evaluating both API-based and local models","Organizations building multi-provider agent systems"],"limitations":["Handler abstraction adds complexity; provider-specific features may not be exposed","Different providers have different capabilities (e.g., function-calling support, context length)","Adding new providers requires implementing handler class","Abstraction may hide important provider differences"],"requires":["API keys for each provider being evaluated","Model handler implementations for each provider","Gorilla evaluation framework","Python 3.9+"],"input_types":["Provider name and model ID","API credentials","Prompts and function schemas"],"output_types":["Unified response format","Function call specifications","Error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_12","uri":"capability://automation.workflow.ci.cd.and.release.process.for.model.versioning","name":"ci/cd and release process for model versioning","description":"Gorilla includes a CI/CD pipeline for managing model versions, running automated evaluations on new model checkpoints, and releasing models to the public endpoint (luigi.millennium.berkeley.edu:8000/v1). The pipeline validates model quality, runs regression tests against prior versions, and gates releases based on performance thresholds. This enables rapid iteration on OpenFunctions models while maintaining quality standards.","intents":["Automatically evaluate new model checkpoints against BFCL before releasing to production","Prevent regressions by comparing new model performance against prior versions","Manage multiple model versions (v0, v1, v2) with clear release criteria and documentation"],"best_for":["Teams maintaining OpenFunctions models and releasing new versions","Organizations with continuous model improvement pipelines","Researchers publishing models and wanting automated quality assurance"],"limitations":["CI/CD pipeline requires significant computational resources (A100 GPUs) — not suitable for resource-constrained teams","Release gates based on performance thresholds may be too strict or too lenient depending on your use case","Automated testing may miss edge cases or domain-specific issues that manual testing would catch"],"requires":["GitHub repository with CI/CD configuration (GitHub Actions or similar)","GPU cluster for running evaluations (A100 recommended)","bfcl_eval package and evaluation datasets","model checkpoints in HuggingFace format"],"input_types":["new model checkpoints (PyTorch or HuggingFace format)","evaluation configuration (which tests to run, performance thresholds)"],"output_types":["evaluation reports (performance vs prior versions)","release approval/rejection decisions","model artifacts (released to public endpoint)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_2","uri":"capability://data.processing.analysis.retrieval.augmented.fine.tuning.raft.for.domain.specific.api.adaptation","name":"retrieval-augmented fine-tuning (raft) for domain-specific api adaptation","description":"RAFT is a dataset generation and fine-tuning pipeline that enables models to adapt to domain-specific APIs by combining retrieval of relevant API documentation with in-context learning. The system generates synthetic training data by retrieving API schemas, creating realistic function-calling prompts, and fine-tuning OpenFunctions models on this domain-specific data. This approach reduces hallucination when invoking proprietary or internal APIs that weren't in the model's training data, enabling accurate function calling on custom API sets without requiring massive labeled datasets.","intents":["Fine-tune function-calling models on internal or proprietary APIs not in public training data","Reduce hallucination when invoking domain-specific APIs by grounding models in actual documentation","Generate synthetic training datasets for function-calling without manual annotation","Adapt pre-trained models to new API ecosystems with minimal labeled examples"],"best_for":["Enterprise teams with internal APIs who need accurate function calling without cloud dependency","Startups building domain-specific agents (e.g., fintech, healthcare) on proprietary APIs","Researchers studying how to adapt LLMs to new tool ecosystems"],"limitations":["RAFT requires GPU resources for fine-tuning; not practical for one-off API integrations","Quality of fine-tuned models depends on quality and completeness of API documentation provided","Synthetic data generation may not cover all edge cases or error conditions in real API usage","Fine-tuned models may overfit to specific API patterns and generalize poorly to new APIs"],"requires":["API documentation in structured format (OpenAPI/Swagger, JSON schema, or markdown)","GPU resources (minimum 1x A100 or equivalent for reasonable fine-tuning time)","RAFT dataset generation pipeline from Gorilla repository","Base OpenFunctions model (v0, v1, or v2)","Python 3.9+ with PyTorch and transformers libraries"],"input_types":["API documentation (OpenAPI specs, JSON schemas, markdown descriptions)","Example function-calling prompts (optional, for seed data)","API endpoint metadata (parameters, return types, error codes)"],"output_types":["Synthetic training dataset (prompt-function call pairs)","Fine-tuned model weights","Evaluation metrics on domain-specific test set","Adapter weights (if using LoRA for efficient fine-tuning)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_3","uri":"capability://safety.moderation.safe.execution.runtime.with.post.facto.validation.and.undo.capabilities.goex","name":"safe execution runtime with post-facto validation and undo capabilities (goex)","description":"GoEx is a Docker-based sandboxed execution environment that safely executes LLM-generated function calls with post-execution validation and rollback capabilities. The system intercepts function calls, executes them in an isolated container, validates outputs against expected schemas, and can undo state changes if validation fails or if the call was determined to be unsafe. This enables agents to take real actions (API calls, database writes) with safety guarantees, preventing hallucinated or malicious function calls from corrupting system state.","intents":["Execute LLM-generated function calls safely without risk of hallucinated or malicious actions","Validate function outputs match expected schemas before committing state changes","Rollback failed or unsafe function calls to restore previous system state","Audit all function calls executed by agents with full execution logs"],"best_for":["Production agents that execute real API calls or database operations","Financial or healthcare systems where incorrect function calls have high consequences","Teams building autonomous agents that need safety guarantees"],"limitations":["Post-facto validation adds latency to function execution (validation time depends on schema complexity)","Rollback capability only works for operations that support transactions; some APIs don't support undo","Docker containerization adds overhead; not suitable for latency-critical applications","Requires defining validation schemas for all functions; incomplete schemas reduce safety guarantees"],"requires":["Docker runtime (Docker Engine 20.10+)","Function schemas in JSON Schema format for validation","Network access from container to target APIs","Gorilla GoEx runtime package","Python 3.9+ for orchestration layer"],"input_types":["Function call specifications (name, parameters)","JSON Schema definitions for output validation","API credentials (injected securely into container)"],"output_types":["Execution result (success/failure)","Validated output matching schema","Execution logs with timestamps","Rollback confirmation if undo was triggered"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_4","uri":"capability://planning.reasoning.agentic.multi.turn.evaluation.with.web.search.and.memory.management","name":"agentic multi-turn evaluation with web search and memory management","description":"BFCL V4 includes specialized evaluation for agentic tasks that require multi-step reasoning, web search integration, and memory management across conversation turns. The evaluation framework provides test scenarios where agents must search the web for information, maintain context across multiple turns, and chain function calls together to solve complex problems. This is implemented as 40% of the overall scoring formula, reflecting the importance of agentic capabilities beyond simple function calling.","intents":["Evaluate whether models can chain multiple function calls to solve multi-step problems","Test if models can integrate web search results into function-calling decisions","Assess memory management and context retention across multi-turn conversations","Benchmark agentic reasoning capabilities that go beyond single-turn function invocation"],"best_for":["Teams building autonomous agents that need to search the web and take actions","Researchers studying how LLMs handle complex multi-step reasoning with tools","Organizations evaluating models for agent-based applications"],"limitations":["Agentic evaluation is computationally expensive; requires orchestrating web searches and multiple function calls per test","Web search results are non-deterministic; same query may return different results, affecting reproducibility","Memory management evaluation requires careful test design to avoid ambiguous scenarios","40% weight on agentic tasks may not reflect all real-world agent use cases"],"requires":["Web search API access (e.g., Google Search, Bing Search)","Function-calling models that support multi-turn conversations","Test scenarios with ground truth multi-step solutions","BFCL evaluation framework with agentic task support"],"input_types":["Complex natural language prompts requiring multi-step reasoning","Web search queries","Multi-turn conversation history","Function schemas for chaining"],"output_types":["Sequence of function calls (ordered by execution)","Web search queries and results used","Final answer or action taken","Accuracy score on agentic task completion"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_5","uri":"capability://memory.knowledge.community.maintained.api.documentation.repository.with.1.600.apis","name":"community-maintained api documentation repository with 1,600+ apis","description":"Gorilla maintains API Zoo, a community-curated repository of 1,600+ API schemas and documentation covering major services (Stripe, GitHub, Twilio, AWS, Google Cloud, etc.). This repository serves as the ground truth for function-calling evaluation and as training data for RAFT fine-tuning. The API Zoo is structured with standardized schema formats, enabling consistent evaluation across diverse APIs and providing a comprehensive dataset for training function-calling models.","intents":["Access standardized API schemas for evaluating function-calling on real-world APIs","Use API documentation as training data for fine-tuning domain-specific models","Contribute new APIs to the community repository for broader evaluation coverage","Benchmark function-calling models against a diverse set of real APIs"],"best_for":["Researchers evaluating function-calling models on realistic API sets","Teams fine-tuning models on specific API ecosystems","Organizations building agent systems that need to invoke diverse APIs"],"limitations":["API schemas may become outdated as services evolve; requires continuous maintenance","Coverage is biased toward popular services; niche or internal APIs may not be represented","Schema quality varies; some APIs may have incomplete or inaccurate documentation","1,600 APIs is large but still a small fraction of all APIs in existence"],"requires":["Access to Gorilla repository (GitHub)","API schema files in JSON or OpenAPI format","For contributions: GitHub account and pull request process"],"input_types":["API schemas (OpenAPI, JSON Schema, or custom format)","API documentation (markdown, HTML, or structured text)"],"output_types":["Standardized API schema files","Function-calling test cases","Training data for RAFT fine-tuning"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_6","uri":"capability://planning.reasoning.head.to.head.agent.comparison.with.elo.rating.system","name":"head-to-head agent comparison with elo rating system","description":"Agent Arena is a competitive evaluation platform where agents are matched against each other on identical tasks, with results aggregated into ELO ratings similar to chess rankings. This enables direct comparison of agent capabilities beyond simple accuracy metrics, capturing relative performance differences and head-to-head matchups. The system tracks agent performance over time as models are updated, providing a dynamic leaderboard that reflects current state-of-the-art.","intents":["Compare agent performance directly in head-to-head matchups rather than isolated accuracy scores","Track how agent capabilities evolve as models are updated","Identify which agents are best for specific task categories","Provide a competitive benchmark that incentivizes model improvements"],"best_for":["Researchers comparing agent architectures and model families","Teams selecting agents for production deployment","Organizations tracking competitive landscape of function-calling models"],"limitations":["ELO ratings require many matchups to stabilize; early ratings may be unreliable","Head-to-head comparison assumes tasks are equally difficult for all agents, which may not hold","ELO system can be gamed by strategic task selection or timing","Ratings don't capture failure modes or edge cases where agents perform poorly"],"requires":["Multiple agents to compare","Standardized task set for fair comparison","ELO rating calculation system","Persistent storage for rating history"],"input_types":["Agent specifications (model, configuration)","Task prompts for head-to-head matchups","Previous match results"],"output_types":["ELO ratings per agent","Head-to-head match results","Agent rankings by category","Rating history over time"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_7","uri":"capability://planning.reasoning.multi.turn.conversation.evaluation.with.context.retention","name":"multi-turn conversation evaluation with context retention","description":"BFCL evaluates models on multi-turn conversations where function calls must be made in context of previous turns, requiring models to maintain conversation state and reference earlier information. This capability tests whether models can handle realistic agent scenarios where context accumulates across turns and function calls depend on previous results. Multi-turn evaluation accounts for 30% of the overall BFCL V4 scoring, reflecting its importance for practical agent applications.","intents":["Evaluate whether models maintain conversation context across multiple turns","Test if models can reference previous function call results in subsequent turns","Assess how models handle ambiguous references that require context to resolve","Benchmark realistic multi-turn agent conversations"],"best_for":["Teams building conversational agents that maintain state across turns","Researchers studying context management in multi-turn LLM interactions","Organizations evaluating models for chatbot or assistant applications"],"limitations":["Multi-turn evaluation requires longer context windows; some models may struggle with long conversations","Context management is harder to evaluate objectively; requires careful test design to avoid ambiguity","30% weight on multi-turn may not reflect all real-world conversation patterns","Evaluation doesn't capture user experience aspects like response quality or naturalness"],"requires":["Models with sufficient context window (minimum 4K tokens, preferably 8K+)","Multi-turn conversation datasets with ground truth function calls","Conversation state management in evaluation framework","BFCL evaluation framework with multi-turn support"],"input_types":["Multi-turn conversation history","Previous function call results","New prompts in context of conversation","Ambiguous references requiring context resolution"],"output_types":["Function calls in context of conversation","Accuracy on context-dependent tasks","Error analysis on context misunderstandings"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_8","uri":"capability://tool.use.integration.live.api.validation.with.real.endpoint.testing","name":"live api validation with real endpoint testing","description":"BFCL includes live API testing where function calls are executed against real API endpoints (Stripe, GitHub, Twilio, etc.) and results are validated against actual API responses. This goes beyond schema validation to test whether generated function calls actually work with real services, catching hallucinations that pass schema checks but fail in practice. Live API testing accounts for 10% of BFCL V4 scoring and requires valid API credentials for each service.","intents":["Test whether function calls actually work with real APIs, not just schema validation","Catch hallucinations that pass schema checks but fail in practice","Validate parameter values are realistic and accepted by real services","Benchmark real-world function-calling accuracy"],"best_for":["Teams deploying agents that invoke real APIs in production","Researchers studying hallucination in function calling","Organizations that need high confidence in function-calling accuracy"],"limitations":["Live API testing requires valid credentials for each service; expensive and complex to set up","API responses are non-deterministic; same function call may return different results","Rate limiting and quota restrictions may prevent comprehensive testing","Live testing may incur costs (e.g., Stripe charges for test transactions)","Only 10% weight in BFCL; may not reflect importance of real-world accuracy"],"requires":["Valid API credentials for services being tested (Stripe, GitHub, Twilio, etc.)","Network access to real API endpoints","Test accounts or sandboxes for each service","Careful credential management to avoid exposing secrets","Budget for API usage costs during testing"],"input_types":["Function call specifications","API credentials (injected securely)","Test data (e.g., test customer IDs for Stripe)"],"output_types":["Real API responses","Success/failure status","Actual error messages from APIs","Validation results comparing expected vs actual responses"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__cap_9","uri":"capability://tool.use.integration.non.live.schema.based.function.call.validation","name":"non-live schema-based function call validation","description":"BFCL includes offline validation where function calls are checked against JSON schemas without executing against real APIs. This tests whether models generate syntactically correct function calls with valid parameters, catching basic hallucinations like incorrect parameter names or types. Non-live validation is fast and doesn't require API credentials, making it suitable for rapid iteration and evaluation of many models. Non-live testing accounts for 10% of BFCL V4 scoring.","intents":["Quickly validate function call syntax and parameter correctness without API access","Catch basic hallucinations like wrong parameter names or types","Evaluate models without requiring API credentials or network access","Enable rapid iteration during model development"],"best_for":["Researchers iterating on function-calling models during development","Teams with limited API access or credentials","Quick validation before running expensive live API tests"],"limitations":["Schema validation doesn't catch semantic errors (e.g., valid parameter but wrong value)","Can't detect hallucinations that pass schema checks but fail in practice","Only 10% weight in BFCL; may underweight importance of basic correctness","Requires accurate JSON schemas; incomplete schemas reduce validation effectiveness"],"requires":["JSON Schema definitions for all functions","JSON schema validator library (e.g., jsonschema in Python)","Function call outputs in JSON format"],"input_types":["Function call JSON","JSON Schema definitions"],"output_types":["Validation pass/fail","Schema violation details","Parameter type mismatches"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gorilla__headline","uri":"capability://tool.use.integration.api.invocation.agent.for.llms","name":"api invocation agent for llms","description":"Gorilla is an advanced agent designed to enable large language models to accurately invoke over 1,600 API calls, significantly reducing hallucinations and enhancing reliable programmatic interactions.","intents":["best API invocation agent","API agent for LLMs","reliable API calling framework","how to reduce LLM hallucinations in API use","top tools for LLM function calling"],"best_for":["developers needing reliable API interactions","researchers evaluating LLM capabilities"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","bfcl_eval PyPI package","API keys for models being evaluated (OpenAI, Anthropic, Google, etc.)","For local models: sufficient GPU memory or CPU resources","For live API testing: valid credentials for external services (Stripe, GitHub, etc.)","OpenAI Python client library (compatible with v1.0+)","API endpoint access to luigi.millennium.berkeley.edu:8000/v1","Model ID specification (gorilla-openfunctions-v0, v1, or v2)","For fine-tuning: RAFT dataset generation pipeline and GPU resources","Test scenarios where function calls are irrelevant or unnecessary"],"failure_modes":["Evaluation requires running inference on 70+ models, which is computationally expensive and time-consuming","Live API testing requires valid API credentials and may incur costs for external services","Agentic task evaluation (40% weight) requires complex multi-step orchestration that may not reflect all real-world agent patterns","Leaderboard results are point-in-time snapshots; model performance changes with updates","OpenFunctions models are smaller than GPT-4 and may have lower accuracy on complex multi-step reasoning","Parallel function execution requires careful orchestration to handle dependencies and error propagation","Fine-tuning with RAFT requires domain-specific training data and GPU resources","Endpoint availability depends on Berkeley's infrastructure; no SLA guarantees for public endpoint","Irrelevance detection is subjective; some requests may legitimately have multiple valid responses","Only 10% weight in BFCL; may underweight importance of avoiding unnecessary tool calls","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.066Z","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=gorilla","compare_url":"https://unfragile.ai/compare?artifact=gorilla"}},"signature":"W5pSQbDTLeU3q+xvWv7Dxn90LI0ZT5JndF9QZlB1vnWixMWhpvu6Q7vtE9zpN8y7Fy5nFRAzYgjY0vcUe3AUBw==","signedAt":"2026-06-15T05:24:59.033Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gorilla","artifact":"https://unfragile.ai/gorilla","verify":"https://unfragile.ai/api/v1/verify?slug=gorilla","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"}}