{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_gooseai","slug":"gooseai","name":"GooseAi","type":"api","url":"https://goose.ai","page_url":"https://unfragile.ai/gooseai","categories":["llm-apis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_gooseai__cap_0","uri":"capability://text.generation.language.cost.optimized.text.generation.via.rest.api","name":"cost-optimized text generation via rest api","description":"Provides HTTP-based access to multiple language models (125M to 20B parameters) with per-token billing and competitive pricing undercut to OpenAI's GPT-3.5. Uses standard REST endpoints for prompt submission and streaming or batch response retrieval, with request/response payloads structured as JSON. The pricing model charges only for tokens consumed, enabling fine-grained cost control for production inference workloads at scale.","intents":["I need to generate text completions at 40-60% lower cost than OpenAI for my production application","I want to switch from GPT-3.5 to a cheaper alternative without rewriting my API integration code","I need to run inference at high volume and want transparent per-token pricing to forecast costs"],"best_for":["startups and small teams with tight budgets building chatbots, content generation, or summarization features","developers optimizing for cost-per-inference in high-volume production systems","teams migrating from OpenAI seeking API-compatible drop-in replacements"],"limitations":["No streaming response support for real-time token-by-token output — responses are buffered and returned in full","Maximum context window and token limits are smaller than GPT-3.5 (exact limits not publicly documented)","No fine-tuning or custom model training available — limited to pre-trained model selection","Pricing advantage erodes as model size increases; larger models (20B) approach OpenAI pricing"],"requires":["API key from GooseAI account (free tier available with usage limits)","HTTP client library (curl, requests, httpx, etc.) or GooseAI Python SDK","Network connectivity to goose.ai API endpoints"],"input_types":["text (plain string prompts)","structured JSON payloads with model selection, temperature, max_tokens parameters"],"output_types":["text (generated completions)","JSON responses with usage metadata (tokens consumed, cost)"],"categories":["text-generation-language","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_1","uri":"capability://text.generation.language.multi.model.size.selection.with.speed.capability.tradeoff","name":"multi-model size selection with speed-capability tradeoff","description":"Exposes a range of model sizes from 125M to 20B parameters as selectable endpoints, allowing developers to choose inference speed vs. output quality based on workload requirements. The API accepts a 'model' parameter in requests to route to different model variants. Smaller models (125M-1B) prioritize latency for real-time applications, while larger models (7B-20B) improve coherence and reasoning at the cost of higher latency and per-token cost.","intents":["I want to use a small, fast model for low-latency autocomplete but a larger model for complex summarization tasks","I need to optimize my inference pipeline to balance response time and quality for different user-facing features","I want to benchmark different model sizes to find the cost-quality sweet spot for my use case"],"best_for":["teams building multi-tier inference systems where different features have different latency/quality requirements","developers prototyping and need to experiment with model size tradeoffs without infrastructure changes","cost-conscious builders who want to use smaller models for simple tasks and reserve larger models for complex reasoning"],"limitations":["No automatic model selection or routing based on input complexity — developers must manually choose model per request","Performance characteristics (latency, throughput) for each model size not publicly documented, requiring empirical testing","Larger models (20B) have significantly higher per-token cost, reducing cost advantage vs. OpenAI for complex tasks","No ensemble or mixture-of-experts approach — single model per request, no dynamic model switching"],"requires":["Knowledge of available model sizes and their parameter counts (125M, 350M, 1.3B, 6B, 20B)","API key with access to desired model tiers (some may be restricted to paid accounts)","Ability to profile and benchmark model performance for your specific use case"],"input_types":["text prompts","model selection parameter (string identifier for model variant)"],"output_types":["text completions","latency and token usage metrics for cost/performance analysis"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_2","uri":"capability://text.generation.language.python.sdk.with.openai.api.compatibility.layer","name":"python sdk with openai api compatibility layer","description":"Provides a Python library that mirrors OpenAI's client interface, allowing developers to swap API endpoints with minimal code changes. The SDK handles HTTP request serialization, response parsing, error handling, and retry logic internally. It supports both synchronous and asynchronous (async/await) patterns, with context managers for resource cleanup. The compatibility layer maps GooseAI model names and parameters to OpenAI's expected format, reducing cognitive load for teams familiar with OpenAI's SDK.","intents":["I want to migrate from OpenAI to GooseAI by changing only the API key and model name, keeping my existing code intact","I need async/await support for concurrent inference requests in my Python application","I want a type-hinted Python client that integrates with my existing OpenAI-based codebase without refactoring"],"best_for":["Python developers already using OpenAI SDK who want to reduce costs with minimal refactoring","teams building async Python applications (FastAPI, asyncio-based services) requiring concurrent inference","developers who value API consistency and want to avoid learning a new SDK interface"],"limitations":["SDK only supports Python 3.7+ — no support for older Python versions or other languages (Go, Rust, Node.js)","Async support may have different concurrency limits or timeout behavior than OpenAI's SDK, requiring testing","Error messages and exception types may differ from OpenAI's SDK, breaking error handling code that assumes OpenAI exceptions","No built-in retry logic with exponential backoff — developers must implement their own or use external libraries"],"requires":["Python 3.7 or higher","pip or poetry for dependency management","GooseAI API key (free or paid account)","Familiarity with OpenAI's Python SDK API surface"],"input_types":["text prompts (string)","model selection (string identifier)","generation parameters (temperature, max_tokens, top_p, etc.)"],"output_types":["text completions (string or async generator for streaming)","response objects with metadata (tokens used, finish reason)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_3","uri":"capability://data.processing.analysis.token.level.usage.tracking.and.cost.attribution","name":"token-level usage tracking and cost attribution","description":"Tracks and reports token consumption at the request level, returning detailed usage metadata (prompt tokens, completion tokens, total tokens) in API responses. This enables developers to calculate per-request costs using published per-token rates and attribute spending to specific features, users, or workloads. The SDK and REST API both expose usage information in response objects, allowing integration with cost monitoring and billing systems.","intents":["I need to track how much each feature or user is costing me to forecast monthly spend and optimize pricing","I want to implement per-user or per-feature billing based on actual token consumption","I need to identify which requests or models are consuming the most tokens to optimize my inference pipeline"],"best_for":["SaaS platforms and startups building usage-based billing models on top of GooseAI","teams with strict cost budgets who need real-time visibility into inference spending","developers optimizing prompt engineering and model selection based on token efficiency metrics"],"limitations":["Usage data is returned per-request only — no aggregated usage reports or dashboards in the GooseAI console","No built-in cost alerts or budget limits — developers must implement their own monitoring and enforcement","Token counting may differ slightly from actual billing due to tokenizer differences, requiring empirical validation","No historical usage data export or API for querying past consumption — requires developers to log and store responses"],"requires":["API key with access to usage tracking (available on all account tiers)","Logging or database system to store and aggregate usage data across requests","Knowledge of GooseAI's per-token pricing for cost calculations"],"input_types":["API requests with prompts and generation parameters"],"output_types":["usage metadata (prompt_tokens, completion_tokens, total_tokens)","cost calculations (tokens × per-token rate)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_4","uri":"capability://automation.workflow.batch.inference.with.asynchronous.job.submission","name":"batch inference with asynchronous job submission","description":"Supports submitting multiple inference requests as a batch job for asynchronous processing, allowing developers to trade latency for throughput and cost savings. Batch jobs are queued and processed during off-peak hours, typically returning results within hours rather than milliseconds. The API returns a job ID for polling or webhook-based result retrieval, enabling developers to decouple request submission from result consumption.","intents":["I have a large corpus of documents to summarize or classify and can tolerate a few hours of latency for 10-20% cost savings","I want to process millions of inference requests without overwhelming the API or my infrastructure with concurrent connections","I need to generate embeddings or completions for a dataset and want to optimize for cost rather than speed"],"best_for":["data processing pipelines and ETL workflows where latency is not critical","teams processing large datasets (millions of documents) with limited budgets","batch analytics and reporting systems that can tolerate multi-hour processing windows"],"limitations":["Batch jobs typically process during off-peak hours, resulting in 2-24 hour turnaround time — not suitable for real-time applications","No guaranteed SLA for batch job completion time — processing time depends on queue depth and system load","Batch API may have different rate limits or quotas than real-time API, requiring separate account configuration","No built-in result streaming or pagination — entire batch results must be retrieved at once, potentially consuming significant memory"],"requires":["API key with batch job permissions (may require paid account)","JSONL (JSON Lines) formatted input file with one request per line","Polling mechanism or webhook endpoint for result retrieval","Storage for batch results (local filesystem, S3, database, etc.)"],"input_types":["JSONL file with batch requests (each line is a JSON object with prompt, model, parameters)","batch job submission endpoint"],"output_types":["job ID for tracking","JSONL file with results (one result per line, matching input order)","usage and cost summary for the batch"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_5","uri":"capability://text.generation.language.temperature.and.sampling.parameter.control.for.output.diversity","name":"temperature and sampling parameter control for output diversity","description":"Exposes standard LLM sampling parameters (temperature, top_p, top_k, frequency_penalty, presence_penalty) in the API, allowing developers to control output randomness and diversity. Temperature scales logits before sampling (0 = deterministic, 1+ = more random), while top_p and top_k implement nucleus and top-k sampling respectively. These parameters are passed per-request, enabling dynamic control over model behavior without retraining or fine-tuning.","intents":["I want deterministic, consistent outputs for classification or structured data extraction tasks","I need to generate diverse, creative outputs for content generation or brainstorming features","I want to prevent the model from repeating the same phrases or tokens in long-form generation"],"best_for":["developers building deterministic systems (classification, extraction) who need reproducible outputs","creative applications (story generation, marketing copy) requiring output diversity","teams fine-tuning model behavior without access to fine-tuning infrastructure"],"limitations":["Parameter effects are model-dependent — optimal temperature/top_p values vary by model size and task, requiring empirical tuning","No guidance on recommended parameter ranges for different use cases — developers must experiment or consult documentation","Extreme parameter values (temperature > 2, top_p < 0.1) may produce nonsensical or repetitive outputs without warning","Parameters do not affect determinism of model weights — same prompt with same parameters may produce different outputs due to floating-point precision"],"requires":["Understanding of LLM sampling mechanics (temperature, nucleus sampling, etc.)","Ability to test and validate parameter effects on your specific use case","API key with access to parameter control (available on all tiers)"],"input_types":["text prompts","sampling parameters (temperature: float, top_p: float, top_k: int, frequency_penalty: float, presence_penalty: float)"],"output_types":["text completions with controlled diversity/determinism","metadata on sampling decisions (if available)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gooseai__cap_6","uri":"capability://automation.workflow.free.tier.with.usage.limits.for.experimentation","name":"free tier with usage limits for experimentation","description":"Offers a free account tier with monthly token allowances (typically 5,000-10,000 free tokens) and rate limits, enabling developers to experiment and prototype without upfront payment. Free tier accounts have reduced rate limits (e.g., 10 requests/minute) and may have access to smaller models only. Upgrading to paid accounts removes rate limits and provides higher monthly allowances with pay-as-you-go billing.","intents":["I want to test GooseAI's API and compare output quality with OpenAI before committing to paid usage","I'm building a hobby project or prototype and need free inference to validate the idea","I want to benchmark model performance and cost without spending money upfront"],"best_for":["individual developers and hobbyists prototyping LLM applications","students and researchers evaluating different inference providers","teams evaluating GooseAI before committing to production usage"],"limitations":["Free tier token allowances are typically exhausted within days for active development, requiring upgrade to paid","Rate limits on free tier (e.g., 10 req/min) are too restrictive for load testing or production validation","Free tier may have access restrictions (e.g., only smaller models available), limiting ability to test full model range","No SLA or uptime guarantees on free tier — service may be deprioritized during high load"],"requires":["Email address to create GooseAI account","No credit card required for free tier signup"],"input_types":["text prompts","API requests within free tier rate limits"],"output_types":["text completions","usage tracking showing remaining free tokens"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["API key from GooseAI account (free tier available with usage limits)","HTTP client library (curl, requests, httpx, etc.) or GooseAI Python SDK","Network connectivity to goose.ai API endpoints","Knowledge of available model sizes and their parameter counts (125M, 350M, 1.3B, 6B, 20B)","API key with access to desired model tiers (some may be restricted to paid accounts)","Ability to profile and benchmark model performance for your specific use case","Python 3.7 or higher","pip or poetry for dependency management","GooseAI API key (free or paid account)","Familiarity with OpenAI's Python SDK API surface"],"failure_modes":["No streaming response support for real-time token-by-token output — responses are buffered and returned in full","Maximum context window and token limits are smaller than GPT-3.5 (exact limits not publicly documented)","No fine-tuning or custom model training available — limited to pre-trained model selection","Pricing advantage erodes as model size increases; larger models (20B) approach OpenAI pricing","No automatic model selection or routing based on input complexity — developers must manually choose model per request","Performance characteristics (latency, throughput) for each model size not publicly documented, requiring empirical testing","Larger models (20B) have significantly higher per-token cost, reducing cost advantage vs. OpenAI for complex tasks","No ensemble or mixture-of-experts approach — single model per request, no dynamic model switching","SDK only supports Python 3.7+ — no support for older Python versions or other languages (Go, Rust, Node.js)","Async support may have different concurrency limits or timeout behavior than OpenAI's SDK, requiring testing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=gooseai","compare_url":"https://unfragile.ai/compare?artifact=gooseai"}},"signature":"0gveJxvfbB2pA4Lv/b3sByFD9Or81WqPi+ZHuQ7q6EDuLob78sn5ynCHc1tKmQ3ZVD7xzJA/p2oVwWQ9t3GiCA==","signedAt":"2026-06-21T16:01:58.276Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gooseai","artifact":"https://unfragile.ai/gooseai","verify":"https://unfragile.ai/api/v1/verify?slug=gooseai","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"}}