{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-fireworks-ai","slug":"pypi-fireworks-ai","name":"fireworks-ai","type":"api","url":"https://pypi.org/project/fireworks-ai/","page_url":"https://unfragile.ai/pypi-fireworks-ai","categories":["llm-apis"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-fireworks-ai__cap_0","uri":"capability://text.generation.language.multi.provider.llm.inference.with.unified.api","name":"multi-provider llm inference with unified api","description":"Provides a standardized Python client interface that abstracts multiple LLM providers (Fireworks, OpenAI-compatible endpoints, and other inference backends) behind a single API. Uses a provider-agnostic request/response schema that maps to each backend's native API format, enabling seamless model switching without code changes. Implements connection pooling and request batching for efficient resource utilization across distributed inference endpoints.","intents":["I want to switch between different LLM providers without rewriting my inference code","I need to compare model outputs across multiple providers for the same prompt","I want to abstract away provider-specific API differences in my application"],"best_for":["teams building LLM applications that need provider flexibility","developers prototyping multi-model comparison workflows","enterprises with hybrid inference infrastructure"],"limitations":["Provider-specific features (like vision capabilities or tool-use schemas) may not be fully abstracted, requiring conditional logic","Latency varies significantly across providers; no built-in load balancing or failover between endpoints","Rate limiting is provider-specific and not unified across the client"],"requires":["Python 3.8+","API credentials for Fireworks AI or compatible endpoint","Network access to inference endpoints"],"input_types":["text prompts","structured message arrays (system/user/assistant roles)","optional: images (if provider supports vision)"],"output_types":["text completions","structured JSON (with response_format parameter)","streaming token sequences"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_1","uri":"capability://text.generation.language.streaming.token.generation.with.backpressure.handling","name":"streaming token generation with backpressure handling","description":"Implements server-sent events (SSE) streaming for real-time token generation with built-in backpressure handling to prevent memory overflow when consuming tokens faster than they arrive. Uses async iterators and generator patterns to allow incremental token consumption without buffering entire responses. Handles connection interruptions and partial token sequences gracefully with automatic reconnection and state recovery.","intents":["I want to display LLM responses token-by-token as they arrive for better UX","I need to process streaming outputs without loading entire responses into memory","I want to cancel long-running generations mid-stream based on user input"],"best_for":["frontend developers building real-time chat interfaces","backend engineers processing large document generations","teams with bandwidth constraints needing incremental output consumption"],"limitations":["Streaming requires persistent HTTP connections; proxies or load balancers with connection timeouts may interrupt streams","Token-level backpressure adds ~5-10ms latency per token in high-throughput scenarios","No built-in deduplication of partial tokens across reconnections; application must handle idempotency"],"requires":["Python 3.8+","httpx or aiohttp for async HTTP streaming","Fireworks API endpoint supporting streaming (SSE)"],"input_types":["text prompts","message arrays with streaming=True parameter"],"output_types":["async iterator of token strings","streaming event objects with metadata (finish_reason, usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_10","uri":"capability://automation.workflow.logging.and.observability.hooks","name":"logging and observability hooks","description":"Provides structured logging and observability hooks for monitoring API calls, latency, errors, and token usage. Integrates with standard Python logging and supports custom handlers for metrics collection. Logs include request/response metadata, timing information, and error details for debugging and performance analysis.","intents":["I want to monitor API latency and identify performance bottlenecks","I need detailed logs of all API calls for debugging and auditing","I want to collect metrics on token usage and costs for billing and optimization"],"best_for":["teams running production LLM applications","developers debugging inference issues","organizations needing audit trails for compliance"],"limitations":["Logging adds overhead; high-volume applications may see 5-10% latency increase","Structured logging requires parsing and aggregation; raw logs are not immediately actionable","Custom metrics handlers must be implemented by the application; no built-in integration with observability platforms"],"requires":["Python 3.8+","Python logging module","optional: observability platform (Datadog, New Relic, etc.)"],"input_types":["logging configuration","custom handler definitions"],"output_types":["structured log entries","metrics data (latency, tokens, errors)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.automatic.chunking.and.result.aggregation","name":"batch inference with automatic chunking and result aggregation","description":"Provides a batch processing interface that accepts large lists of prompts and automatically chunks them into API-compliant batch sizes, submitting them in parallel while respecting rate limits. Aggregates results back into the original order and handles partial failures with retry logic. Implements exponential backoff for transient errors and exposes detailed error reporting per-batch item.","intents":["I want to process thousands of prompts efficiently without manual batching logic","I need to parallelize inference across multiple requests while respecting API rate limits","I want detailed error reporting for failed items in a large batch without losing successful results"],"best_for":["data scientists running inference on large datasets","teams building ETL pipelines with LLM enrichment steps","applications needing cost-optimized bulk inference"],"limitations":["Automatic chunking assumes uniform token counts; highly variable prompt lengths may cause some batches to exceed token limits","Result aggregation requires holding all results in memory; very large batches (>100k items) may cause memory pressure","No built-in deduplication; duplicate prompts in the input will be processed separately"],"requires":["Python 3.8+","Fireworks API key with batch processing quota","Sufficient memory for result aggregation (roughly 1-2KB per result)"],"input_types":["list of text prompts","list of message arrays","optional: per-item parameters (temperature, max_tokens, etc.)"],"output_types":["list of completions in original input order","structured error report with per-item status","aggregated usage statistics (total tokens, cost)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_3","uri":"capability://tool.use.integration.function.calling.with.schema.validation.and.type.coercion","name":"function calling with schema validation and type coercion","description":"Provides a structured function-calling interface that accepts Python function signatures or JSON schemas, validates LLM-generated tool calls against the schema, and automatically coerces response types to match declared parameter types. Uses Python's inspect module to extract type hints from functions and converts them to OpenAI-compatible tool schemas. Implements a call dispatcher that routes validated function calls to registered handlers with type safety.","intents":["I want to define callable tools as Python functions and let the LLM invoke them with type safety","I need to validate that LLM-generated function calls match my expected schemas before execution","I want automatic type coercion so the LLM's string outputs are converted to the correct Python types"],"best_for":["developers building LLM agents with deterministic tool interactions","teams needing strict validation of LLM outputs before executing side effects","Python-first applications where type hints are already in use"],"limitations":["Type coercion only works for built-in Python types and common libraries; custom classes require manual serialization","Schema generation from type hints may not capture all validation constraints (e.g., string length limits, enum restrictions)","No built-in retry logic if the LLM generates invalid function calls; application must implement its own agentic loop"],"requires":["Python 3.8+ with type hints support","Fireworks API with function-calling capability","Function definitions with type annotations (or explicit JSON schemas)"],"input_types":["Python function objects with type hints","JSON schema objects","LLM responses with tool_calls field"],"output_types":["validated function call objects","type-coerced function arguments","execution results from registered handlers"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_4","uri":"capability://memory.knowledge.context.window.management.with.automatic.truncation.and.summarization","name":"context window management with automatic truncation and summarization","description":"Manages conversation history and context windows by tracking token counts, automatically truncating or summarizing older messages when approaching model limits, and maintaining semantic coherence across truncation boundaries. Uses token counting APIs to estimate message sizes and implements configurable truncation strategies (sliding window, importance-based, or LLM-generated summaries). Preserves system prompts and recent messages while compressing historical context.","intents":["I want to maintain long conversations without manually managing context window limits","I need to keep recent messages intact while compressing older conversation history","I want to avoid token limit errors by automatically truncating context before they occur"],"best_for":["chatbot developers building multi-turn conversation systems","teams with long-running agent interactions","applications where conversation history is important but token budgets are limited"],"limitations":["Automatic summarization requires additional LLM calls, adding latency and cost","Truncation may lose important context if the strategy doesn't account for semantic importance","Token counting is approximate; actual token usage may vary by 5-10% due to tokenizer differences"],"requires":["Python 3.8+","Fireworks API with token counting support","Model specification for accurate token estimation"],"input_types":["message arrays with role/content pairs","context window size limit","optional: truncation strategy configuration"],"output_types":["truncated message arrays","metadata about truncation (removed messages, compression ratio)","updated token count estimates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_5","uri":"capability://data.processing.analysis.response.formatting.with.structured.output.validation","name":"response formatting with structured output validation","description":"Enforces structured output formats (JSON, YAML, or custom schemas) by specifying response_format parameters and validating LLM outputs against declared schemas before returning to the application. Uses JSON schema validation libraries to check structure, type, and constraint compliance. Implements fallback parsing strategies (e.g., extracting JSON from markdown code blocks) when LLM outputs are malformed.","intents":["I want the LLM to always return valid JSON that matches my expected schema","I need to extract structured data from LLM responses without manual parsing","I want validation errors with clear feedback about what went wrong in the LLM output"],"best_for":["developers building data extraction pipelines","teams needing deterministic LLM outputs for downstream processing","applications where response structure is critical for correctness"],"limitations":["Structured output mode may reduce model quality or creativity; some models perform worse with strict formatting constraints","Fallback parsing (e.g., extracting JSON from markdown) is heuristic-based and may fail on edge cases","Schema validation doesn't guarantee semantic correctness; a valid JSON structure may still contain nonsensical values"],"requires":["Python 3.8+","Fireworks API supporting response_format parameter","JSON schema definition or Pydantic model"],"input_types":["JSON schema objects","Pydantic models","response_format specification"],"output_types":["validated structured objects","parsed JSON/YAML","validation error reports"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_6","uri":"capability://planning.reasoning.model.routing.and.dynamic.provider.selection","name":"model routing and dynamic provider selection","description":"Automatically routes requests to different models or providers based on configurable criteria (prompt complexity, latency requirements, cost budgets, or model capabilities). Implements a routing policy engine that evaluates conditions at request time and selects the optimal model. Supports A/B testing by probabilistically routing requests to different models and collecting performance metrics.","intents":["I want to use cheaper models for simple queries and more capable models for complex ones","I need to A/B test different models to measure quality and cost tradeoffs","I want to automatically failover to a backup model if the primary one is unavailable"],"best_for":["teams optimizing for cost-quality tradeoffs","applications running A/B tests on model selection","systems requiring high availability with fallback models"],"limitations":["Routing decisions are made at request time; no global optimization across all requests","A/B testing requires sufficient traffic to achieve statistical significance; low-volume applications may not get reliable results","Routing policies must be manually configured; no automatic learning from historical performance"],"requires":["Python 3.8+","Multiple models available in Fireworks or other providers","Routing policy configuration (rules or weights)"],"input_types":["routing policy configuration","request metadata (prompt, user, context)","optional: performance metrics for learning"],"output_types":["selected model identifier","routing decision metadata","performance metrics (latency, cost, quality)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_7","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Provides accurate token counting for prompts and completions using model-specific tokenizers, enabling cost estimation before making API calls. Implements caching of tokenizer instances and supports batch token counting for efficiency. Calculates estimated costs based on model pricing and token counts, with support for different pricing tiers and volume discounts.","intents":["I want to estimate the cost of a request before sending it to the API","I need accurate token counts for context window management and billing","I want to track cumulative costs across multiple requests for budget monitoring"],"best_for":["developers building cost-aware LLM applications","teams with strict budget constraints","applications needing transparent cost tracking"],"limitations":["Token counts are estimates; actual API usage may vary by 5-10% due to tokenizer updates or edge cases","Pricing information must be manually updated when models change pricing","Batch token counting still requires sequential processing; no parallelization"],"requires":["Python 3.8+","Fireworks API key (for token counting endpoint)","Model specification for tokenizer selection"],"input_types":["text strings","message arrays","model identifiers"],"output_types":["token count integers","cost estimates (in USD or other currency)","usage summaries"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_8","uri":"capability://automation.workflow.retry.logic.with.exponential.backoff.and.jitter","name":"retry logic with exponential backoff and jitter","description":"Implements automatic retry logic for transient failures (rate limits, timeouts, temporary service unavailability) using exponential backoff with jitter to prevent thundering herd problems. Configurable retry budgets and maximum wait times prevent infinite retries. Distinguishes between retryable errors (429, 503) and permanent failures (401, 404) to avoid wasting retries on unrecoverable errors.","intents":["I want transient failures to be automatically retried without my code having to handle it","I need to avoid overwhelming the API with retry storms when it's under load","I want to know when a failure is permanent versus transient so I can handle it appropriately"],"best_for":["production applications requiring high reliability","batch processing systems that can tolerate delays","teams without sophisticated error handling infrastructure"],"limitations":["Exponential backoff can add significant latency (up to minutes) for heavily rate-limited scenarios","Jitter is randomized; retry timing is not deterministic, making debugging harder","No built-in circuit breaker; if the service is down, retries will continue until the budget is exhausted"],"requires":["Python 3.8+","Fireworks API","Configuration for max retries and backoff parameters"],"input_types":["API requests (any inference call)","retry configuration (max_retries, initial_delay, max_delay)"],"output_types":["successful API response (after retries if needed)","permanent failure exception (if all retries exhausted)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fireworks-ai__cap_9","uri":"capability://automation.workflow.async.await.support.with.concurrent.request.handling","name":"async/await support with concurrent request handling","description":"Provides full async/await support using Python's asyncio, allowing concurrent inference requests without blocking. Implements connection pooling with configurable concurrency limits to prevent overwhelming the API or local resources. Supports both async context managers and traditional callback patterns for flexibility.","intents":["I want to make multiple inference requests concurrently without blocking","I need to limit concurrent requests to avoid overwhelming the API or my system","I want to integrate with async web frameworks like FastAPI or aiohttp"],"best_for":["web applications using async frameworks (FastAPI, Quart, etc.)","high-concurrency systems processing multiple requests simultaneously","teams already using asyncio in their codebase"],"limitations":["Async code is more complex to debug than synchronous code","Connection pooling adds memory overhead; very high concurrency (>1000 concurrent requests) may cause memory pressure","Mixing sync and async code in the same application can cause deadlocks if not carefully managed"],"requires":["Python 3.8+","asyncio event loop","async-compatible HTTP client (httpx, aiohttp)"],"input_types":["async inference requests","concurrency configuration"],"output_types":["coroutines returning inference results","async iterators for streaming"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.8+","API credentials for Fireworks AI or compatible endpoint","Network access to inference endpoints","httpx or aiohttp for async HTTP streaming","Fireworks API endpoint supporting streaming (SSE)","Python logging module","optional: observability platform (Datadog, New Relic, etc.)","Fireworks API key with batch processing quota","Sufficient memory for result aggregation (roughly 1-2KB per result)","Python 3.8+ with type hints support"],"failure_modes":["Provider-specific features (like vision capabilities or tool-use schemas) may not be fully abstracted, requiring conditional logic","Latency varies significantly across providers; no built-in load balancing or failover between endpoints","Rate limiting is provider-specific and not unified across the client","Streaming requires persistent HTTP connections; proxies or load balancers with connection timeouts may interrupt streams","Token-level backpressure adds ~5-10ms latency per token in high-throughput scenarios","No built-in deduplication of partial tokens across reconnections; application must handle idempotency","Logging adds overhead; high-volume applications may see 5-10% latency increase","Structured logging requires parsing and aggregation; raw logs are not immediately actionable","Custom metrics handlers must be implemented by the application; no built-in integration with observability platforms","Automatic chunking assumes uniform token counts; highly variable prompt lengths may cause some batches to exceed token limits","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:10.823Z","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=pypi-fireworks-ai","compare_url":"https://unfragile.ai/compare?artifact=pypi-fireworks-ai"}},"signature":"YTobZSS2njCQdUhROsyPaYaXn3Ee6FIsgtBUnnSho8z8MorvOaY9NB6S0753Cc9GsmlDfzDsx4T/TYJb29v8Dg==","signedAt":"2026-06-21T09:27:30.268Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-fireworks-ai","artifact":"https://unfragile.ai/pypi-fireworks-ai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-fireworks-ai","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"}}