{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-partial-json","slug":"partial-json","name":"partial-json","type":"repo","url":"https://promplate.dev/partial-json-parser","page_url":"https://unfragile.ai/partial-json","categories":["data-analysis"],"tags":["JSON","parser","LLM","nlp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-partial-json__cap_0","uri":"capability://data.processing.analysis.incremental.json.parsing.with.llm.streaming.tolerance","name":"incremental json parsing with llm streaming tolerance","description":"Parses incomplete or malformed JSON generated by LLMs during token-by-token streaming, using a state machine that tracks bracket/brace nesting depth and validates structure incrementally. The parser maintains a buffer of partial input and attempts to extract valid JSON objects/arrays even when the stream is cut off mid-token, enabling real-time consumption of LLM outputs without waiting for completion.","intents":["I need to extract structured data from an LLM response as it streams, without waiting for the full response to complete","I want to handle incomplete JSON from LLM outputs that may be truncated or malformed due to token limits","I need to validate and parse JSON fragments that an LLM generates incrementally in a chat interface"],"best_for":["LLM application developers building real-time chat interfaces with structured output","Teams implementing streaming JSON extraction from OpenAI, Anthropic, or other LLM APIs","Developers building agents that need to parse tool calls or structured responses mid-generation"],"limitations":["Cannot recover from deeply nested structures with missing closing brackets — requires heuristic closing or manual intervention","Performance degrades with very large JSON payloads (>10MB) due to full-buffer parsing on each token","Does not validate JSON schema — only structural validity of brackets/braces/quotes","No support for JSON5 extensions (comments, trailing commas, unquoted keys)"],"requires":["Node.js 12+ or browser with ES6 support","Input stream from LLM API (OpenAI, Anthropic, Ollama, etc.)","No external dependencies"],"input_types":["text (partial JSON string)","stream (token-by-token LLM output)","string (incomplete JSON fragments)"],"output_types":["object (parsed JSON as JavaScript object)","array (parsed JSON array)","null (if no valid JSON can be extracted)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-partial-json__cap_1","uri":"capability://data.processing.analysis.automatic.bracket.quote.balancing.and.recovery","name":"automatic bracket/quote balancing and recovery","description":"Detects unclosed brackets, braces, and quotes in partial JSON and automatically closes them using heuristic rules (e.g., closing all open structures in reverse nesting order). The parser tracks quote context to distinguish between structural delimiters and string content, enabling recovery from truncated JSON without manual intervention.","intents":["I want to automatically fix truncated JSON from an LLM that cuts off mid-string or mid-object","I need to handle JSON responses where the LLM ran out of tokens before completing the structure","I want to extract usable data from incomplete LLM outputs without throwing parse errors"],"best_for":["Developers building fault-tolerant LLM pipelines that must extract value from incomplete outputs","Teams using token-limited models (GPT-3.5, smaller open-source LLMs) that frequently truncate responses","Applications requiring graceful degradation when LLM outputs are malformed or incomplete"],"limitations":["Heuristic closing may produce semantically incorrect JSON if the LLM intended a different structure","Cannot infer missing object keys or array elements — only closes structural delimiters","May produce valid JSON that does not match the LLM's original intent if truncation occurs mid-value","No configurable recovery strategies — uses fixed heuristic rules"],"requires":["Node.js 12+ or browser with ES6 support","Partial JSON string with unclosed brackets/braces/quotes"],"input_types":["text (incomplete JSON with unclosed delimiters)"],"output_types":["text (repaired JSON string)","object (parsed and balanced JSON as JavaScript object)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-partial-json__cap_2","uri":"capability://data.processing.analysis.streaming.json.extraction.with.progressive.object.emission","name":"streaming json extraction with progressive object emission","description":"Processes token streams from LLM APIs and emits complete JSON objects/arrays as soon as they are structurally valid, without waiting for the entire stream to complete. Uses an event-driven architecture where each token is fed to the parser, which emits 'data' events when valid JSON boundaries are detected, enabling downstream consumers to process results incrementally.","intents":["I want to emit parsed JSON objects to my UI as soon as they complete, rather than waiting for the full LLM response","I need to build a streaming pipeline where each complete JSON object triggers a downstream action (e.g., API call, database write)","I want to reduce latency in LLM-powered applications by processing partial results as they arrive"],"best_for":["Real-time chat applications that display structured data (tables, lists, cards) as the LLM generates them","Streaming agent frameworks that need to trigger tool calls as soon as JSON function calls are complete","Low-latency LLM pipelines where processing delay is critical (e.g., live transcription, real-time recommendations)"],"limitations":["Emits objects only at structural boundaries — cannot emit partial objects or arrays mid-generation","Requires consumer to handle out-of-order or interleaved objects if multiple JSON structures are generated in parallel","No built-in buffering or backpressure handling — fast consumers may miss events if not properly subscribed","Event-driven model adds complexity vs simple blocking parse calls"],"requires":["Node.js 12+ with EventEmitter support or browser with custom event system","Streaming input source (fetch API with ReadableStream, Node.js stream, WebSocket, etc.)","Event listener setup for 'data' and 'error' events"],"input_types":["stream (token-by-token LLM output)","text (individual tokens fed to parser)"],"output_types":["event (emitted objects/arrays as they complete)","object (parsed JSON as JavaScript object)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-partial-json__cap_3","uri":"capability://data.processing.analysis.multi.format.json.output.handling","name":"multi-format json output handling","description":"Supports extraction and parsing of JSON embedded in various text formats: raw JSON, JSON wrapped in markdown code blocks ( ... ), JSON with leading/trailing whitespace or comments, and JSON mixed with natural language text. The parser uses pattern matching to detect and isolate JSON structures before parsing, enabling compatibility with LLM outputs that include explanatory text.","intents":["I need to extract JSON from an LLM response that includes markdown formatting or code blocks","I want to parse JSON that the LLM wrapped in explanatory text or comments","I need to handle LLM outputs that may include both natural language and structured JSON"],"best_for":["Developers building LLM applications where the model outputs mixed text and JSON (e.g., 'Here is the data: {...}')","Teams using models that default to markdown-formatted responses (ChatGPT, Claude, etc.)","Applications that need to extract JSON from unstructured LLM outputs without strict formatting requirements"],"limitations":["Pattern matching for markdown blocks may fail if JSON contains triple backticks in strings","Cannot disambiguate multiple JSON objects in a single response — extracts first valid object only","Whitespace and comment stripping may remove intentional formatting or data","No support for JSONL (newline-delimited JSON) or other streaming JSON formats"],"requires":["Node.js 12+ or browser with ES6 regex support","Text input containing JSON (raw or embedded in markdown/comments)"],"input_types":["text (raw JSON)","text (JSON in markdown code blocks)","text (JSON with leading/trailing text or comments)"],"output_types":["object (parsed JSON as JavaScript object)","string (extracted JSON string before parsing)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-partial-json__cap_4","uri":"capability://data.processing.analysis.configurable.parsing.strategies.and.fallback.chains","name":"configurable parsing strategies and fallback chains","description":"Provides multiple parsing strategies (strict, lenient, recovery) that can be chained together as fallbacks. The parser attempts strict parsing first, then falls back to lenient mode (ignoring minor errors), then to recovery mode (auto-closing brackets), allowing applications to define their own tolerance levels and error handling behavior.","intents":["I want to try strict JSON parsing first, then fall back to lenient parsing if that fails","I need to configure how aggressively the parser should attempt to recover from malformed JSON","I want different parsing behavior for different LLM models or response types"],"best_for":["Teams building production LLM systems that need tunable robustness across different models and use cases","Developers who want to experiment with different error tolerance levels without code changes","Applications that need to balance between data accuracy (strict parsing) and availability (lenient parsing)"],"limitations":["Fallback chains add complexity and may mask underlying issues with LLM output quality","No built-in metrics or logging for which strategy succeeded — requires custom instrumentation","Strategy selection is static per parser instance — cannot dynamically switch strategies per input","Performance cost of trying multiple strategies sequentially (e.g., strict then lenient then recovery)"],"requires":["Node.js 12+ or browser with ES6 support","Configuration object specifying strategy chain and options"],"input_types":["text (partial or malformed JSON)","object (configuration specifying parsing strategies)"],"output_types":["object (parsed JSON as JavaScript object)","metadata (which strategy succeeded, error details)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-partial-json__cap_5","uri":"capability://data.processing.analysis.type.aware.json.validation.and.coercion","name":"type-aware json validation and coercion","description":"Validates parsed JSON against expected types (string, number, boolean, object, array) and optionally coerces values to match schema expectations. The parser can detect type mismatches (e.g., string where number expected) and either reject the value, coerce it, or emit a warning, enabling downstream code to work with guaranteed types.","intents":["I want to ensure that parsed JSON fields have the correct types before passing them to downstream code","I need to coerce string numbers to actual numbers or vice versa to match my schema","I want to detect when an LLM generated the wrong type and handle it gracefully"],"best_for":["Developers building type-safe LLM integrations (e.g., with TypeScript or strict type checking)","Teams using LLM outputs to populate databases or APIs with strict schema requirements","Applications that need to validate LLM-generated data before processing or storage"],"limitations":["Type validation requires schema definition — adds configuration overhead","Coercion rules are heuristic-based and may produce unexpected results (e.g., 'true' string → boolean true)","No support for complex types (nested objects, conditional schemas, unions) — only primitive types","Type mismatches may indicate LLM confusion rather than recoverable errors"],"requires":["Node.js 12+ or browser with ES6 support","Schema definition specifying expected types for JSON fields","Configuration for coercion behavior (strict, lenient, auto-coerce)"],"input_types":["object (parsed JSON)","object (schema definition)"],"output_types":["object (validated/coerced JSON)","array (validation errors or warnings)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"low","permissions":["Node.js 12+ or browser with ES6 support","Input stream from LLM API (OpenAI, Anthropic, Ollama, etc.)","No external dependencies","Partial JSON string with unclosed brackets/braces/quotes","Node.js 12+ with EventEmitter support or browser with custom event system","Streaming input source (fetch API with ReadableStream, Node.js stream, WebSocket, etc.)","Event listener setup for 'data' and 'error' events","Node.js 12+ or browser with ES6 regex support","Text input containing JSON (raw or embedded in markdown/comments)","Configuration object specifying strategy chain and options"],"failure_modes":["Cannot recover from deeply nested structures with missing closing brackets — requires heuristic closing or manual intervention","Performance degrades with very large JSON payloads (>10MB) due to full-buffer parsing on each token","Does not validate JSON schema — only structural validity of brackets/braces/quotes","No support for JSON5 extensions (comments, trailing commas, unquoted keys)","Heuristic closing may produce semantically incorrect JSON if the LLM intended a different structure","Cannot infer missing object keys or array elements — only closes structural delimiters","May produce valid JSON that does not match the LLM's original intent if truncation occurs mid-value","No configurable recovery strategies — uses fixed heuristic rules","Emits objects only at structural boundaries — cannot emit partial objects or arrays mid-generation","Requires consumer to handle out-of-order or interleaved objects if multiple JSON structures are generated in parallel","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46602830571787707,"quality":0.22,"ecosystem":0.52,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":2286931,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=partial-json","compare_url":"https://unfragile.ai/compare?artifact=partial-json"}},"signature":"5V6vLG8wLBHq8BQ5PCUbF9DzE6xldTg3wElgkG21TW77FxQZdaavelfQUMgAC3lHSr1Zt/d7zb+rpKqyFiZaAQ==","signedAt":"2026-06-20T14:30:28.732Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/partial-json","artifact":"https://unfragile.ai/partial-json","verify":"https://unfragile.ai/api/v1/verify?slug=partial-json","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"}}