partial-json
RepositoryFreeParse partial JSON generated by LLM
Capabilities6 decomposed
incremental json parsing with llm streaming tolerance
Medium confidenceParses 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.
Implements a bracket-depth-aware state machine that tolerates incomplete JSON by tracking open/close balance and attempting extraction at valid boundaries, rather than requiring complete, well-formed JSON before parsing — specifically designed for token-streaming scenarios where LLMs emit JSON incrementally
Faster and more pragmatic than regex-based JSON extraction because it maintains parse state across tokens and extracts valid objects as soon as closing brackets appear, avoiding the need to buffer entire responses or retry on malformed input
automatic bracket/quote balancing and recovery
Medium confidenceDetects 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.
Uses a quote-aware state machine to distinguish between structural delimiters and string content, then applies reverse-nesting-order closure rules to automatically balance unclosed brackets without requiring manual schema knowledge or external validation
More robust than simple regex-based bracket counting because it respects quote context and nesting depth, avoiding false positives from brackets inside strings and producing valid JSON even from severely truncated LLM outputs
streaming json extraction with progressive object emission
Medium confidenceProcesses 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.
Implements an event-emitter pattern where the parser maintains internal state across token boundaries and fires 'data' events only when complete JSON objects/arrays are detected, enabling true streaming consumption without buffering the entire response
More efficient than line-by-line or chunk-based parsing because it respects JSON structure rather than arbitrary delimiters, and more responsive than waiting for full completion because it emits results as soon as closing brackets appear
multi-format json output handling
Medium confidenceSupports 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.
Uses regex-based pattern matching to detect and extract JSON from markdown code blocks and mixed-format text, then applies the core partial JSON parser to the extracted content, enabling single-pass handling of both raw and formatted LLM outputs
More flexible than strict JSON parsers because it tolerates markdown formatting and surrounding text, and more reliable than simple regex extraction because it validates JSON structure after extraction rather than relying on delimiters alone
configurable parsing strategies and fallback chains
Medium confidenceProvides 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.
Implements a strategy pattern with configurable fallback chains, allowing applications to define their own error tolerance hierarchy (strict → lenient → recovery) rather than forcing a single parsing approach for all inputs
More flexible than single-strategy parsers because it allows tuning error tolerance per use case, and more pragmatic than all-or-nothing approaches because it gracefully degrades from strict to lenient parsing based on input quality
type-aware json validation and coercion
Medium confidenceValidates 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.
Adds a post-parsing validation layer that checks field types against a schema and optionally coerces values, enabling type-safe consumption of LLM-generated JSON without requiring strict LLM output formatting
More robust than relying on LLM instruction-following because it validates types after parsing, and more flexible than strict schema enforcement because it can coerce values rather than rejecting them outright
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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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
- ✓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
- ✓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
Known 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)
- ⚠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
Requirements
Input / Output
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Parse partial JSON generated by LLM
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