{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-haibiiin--json-repair","slug":"haibiiin--json-repair","name":"json-repair","type":"repo","url":"https://haibiiin.github.io/json-repair/","page_url":"https://unfragile.ai/haibiiin--json-repair","categories":["data-pipelines"],"tags":["ai-tool","java","json","json-repair","llm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-haibiiin--json-repair__cap_0","uri":"capability://data.processing.analysis.antlr.based.malformed.json.structural.repair.with.strategy.pattern.orchestration","name":"antlr-based malformed json structural repair with strategy pattern orchestration","description":"Repairs syntactically broken JSON by using ANTLR parser to identify structural errors (missing braces, brackets, parentheses) and applies configurable repair strategies (SimpleRepairStrategy, CorrectRepairStrategy) to fix them. The JSONRepair orchestrator class manages the repair pipeline, attempting fixes iteratively up to a configurable limit, with error context tracking via the Expecting class to understand what tokens are missing at failure points.","intents":["Fix JSON output from LLMs that is missing closing braces or brackets","Repair unquoted JSON keys and values that break parsing","Handle partially-formed JSON structures with missing outer braces","Automatically patch redundant commas in arrays and objects"],"best_for":["Java backend developers integrating LLM outputs into production systems","Teams building LLM-powered APIs that need post-processing guardrails","Developers working with streaming JSON responses that may be truncated"],"limitations":["ANTLR parsing adds ~0.04-0.09ms per repair operation; not suitable for real-time sub-millisecond requirements","Repair strategies are heuristic-based and may produce semantically incorrect JSON if the original intent is ambiguous","No support for repairing deeply nested structures with multiple simultaneous errors; repairs are applied iteratively with diminishing returns","Limited to JSON format only; cannot repair JSONL, NDJSON, or other streaming JSON variants"],"requires":["Java 8 or higher","ANTLR 4.x runtime library (included in Maven dependencies)","Maven or Gradle for dependency management"],"input_types":["malformed JSON string","partial JSON fragments","JSON with unquoted keys/values","JSON with missing structural delimiters"],"output_types":["valid JSON string","JSONObject (parsed representation)","repair metadata (error context, strategy applied)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_1","uri":"capability://data.processing.analysis.json.content.extraction.from.mixed.text.with.fallback.repair","name":"json content extraction from mixed text with fallback repair","description":"Extracts valid JSON objects or arrays from larger text blocks (e.g., LLM responses with explanatory text before/after JSON) using SimpleExtractStrategy, which scans for JSON delimiters and isolates contiguous JSON content. Extracted JSON is then passed through the repair pipeline if it contains anomalies, enabling end-to-end recovery of structured data from unstructured LLM outputs.","intents":["Extract JSON from LLM responses that include markdown code blocks or explanatory text","Isolate multiple JSON objects from a single text response","Recover JSON from responses where the model explains its output before providing the data structure"],"best_for":["Developers parsing LLM chat completions that mix natural language with JSON","Teams building chatbot backends that need to extract structured data from conversational responses","Builders of multi-step LLM pipelines where intermediate outputs are JSON embedded in text"],"limitations":["Extraction assumes JSON is contiguous; cannot handle interleaved JSON and text (e.g., 'key: {nested' followed by explanation then '}' )","Extraction heuristic is delimiter-based and may incorrectly identify JSON-like structures in code blocks or examples","No support for extracting multiple separate JSON objects from a single response; extracts the first valid JSON block found","Performance degrades on very large text blocks (>100KB) due to linear scanning for delimiters"],"requires":["Java 8 or higher","ANTLR 4.x runtime","Input text must contain at least one '{' or '[' character to trigger extraction"],"input_types":["mixed text with embedded JSON","markdown-formatted JSON code blocks","LLM chat responses with JSON payloads"],"output_types":["extracted JSON string","JSONObject or JSONArray","extraction metadata (start/end position, confidence)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_10","uri":"capability://automation.workflow.integration.test.suite.with.diverse.json.anomaly.scenarios","name":"integration test suite with diverse json anomaly scenarios","description":"Includes comprehensive integration tests (IntegrationTests class) covering a wide range of JSON anomalies produced by LLMs: missing braces/brackets, unquoted keys/values, trailing commas, missing outer delimiters, and nested structure errors. Tests are organized by anomaly type and include both positive cases (repair succeeds) and negative cases (repair fails gracefully), providing confidence in repair behavior across different LLM output patterns.","intents":["Verify repair behavior across diverse JSON anomaly types","Ensure repair does not break valid JSON","Identify edge cases and limitations of repair strategies"],"best_for":["Teams evaluating json-repair for production use","Developers extending repair strategies and needing regression test coverage","Builders of LLM output processing pipelines wanting to understand repair behavior"],"limitations":["Tests are synthetic and may not cover all real-world LLM output patterns","No performance regression tests; cannot detect if repair latency degrades between versions","Tests do not validate semantic correctness of repaired JSON; only syntactic validity","Limited coverage of edge cases like very large JSON structures or deeply nested objects"],"requires":["Java 8 or higher","JUnit 4 or 5 (included in Maven dependencies)","Maven to run tests"],"input_types":["test case JSON strings with known anomalies","expected repair results"],"output_types":["test pass/fail results","coverage metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_2","uri":"capability://automation.workflow.configurable.multi.pass.repair.attempt.strategy.with.iteration.limits","name":"configurable multi-pass repair attempt strategy with iteration limits","description":"Implements a configurable repair pipeline via JSONRepairConfig that allows developers to set maximum repair attempt counts and extraction modes. The JSONRepair orchestrator applies repair strategies iteratively, re-parsing after each fix attempt until either the JSON is valid or the attempt limit is reached. This prevents infinite loops while allowing heuristic-based repairs to converge on valid output through multiple passes.","intents":["Control repair aggressiveness and resource consumption via attempt limits","Enable/disable extraction mode based on input characteristics","Tune repair behavior for different LLM output patterns (e.g., stricter for GPT-4, more lenient for smaller models)"],"best_for":["Production systems needing tunable repair behavior across different LLM providers","Teams with strict latency budgets who want to limit repair overhead","Developers building adaptive pipelines that adjust repair strategy based on model quality"],"limitations":["Configuration is static per JSONRepair instance; cannot dynamically adjust strategy mid-repair based on error patterns","No built-in metrics or observability for repair attempt counts or strategy effectiveness; requires external logging","Attempt limits are global; cannot set different limits for different error types (e.g., 5 attempts for missing braces, 2 for unquoted keys)","No rollback mechanism; if repair attempt N produces invalid output, there is no way to revert to attempt N-1 state"],"requires":["Java 8 or higher","JSONRepairConfig object instantiation with desired parameters","Understanding of repair strategy semantics to set appropriate attempt limits"],"input_types":["JSONRepairConfig object with maxAttempts and extractionMode settings","malformed JSON string"],"output_types":["repaired JSON string","repair attempt count (implicit in execution)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_3","uri":"capability://planning.reasoning.error.context.tracking.and.missing.token.identification.via.expecting.class","name":"error context tracking and missing token identification via expecting class","description":"Tracks parse error context through the Expecting class, which records what tokens the parser expected at the point of failure (e.g., 'expected }' or 'expected ]'). This error context is used by repair strategies to make targeted fixes rather than blind string manipulation. When ANTLR parsing fails, the Expecting object captures the expected token type and position, enabling the repair strategy to insert the correct missing delimiter at the right location.","intents":["Understand why JSON parsing failed and what token is missing","Make targeted repairs based on expected tokens rather than heuristic guessing","Provide diagnostic information for debugging LLM output quality"],"best_for":["Developers building observability into LLM output processing pipelines","Teams analyzing LLM failure patterns and wanting to understand common error types","Builders of adaptive repair systems that adjust strategy based on error context"],"limitations":["Error context is captured at first parse failure only; does not track cascading errors or multiple simultaneous issues","Expecting class is internal to repair logic; error context is not exposed in public API for external analysis","No aggregation or metrics collection across multiple repair operations; each repair is independent","Error context does not include semantic information (e.g., 'value is wrong type'), only syntactic token expectations"],"requires":["Java 8 or higher","ANTLR 4.x parser integration (internal dependency)","Access to JSONRepair internals for error context inspection (not exposed in standard API)"],"input_types":["malformed JSON string","ANTLR parse error event"],"output_types":["Expecting object with expected token type and position","repair strategy decision based on error context"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_4","uri":"capability://data.processing.analysis.unquoted.key.and.value.normalization.with.automatic.quote.insertion","name":"unquoted key and value normalization with automatic quote insertion","description":"Repairs JSON where keys or values lack quotation marks (e.g., {f:v} instead of {\"f\":\"v\"}) by detecting unquoted identifiers and automatically inserting quotes around them. This is handled as part of the SimpleRepairStrategy, which identifies tokens that should be strings but lack delimiters and wraps them in quotes during the repair pass.","intents":["Fix JSON from LLMs that omit quotes around string keys or values","Handle JavaScript-like object notation that is not valid JSON","Normalize JSON from models trained on code datasets that may use unquoted keys"],"best_for":["Teams working with smaller LLMs or fine-tuned models that produce JavaScript-like output","Developers building JSON APIs that accept LLM-generated payloads","Builders of data pipelines that need to normalize heterogeneous JSON sources"],"limitations":["Quote insertion is heuristic-based; if an unquoted value is actually a number or boolean, it will be incorrectly quoted (e.g., true becomes \"true\")","No type inference; cannot distinguish between unquoted strings, numbers, and booleans without additional context","Repair may produce semantically incorrect JSON if the original intent was to use a number or boolean (e.g., {count:5} becomes {\"count\":\"5\"})","Does not handle complex cases like unquoted keys with special characters or spaces"],"requires":["Java 8 or higher","ANTLR 4.x parser","Input JSON with unquoted keys or values"],"input_types":["JSON with unquoted keys","JSON with unquoted string values","JavaScript-like object notation"],"output_types":["valid JSON with quoted keys and values","JSONObject with normalized string representation"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_5","uri":"capability://data.processing.analysis.redundant.comma.removal.and.array.object.cleanup","name":"redundant comma removal and array/object cleanup","description":"Removes redundant or trailing commas in JSON arrays and objects (e.g., [1,2,] becomes [1,2]) as part of the SimpleRepairStrategy. The repair logic detects comma tokens that appear before closing brackets or braces and removes them, producing valid JSON that conforms to the JSON specification which disallows trailing commas.","intents":["Fix JSON arrays with trailing commas from LLM generation","Clean up JSON objects with redundant commas between properties","Handle JSON from models that generate comma-separated lists without proper termination"],"best_for":["Developers processing LLM outputs that include trailing commas (common in code-trained models)","Teams building JSON validation pipelines that need to normalize input","Builders of data ingestion systems that accept LLM-generated structured data"],"limitations":["Comma removal is unconditional; if a trailing comma is intentional (e.g., in a code generation context), it will be removed","Does not handle commas in string values; if a string contains a comma before a closing bracket, it may be incorrectly removed","No semantic understanding of whether a comma is truly redundant or part of the data structure","Performance impact is minimal (~0.01ms) but scales linearly with JSON size"],"requires":["Java 8 or higher","ANTLR 4.x parser","Input JSON with redundant or trailing commas"],"input_types":["JSON arrays with trailing commas","JSON objects with redundant commas","malformed JSON with comma syntax errors"],"output_types":["valid JSON without trailing commas","JSONArray or JSONObject with cleaned structure"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_6","uri":"capability://data.processing.analysis.missing.outer.brace.bracket.completion.for.partial.json.structures","name":"missing outer brace/bracket completion for partial json structures","description":"Automatically adds missing outermost braces or brackets to convert partial JSON fragments into valid JSON objects or arrays. For example, converts [1,2,3 to [1,2,3] or {\"key\":\"value\" to {\"key\":\"value\"}. This is implemented in SimpleRepairStrategy by detecting unclosed top-level delimiters and inserting the corresponding closing delimiter at the end of the input.","intents":["Complete partial JSON from streaming LLM responses that are truncated","Fix JSON fragments that are missing only the final closing delimiter","Handle JSON from models that generate valid content but forget to close the outermost structure"],"best_for":["Developers working with streaming LLM APIs where responses may be cut off mid-generation","Teams building real-time chat interfaces that need to parse incomplete JSON","Builders of LLM output pipelines that handle truncated or partial responses"],"limitations":["Only handles missing outermost delimiters; does not repair missing inner braces or brackets","Assumes the JSON is otherwise well-formed except for the missing closing delimiter; cannot repair multiple simultaneous structural errors","May produce incorrect JSON if the input is ambiguous (e.g., {\"a\":{\"b\":1 could close as {\"a\":{\"b\":1}} or {\"a\":{\"b\":1}}, both valid but different)","No validation that the added closing delimiter actually closes the structure correctly; relies on subsequent ANTLR parsing to validate"],"requires":["Java 8 or higher","ANTLR 4.x parser","Input JSON with missing outermost closing delimiter"],"input_types":["partial JSON arrays (missing ])","partial JSON objects (missing })","truncated JSON from streaming responses"],"output_types":["complete JSON with closing delimiter added","JSONObject or JSONArray"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_7","uri":"capability://data.processing.analysis.null.value.insertion.for.missing.object.properties.and.array.elements","name":"null value insertion for missing object properties and array elements","description":"Fills missing values in JSON objects and arrays with null when a key is present but has no value, or when an array element is missing. For example, converts {\"key\":} to {\"key\":null} or [1,,3] to [1,null,3]. This is part of SimpleRepairStrategy and ensures that all keys have values and all array positions are filled, producing valid JSON that can be parsed without type errors.","intents":["Fix JSON where LLMs generate keys without values (incomplete object properties)","Handle arrays with missing elements or double commas","Ensure all JSON properties have values, even if the original intent was unclear"],"best_for":["Developers building JSON APIs that require all properties to be present","Teams processing LLM outputs where incomplete objects are common","Builders of data pipelines that need to normalize JSON structure"],"limitations":["Null insertion is a lossy operation; the original intent (e.g., omit the property entirely vs. set it to null) is lost","May produce semantically incorrect JSON if the original intent was to omit optional properties","No type inference; cannot determine if a missing value should be null, empty string, 0, or false","Null insertion may cause downstream type validation errors if the schema expects non-null values"],"requires":["Java 8 or higher","ANTLR 4.x parser","Input JSON with missing values or array elements"],"input_types":["JSON objects with keys but no values","JSON arrays with missing elements","malformed JSON with incomplete properties"],"output_types":["valid JSON with null values inserted","JSONObject or JSONArray with complete structure"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_8","uri":"capability://automation.workflow.jmh.based.performance.benchmarking.and.latency.profiling","name":"jmh-based performance benchmarking and latency profiling","description":"Includes built-in JMH (Java Microbenchmark Harness) benchmarks via BenchmarkTests class that measure repair latency for different JSON complexity levels (simple objects, arrays with missing brackets, nested structures, unquoted keys). Benchmarks are executed as part of the test suite and generate detailed performance reports (benchmark_0.2.2.json) showing nanosecond-level timing for each repair operation, enabling developers to understand repair overhead and optimize for their use case.","intents":["Measure repair latency for different JSON patterns and complexity levels","Understand performance impact of repair operations in production systems","Identify bottlenecks and optimize repair strategy selection"],"best_for":["Performance-sensitive teams building real-time LLM processing pipelines","Developers optimizing JSON repair for high-throughput systems","Teams evaluating json-repair for production use and needing latency guarantees"],"limitations":["JMH benchmarks measure isolated repair operations; do not account for GC pauses, JIT compilation, or other JVM overhead in production","Benchmarks are synthetic and may not reflect real-world LLM output patterns","No built-in comparison with alternative JSON repair libraries; benchmarks are absolute, not relative","Benchmark results are JVM-dependent and may vary significantly across different Java versions and hardware"],"requires":["Java 8 or higher","JMH library (included in Maven dependencies)","Maven or Gradle to run benchmarks","Ability to execute JMH tests (requires special Maven plugin configuration)"],"input_types":["JSON test cases of varying complexity","benchmark configuration parameters (iterations, warmup, etc.)"],"output_types":["JMH benchmark results (JSON format)","latency metrics (nanoseconds, milliseconds)","performance reports with statistical analysis"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-haibiiin--json-repair__cap_9","uri":"capability://automation.workflow.maven.based.build.and.dependency.management.with.antlr.code.generation","name":"maven-based build and dependency management with antlr code generation","description":"Uses Maven as the build system with integrated ANTLR code generation plugin (maven-antlr4-plugin) that automatically generates parser and lexer classes from ANTLR grammar files during the build process. The pom.xml configuration manages dependencies (ANTLR runtime, JSON libraries, testing frameworks) and build phases, enabling reproducible builds and easy integration into Java projects via Maven Central repository.","intents":["Integrate json-repair into Maven-based Java projects with automatic dependency resolution","Regenerate ANTLR parser code when grammar files are updated","Manage transitive dependencies and ensure version compatibility"],"best_for":["Java teams using Maven as their build system","Developers building LLM integration layers in enterprise Java applications","Teams needing reproducible builds with locked dependency versions"],"limitations":["Maven-only; no Gradle or other build system support (though Gradle can consume Maven artifacts)","ANTLR code generation adds ~5-10 seconds to build time; not suitable for rapid iteration workflows","Maven Central deployment requires manual release process; no automatic CI/CD publishing","Transitive dependencies may conflict with other libraries in large projects; requires careful version management"],"requires":["Java 8 or higher","Maven 3.6 or higher","ANTLR 4.x (managed by Maven)","Internet access to download dependencies from Maven Central"],"input_types":["pom.xml configuration","ANTLR grammar files (.g4)","source code"],"output_types":["compiled JAR artifact","generated ANTLR parser and lexer classes","dependency tree"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Java 8 or higher","ANTLR 4.x runtime library (included in Maven dependencies)","Maven or Gradle for dependency management","ANTLR 4.x runtime","Input text must contain at least one '{' or '[' character to trigger extraction","JUnit 4 or 5 (included in Maven dependencies)","Maven to run tests","JSONRepairConfig object instantiation with desired parameters","Understanding of repair strategy semantics to set appropriate attempt limits","ANTLR 4.x parser integration (internal dependency)"],"failure_modes":["ANTLR parsing adds ~0.04-0.09ms per repair operation; not suitable for real-time sub-millisecond requirements","Repair strategies are heuristic-based and may produce semantically incorrect JSON if the original intent is ambiguous","No support for repairing deeply nested structures with multiple simultaneous errors; repairs are applied iteratively with diminishing returns","Limited to JSON format only; cannot repair JSONL, NDJSON, or other streaming JSON variants","Extraction assumes JSON is contiguous; cannot handle interleaved JSON and text (e.g., 'key: {nested' followed by explanation then '}' )","Extraction heuristic is delimiter-based and may incorrectly identify JSON-like structures in code blocks or examples","No support for extracting multiple separate JSON objects from a single response; extracts the first valid JSON block found","Performance degrades on very large text blocks (>100KB) due to linear scanning for delimiters","Tests are synthetic and may not cover all real-world LLM output patterns","No performance regression tests; cannot detect if repair latency degrades between versions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1686903065794265,"quality":0.32,"ecosystem":0.55,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:13.678Z","last_commit":"2026-02-14T07:24:57Z"},"community":{"stars":100,"forks":6,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=haibiiin--json-repair","compare_url":"https://unfragile.ai/compare?artifact=haibiiin--json-repair"}},"signature":"Yos+S29gfDUjDuWNW0beEET1dcV+lI58BAEJeCj1ZBEwFy5XJpz858pxOXOTZnMTytsnnh+/2pCABJ/Dy2MfDg==","signedAt":"2026-06-21T21:37:06.545Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/haibiiin--json-repair","artifact":"https://unfragile.ai/haibiiin--json-repair","verify":"https://unfragile.ai/api/v1/verify?slug=haibiiin--json-repair","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"}}