jsonmend

Mends the JSON your LLM almost wrote.

Truncated tool calls, markdown fences, single quotes, bare keys, Python literals, comments, trailing commas, prose around the payload — jsonmend turns them into valid JSON. It is a drop-in replacement for json_repair that is 5–10× faster on batch repair, ~50× faster on streaming, ships a true incremental streaming API (O(new bytes) per chunk, not O(buffer)), and is the reference implementation of an open, cross-language conformance corpus for JSON repair.

Pure Python, zero dependencies, zero binaries. Works on CPython 3.9–3.14, PyPy, Pyodide/WASM, AWS Lambda — anywhere pip install works.

pip install jsonmend

Scoreboard

JSON repair has no standard: the same broken input is repaired differently by the Python and JavaScript incumbents, which is a real source of production bugs. The jsonmend conformance corpus (485 cases, 20 categories, CC0) defines repair semantics as data — including the genuinely ambiguous cases, where every defensible answer is accepted.

	jsonmend 0.1.1	json_repair 0.60.1	jsonrepair 3.14.0 (JS)
corpus pass rate	485/485 (100%)	339/485 (69.9%)	365/485 (75.3%)

Per-category breakdown: corpus/scoreboard.md. Reproduce: python tools/referee.py --write (needs pip install json_repair and npm install jsonrepair, dev-only).

Performance

Median of 7, three independent rounds within ±5%, Python 3.12, M-series macOS. All inputs are broken JSON (the json.loads fast path never runs). Verified-then-timed: outputs are checked equal before timing. Reproduce: python tools/bench.py --verify && python tools/bench.py.

workload	size	jsonmend	json_repair	speedup
truncated tool call	1 KB	0.027 ms	0.199 ms	7.3×
truncated row payload	75 KB	1.48 ms	12.6 ms	8.5×
markdown-fenced output	49 KB	0.25 ms	2.6 ms	10.6×
dirty (quotes/keys/literals)	5 KB	0.38 ms	2.6 ms	7.0×

Streaming is a different complexity class

A streaming UI re-renders the partial value on every chunk. With a batch repairer you must re-parse the whole buffer each time — O(n²) total. The stateful Mender only pays for the new bytes (amortised O(1) per fed character on CPython — guarded by a buffer-copy invariant test, not a flaky timer):

workload	jsonmend Mender	json_repair (stream_stable=True)
150 KB in 4 KB chunks	6.9 ms	323 ms	47×
10 MB in 4 KB chunks	1.2 s	est. >20 min (quadratic)	—

Usage

Drop-in for json_repair

# before
from json_repair import repair_json, loads
# after — same call sites
from jsonmend import repair_json, loads

repair_json("{'name': 'John', age: 31")     # '{"name": "John", "age": 31}'
loads('```json\n{"ok": true,}\n```')         # {'ok': True}

repair_json(json_str, return_objects=..., skip_json_loads=..., ensure_ascii=..., **json_dumps_args), loads, load(fd), from_file(path) match json_repair's signatures. Valid JSON short-circuits through C-speed json.loads.

Streaming

from jsonmend import Mender

m = Mender()
for chunk in llm_stream:           # feed as the tokens arrive
    partial = m.feed(chunk)        # best-effort value, O(new bytes)
    render(partial)                # e.g. {"answer": "The capital of Fr"}
value = m.close()                  # final mended value

feed() returns a live view that grows in place — including the string that is currently streaming in. Any chunking gives byte-identical results to batch repair (property-tested over the whole corpus).

Strict mode

from jsonmend import loads, JSONMendError

loads("complete garbage")                  # "" (json_repair-compatible)
loads("complete garbage", strict=True)     # raises JSONMendError

What it fixes

truncated objects/arrays/strings/numbers/literals · markdown fences with prose around them · single/smart/backtick quotes · unescaped inner quotes · missing quotes · bare keys and values · True/False/None/undefined/NaN/ Infinity · //, #, /* */ comments · trailing/missing/extra commas · missing colons · mismatched brackets · concatenated/NDJSON documents · string concatenation ("a" + "b") · JSONP/MongoDB wrappers (ObjectId("…")) · Python tuples/sets · ellipsis placeholders · non-string keys · BOM and exotic whitespace · escaped-JSON documents ({\"a\": 1}) · broken \u escapes and surrogate pairs · 100k-deep nesting (no recursion anywhere)

Why it's fast

• One resumable state machine serves batch and streaming — batch is a single feed that never suspends, so there is no streaming tax.
• Strings cost one str.find + one slice when clean; never a per-character Python loop.
• Speculative C parsing: complete sub-trees inside broken documents are recognized and handed to the C json scanner, with a salvage step that parses the longest clean prefix of a broken container in one shot. Semantics-affecting inputs (NaN, control chars, surrogate escapes) fall back to the machine, so behavior never changes.
• Bounded backtracking: a string-close decision can revisit one recorded candidate quote, never rescan; adversarial quote storms stay linear (tested).

Guarantees

• Output is always valid RFC 8259 JSON (or ""/an exception). Unlike json_repair, NaN/Infinity never leak into the output text — they serialize as null (loads still gives you the floats).
• Output is always UTF-8 encodable (lone surrogates are replaced).
• Never crashes, never recurses: fuzzed and property-tested, 100k-deep inputs are fine.
• Mender.close() ≡ batch result, for every chunking (property-tested).

Honest differences vs json_repair

• logging=True is not supported (it is incompatible with the single-pass design and is one reason json_repair is slow); a no-op shim raises TypeError so you notice.
• Schema-guided repair (schema=) is not implemented in v0.1.
• json_repair's stream_stable=True flag changes how truncated escapes render mid-stream; jsonmend's Mender is always stream-stable.
• On ambiguous corpus cases the libraries may legitimately differ; jsonmend's choices are documented case-by-case in the corpus rationales.

The corpus is the point

If you maintain a JSON-repair library in any language: please steal corpus/. It is CC0, the format is three fields, and 485 cases with rationales are more valuable than any of our engines. Cross-language agreement on repair semantics helps everyone shipping LLM systems.

License

MIT. The conformance corpus is CC0.