Disclosure: I wrote this article together with an AI assistant (Claude). The AI ran all the code below and checked the results. I am not a professional programmer, so please read it with that in mind. When you pull JSON out of an LLM's reply, json.loads raising an error is the easy case. The harder
Disclosure: I wrote this article together with an AI assistant (Claude). The AI ran all the code below and checked the results. I am not a professional programmer, so please read it with that in mind. When you pull JSON out of an LLM's reply, json.loads raising an error is the easy case. The harder case is when nothing fails and the wrong JSON quietly comes back. Some reasoning models print their thinking before the answer, wrapped in tags like <think>...</think>. Sometimes that thinking contains a draft of the JSON: <think> The user wants {"name": "...", "age": ...}. Example: {"x": 1} </think> {"name": "Ann", "age": 30} (Depending on the model or provider, the thinking may come in a separate field instead. This article is about the case where it arrives mixed into the text.) text[text.find("{"): text.rfind("}") + 1] This gave me a JSONDecodeError, because the draft and the answer get cut out as one piece. At least it fails loudly. I passed the same text to json-repair (version 0.63.5): from json_repair import repair_json repair_json(text, return_objects=True) [{'name': '...', 'age': '...'}, {'x': 1}, {'name': 'Ann', 'age': 30}] No error. All three JSON objects come back as a list. If you do not validate against a schema, this slips through. If the output stops in the middle of the thinking (for example, at a token limit): <think>Hmm, maybe {"name": "Ann"} the same library returns: {'name': 'Ann'} An unfinished draft is returned as if it were the final answer. The value is often "almost right", so it is easy to miss. You can reproduce this in a few seconds: pip install json-repair python -c "from json_repair import repair_json; print(repair_json('<think>Hmm, {"name": "Ann"} maybe', return_objects=True))" Two steps: Remove the <think> block before looking for JSON (including an unclosed one). From what is left, take the last complete JSON value. Standard library only: import json import re THINK_BLOCK = re.compile(r"<(think|thinking|reasoning)\b[^>]>.?</\1\s*>", re.S | re.I) THINK_OPEN = re.compile(r"<(think|thinking|reasoning)\b[^>]>.\Z", re.S | re.I) def extract_json(text: str): """Return the last complete JSON value found, or None.""" text = THINK_BLOCK.sub("", text) # drop closed thinking blocks text = THINK_OPEN.sub("", text) # drop an unclosed (cut-off) one dec = json.JSONDecoder() found, i = None, 0 while i < len(text): if text[i] in "{[": try: found, end = dec.raw_decode(text, i) i = end # skip what was read (no nested picks) continue except json.JSONDecodeError: pass i += 1 return found raw_decode tells you whether JSON can be read from a given position, and how far it reaches. That is why chatter before or after the JSON does not matter. Input Result Draft JSON inside the thinking only the answer Text before and after the JSON only the JSON JSON inside a Markdown code fence only the JSON Nested JSON the whole outer object Truncated JSON None Unclosed thinking block None The last two matter most: if there is no answer yet, return None instead of guessing. That lets the caller treat it as a failure. Trailing commas, single quotes, and truncated JSON like {"a": 1 still need a repair library. But order matters: text = THINK_BLOCK.sub("", text) text = THINK_OPEN.sub("", text) data = repair_json(text, return_objects=True) Drop the thinking, then repair the syntax, then validate against a schema. Never report a failed repair as a success. Return None or raise. Log what you removed (for example, "removed 1 thinking block"). Validate with a schema as the last line of defense. I am not an expert in this area. The code and results come from actually running them with an AI assistant, and output formats differ between models and providers, so your results may differ. If you know other failure modes or better approaches, I would be glad to hear them in the comments.
Key Takeaways #
- •Disclosure: I wrote this article together with an AI assistant (Claude)
- •This story was reported by Dev.to , covering developments in thedev space.
- •AI advancements continue to reshape industries — read the full article on Dev.to for complete coverage.
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