ai-data-extractor: the address we list did not respond · last checked 13 Nov 2026
ai-data-extractor
Free MIT Python CLI that turns AI coding assistant chat history into one normalized JSONL dataset for fine-tuning and analytics.
Ten parsers, one schema, MIT license, no upload — for terminal-comfortable developers that is a genuinely useful local harvester covering Claude Code, Codex, Cursor, Cline, Roo Code, and Aider. It is not a product, though: no support channel, and the Windsurf and Trae heuristic parsers will crack the moment those apps change their storage layout. Pick it for fine-tuning data or a pre-reinstall backup, not because you want someone to call when it breaks.
Verdict generatedLast checked 19d ago · cite: rightaichoice.com/tools/ai-data-extractor
- Developers building fine-tuning datasets from their own assistant conversation history
- Engineers backing up chat logs before an app clears its local database
- Privacy-focused users who want local-only extraction with no account or upload
- Researchers comparing how different AI coding assistants structure and store sessions
- Teams needing an accountable vendor with a support SLA
- Non-technical users who need a GUI or one-click installer
- Anyone requiring schema-stable parsing for Windsurf or Trae
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Skip ai-data-extractor if you need a supported, schema-stable connector for Windsurf or Trae, or if you want a vendor you can escalate a bug to — the heuristic parsers and the short commit history are yours to absorb.
Heuristic Windsurf and Trae parsers can break after an app update, and you pay for that in engineering time rather than dollars.
There is no pricing to fit: ai-data-extractor is MIT-licensed and free to run locally, so the trade-off is your time rather than a subscription. Compared with paid transcript exporters that bill per seat or per export, it costs nothing and gives you ten parsers; compared with a hand-rolled scraper, it costs you the effort of updating heuristic parsers when Windsurf or Trae change their storage.
In short
ai-data-extractor — Free MIT Python CLI that turns AI coding assistant chat history into one normalized JSONL dataset for fine-tuning and analytics. Best for Developers building fine-tuning datasets from their own assistant conversation history, Engineers backing up chat logs before an app clears its local database, Privacy-focused users who want local-only extraction with no account or upload. Free to use.
What is ai-data-extractor?
ai-data-extractor — Free MIT Python CLI that turns AI coding assistant chat history into one normalized JSONL dataset for fine-tuning and analytics. Best for Developers building fine-tuning datasets from their own assistant conversation history, Engineers backing up chat logs before an app clears its local database, Privacy-focused users who want local-only extraction with no account or upload. Free to use.
ai-data-extractor solves a niche but real problem: your AI coding assistant conversation history is scattered across a dozen incompatible log formats, and you want one corpus. It's a free, MIT-licensed Python command-line tool that reads those logs straight off your local disk and writes a single normalized JSONL file — one conversation per line — usable for fine-tuning datasets, usage analytics, or offline archiving. No account, no upload, no server; everything stays on the machine. Source breadth is the pitch. Ten extractors ship in the repo, covering Claude Code session JSONL under ~/.claude/projects, Codex CLI rollout files under ~/.codex/sessions, Cursor's SQLite state.vscdb in global and workspace storage, Continue sessions, Gemini CLI chats, OpenCode's session/message/part trees, and heuristic parsers for Windsurf and Trae. In January 2026 Cline and its fork Roo Code were added — they store raw Anthropic-format message arrays one folder per task — and Aider support arrived, pulling the markdown transcript Aider keeps per project directory. Records carry user messages, assistant responses, code context (file paths, selections, snippets), diffs and suggested edits where the source provides them, plus tool calls and results, timestamps, session IDs, project paths, and model names. Only messages, source, and session_id are guaranteed field-by-field, so budget for normalization on the rest. macOS, Linux, and Windows data roots are auto-detected — no OS flag to pass — and output lands as timestamped files under extracted_data/, with --merge concatenating everything into all_conversations.jsonl. Against paid transcript exporters, the trade is breadth at zero cost versus no support channel and no schema guarantees on the heuristic parsers. If your chats live on local disk and you are comfortable in a terminal, it earns its place; if you need a vendor to answer a ticket when a parser breaks, look elsewhere.
Key features
- Extract Claude Code session history from ~/.claude/projects/**/*.jsonl
- Parse Codex CLI rollout files from ~/.codex/sessions/**/rollout-*.jsonl
- Read Cursor chat data from SQLite state.vscdb in global and workspace storage
- Heuristic extraction from Windsurf SQLite storage with an undocumented schema
- Trae support combining SQLite and JSONL sources via heuristic parsing
- Extract Continue sessions from ~/.continue/sessions/*.json
- Extract Gemini CLI chats from ~/.gemini/tmp/<hash>/chats/*.json
- Parse OpenCode session, message, and part trees
- Extract Cline and Roo Code tasks storing raw Anthropic-format message arrays (added Jan 2026)
- Extract Aider markdown chat transcripts per project directory (added Jan 2026)
- Auto-detect macOS, Linux, and Windows data roots without OS flags
- Normalize every source into one JSONL conversation-per-line format
- Capture user messages, assistant responses, code context, diffs, and tool calls
- CLI flags for --all, --sources, --list, --output-dir, --search-path, and --merge
- Run extractors standalone with python -m extractors.<name> for debugging
Behind the verdict
The first question to ask is where your chats actually live. If they are on your own disk — Claude Code's JSONL under ~/.claude/projects, Codex's rollout files, a Cursor SQLite database — this tool gets you a usable corpus in a few minutes with no account and nothing leaving the machine. That is the whole value proposition, and for fine-tuning prep or a pre-reinstall backup it holds up. Source breadth is the reason to choose it over a single-tool exporter. Switching between Cursor, Claude Code, and Codex over a year leaves you with three incompatible formats; --merge flattens them into one all_conversations.jsonl. The Cline/Roo Code extractor is also the clearest worked example in the repo if you want to write your own parser — copy the pattern, add a source. Where it bites: the Windsurf and Trae parsers are heuristic over undocumented SQLite layouts, so a vendor storage change breaks them without warning. Only messages, source, and session_id are guaranteed fields; everything else (diffs, code context, model names) depends on the source and may be absent. Teams that need schema-stable parsing or someone accountable should look at a paid exporter instead. In practice, reach for it when you are comfortable in a terminal and want the raw data on your terms. Pass when you need a GUI, a support SLA, or guaranteed field coverage on the weaker parsers. It is a utility, not a service — treat it accordingly.
Limitations
- Extraction fidelity is bounded by what each assistant actually persists locally — tools that don't record diffs or tool-call results produce thinner records, and cloud-side history is out of reach entirely.
- The Windsurf and Trae parsers are heuristics against undocumented schemas, so they are the most likely to break or mis-parse after an app update.
- Only messages, source, and session_id are guaranteed on every record; timestamps, project paths, diffs, and model names appear only when the source tool records them.
- It is a local CLI script with no documented support channel, SLA, or hosted API surface.
as of 2026-10-09
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Is ai-data-extractor still active?
ai-data-extractor: the address we list did not respond · last checked 13 Nov 2026.
Viability score
How well maintained and how widely used is ai-data-extractor? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about ai-data-extractor?
What people actually say about ai-data-extractor — is it worth it?
We scanned public community sources for ai-data-extractor on Sep 30, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 3 of the posts we fetched could be positively tied to ai-data-extractor. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about ai-data-extractor — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified ai-data-extractor 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed pricing page, limitations
- — changed features, summary, description
- — changed pricing page
- — changed who should skip it, description, summary, our verdict
- — changed pricing page
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does ai-data-extractor cost in 2026?
ai-data-extractor: Free to use. Pricing last verified 25 Oct 2026.
Where the pricing makes sense
The company stage and team size where ai-data-extractor's pricing actually pencils out — and where peers do it cheaper.
There is no pricing to fit: ai-data-extractor is MIT-licensed and free to run locally, so the trade-off is your time rather than a subscription. Compared with paid transcript exporters that bill per seat or per export, it costs nothing and gives you ten parsers; compared with a hand-rolled scraper, it costs you the effort of updating heuristic parsers when Windsurf or Trae change their storage.
Has ai-data-extractor's pricing changed?
No plan price for ai-data-extractor has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick ai-data-extractor?
Pick ai-data-extractor if you are Developers building fine-tuning datasets from their own assistant conversation history, Engineers backing up chat logs before an app clears its local database, Privacy-focused users who want local-only extraction with no account or upload, Researchers comparing how different AI coding assistants structure and store sessions.
Look elsewhere if you are Teams needing an accountable vendor with a support SLA, Non-technical users who need a GUI or one-click installer, Anyone requiring schema-stable parsing for Windsurf or Trae, Users whose chat history only ever existed in a vendor cloud.
Skip it if Skip ai-data-extractor if you need a supported, schema-stable connector for Windsurf or Trae, or if you want a vendor you can escalate a bug to — the heuristic parsers and the short commit history are yours to absorb.
GeneratedReal-world workflow fit
Concrete scenarios for the personas ai-data-extractor actually fits — and what changes day-one when you adopt it.
Run python -m extractors.claude_code and python -m extractors.codex_cli against your local data roots, then re-run with --merge to collapse the timestamped output into all_conversations.jsonl.
Outcome: One conversation-per-line JSONL file with user messages, assistant responses, code context, and model names ready for your training pipeline.
Run extract_all.sh before wiping app data so Cursor's state.vscdb and the other supported sources are harvested into extracted_data/ first.
Outcome: A dated local archive of every supported assistant's history that survives the reinstall.
Extract from several sources and group records by the source, timestamp, and project path fields carried on each conversation.
Outcome: A comparable usage picture across Claude Code, Cursor, Cline, Aider, and the rest, in one schema.
Use cases
- Export your Claude Code and Codex CLI sessions into one JSONL file for fine-tuning.
- Back up Cursor and Windsurf chat history before reinstalling or clearing app data.
- Run extract_all.sh to batch-harvest every supported assistant on your machine at once.
- Analyze timestamps, project paths, and model names to see where you actually spend AI time.
- Fork the repo and patch a heuristic parser when Windsurf or Trae changes its storage schema.
- Preserve years of local conversations as a portable archive independent of any vendor.
Setup time & first value
How long it actually takes to get something useful out of ai-data-extractor — broken out by persona, not the marketing-page minute.
For a Python developer, cloning the repo and running your first extractor takes a few minutes, since macOS, Linux, and Windows data roots are auto-detected and no OS flag is needed. Getting every source harvested takes a single extract_all.sh run. Extending the toolkit to a new assistant means writing a parser against that app's storage layout — the Cline/Roo Code and Aider extractors are the
Switching to or from ai-data-extractor
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hand-rolled scraper: point it at the repo's extractors/ folder and reuse the normalized JSONL schema instead of maintaining your own.
- →From manually copying chat files: run extract_all.sh and keep the timestamped files under extracted_data/ as your archive.
- →From a paid transcript exporter: export locally with the matching extractor, then feed all_conversations.jsonl into your existing downstream tooling.
- ↗To your own pipeline: consume all_conversations.jsonl directly, since each line is one complete conversation.
- ↗To a custom fork: copy the extractors/ directory and adapt the parser closest to your target app's storage format.
- ↗To a different archive format: transform the normalized JSONL with any line-oriented script, keeping messages, source, and session_id as the guaranteed keys.
Tutorials & Learning
YouTube returned 6 videos for “ai-data-extractor”, and we withheld 5: 5 did not mention ai-data-extractor. Showing the 1 we can prove is about ai-data-extractor.
Official links
What are the best ai-data-extractor alternatives?
The closest alternatives to ai-data-extractor we list are Datasets, Fashion Mnist and PublicAI. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Datasets | Freemium | Pricing not published | — |
| Fashion Mnist | Free | Free | — |
| PublicAI | Freemium | Pricing not published | — |
Alternatives to ai-data-extractor
View all
Datasets
Load, stream, and preprocess Hugging Face datasets in one line of Python with an Arrow-backed pipeline
Fashion Mnist
A free 70,000-image Zalando clothing dataset built as a drop-in replacement for MNIST.
Tools that pair well with ai-data-extractor
Common stack mates teams adopt alongside ai-data-extractor, with the specific reason each pairing earns its keep.
Datasets
Load, stream, and preprocess Hugging Face datasets in one line of Python with an Arrow-backed pipeline
Fashion Mnist
A free 70,000-image Zalando clothing dataset built as a drop-in replacement for MNIST.
PublicAI
PublicAI is a Web3 data infrastructure platform that pays a global workforce in crypto for multi-modal annotation, coding trajectories, and LLM benchmarks.
ai-data-extractor vs its closest rival
ai-data-extractor's closest head-to-head is with Mineral Alphabet X — These aren't competitors — they don't share a buyer, a problem, or a budget line. ai-data-extractor is a free, MIT-licensed Python CLI for developers who want their Claude Code, Codex, Cursor, or Aider chat history flattened into one normalized JSONL file for fine-tuning, analytics, or backup. Mineral is agricultural AI: a solar-powered rover that imaged plants at scale and was acquired by Driscoll's and John Deere in 2024, ending the standalone product. If you're a developer with local chat logs, only one of these is even installable by you. If you're a berry or specialty-crop producer, you'd go through Driscoll's or John Deere — not a Python CLI. Verdict generated
Head-to-head comparisons
Ai Data Extractor vs Mineral Alphabet X
These aren't competitors — they don't share a buyer, a problem, or a budget line. ai-data-extractor is a free, MIT-licensed Python CLI for developers who want their Claude Code, Codex, Cursor, or Aider chat history flattened into one normalized JSONL file for fine-tuning, analytics, or backup. Mineral is agricultural AI: a solar-powered rover that imaged plants at scale and was acquired by Driscoll's and John Deere in 2024, ending the standalone product. If you're a developer with local chat logs, only one of these is even installable by you. If you're a berry or specialty-crop producer, you'd go through Driscoll's or John Deere — not a Python CLI.
GeneratedAi Data Extractor vs Air Ai
These two products do not belong in the same buying conversation. If you are a developer who wants your own Claude Code, Codex, Cursor, or Aider chat logs flattened into one JSONL file for fine-tuning, backup, or analysis, ai-data-extractor is a free MIT-licensed CLI you run locally — nothing to buy, nothing to negotiate. If you are a defense agency or military command trying to compress materiel release timelines and raise equipment readiness, Air is an enterprise platform sold through a vendor-led deployment cycle and priced by contact. Neither is a substitute for the other at any budget.
GeneratedAi Data Extractor vs Mostly Ai
These two are not competitors, so the 'choice' is really about which problem you have. If you're a developer who wants your own Claude Code, Cursor, or Aider history flattened into a JSONL dataset for fine-tuning or backup — and you want zero cost, zero accounts, and zero uploads — reach for ai-data-extractor. If you're a data team that needs privacy-safe synthetic or mock data at enterprise scale, with multi-table referential integrity, time-series support, and differential privacy on Kubernetes — and you have a budget and the infrastructure — Mostly AI is built for exactly that. Buying both makes sense only if your org separately wants local chat-log extraction alongside synthetic data; they don't substitute for each other.
GeneratedAi Data Extractor vs Surge Ai
These are not competitors and you should never be choosing between them. ai-data-extractor is a free MIT Python CLI that scrapes the chat logs already sitting on your own disk — Claude Code, Codex CLI, Cursor, Cline, Aider and six more — into one normalized JSONL file for fine-tuning datasets, backups, or personal analytics. Surge AI sells the opposite thing: a vetted human workforce producing RLHF preference data, red-team results, and benchmarks like GDP.pdf and ComplexConstraints, priced by sales call and aimed at frontier labs. Pick ai-data-extractor if you're one developer with local history and no budget; talk to Surge if you're post-training a model and need expert-graded feedback. There is no overlap to weigh.
GeneratedAi Data Extractor vs Persefoni
These products are not competitors and you will never choose between them. ai-data-extractor is a free MIT Python CLI for developers who want their own Claude Code, Codex CLI, Cursor, or Aider chat logs in one JSONL file for fine-tuning, analytics, or backup — no account, no upload. Persefoni is a commercial carbon accounting platform for enterprises that must file CSRD, ISSB, CA SB-253, or CDP disclosures and for banks doing PCAF financed emissions, and it is sold through a sales conversation. Pick ai-data-extractor if you need local chat-log extraction; buy Persefoni if you need audit-ready emissions reporting. The only shared trait is the word "data."
GeneratedAi Data Extractor vs Truleo
These products do not compete. ai-data-extractor is a free MIT-licensed CLI for developers who want their Claude Code, Codex, Cursor, or Aider history on disk folded into one JSONL file; Truleo is freemium case-intelligence software that cross-references jail calls, RMS, CAD, ALPR, and 140+ OSINT sources for agencies. A solo developer building a fine-tuning dataset has no reason to evaluate a $100/month-per-connected-application investigative platform, and a police department has no use for a Python script that reads ~/.claude/projects. Pick based on which job you actually have — not by comparing them.
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