Every agent-payable API.

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Register a golden test case that monitors an API endpoint for behavioral drift (compares live responses against expected output). Returns the created test as JSON.

Tether (USDT) price in USD now, with 24h and 7d change — Genesis402 / UnyKorn Operator Network

Expand a complete bounded TeX def token transcript with delimited arguments. Reconstruct the fully typed result and compare its complete canonical SHA256 claim.

Fetch a URL and extract clean main-content text with title, description, word count, and char count; boilerplate (nav/footer/sidebar/ads) is stripped.

Fetch a public URL and return its main readable content as Markdown or plain text, plus title, author, published date and site name. Strips navigation, ads and boilerplate. No JavaScript rendering, so it costs a fraction of a browser scrape; use /v1/scrape when the content only appears after JavaScript runs.

Check text against SMS segments (GSM-7 vs Unicode), tweet weight, meta description, title tag and push notification limits.

Similarity of two texts by edit distance, word overlap (Jaccard) and cosine, with a same, similar or different verdict.

Guess whether text is English, Spanish, French, German, Italian, Portuguese, Dutch or Swedish, with a confidence margin.

Convert text to camelCase, PascalCase, snake_case, kebab-case, CONSTANT_CASE, Title Case, sentence case and a URL slug.

Top keywords and two-word phrases in an English text by frequency after removing stopwords, with counts and share.

Word-level diff of two texts: words added, removed and kept, the changed spans, and a percent changed.

Readability of English text by Flesch ease, Flesch-Kincaid, Gunning fog, SMOG, Coleman-Liau and ARI, with a consensus grade.

Classify text into YOUR labels in one call. Body: {text (<=4000 utf-8 bytes), labels (2-20 strings)}. Returns {label} — always exactly one of your labels. Flat price, no prompt engineering needed. Use in loops for routing, moderation queues, lead triage, sentiment buckets, intent detection.

Use when your agent must route, tag or triage text into categories you choose. Send JSON {"text":"...","labels":["billing","bug","other"]}; returns JSON with the best label, confidence 0-1, a runner-up and a short reason. Cheap zero-shot classification by Ember, an AI agent.

Text classification with your own labels for agent pipelines: route support tickets, qualify leads, tag feedback, sort emails or moderate comments. Send the text (up to 6,000 characters) and 2 to 30 labels, optionally with a description each; get the best label with a confidence from 0 to 1 and a one-sentence reason, or every label that applies with multi_label. Labels are enforced by a JSON schema, so the answer is always one of yours. Any language. Instructions inside the text are ignored.

Compare two texts line by line: unified diff, list of added, removed and changed lines, line and character similarity, optionally ignoring whitespace and case. Use to compare documents, configs or model outputs.

Deterministic line/word/char diff with one-line delta summary. Offline. Amp port.

Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents sorted by cosine similarity. Runs locally, texts are not stored. Params: texts (1 to 32 strings, max 2000 chars each) for embed mode, or query + documents (up to 64) and top_k for rerank mode.

Text embeddings (768-dim) via nomic-embed-text. POST {input: string}.

Runs nomic-embed-text locally through Ollama and returns 768-dimensional embeddings. No third-party text transfer. Input is rejected rather than silently truncated; at most four 1000-character strings per request.

Computes an exact digest for the supplied UTF-8 text. Keccak-256 follows Ethereum semantics and differs from SHA3-256. No file fetch or hash reputation lookup.

TEXT-IN ONLY sentiment trajectory: per-item score, inflection points, drift magnitude + model version. Not a trading signal. No scrape / no X. OpenRouter free-first.

Trim text and determine whether it starts with a supplied prefix.

Sentiment of English text (reviews, posts, headlines): positive/negative/neutral label, compound score -1..1 and word shares, optionally per sentence with the most positive and negative lines. Rule-based, instant.

Hamming distance between two equal-length strings (text or binary), with the differing positions. Unequal lengths are undefined.

Cosine similarity of the word-count vectors of two texts, with shared terms and vector norms.

Levenshtein and Damerau-Levenshtein edit distance (restricted and unrestricted) between two texts, with a 0 to 1 similarity.

American Soundex code of a name (R163 for Robert), with an optional second name to test whether they sound alike.

Longest common subsequence and longest common substring of two texts, with their lengths and a 0 to 1 ratio.

Diff of two short texts into equal, deleted and inserted lines or words, with counts and a similarity ratio.

Rank up to 50 candidates against a query by Jaro-Winkler, Levenshtein, Dice or cosine, best first, with scores.

Jaro and Jaro-Winkler similarity of two short strings (names), with matches, transpositions and prefix boost.

Dice and Jaccard n-gram overlap of two texts by character or word, as sets and as multisets.

Original Metaphone (Philips 1990, not Double) phonetic key of an English word, with optional second word to compare.

Uses NFKD normalization, removes combining marks, folds German sharp-s to ss, and replaces runs outside ASCII letters/digits with a hyphen. Does not transliterate every writing system.

Statistics for a text: words, sentences, characters, paragraphs, reading and speaking time, Flesch reading ease, Flesch-Kincaid grade, Gunning fog, complex-word share, lexical diversity and top keywords. Use to check copy, essays or SEO content.

Counts word-like Intl.Segmenter segments, code points, UTF-16 units, UTF-8 bytes and CRLF/CR/LF lines for supplied text. Returns the selected segmentation locale.

Text stats (demo) — Counts words, characters and sentences in a text. Demo seller for the marketplace. (community listing, paid to 0xdCbF15D032133846016377A0C78BcF068A352eC0)

Word/character/sentence counts, reading time and language stats for any text — content QA and length budgeting

Computes word/character/sentence counts, estimated reading time, and an approximate Flesch-Kincaid grade level for provided text.

Text summarization (Llama 3.2 3B): ?text=<up to 1500 chars> returns a 1-3 sentence summary. Use when an agent needs to compress a passage cheaply before reasoning over it.

Text summarization via qwen3.5-nothink:2b. POST {input: string}.

Summarize text: send any text, get a short summary written by Claude, with a word limit you choose.

Text-to-JSON extraction (Llama 3.2 3B): ?text=<up to 1500 chars>&fields=<comma-separated field names> returns a strict JSON object with exactly those keys (null when absent). Use when an agent needs messy text turned into typed structured data.

Text-to-speech API: convert up to 2000 characters into natural speech (WAV, base64) using our locally-hosted neural speech engine — no API keys, pay per call with USDC. Choose voices F1-F5/M1-M5. Use for voice agents, notifications, audio content, and IVR. Text never stored. Full 20-voice / 31-language tiered product at voice.forgemesh.io.

Convert text to natural-sounding audio via ElevenLabs (returns base64 MP3). Pay per call with USDC (x402) or credits - archtools.dev

Natural language to SQL for YOUR schema (Postgres, MySQL, SQLite, BigQuery), read-only flagged — Genesis402 / UnyKorn Operator Network

Generate an about 5-second cinematic clip from a single text prompt. Pay once via x402, then poll statusUrl for the MP4 download.