{ "data": { "a": { "slug": "convai-laya", "name": "Laya", "vendor": "Convai Innovations", "vendorUrl": "https://convaiinnovations.com", "kind": "model", "category": "decision-models", "summary": "Open-source decision engine from Convai Innovations, released under Apache-2.0.", "url": "https://www.anchorterminal.com/tools/convai-laya", "markdownUrl": "https://www.anchorterminal.com/tools/convai-laya.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/convai-laya.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/convai-laya.json", "repo": "https://github.com/NandhaKishorM/laya", "license": "Apache-2.0", "transports": [ "http", "stdio" ], "packages": [ { "registry": "pypi", "name": "laya" } ], "auth": "none", "authNotes": "No account. `laya-serve` binds to 0.0.0.0:8000 and is open unless `LAYA_API_KEY` is set, after which it requires `Authorization: Bearer \u003ckey\u003e` on every route but liveness. The MCP server runs over stdio. Weights download from Hugging Face without an account.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy. You pay for the hardware, and the README's speed figures were measured on a Tesla T4. The maintainer takes donations through Buy Me A Coffee (https://github.com/NandhaKishorM/laya).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Laya is software you run, and its server has no payment route (checked 2026-10-02).", "endpoints": [] }, "toolCount": 8, "popularity": { "githubStars": 29200, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-02" }, "docsUrl": "https://nandhakishorm.github.io/laya/", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "mcp", "batch", "pre-1.0" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 69.2, "grade": "B", "agentReady": false, "rank": 183, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 83, "payments": 60, "reliability": 65, "schema": 80, "security": 57, "transparency": 62 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.", "bestFor": "Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.", "strengths": [ "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI", "421M and 322M-parameter encoders that the README times at 32.8 to 39.5 ms for one question on a Tesla T4", "A Jev-compatible HTTP server with a batch route for up to 64 states, an 8-tool MCP server, and LangChain, LlamaIndex and CrewAI wrappers", "A multilingual checkpoint for 100+ languages and a router that picks the checkpoint per request", "An honest-limits section in the README, SECURITY.md with private reporting, and CI that runs gitleaks, pip-audit and CodeQL" ], "weaknesses": [ "Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning", "Choice options share a 192 or 256-token budget, and the README reports 0.425 on Banking77's 77 labels", "512 tokens of context on the English checkpoint and 1,024 by default on the others", "`laya-serve` listens on 0.0.0.0 with no key unless `LAYA_API_KEY` is set", "26 releases in 13 days, still 0.x and marked beta, with 61 open issues and 81 open pull requests" ], "agentNotes": [ "Set `LAYA_API_KEY` before starting `laya-serve`. It listens on every interface by default", "Gate on `answer_confidence`, not `confidence`, which measures entropy and doesn't match Jev's field", "Shortlist choice questions with more than about 20 options using `predict_shortlist` or the `laya_shortlist` tool", "Use semantic or opaque labels such as `A` and `B`, not `yes` and `no`, in choice questions. The checkpoints can follow the label text", "Pass `model=\"multilingual\"` and `max_len=8192` for long documents. The English checkpoint stops at 512 tokens" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.2 } ], "editorialScores": { "ergonomics": 80, "maintenance": 83, "payments": 60, "reliability": 65, "schema": 80, "security": 57, "transparency": 70 }, "provenanceScore": 53 }, "connect": { "install": "pip install \"laya[serve]\"\nLAYA_API_KEY=change-me LAYA_DEVICE=cuda LAYA_PRELOAD=1 laya-serve # 0.0.0.0:8000", "http": "curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -H \"Authorization: Bearer $LAYA_API_KEY\" \\\n -d '{\"state\":\"Checkout has failed for every customer for an hour.\",\"questions\":{\"urgent\":{\"type\":\"noul\",\"instructions\":\"Is this request urgent?\"},\"team\":{\"type\":\"choice\",\"criteria\":{\"billing\":\"Payments and refunds\",\"technical\":\"Outages and errors\"}}}}'", "claudeCode": "pip install \"laya[mcp]\" \u0026\u0026 claude mcp add laya -- laya-mcp-server", "config": { "mcpServers": { "laya": { "command": "laya-mcp-server", "env": { "LAYA_DEVICE": "cpu" } } } } }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/convai-laya" }, "area": "models", "provenance": { "legalEntity": "Convai Innovations Pvt. Ltd.", "domain": "convaiinnovations.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/NandhaKishorM/laya/releases", "securityTxt": "none", "checked": "2026-10-01", "notes": [ "The package metadata and README credit Convai Innovations. The company's site names Convai Innovations Pvt. Ltd. at the Kerala Startup Mission office in Kasaragod, Kerala, India, but doesn't mention Laya.", "convaiinnovations.com/.well-known/security.txt returns 404. SECURITY.md in the repository takes private reports through GitHub Security Advisories.", "We didn't read the domain's registration date.", "Software you run, so there's no hosted endpoint. The Apache-2.0 licence stands in for terms." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/convai-laya.json", "live": { "slug": "convai-laya", "versions": [ { "registry": "github", "name": "NandhaKishorM/laya", "version": "v0.4.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:59.058927231Z" }, { "registry": "pypi", "name": "laya", "version": "0.4.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:58.941467636Z" } ], "githubStars": 31667, "pypiWeekly": 77372, "securityTxt": { "url": "https://convaiinnovations.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:09.474636066Z" }, "domain": { "domain": "convaiinnovations.com", "registered": "2021-09-01", "source": "https://rdap.verisign.com/com/v1/domain/convaiinnovations.com", "checkedAt": "2026-10-04T13:08:27.291006176Z" }, "updatedAt": "2026-10-08T16:06:59.058927231Z" } }, "answer": "Laya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories.", "b": { "slug": "gliclass", "name": "GLiClass", "vendor": "Knowledgator", "vendorUrl": "https://www.knowledgator.com", "kind": "model", "category": "decision-models", "summary": "GLiClass is an open-source Python library and family of open-weight zero-shot text classifiers from Knowledgator. It scores every candidate label in one forward pass and runs locally through a pipeline or a Ray Serve endpoint.", "url": "https://www.anchorterminal.com/tools/gliclass", "markdownUrl": "https://www.anchorterminal.com/tools/gliclass.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gliclass.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gliclass.json", "repo": "https://github.com/Knowledgator/GLiClass", "license": "Apache-2.0 (library and the model weights we checked)", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "gliclass" } ], "auth": "none", "authNotes": "No account or key. The weights are public and ungated on Hugging Face. The bundled Ray Serve deployment has no authentication of any kind, and `python -m gliclass.serve` binds to 0.0.0.0 unless `--host` is passed, so the owner has to restrict the port.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy for the library or the weights. Hardware is the owner's cost. Knowledgator's site links a hosted platform with plans at platform.knowledgator.com, which we couldn't reach on 8 October 2026, so whether it serves GLiClass and at what price is unchecked.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. GLiClass is software the owner runs, and its server has no payment route (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 555, "npmWeekly": null, "pypiWeekly": 13204, "asOf": "2026-10-08" }, "docsUrl": "https://docs.knowledgator.com/docs/frameworks/gliclass/", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "no-auth" ], "lastRelease": "2026-07-21", "graded": true, "anchor": { "graded": true, "score": 49.9, "grade": "D", "agentReady": false, "rank": 697, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 44, "payments": 60, "reliability": 43, "schema": 49, "security": 38, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.", "bestFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "strengths": [ "Apache-2.0 code and weights, with `train.py`, the training datasets named on the model cards and an arXiv paper (2508.07662)", "One forward pass scores every label. The base v3.0 card reports 51.6 examples a second averaged over 1 to 128 labels on an A6000 (vendor figures)", "Single-label (softmax) and multi-label (sigmoid) modes, hierarchical label sets, task prompts and few-shot examples in one pipeline call", "A Ray Serve deployment with dynamic batching ships in the `gliclass[serve]` extra, with a documented request table for `POST /gliclass`", "31 public, ungated GLiClass checkpoints on Hugging Face, from 32.7M to 439M parameters, as safetensors" ], "weaknesses": [ "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic", "The bundled server has no authentication, and `python -m gliclass.serve` binds to 0.0.0.0 by default", "The last three runs of the Tests workflow on main, all on 24 September 2026, failed", "Still 0.1.x with no changelog file. 0.1.18 raised the `transformers` floor to 5.0, and issue #44 on v5 loading has been open since 6 July 2026", "No OpenAPI file, no llms.txt, no SECURITY.md and no documented error responses" ], "agentNotes": [ "Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential", "On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA", "Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error", "Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1", "Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.9 } ], "editorialScores": { "ergonomics": 60, "maintenance": 44, "payments": 60, "reliability": 43, "schema": 49, "security": 38, "transparency": 60 }, "provenanceScore": 68 }, "connect": { "install": "pip install gliclass", "http": "pip install \"gliclass[serve]\"\npython -m gliclass.serve --model knowledgator/gliclass-edge-v3.0 --port 8000\ncurl -X POST http://localhost:8000/gliclass \\\n -H \"Content-Type: application/json\" \\\n -d '{\"text\": \"This is a great product.\", \"labels\": [\"positive\", \"negative\", \"neutral\"], \"threshold\": 0.3, \"multi_label\": true}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/gliclass" }, "area": "models", "provenance": { "legalEntity": "Knowledgator Engineering Ltd.", "domain": "knowledgator.com", "domainRegistered": "2021-06-25", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/Knowledgator/GLiClass/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The terms of use and privacy policy linked from knowledgator.com name Knowledgator Engineering Ltd., London, United Kingdom. Both are dated 21 September 2023.", "Those two documents govern the website and Knowledgator's hosted services, not the library, so `terms` and `privacy` are left out. The Apache-2.0 licence governs what an agent would run.", "Software the owner runs, so there is no hosted endpoint and no status page for it.", "www.knowledgator.com/.well-known/security.txt returns 404, and the repository has no SECURITY.md.", "RDAP for knowledgator.com gives a registration date of 2021-06-25.", "The code is on github.com under the Knowledgator organisation and the weights on huggingface.co under knowledgator." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gliclass.json" }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Convai Innovations", "b": "Knowledgator", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, stdio", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0 (library and the model weights we checked)", "name": "Licence" }, { "a": "8", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-01", "b": "2026-07-21", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "29k stars", "b": "555 stars, 13k PyPI/wk", "name": "Popularity" }, { "a": "2.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Laya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, Laya or GLiClass?" }, { "answer": "Neither needs a key.", "question": "Do Laya and GLiClass need an API key?" }, { "answer": "Laya runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for GLiClass.", "question": "Can an agent call Laya and GLiClass without installing anything?" }, { "answer": "Yes. Laya is open source (Apache-2.0). GLiClass is open source (Apache-2.0 (library and the model weights we checked)).", "question": "Are Laya and GLiClass open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 43", "Schema \u0026 documentation, 80 against 49", "Agent ergonomics, 80 against 60", "Security \u0026 auth, 57 against 38", "Maintenance \u0026 community, 83 against 44" ], "also": [ "Runs on your own machine" ], "goodFor": "Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.", "slug": "convai-laya", "watchFor": "Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning" }, { "aheadOn": null, "also": null, "goodFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "slug": "gliclass", "watchFor": "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic" } ], "job": { "capability": "inference.decision", "name": "Inference decision" }, "others": [ { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.json", "title": "Celeris-1 Decision vs Laya", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.json", "title": "Celeris-1 Decision vs GLiClass", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya.json", "title": "Clef vs Laya", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.json", "title": "Clef vs GLiClass", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-decider.json", "title": "Laya vs Decider", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-decider" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev.json", "title": "Laya vs Kev", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1.json", "title": "Laya vs Liquid d1", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api.json", "title": "Laya vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider.json", "title": "Laya vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev.json", "title": "Laya vs Jev", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-vela.json", "title": "Laya vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/decider-vs-gliclass.json", "title": "Decider vs GLiClass", "url": "https://www.anchorterminal.com/compare/decider-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.json", "title": "GLiClass vs Kev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.json", "title": "GLiClass vs Liquid d1", "url": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.json", "title": "GLiClass vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json", "title": "GLiClass vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.json", "title": "GLiClass vs Jev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-vela.json", "title": "GLiClass vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/gliclass-vs-vela" } ], "scores": [ { "by": 22, "convai-laya": 65, "edge": "convai-laya", "gliclass": 43, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 31, "convai-laya": 80, "edge": "convai-laya", "gliclass": 49, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 20, "convai-laya": 80, "edge": "convai-laya", "gliclass": 60, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 19, "convai-laya": 57, "edge": "convai-laya", "gliclass": 38, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "convai-laya": 60, "edge": "", "gliclass": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 39, "convai-laya": 83, "edge": "convai-laya", "gliclass": 44, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 2, "convai-laya": 62, "edge": "gliclass", "gliclass": 64, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Laya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. Both do inference decision.", "verdicts": { "convai-laya": "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.", "gliclass": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass", "json": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md", "slim": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.min.md" }, "markdown": "Laya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. Both do inference decision.\n\n- Laya: grade B, 69.2/100, rank #183 of 842. Markdown https://www.anchorterminal.com/tools/convai-laya.md · JSON https://www.anchorterminal.com/api/v1/tools/convai-laya.json\n- GLiClass: grade D, 49.9/100, rank #697 of 842. Markdown https://www.anchorterminal.com/tools/gliclass.md · JSON https://www.anchorterminal.com/api/v1/tools/gliclass.json\n\n## Which one, for what\n\n### Laya (B)\n\nGood for: Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.\n\nAhead on:\n- Reliability, 65 against 43\n- Schema \u0026 documentation, 80 against 49\n- Agent ergonomics, 80 against 60\n- Security \u0026 auth, 57 against 38\n- Maintenance \u0026 community, 83 against 44\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning\n\n### GLiClass (D)\n\nGood for: Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.\n\nWatch for: No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic\n\n\n## Score by category\n\n| Category | Weight | Laya | GLiClass | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 43 | Laya +22 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 49 | Laya +31 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 60 | Laya +20 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 38 | Laya +19 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 44 | Laya +39 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 62 | 64 | GLiClass +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.2 · B** | **49.9 · D** | |\n\n## Facts side by side\n\n| Fact | Laya | GLiClass |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Convai Innovations | Knowledgator |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP, stdio | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 (library and the model weights we checked) |\n| Tools exposed | 8 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-01 | 2026-07-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 29k stars | 555 stars, 13k PyPI/wk |\n| Agent reviews | 2.5/5 (2) | none |\n\n## Verdicts\n\n**Laya.** Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.\n\n**GLiClass.** An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.\n\n## Before you call either\n\n### Laya\n\n1. Set `LAYA_API_KEY` before starting `laya-serve`. It listens on every interface by default\n2. Gate on `answer_confidence`, not `confidence`, which measures entropy and doesn't match Jev's field\n3. Shortlist choice questions with more than about 20 options using `predict_shortlist` or the `laya_shortlist` tool\n4. Use semantic or opaque labels such as `A` and `B`, not `yes` and `no`, in choice questions. The checkpoints can follow the label text\n5. Pass `model=\"multilingual\"` and `max_len=8192` for long documents. The English checkpoint stops at 512 tokens\n\n### GLiClass\n\n1. Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential\n2. On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA\n3. Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error\n4. Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1\n5. Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently\n\n## Questions\n\n### Which is better for AI agents, Laya or GLiClass?\n\nLaya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories.\n\n### Do Laya and GLiClass need an API key?\n\nNeither needs a key.\n\n### Can an agent call Laya and GLiClass without installing anything?\n\nLaya runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for GLiClass.\n\n### Are Laya and GLiClass open source?\n\nYes. Laya is open source (Apache-2.0). GLiClass is open source (Apache-2.0 (library and the model weights we checked)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.json, and with the fewest tokens: https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"convai-laya\", \"b\": \"gliclass\"}`. From a terminal: `anchor compare convai-laya gliclass`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/convai-laya.json and https://www.anchorterminal.com/api/v1/tools/gliclass.json\n\n## Other comparisons with Laya or GLiClass\n\n- [Celeris-1 Decision vs Laya](https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.md)\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Clef vs Laya](https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Laya vs Decider](https://www.anchorterminal.com/compare/convai-laya-vs-decider.md)\n- [Laya vs Kev](https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev.md)\n- [Laya vs Liquid d1](https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1.md)\n- [Laya vs OpenAI Decisions API](https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api.md)\n- [Laya vs Strands Decider 2B](https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider.md)\n- [Laya vs Jev](https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev.md)\n- [Laya vs Vela 2.0](https://www.anchorterminal.com/compare/convai-laya-vs-vela.md)\n- [Decider vs GLiClass](https://www.anchorterminal.com/compare/decider-vs-gliclass.md)\n- [GLiClass vs Kev](https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.md)\n- [GLiClass vs Strands Decider 2B](https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md)\n- [GLiClass vs Jev](https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.md)\n- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-vs-vela.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Laya vs GLiClass", "url": "" } ], "description": "Laya scores 69.2 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 5 of 7 scored categories. Both do inference decision. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Laya B 69.2", "GLiClass D 49.9", "scores" ], "h1": "Laya vs GLiClass", "image": "https://www.anchorterminal.com/assets/og/compare-convai-laya-vs-gliclass.png", "path": "/compare/convai-laya-vs-gliclass", "published": "2026-10-01", "section": "tools", "title": "Laya vs GLiClass for AI agents, B 69.2 vs D 49.9 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass" }, "tokens": { "markdown": 2150, "slim": 530 }, "version": 1 }