{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Cohere analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/analysis/cohere","json_url":"https://onlylabs.fyi/analysis/cohere/evidence.json","generated_at":"2026-06-11T15:12:02.093Z","org":{"slug":"cohere","name":"Cohere","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/cohere"},"analysis":{"url":"https://onlylabs.fyi/analysis/cohere","json_url":"https://onlylabs.fyi/analysis/cohere/analysis.json","generated_at":"2026-06-08T15:59:08.543+00:00"},"workflow":{"version":"synthesize-analyses","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":140,"web":0,"evidence":88,"signal_desks":{"hiring":30,"forks":3,"releases":27,"talking":0,"repos":0},"data_radar_lanes":{"data":2,"evals":0,"infrastructure":6,"safety":4,"product":10},"data_radar_matches":18,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"cohere-ai/cohere-go repository metadata","date":"2026-06-11T03:58:59.796731+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-go","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-go\n\nDescription: Go Library for Accessing the Cohere API\n\nLanguage: Go\n\nLicense: MIT\n\nStars: 72\n\nForks: 12\n\nOpen issues: 4\n\nCreated: 2021-09-21T18:23:14Z\n\nPushed: 2026-04-07T22:46:27Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cohere Go Library\n\n![](https://raw.githubusercontent.com/cohere-ai/cohere-typescript/5188b11a6e91727fdd4d46f4a690419ad204224d/banner.png)\n\n[![fern shield](https://img.shields.io/badge/%F0%9F%8C%BF-SDK%20generated%20by%20Fern-brightgreen)](https://github.com/fern-api/fern)\n[![go shield](https://img.shields.io/badge/go-docs-blue)](https://pkg.go.dev/github.com/cohere-ai/cohere-go/v2)\n\nThe Cohere Go library provides convenient access to the Cohere API from Go.\n\n## Requirements\n\nThis module requires Go version >= 1.18.\n\n## Installation\n\nRun the following command to use the Cohere Go library in your module:\n\n```sh\ngo get github.com/cohere-ai/cohere-go/v2\n```\n\n## Usage\n\n```go\nimport cohereclient \"github.com/cohere-ai/cohere-go/v2/client\"\n\nclient := cohereclient.NewClient(cohereclient.WithToken(\"<YOUR_AUTH_TOKEN>\"))\n```\n\n## Chat\n\n```go\nimport (\ncohere \"github.com/cohere-ai/cohere-go/v2\"\ncohereclient \"github.com/cohere-ai/cohere-go/v2/client\"\n)\n\nclient := cohereclient.NewClient(cohereclient.WithToken(\"<YOUR_AUTH_TOKEN>\"))\nresponse, err := client.Chat(\ncontext.TODO(),\n&cohere.ChatRequest{\nMessage: \"How is the weather today?\",\n},\n)\n```\n\n## Timeouts\n\nSetting a timeout for each individual request is as simple as using the standard\n`context` library. Setting a one second timeout for an individual API call looks\nlike the following:\n\n```go\nctx, cancel := context.WithTimeout(context.TODO(), time.Second)\ndefer cancel()\n\nresponse, err := client.Chat(\ncontext.TODO(),\n&cohere.ChatRequest{\nMessage: \"How is the weather today?\",\n},\n)\n```\n\n## Client Options\n\nA variety of client options are included to adapt the behavior of the library, which includes\nconfiguring authorization tokens to be sent on every request, or providing your own instrumented\n`*http.Client`. Both of these options are shown below:\n\n```go\nclient := cohereclient.NewClient(\ncohereclient.WithToken(\"<YOUR_AUTH_TOKEN>\"),\ncohereclient.WithHTTPClient(\n&http.Client{\nTime"},{"ref":"P2","kind":"page","title":"cohere-ai/cohere-python repository metadata","date":"2026-06-11T03:58:59.631127+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-python","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-python\n\nDescription: Python Library for Accessing the Cohere API\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 389\n\nForks: 90\n\nOpen issues: 11\n\nCreated: 2021-01-20T22:43:24Z\n\nPushed: 2026-06-10T21:24:58Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cohere Python SDK\n\n![](banner.png)\n\n[![version badge](https://img.shields.io/pypi/v/cohere)](https://pypi.org/project/cohere/)\n![license badge](https://img.shields.io/github/license/cohere-ai/cohere-python)\n[![fern shield](https://img.shields.io/badge/%F0%9F%8C%BF-SDK%20generated%20by%20Fern-brightgreen)](https://github.com/fern-api/fern)\n\nThe Cohere Python SDK allows access to Cohere models across many different platforms: the cohere platform, AWS (Bedrock, Sagemaker), Azure, GCP and Oracle OCI. For a full list of support and snippets, please take a look at the [SDK support docs page](https://docs.cohere.com/docs/cohere-works-everywhere).\n\n## Documentation\n\nCohere documentation and API reference is available [here](https://docs.cohere.com/).\n\n## Installation\n\n```\npip install cohere\n```\n\n## Usage\n\n```Python\nimport cohere\n\nco = cohere.ClientV2()\n\nresponse = co.chat(\nmodel=\"command-r-plus-08-2024\",\nmessages=[{\"role\": \"user\", \"content\": \"hello world!\"}],\n)\n\nprint(response)\n```\n\n> [!TIP]\n> You can set a system environment variable `CO_API_KEY` to avoid writing your api key within your code, e.g. add `export CO_API_KEY=theapikeyforyouraccount`\n> in your ~/.zshrc or ~/.bashrc, open a new terminal, then code calling `cohere.Client()` will read this key.\n\n## Streaming\n\nThe SDK supports streaming endpoints. To take advantage of this feature for chat,\nuse `chat_stream`.\n\n```Python\nimport cohere\n\nco = cohere.ClientV2()\n\nresponse = co.chat_stream(\nmodel=\"command-r-plus-08-2024\",\nmessages=[{\"role\": \"user\", \"content\": \"hello world!\"}],\n)\n\nfor event in response:\nif event.type == \"content-delta\":\nprint(event.delta.message.content.text, end=\"\")\n```\n\n## Oracle Cloud Infrastructure (OCI)\n\nThe SDK supports Oracle Cloud Infrastructure (OCI) Generative AI service. First, install the OCI SDK:\n\n```\npip install 'cohere[oci]'\n```\n\nThen use the `OciClient` or `OciClientV2`:\n\n```Python\nimport cohere\n\n# Using OCI config f"},{"ref":"P3","kind":"page","title":"cohere-ai/cohere-typescript repository metadata","date":"2026-06-11T03:58:59.601327+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-typescript","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-typescript\n\nDescription: The Cohere TypeScript SDK\n\nLanguage: TypeScript\n\nLicense: MIT\n\nStars: 173\n\nForks: 31\n\nOpen issues: 9\n\nCreated: 2021-05-11T14:42:23Z\n\nPushed: 2026-04-01T14:13:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cohere TypeScript SDK\n\n![](banner.png)\n\n[![npm shield](https://img.shields.io/npm/v/cohere-ai)](https://www.npmjs.com/package/cohere-ai)\n[![fern shield](https://img.shields.io/badge/%F0%9F%8C%BF-SDK%20generated%20by%20Fern-brightgreen)](https://github.com/fern-api/fern)\n\nThe Cohere Typescript SDK allows access to Cohere models across many different platforms: the cohere platform, AWS (Bedrock, Sagemaker), Azure, GCP and Oracle OCI. For a full list of support and snippets, please take a look at the [SDK support docs page](https://docs.cohere.com/docs/cohere-works-everywhere).\n\n## Documentation\n\nCohere documentation and API reference is available [here](https://docs.cohere.com/).\n\n## Installation\n\n```\nnpm i -s cohere-ai\n```\n\n## Usage\n\n```typescript\nimport { CohereClientV2 } from \"cohere-ai\";\n\nconst cohere = new CohereClientV2({});\n\n(async () => {\nconst response = await cohere.chat({\nmodel: 'command-a-03-2025',\nmessages: [\n{\nrole: 'user',\ncontent: 'hello world!',\n},\n],\n});\n\nconsole.log(response);\n})();\n```\n\n## Streaming\n\nThe SDK supports streaming endpoints. To take advantage of this feature for chat,\nuse `chatStream`.\n\n```typescript\nimport { CohereClientV2 } from \"cohere-ai\";\n\nconst cohere = new CohereClientV2({});\n\n(async () => {\nconst stream = await cohere.chatStream({\nmodel: 'command-a-03-2025',\nmessages: [\n{\nrole: 'user',\ncontent: 'hello world!',\n},\n],\n});\n\nfor await (const chatEvent of stream) {\nif (chatEvent.type === 'content-delta') {\nconsole.log(chatEvent.delta?.message);\n}\n}\n})();\n```\n\n## Errors\n\nWhen the API returns a non-success status code (4xx or 5xx response),\na subclass of [CohereError](./src/errors/CohereError.ts) will be thrown:\n\n```TypeScript\nimport { CohereClientV2, CohereError, CohereTimeoutError } from \"cohere-ai\";\n\nconst cohere = new CohereClient({\ntoken: \"YOUR_API_KEY\",\n});\n\n(async () => {\ntry {\nawait cohere.generate(/* ... */);\n} catch (err) {\nif (err instanceof CohereTimeoutError) "},{"ref":"P4","kind":"page","title":"cohere-ai/notebooks repository metadata","date":"2026-06-11T03:58:59.247812+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/notebooks","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/notebooks\n\nDescription: Code examples and jupyter notebooks for the Cohere Platform\n\nLanguage: Jupyter Notebook\n\nStars: 506\n\nForks: 139\n\nOpen issues: 25\n\nCreated: 2021-10-06T16:51:34Z\n\nPushed: 2025-01-16T15:16:11Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\nThis file has been moved to https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/README.md."},{"ref":"P5","kind":"page","title":"cohere-ai/tokenizer repository metadata","date":"2026-06-11T03:58:58.88663+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/tokenizer","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/tokenizer\n\nDescription: BPE tokenization implemented in Golang 💙\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 12\n\nForks: 2\n\nOpen issues: 7\n\nCreated: 2022-01-03T01:15:49Z\n\nPushed: 2023-10-02T13:59:32Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n## Outdated\n\nPlease refer to [tokenize](https://docs.cohere.com/reference/tokenize) and [detokenize](https://docs.cohere.com/reference/detokenize) APIs for up to date Cohere tokenizers."},{"ref":"P6","kind":"page","title":"cohere-ai/samples repository metadata","date":"2026-06-11T03:58:58.803553+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/samples","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/samples\n\nDescription: Sample integrations built by Cohere.\n\nLanguage: JavaScript\n\nStars: 26\n\nForks: 7\n\nOpen issues: 6\n\nCreated: 2022-04-01T13:52:28Z\n\nPushed: 2024-03-28T03:39:10Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# samples\n\n<h1 align=\"center\">Cohere Samples</h1>\n\n<br>\n\n<p align=\"center\">\n<b>Sample integrations built by Cohere.</b><br>\n</p>\n<br>\n\nThis project showcases sample uses of Cohere's API. Samples include:\n\n- **cohere-slack-starter-app**: A Slack App starter project to generate chat bot dialog or summarize channel activity.\n- **trivia-generator**: A React sample project to generate trivia questions from a desired topic.\n- **six-degrees-of-wikipedia**: A command-line example of using embeddings to form a natural language understanding solution for [\"six degrees of wikipedia\"][1].\n- **piracy**: A chat bot example that illustrates the classification and rephrasing capabilities of our LLMs.\n- **google-sheets-nlp**: Add text classification, sentiment analysis, and summarization features to Google Sheets documents.\n- **startup-idea-generator**: An example of how, using Cohere and Streamlit, you can quickly turn your language AI product idea into a basic prototype.\n- **nlp-nextjs**: A Next.js application that does sentiment analysis on hotel reviews.\n\n[1]: https://github.com/jwngr/sdow"},{"ref":"P7","kind":"page","title":"cohere-ai/examples repository metadata","date":"2026-06-11T03:58:58.799692+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/examples","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/examples\n\nDescription: Examples and Demos using the Cohere APIs\n\nLanguage: Jupyter Notebook\n\nLicense: MIT\n\nStars: 24\n\nForks: 19\n\nOpen issues: 6\n\nCreated: 2022-08-23T05:26:50Z\n\nPushed: 2023-11-03T17:41:13Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Application Examples\nA repository for demos showcased on Cohere's [Application Examples page](https://docs.cohere.ai/page/application-examples).\n\nThe list of demos:\n\n## Topic Modeler\nSurface the main topics from a large collection of text documents.\n\n[Guide](https://docs.cohere.ai/page/topic-modeling) | [Code](https://github.com/cohere-ai/examples/tree/main/topic-modeling)\n\n## Invoice Extractor\nExtract information from invoices and receipts automatically.\n\n[Guide](https://docs.cohere.ai/page/invoice-extractor) | [Code](https://github.com/cohere-ai/examples/tree/main/invoice-extractor)\n\n## News Article Recommender\nRecommend other similar articles to the one a user is reading.\n\n[Guide](https://docs.cohere.ai/page/news-article-recommender) | [Code](https://github.com/cohere-ai/examples/tree/main/article-recommender)\n\n## Startup Idea Generator\nGenerate a startup idea and name, given an industry.\n\n[Guide](https://docs.cohere.ai/page/startup-idea-generator) | [Code](https://github.com/cohere-ai/examples/tree/main/startup-idea-generator)\n\n## Lazywriter\nWrite content for blogs, product descriptions, and other use cases.\n\n[Guide](https://docs.cohere.ai/page/lazywriter) | [Code](https://github.com/cohere-ai/examples/tree/main/lazywriter)\n\n## Pondr\nGenerate interesting conversation questions that turn strangers into friends.\n\n[Guide](https://docs.cohere.ai/page/pondr) | [Code](https://github.com/cohere-ai/examples/tree/main/pondr)\n\n## Document Question Answering\nGenerate answers to questions about a passage of text.\n[Guide](https://docs.cohere.ai/page/document-question-answering) | [Code](https://github.com/cohere-ai/examples/tree/main/document-qa)"},{"ref":"P8","kind":"page","title":"cohere-ai/sandbox-grounded-qa repository metadata","date":"2026-06-11T03:58:58.759704+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-grounded-qa","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-grounded-qa\n\nDescription: A sandbox repo for grounded question answering with Cohere and Google Search\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 137\n\nForks: 16\n\nOpen issues: 9\n\nCreated: 2022-10-11T12:20:46Z\n\nPushed: 2023-07-25T20:47:57Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [nickfrosst](https://github.com/nickfrosst) \\\n**Project maintained until at least (YYYY-MM-DD):** 2023-01-01\n\n# Grounded Question Answering\n\nThis is a [Cohere](https://cohere.ai/) API / Serp API powered contextualized factual question answering bot! \n\nIt responds to question in discord or in the cli by understanding the context, google \nsearching what it believes to be the appropriate question, finding relevant \ninformation on the google result pages and then answering the question based on \nwhat it found.\n\n## Motivation\n\nLanguage models are very good at creating sensible answers to complex questions. They are not however very good at creating truthful answe"},{"ref":"P9","kind":"page","title":"cohere-ai/public-demos repository metadata","date":"2026-06-11T03:58:58.335623+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/public-demos","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/public-demos\n\nDescription: Public demos using the Cohere platform!\n\nLanguage: TypeScript\n\nStars: 12\n\nForks: 2\n\nOpen issues: 12\n\nCreated: 2022-10-11T12:39:16Z\n\nPushed: 2023-05-24T00:34:20Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# Public Demos Using The Cohere Platform\n\nThis repository contains publicly available demos from the Cohere NLP platform. Get started building!\n\nIf you have an LLM use case that you'd like us to show a demo for, please fill out <a href=\"https://forms.gle/RzRRtQXT4LZaMJVX9\">this form</a>. \n\n## 1. <a href=\"https://txt.cohere.ai/top-js-frameworks-using-a-tweet-analysis-bot/\">Tweet Analysis Bot</a>\n\nCreate a tweet analysis bot to deduce which are the top JS frameworks: [<a href=\"https://github.com/cohere-ai/public-demos/tree/main/python-analysis-bot\">Code</a>]\n\n<img src=\"https://user-images.githubusercontent.com/26754576/195356562-0a527d78-e3e1-46bc-be4c-ae85cc415528.png\" width=\"640\"/>\n\n## 2. <a href=\"https://txt.cohere.ai/automating-user-feedback-monitoring-on-discord-using-ai/\">Automate User Feedback Monitoring on Discord with AI</a>\n\nAutomatically analyze feedback and classify it into different product areas. [<a href=\"https://github.com/cohere-ai/public-demos/tree/main/js-discord-feedbackbot\">Code</a>]\n\n<img src=\"https://user-images.githubusercontent.com/26754576/195356647-bfec3d60-7b0c-4e03-8cd6-9247769c54b7.png\" width=\"640\"/>\n\n## 3. <a href=\"https://txt.cohere.ai/extract-entities-from-invoices-using-large-language-models/\">Extract Entities from Invoices Using Large Language Models</a>\n\nAutomate invoice scanning and extract entities from invoices using LLMs. [<a href=\"https://github.com/cohere-ai/public-demos/tree/main/js-extract-entities\">Code</a>]\n\n<img src=\"https://user-images.githubusercontent.com/26754576/198416367-a93ae5c1-a576-40ee-8a44-cc75e5b55c8c.png\" width=\"640\"/> \n\n## 4. <a href=\"https://txt.cohere.ai/text-summarizer-app-user-reviews/\">Integrate a Text Summarizer to your App to Analyze User Reviews</a>\n\nIntegrate Cohere's Python SDK with a Python app to add NLP functionality and take advantage of LLMs. [<a href=\"https://github.com/cohere-ai/public-demos/tree/main/python-user-reviews/\">Code</a>] \n\n<"},{"ref":"P10","kind":"page","title":"cohere-ai/sandbox-topically repository metadata","date":"2026-06-11T03:58:58.066158+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-topically","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-topically\n\nDescription: Topic modeling helpers using managed language models from Cohere. Name text clusters using large GPT models.\n\nLanguage: Jupyter Notebook\n\nLicense: MIT\n\nStars: 223\n\nForks: 20\n\nOpen issues: 4\n\nCreated: 2022-10-13T10:22:26Z\n\nPushed: 2022-12-15T07:08:21Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [jalammar](https://github.com/jalammar) \\\n**Project maintained until at least:** 2023-04-30\n\n# A picture is worth a thousand sentences\n\n<img src=\"./assets/topic-modeling-picture-thousand-texts.png\" />\nWhen you want to explore thousands or millions of texts (messages, emails, news headlines), topic modeling tools help you make sense of them rapidly and visually.\n\n# Topically\n\nTopically is a \\[work-in-progress\\] suite of tools that help make sense of text collections (messages, articles, emails, news headlines) using large language models.\n\nTopically's first feature is to name clusters of short texts based on their conte"},{"ref":"P11","kind":"page","title":"cohere-ai/sandbox-conversant-lib repository metadata","date":"2026-06-11T03:58:58.023814+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-conversant-lib","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-conversant-lib\n\nDescription: Conversational AI tooling & personas built on Cohere's LLMs\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 176\n\nForks: 31\n\nOpen issues: 12\n\nCreated: 2022-10-24T14:17:36Z\n\nPushed: 2023-08-14T23:10:55Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [Cohere ConvAI Team](mailto:convai@cohere.com) \\\n**Project maintained until at least (YYYY-MM-DD):** 2023-03-01\n\n# Conversant\n[![tests](https://github.com/cohere-ai/sandbox-conversant-lib/actions/workflows/run_tests.yaml/badge.svg)](https://github.com/cohere-ai/sandbox-conversant-lib/actions/workflows/run_tests.yaml/badge.svg)\n[![PyPI](https://img.shields.io/pypi/v/conversant.svg)](https://img.shields.io/pypi/v/conversant.svg)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\nBuild conversational AI on top of [Cohere](https://cohere.ai/)'s [large language models](https://docs.cohere.ai/generate-reference/)\n- 🗣 Us"},{"ref":"P12","kind":"page","title":"cohere-ai/sandbox-toy-semantic-search repository metadata","date":"2026-06-11T03:58:57.982203+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-toy-semantic-search","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-toy-semantic-search\n\nDescription: A demonstration of how a toy (but usable!) semantic search engine can be quickly built using Cohere's platform.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 118\n\nForks: 6\n\nOpen issues: 3\n\nCreated: 2022-11-02T12:03:13Z\n\nPushed: 2023-07-25T22:24:53Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [jcudit](https://github.com/jcudit) and [lsgos](https://github.com/lsgos) \\\n**Project maintained until at least (YYYY-MM-DD):** 2023-03-14\n\n# Toy Semantic Search\n\nThis is an example of how to use the [Cohere API](https://docs.cohere.ai/) to build a simple semantic search engine. It is not meant to be \nproduction-ready or scale efficiently (although could be adapted to these ends), but rather serves to showcase the\nease of producing a search engine powered by representations produced by [Cohere](https://cohere.ai/)'s Large Language Models (LLMs).\n\nThe search algorithm used here is fairly simple: it simply finds the pa"},{"ref":"P13","kind":"page","title":"cohere-ai/cohere-aws repository metadata","date":"2026-06-11T03:58:57.840452+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-aws","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-aws\n\nLanguage: Jupyter Notebook\n\nLicense: MIT\n\nStars: 65\n\nForks: 10\n\nOpen issues: 14\n\nCreated: 2022-11-22T17:29:40Z\n\nPushed: 2025-04-25T17:23:12Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n> [!IMPORTANT]\n> Cohere [python](https://github.com/cohere-ai/cohere-python) and [typescript](https://github.com/cohere-ai/cohere-typescript) SDKs now also support bedrock and sagemaker! This is part of an effort to make it as frictionless as possible to get started with and switch between Cohere providers.\n>\n> As such, package will soon be deprecated. Please see the [platform support docs](https://docs.cohere.com/docs/cohere-works-everywhere) for code snippets and the status of this transition or go straight to the [python](https://github.com/cohere-ai/cohere-python) and [typescript](https://github.com/cohere-ai/cohere-typescript) repos to start using bedrock/sagemaker straight away\n\n# Cohere Python SDK (AWS SageMaker & Bedrock)\n\nThis package provides functionality developed to simplify interfacing with the [Cohere models via AWS SageMaker Marketplace](https://aws.amazon.com/marketplace/pp/prodview-6dmzzso5vu5my) and the [Cohere models via AWS Bedrock](https://aws.amazon.com/marketplace/pp/prodview-r6zvppobprqmy) in Python >=3.9\n\n## Installation\n\nThe package can be installed with pip:\n```bash\npip install --upgrade cohere-aws\n```\n\nInstall from source:\n```bash\npython setup.py install\n```\n\n## Quick Start\n\nTo use this library, you need to configure your AWS credentials. You can do this by running `aws configure`. Once that's set up, please refer to one of the Jupyter notebooks to get started. Here is the one for our [medium command model](https://github.com/cohere-ai/cohere-aws/blob/main/notebooks/sagemaker/Deploy%20command%20medium.ipynb)\n\nNote: by default we assume region configured in AWS CLI (`aws configure get region`), to override use `region_name` parameter, e.g.\n```python\nclient = Client(region_name='us-east-1')\n```"},{"ref":"P14","kind":"page","title":"cohere-ai/sandbox-multilingual repository metadata","date":"2026-06-11T03:58:57.497313+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-multilingual","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-multilingual\n\nDescription: A demonstration of a multilingual semantic search engine you can be quickly built using Cohere's platform.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 63\n\nForks: 5\n\nOpen issues: 3\n\nCreated: 2022-12-05T11:59:50Z\n\nPushed: 2023-07-25T20:53:09Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [amrmkayid](https://github.com/AmrMKayid)\n**Project maintained until at least (YYYY-MM-DD):** 2023-03-14\n\n# Multilingual Semantic Search\n\nThis is an example of how to use the [Cohere API](https://docs.cohere.ai/) to build a multilingual semantic search engine. It is not meant to be\nproduction-ready or scale efficiently (although could be adapted to these ends), but rather serves to showcase the\nease of producing a search engine powered by representations produced by [Cohere](https://cohere.ai/)'s Large Language Models (LLMs).\n\nOur Multilingual Model maps text to a semantic vector space, positioning text with a similar meaning in "},{"ref":"P15","kind":"page","title":"cohere-ai/sandbox-condense repository metadata","date":"2026-06-11T03:58:57.258623+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/sandbox-condense","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/sandbox-condense\n\nDescription: Webpage Summarizer\n\nLanguage: JavaScript\n\nLicense: MIT\n\nStars: 27\n\nForks: 8\n\nOpen issues: 1\n\nCreated: 2023-05-25T21:46:42Z\n\nPushed: 2024-11-29T17:56:33Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n```\n################################################################################\n# ____ _ ____ _ _ #\n# / ___|___ | |__ ___ _ __ ___ / ___| __ _ _ __ __| | |__ _____ __ #\n# | | / _ \\| '_ \\ / _ \\ '__/ _ \\ \\___ \\ / _` | '_ \\ / _` | '_ \\ / _ \\ \\/ / #\n# | |__| (_) | | | | __/ | | __/ ___) | (_| | | | | (_| | |_) | (_) > < #\n# \\____\\___/|_| |_|\\___|_| \\___| |____/ \\__,_|_| |_|\\__,_|_.__/ \\___/_/\\_\\ #\n# #\n# This project is part of Cohere Sandbox, Cohere's Experimental Open Source #\n# offering. This project provides a library, tooling, or demo making use of #\n# the Cohere Platform. You should expect (self-)documented, high quality code #\n# but be warned that this is EXPERIMENTAL. Therefore, also expect rough edges, #\n# non-backwards compatible changes, or potential changes in functionality as #\n# the library, tool, or demo evolves. Please consider referencing a specific #\n# git commit or version if depending upon the project in any mission-critical #\n# code as part of your own projects. #\n# #\n# Please don't hesitate to raise issues or submit pull requests, and thanks #\n# for checking out this project! #\n# #\n################################################################################\n```\n\n**Maintainer:** [leilacc](https://github.com/leilacc) \\\n**Project maintained until at least (YYYY-MM-DD):** 2023-12-15\n\n# Condense\nCondense is a Chrome Extension that summarizes webpages using Cohere's cutting-edge chat endpoint, [co:chat](https://docs.cohere.com/reference/chat).\nSummarization is one of many tasks that the chat endpoint can be used for, beyond standard user conversations.\nThis Chrome Extension passes the visible text from websites we visit to co:chat and generates a purple tl;dr header at the top of every page.\nIt shines when it comes to summarizing pages with a lot of text about a specific topic, like Wikipedia and news articles - or even this one!\n\nYou can see the Condense summary in the purple header here:\n\n![Con"},{"ref":"P16","kind":"page","title":"cohere-ai/human-feedback-paper repository metadata","date":"2026-06-11T03:58:57.196013+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/human-feedback-paper","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/human-feedback-paper\n\nDescription: Code and data from the paper 'Human Feedback is not Gold Standard'\n\nLanguage: Jupyter Notebook\n\nStars: 21\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-09-22T10:48:22Z\n\nPushed: 2026-05-05T13:32:46Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# [Human Feedback is not Gold Standard](https://arxiv.org/abs/2309.16349)\n\nTom Hosking, Phil Blunsom, Max Bartolo\n\n## Description\n\nHuman feedback has become the de facto standard for evaluating the performance of Large Language Models, and is increasingly being used as a training objective. However, it is not clear which properties of a generated output this single `preference' score captures. We hypothesise that preference scores are subjective and open to undesirable biases. We critically analyse the use of human feedback for both training and evaluation, to verify whether it fully captures a range of crucial error criteria. We find that while preference scores have fairly good coverage, they under-represent important aspects like factuality. We further hypothesise that both preference scores and error annotation may be affected by confounders, and leverage instruction-tuned models to generate outputs that vary along two possible confounding dimensions: assertiveness and complexity. We find that the assertiveness of an output skews the perceived rate of factuality errors, indicating that human annotations are not a fully reliable evaluation metric or training objective. Finally, we offer preliminary evidence that using human feedback as a training objective disproportionately increases the assertiveness of model outputs. We encourage future work to carefully consider whether preference scores are well aligned with the desired objective.\n\n## Repo Overview\n\n`./interface` contains our modified version of Potato and the configs used for annotation.\n\n`./notebooks` contains the notebooks used to sample from the models, filter the datasets, and run the analysis:\n`DataPreparation.ipynb` for data preprocessing\n`AnalysisPart1.ipynb` for analysis and plot generation for the first set of experiments\n`AnalysisPart2.ipynb` for analysis and plot generation for the second set of experim"},{"ref":"P17","kind":"page","title":"cohere-ai/quick-start-connectors repository metadata","date":"2026-06-11T03:58:57.111117+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/quick-start-connectors","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/quick-start-connectors\n\nDescription: This open-source repository offers reference code for integrating workplace datastores with Cohere's LLMs, enabling developers and businesses to perform seamless retrieval-augmented generation (RAG) on their own data.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 155\n\nForks: 28\n\nOpen issues: 14\n\nCreated: 2023-10-30T16:31:54Z\n\nPushed: 2024-10-08T18:41:35Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n![Cohere](banner.png)\n\n**Quick Start Connectors**\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n![](https://img.shields.io/badge/PRs-Welcome-red)\n\n---\n\n# Table of Contents\n\n- [Table of Contents](#table-of-contents)\n- [Overview](#overview)\n- [Features](#features)\n- [Getting Started](#getting-started)\n- [Contributing](#contributing)\n\n# Overview\n\nCohere's Build-Your-Own-Connector framework allows you to integrate Cohere's Command LLM via the [Chat api endpoint](https://docs.cohere.com/reference/chat) to any datastore/software that holds text information and has a corresponding search endpoint exposed in its API. This allows the Command model to generated responses to user queries that are grounded in proprietary information.\n\nSome examples of the use-cases you can enable with this framework:\n\n- Generic question/answering around broad internal company docs\n- Knowledge working with specific sub-set of internal knowledge\n- Internal comms summary and search\n- Research using external providers of information, allowing researchers and writers to explore to information from 3rd parties\n\nThis open-source repository contains code that will allow you to get started integrating with some of the most popular datastores. There is also an [empty template connector](https://github.com/cohere-ai/quick-start-connectors/tree/main/_template_) which you can expand to use any datasource. Note that different datastores may have different requirements or limitations that need to be addressed in order to to get good quality responses. While some of the quickstart code has been enhanced to address some of these limitations, others only provide the basics of the integration, and you"},{"ref":"P18","kind":"page","title":"cohere-ai/cohere-java repository metadata","date":"2026-06-11T03:58:56.986727+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-java","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-java\n\nDescription: Cohere's Java SDK\n\nLanguage: Java\n\nLicense: MIT\n\nStars: 29\n\nForks: 3\n\nOpen issues: 4\n\nCreated: 2023-11-06T17:02:58Z\n\nPushed: 2026-03-30T18:22:30Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cohere Java Library\n\n![](banner.png)\n\n[![Maven Central](https://img.shields.io/maven-central/v/com.cohere/cohere-java)](https://central.sonatype.com/artifact/com.cohere/cohere-java)\n[![fern shield](https://img.shields.io/badge/%F0%9F%8C%BF-SDK%20generated%20by%20Fern-brightgreen)](https://github.com/fern-api/fern)\n\n## ✨🪩✨ Announcing Cohere's new Java SDK ✨🪩✨\n\nWe are very excited to publish this brand new Java SDK. We now officially support Java and will continuously update this library with all of the latest features in our API. Please create issues where you have feedback so that we can continue to improve the developer experience!\n\n## Documentation\n\nAPI reference documentation is available [here](https://docs.cohere.com/docs).\n\n## Installation\n\n### Gradle\n\nAdd the dependency in your `build.gradle`:\n\n```groovy\ndependencies {\nimplementation 'com.cohere:cohere-java:+'\n}\n```\n\n### Maven\n\nAdd the dependency in your `pom.xml`:\n\n```xml\n<dependency>\n<groupId>com.cohere</groupId>\n<artifactId>cohere-java</artifactId>\n<version>1.x.x</version>\n</dependency>\n```\n\n## Usage\n```java\nimport com.cohere.api.Cohere;\nimport com.cohere.api.requests.ChatRequest;\nimport com.cohere.api.types.ChatMessage;\nimport com.cohere.api.types.Message;\nimport com.cohere.api.types.NonStreamedChatResponse;\n\nimport java.util.List;\n\npublic class ChatPost {\npublic static void main(String[] args) {\nCohere cohere = Cohere.builder().token(\"<<apiKey>>\").clientName(\"snippet\").build();\n\nNonStreamedChatResponse response = cohere.chat(\nChatRequest.builder()\n.message(\"What year was he born?\")\n.chatHistory(\nList.of(Message.user(ChatMessage.builder().message(\"Who discovered gravity?\").build()),\nMessage.chatbot(ChatMessage.builder().message(\"The man who is widely credited with discovering gravity is Sir Isaac Newton\").build()))).build());\n\nSystem.out.println(response);\n}\n}\n```\n\n### Handling Errors\nWhen the API returns a non-success status code (4xx or 5xx response),\na subclas"},{"ref":"P19","kind":"page","title":"cohere-ai/BinaryVectorDB repository metadata","date":"2026-06-11T03:58:56.65863+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/BinaryVectorDB","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/BinaryVectorDB\n\nDescription: Efficient vector database for hundred millions of embeddings.\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 215\n\nForks: 14\n\nOpen issues: 0\n\nCreated: 2024-03-21T22:40:59Z\n\nPushed: 2024-05-17T14:06:20Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# BinaryVectorDB - Efficient Search on Large Datasets\n\nThis repository contains a Binary Vector Database for efficient search on large datasets, aimed for educational purposes.\n\nMost embedding models represent their vectors as float32: These consume a lot of memory and search on these is very slow. At Cohere, we introduced the first embedding model with native [int8 and binary support](https://txt.cohere.com/int8-binary-embeddings/), which give you excellent search quality for a fraction of the cost:\n\n| Model | Search Quality MIRACL | Time to Search 1M docs | Memory Needed 250M Wikipedia Embeddings | Price on AWS (x2gb instance) |\n| ----- |:---------------------:|:----------------------:|:---------------------------------------:|:------------:|\n| OpenAI text-embedding-3-small | 44.9 | 680 ms | 1431 GB | $65,231 / yr |\n| OpenAI text-embedding-3-large | 54.9 | 1240 ms | 2861 GB | $130,463 / yr |\n| **Cohere Embed v3 (Multilingual)** | | | | |\n| Embed v3 - float32 | 66.3 | 460 ms | 954 GB | $43,488 / yr |\n| Embed v3 - binary | 62.8 | 24 ms | 30 GB | $1,359 / yr |\n| Embed v3 - binary + int8 rescore | 66.3 | 28 ms | 30 GB memory + 240 GB disk | $1,589 / yr |\n\n# Demo\n\nWe created a demo that allows you to search on 100M Wikipedia Embeddings for a VM that costs just $15/month:\n[Demo - Search on 100M Wikipedia Embeddings for just $15/mo](http://binaryvectordb.nils-reimers.de/)\n\n# Setup\n\nYou can easily use BinaryVectorDB on your own data. \n\nThe setup is easy:\n```\npip install BinaryVectorDB\n```\n\nTo use some of the below examples you need a **Cohere API key** (free or paid) from [cohere.com](https://cohere.com/). You must set this API key as an environment variable: `export COHERE_API_KEY=your_api_key` \n\n# Usage - Load an Existing Binary Vector Database\n\nWe will show later how to build a vector DB on your own data. For the start, let us use a **pre-build binary vector database**. We "},{"ref":"P20","kind":"page","title":"cohere-ai/cohere-toolkit repository metadata","date":"2026-06-11T03:58:56.435683+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-toolkit","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-toolkit\n\nDescription: Cohere Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications.\n\nLanguage: TypeScript\n\nLicense: MIT\n\nStars: 3179\n\nForks: 431\n\nOpen issues: 60\n\nCreated: 2024-04-19T09:59:08Z\n\nPushed: 2026-03-31T13:26:56Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n![](/docs/assets/banner.png)\n\n# Cohere Toolkit\n\nToolkit is a deployable all-in-one RAG application that enables users to quickly build their LLM-based product.\n\n- [Try Toolkit](#try-now)\n- [About Toolkit](#about-toolkit)\n- [Toolkit Setup](/docs/setup.md)\n- [Troubleshooting](/docs/troubleshooting.md)\n- [How to guides](/docs/how_to_guides.md)\n- [How to set up command model providers](/docs/command_model_providers.md)\n- [How to add tools](/docs/custom_tool_guides/tool_guide.md)\n- [How to add auth to your tools](/docs/custom_tool_guides/tool_auth_guide.md)\n- [How to setup Google Drive](/docs/custom_tool_guides/google_drive.md)\n- [How to setup Gmail](/docs/custom_tool_guides/gmail.md)\n- [How to setup Slack Tool](/docs/custom_tool_guides/slack.md)\n- [How to setup Github Tool](/docs/custom_tool_guides/github.md)\n- [How to setup Sharepoint](/docs/custom_tool_guides/sharepoint.md)\n- [How to setup Google Text-to-Speech](/docs/text_to_speech.md)\n- [How to add authentication](/docs/auth_guide.md)\n- [How to deploy toolkit services](/docs/service_deployments.md)\n- [How to debug dockerized Toolkit API with VSCode/PyCharm](/docs/debugging.md)\n- [How to set up Github Actions for automated DB migrations](/docs/github_migrations_action.md)\n- [How to customize the theme](/docs/theming.md)\n- [How to contribute](#contributing)\n- [Try Cohere's Command Showcase](https://coral.cohere.com/)\n\n![](/docs/assets/toolkit.gif)\n\n## Try Now:\n\nThere are two main ways for quickly running Toolkit: local and cloud. See the specific instructions given below.\n\n### Local\n\n_You will need to have [Docker](https://www.docker.com/products/docker-desktop/), [Docker-compose >= 2.22](https://docs.docker.com/compose/install/), and [Poetry](https://python-poetry.org/docs/#installation) installed. [Go here for a more detailed setup.](/docs/setup.md)_\nNote: to include "},{"ref":"P21","kind":"page","title":"cohere-ai/cohere-terrarium repository metadata","date":"2026-06-11T03:58:56.342879+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-terrarium","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-terrarium\n\nDescription: A simple Python sandbox for helpful LLM data agents\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 314\n\nForks: 55\n\nOpen issues: 8\n\nCreated: 2024-04-24T15:17:01Z\n\nPushed: 2026-04-22T15:31:40Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# ⚠️ No longer maintained: This repository is archived and no longer supported. If you wish to continue development, please fork the project.\n\n# Terrarium - A Simple Python Sandbox\n\nTerrarium is a relatively low latency, easy to use, and economical Python sandbox - to be used as a docker deployed container, for example in GCP Cloud Run - for executing untrusted user or LLM generated ``python`` code.\n\n- **Terrarium is fast:** 900ms runtime to generate a 200 dpi png with a simple matplotlib barchart - 500 ms for a svg version. (hosted on GCP Cloud Run)\n- **Terrarium is cheap:** We spent less than $30 a month hosting terrarium on GCP during internal annotations (2GB mem + 1vCPU and at least 1 alive instance + autoscale on demand) \n- **Terrarium is fully compartmentalized:** The sandbox gets completely recycled after every invocation. No state whatsoever is carried over between calls. *Cohere does not give any guarantees for the sandbox integrity.*\n- **Terrarium supports native input & output files:** You can send any number & type of files as part of the request and we put it them in the python filesystem. After the code execution we gather up all generated files and return them with the response.\n- **Terrarium supports many common packages:** Terrarium runs on [Pyodide](https://pyodide.org/en/stable/index.html), therefore it supports numpy, pandas, matplotlib, sympy, and other standard python packages.\n\n## Using Terrarium\n\nUsing the deployed Cloud Run is super easy - just call it with the `code` to run & authorization bearer (if so configured) as follows:\n\n```bash\ncurl -X POST --url <name of your deployed gcp cloud run> \\\n-H \"Authorization: bearer $(gcloud auth print-identity-token)\" \\\n-H \"Content-Type: application/json\" \\\n--no-buffer \\\n--data-raw '{\"code\": \"1 + 1\"}'\n```\n\nwhich returns:\n```json\n{\"output_files\":[],\"final_expression\":2,\"success\":true,\"std_out\":\"\",\"std_err\":\"\",\"code_runtim"},{"ref":"P22","kind":"page","title":"cohere-ai/meeting-notes-summaries repository metadata","date":"2026-06-11T03:58:56.247462+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/meeting-notes-summaries","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/meeting-notes-summaries\n\nStars: 1\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2024-05-08T18:42:54Z\n\nPushed: 2024-05-08T22:21:17Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Meeting notes summaries\n\nThis repo contains a snapshot of the QMSum variant dataset cited in the Command R Fine-Tuning summarization benchmark."},{"ref":"P23","kind":"page","title":"cohere-ai/magikarp repository metadata","date":"2026-06-11T03:58:56.152276+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/magikarp","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/magikarp\n\nDescription: Code for the paper \"Fishing for Magikarp\"\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 191\n\nForks: 16\n\nOpen issues: 2\n\nCreated: 2024-04-29T14:29:13Z\n\nPushed: 2026-06-03T23:59:39Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Code for the paper \"Fishing for Magikarp\"\n\nThis repository contains the code and extended results for the paper Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models. \n\nThe paper is available on [arXiV](https://arxiv.org/abs/2405.05417) and [ACL Anthology](https://aclanthology.org/2024.emnlp-main.649/).\n\n## Exploring Results\n\nThe most interesting thing in this repository is probably the detailed reports and summary table found in [results/](results/summary.md).\nFor each model, there is a 'full' and 'mini' report. The 'mini' version can always be opened on github, but the full version may require downloading and viewing locally due to file size limitations.\n\nIn these reports:\n\n* `▁` is a space (but not `_`)\n* `¿entry?` represents tokens with a vocabulary `entry` which was not encoded as expected.\n\n## Running on other models\n\n### Setup\n\n<details><summary>This is a standard <a href=\"https://python-poetry.org/\">poetry</a> project.</summary>\n\n```bash\npoetry shell # make/activate your virtual environment\npoetry install # only the first time or on updates\n```\n\nFor some newer models you may need to install a newer transformers version using `pip install git+https://github.com/huggingface/transformers.git`\n\n</details>\n\n### Running\n\nSee `run_verification.sh` for some example commands for running new models. The script itself is mainly a reference for reproducibility and it is not recommended to run.\n\nFor models with tied embeddings, or for nicer visualizations and results, you will need to hard-code some unused token ids in `magikarp/unused_tokens.py`.\n\n* If a related model already exists, copying the token ids is likely to work just fine.\n* For non-tied embeddings you can typically just let verification finish, and update unused tokens after you get the results.\n* For the rare case of new model families with tied embeddings:\n* Take a guess, like `[0]`, or use the to"},{"ref":"P24","kind":"page","title":"cohere-ai/cohere-compass-sdk repository metadata","date":"2026-06-11T03:58:55.855223+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-compass-sdk","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-compass-sdk\n\nDescription: Provides an SDK for interacting with Compass, and Compass Parsing\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 14\n\nForks: 4\n\nOpen issues: 9\n\nCreated: 2024-06-21T18:28:56Z\n\nPushed: 2026-06-01T07:36:53Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cohere Compass SDK\n\n[![Checked with pyright](https://microsoft.github.io/pyright/img/pyright_badge.svg)](https://microsoft.github.io/pyright/)\n\nThe Compass SDK is a Python library that allows you to parse documents and insert them\ninto a Compass index.\n\nIn order to parse documents, the Compass SDK relies on the Compass Parser API, which is\na RESTful API that receives files and returns parsed documents. This requires a hosted\nCompass server.\n\nThe Compass SDK provides a `CompassParserClient` that allows to interact with the parser\nAPI from your Python code in a convenient manner. The `CompassParserClient` provides\nmethods to parse single and multiple files, as well as entire folders, and supports\nmultiple file types (e.g., `pdf`, `docx`, `json`, `csv`, etc.) as well as different file\nsystems (e.g., local, S3, GCS, etc.).\n\nTo insert parsed documents into a `Compass` index, the Compass SDK provides a\n`CompassClient` class that allows to interact with a Compass API server. The Compass API\nis also a RESTful API that allows to create, delete and search documents in a Compass\nindex.\n\n## Table of Contents\n\n<!--\nDo NOT remove the line below; it is used by markdown-toc to automatically generate the\nTable of Contents.\n\nTo update the Table Of Contents, execute the following command in the repo root dir:\n\nmarkdown-toc -i README.md\n\nIf you don't have the markdown-toc tool, you can install it with:\n\nnpm i -g markdown-toc # use sudo if you use a system-wide node installation.\n>\n\n<!-- toc -->\n\n- [Getting Started](#getting-started)\n* [Installation](#installation)\n- [V2 Migration Guide](#v2-migration-guide)\n- [Local Development](#local-development)\n* [Create Python Virtual Environment](#create-python-virtual-environment)\n* [Running Tests Locally](#running-tests-locally)\n+ [VSCode Users](#vscode-users)\n* [Pre-commit](#pre-commit)\n\n<!-- tocstop -->\n\n## Getting Started\n\n### Installation\n\nTo i"},{"ref":"P25","kind":"page","title":"cohere-ai/DiskVectorIndex repository metadata","date":"2026-06-11T03:58:55.637458+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/DiskVectorIndex","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/DiskVectorIndex\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 210\n\nForks: 12\n\nOpen issues: 3\n\nCreated: 2024-07-02T19:08:30Z\n\nPushed: 2025-06-26T21:40:29Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# DiskVectorIndex - Ultra-Low Memory Vector Search on Large Dataset\n\nIndexing large datasets (100M+ embeddings) requires a lot of memory in most vector databases: For 100M documents/embeddings, most vector databases require about **500GB of memory**, driving the cost for your servers accordingly high.\n\nThis repository offers methods to be able to search on very large datasets (100M+) with just **300MB of memory**, making semantic search on such large datasets suitable for the Memory-Poor developers.\n\nWe provide various pre-build indices, that can be used to semantic search and powering your RAG applications.\n\n## Pre-Build Indices\n\nBelow you find different pre-build indices. The embeddings are downloaded at the first call, the size is specified under Index Size. Most of the embeddings are memory mapped from disk, e.g. for the `Cohere/trec-rag-2024-index` corpus you need 15 GB of disk, but just 380 MB of memory to load the index.\n\n| Name | Description | #Docs | Index Size (GB) | Memory Needed |\n| --- | --- | :---: | :---: | :---: | \n| [Cohere/trec-rag-2024-index](https://huggingface.co/datasets/Cohere/trec-rag-2024-index) | Segmented corpus for [TREC RAG 2024](https://trec-rag.github.io/annoucements/2024-corpus-finalization/) | 113,520,750 | 15GB | 380MB |\n| [Cohere/fineweb-edu-10B-index](https://huggingface.co/datasets/Cohere/fineweb-edu-10B-index) | 10B token sample from [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) embedded and indexed on document level. | 9,267,429 | 1.4GB | 230MB |\n| [Cohere/fineweb-edu-100B-index](https://huggingface.co/datasets/Cohere/fineweb-edu-100B-index) | 100B token sample from [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) embedded and indexed on document level. | 69,672,066 | 9.2GB | 380MB\n| [Cohere/fineweb-edu-350B-index](https://huggingface.co/datasets/Cohere/fineweb-edu-350B-index) | 350B token sample from [fineweb-edu](https://huggingface.co/datasets/HuggingFa"},{"ref":"P26","kind":"page","title":"cohere-ai/cohere-developer-experience repository metadata","date":"2026-06-11T03:58:55.500221+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-developer-experience","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-developer-experience\n\nDescription: Docs, Snippets, Guides\n\nLanguage: Jupyter Notebook\n\nStars: 108\n\nForks: 75\n\nOpen issues: 48\n\nCreated: 2024-07-12T10:11:02Z\n\nPushed: 2026-06-10T19:30:51Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n![](./banner.png)\n\n# Cohere Developer Experience\n\nAt Cohere we aim to maximize developer productivity by meeting developers at their existing workflows: whether you want to use our model on our platform or on an external cloud platform, our aim is to provide the best experience available.\n\nThis repository is a one-stop-shop for Cohere developer experience. Here you can find docs, snippets, api specs and more to help you develop on the Cohere platform, or on AWS, Azure, OCI, or cohere-toolkit.\n\n## How to use this repository\n\nThis repository is organized into the following sections:\n\n- `fern/` contains content and configuration for https://docs.cohere.com\n- `cohere-openapi.yaml` contains the OpenAPI spec for the Cohere platform API\n- `snippets/` contains code snippets for using Cohere in various environments\n\n## Other developer resources\n\n### SDKs\n\nWe support the following SDKs which can be used to interact with the Cohere platform and cloud providers:\n\n- [Python](https://github.com/cohere-ai/cohere-python)\n- [Typescript](https://github.com/cohere-ai/cohere-typescript)\n- [Java](https://github.com/cohere-ai/cohere-java)\n- [golang](https://github.com/cohere-ai/cohere-go)\n\n## How to contribute to this repository\n\nWe welcome contributions to this repository! Feel free to pull request any of the content you see and we will work with you to merge it. The OpenAPI sepcs and snippets are one-way synced from our internal repositories so we will need to take your changes and merge them behind the scenes.\n\n## Making local builds\n\n### About local builds\n\nWe encourage our contributors to build the site locally to validate rendering and links.\n\n### Prerequisites\n\n- [NPM](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)\n\n### Installing the local build components\n\nTo install the components necessary to make local builds, run the following command from\nthe root of the repo.\n\n```bash\nmake install\n```\n\n### Mak"},{"ref":"P27","kind":"page","title":"cohere-ai/self-reflection-planning-paper repository metadata","date":"2026-06-11T03:58:55.276944+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/self-reflection-planning-paper","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/self-reflection-planning-paper\n\nDescription: This repository contains the code for our paper: \"On the Efficacy of Self-reflection for Improving LLM Agent Planning\".\n\nLanguage: Python\n\nStars: 2\n\nForks: 1\n\nOpen issues: 2\n\nCreated: 2024-11-13T14:06:19Z\n\nPushed: 2025-04-29T16:25:12Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# On the Efficacy of Self-reflection for Improving LLM Agent Planning\n\nThis repository contains the code for our paper: \"On the Efficacy of Self-reflection for Improving LLM Agent Planning\".\n\n## Navigation\nThe root of the repository contains the following sub-repositories:\n- `SEA/`: Contains the code for our self-relection framework, SEA (Sample-Evaluate-Aggregate).\n- `ToolTalk/`: Contains our version of the [ToolTalk benchmark repository](https://github.com/microsoft/ToolTalk).\n- `ToolSandbox/`: Contains our version of the [ToolSandbox benchmark repository](https://github.com/apple/ToolSandbox).\n\n## Experiments\n\nThe `ToolTalk/` and `ToolSandbox/` sub-repositories contain the code necessary to run our experiments on each benchmark, with instructions provided in the top the respective READMEs. Note that the different environments will likely be required to run each benchmark - after creating a new python environment, you will have to: 1) follow the benchmark installation instructions described in its README, and 2) locally install the SEA repository as a package by running `pip install .` within the `SEA/` subrepository.\nThe respective experimental results for each benchmark can be found in the `results` folder in the root of each sub-repository.\n\n## Citation\n```\n```"},{"ref":"P28","kind":"page","title":"cohere-ai/cohere-finetune repository metadata","date":"2026-06-11T03:58:55.273205+00:00","date_source":null,"source_url":"https://github.com/cohere-ai/cohere-finetune","signal_url":null,"signal_json_url":null,"text":"# cohere-ai/cohere-finetune\n\nDescription: A tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's models\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 82\n\nForks: 5\n\nOpen issues: 2\n\nCreated: 2024-10-07T20:21:46Z\n\nPushed: 2025-03-14T20:05:26Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# cohere-finetune\nCohere-finetune is a tool that facilitates easy, efficient and high-quality fine-tuning of Cohere's models on users' own data to serve their own use cases.\n\nCurrently, we support the following base models for fine-tuning:\n- [Cohere's Command R in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r-v01)\n- [Cohere's Command R 08-2024 in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024)\n- [Cohere's Command R Plus in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r-plus)\n- [Cohere's Command R Plus 08-2024 in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024)\n- [Cohere's Command R 7B 12-2024 in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024)\n- [Cohere's Command A 03-2025 in HuggingFace](https://huggingface.co/CohereForAI/c4ai-command-a-03-2025)\n- [Cohere's Aya Expanse 8B in HuggingFace](https://huggingface.co/CohereForAI/aya-expanse-8b)\n- [Cohere's Aya Expanse 32B in HuggingFace](https://huggingface.co/CohereForAI/aya-expanse-32b)\n\nWe also support any customized base model built on one of these supported models (see [Step 4](#step-4-submit-the-request-to-start-the-fine-tuning) for more details).\n\nCurrently, we support the following fine-tuning strategies:\n- [Parameter efficient fine-tuning by LoRA](https://arxiv.org/pdf/2106.09685)\n- [Parameter efficient fine-tuning by QLoRA](https://arxiv.org/pdf/2305.14314)\n\nWe will keep extending the base models and fine-tuning strategies we support, and keep adding more features, to help our users fine-tune Cohere's models more easily, more efficiently and with higher quality.\n\n## 1. Prerequisites\n- You need to have access to a machine with at least one GPU, e.g., H100, H200, etc. The specific required number, memory and model of GPUs depend on your specific use case, e.g., the model to fine-tune, the batc"},{"ref":"E1","kind":"event","title":"CohereLabs/cohere-transcribe-03-2026","date":"2026-03-24T10:35:54+00:00","date_source":"source","source_url":"https://huggingface.co/CohereLabs/cohere-transcribe-03-2026","signal_url":"https://onlylabs.fyi/signals/19a1a15e-7e0f-4f05-921c-6f968b079a04","signal_json_url":"https://onlylabs.fyi/signals/19a1a15e-7e0f-4f05-921c-6f968b079a04/signal.json","text":"model_released · CohereLabs/cohere-transcribe-03-2026 · signal_desk=releases · occurred_at=2026-03-24T10:35:54+00:00 · url=https://huggingface.co/CohereLabs/cohere-transcribe-03-2026 · hf_downloads=551926 · hf_likes=987 · hf_params=2065804048 · pipeline=automatic-speech-recognition · 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cohere-ai/cohere-developer-experience python@7.0.2 · signal_desk=releases · occurred_at=2026-05-29T13:33:25+00:00 · url=https://github.com/cohere-ai/cohere-developer-experience/releases/tag/python%407.0.2 · raw={\"repo\":\"cohere-ai/cohere-developer-experience\"}"},{"ref":"E27","kind":"event","title":"cohere-ai/TNG v0.0.1","date":"2026-05-28T19:00:47+00:00","date_source":"source","source_url":"https://github.com/cohere-ai/TNG/releases/tag/v0.0.1","signal_url":"https://onlylabs.fyi/signals/91bd9bc8-9050-441f-8a40-23f2b32546c0","signal_json_url":"https://onlylabs.fyi/signals/91bd9bc8-9050-441f-8a40-23f2b32546c0/signal.json","text":"release · cohere-ai/TNG v0.0.1 · signal_desk=releases · occurred_at=2026-05-28T19:00:47+00:00 · url=https://github.com/cohere-ai/TNG/releases/tag/v0.0.1 · raw={\"repo\":\"cohere-ai/TNG\"}"},{"ref":"E28","kind":"event","title":"cohere-ai/cohere-python 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url=https://github.com/cohere-ai/cohere-developer-experience/releases/tag/python%407.0.1 · raw={\"repo\":\"cohere-ai/cohere-developer-experience\"}"},{"ref":"E31","kind":"event","title":"Chief Information Security Officer (CISO)","date":"2026-05-28T02:12:40.821+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/e0c86d1a-ee3a-4da6-9244-53f0909ab236","signal_url":"https://onlylabs.fyi/signals/114ee5a9-0bec-48e6-8be1-93ed3af49586","signal_json_url":"https://onlylabs.fyi/signals/114ee5a9-0bec-48e6-8be1-93ed3af49586/signal.json","text":"job_opened · Chief Information Security Officer (CISO) · signal_desk=hiring · occurred_at=2026-05-28T02:12:40.821+00:00 · url=https://jobs.ashbyhq.com/cohere/e0c86d1a-ee3a-4da6-9244-53f0909ab236 · data_radar_lanes=Safety and policy · data_radar_terms=security · data_radar_reason=Cohere has a job signal matching safety and policy. · raw={\"location\":\"Toronto\",\"team\":\"Security\",\"ats\":\"ashby\"}"},{"ref":"E32","kind":"event","title":"Infrastructure Security Engineer (Secret + Clearance)","date":"2026-05-27T17:34:11.592+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/e48c5456-6ff5-4d4b-a632-df0a3dc497ab","signal_url":"https://onlylabs.fyi/signals/0ac7dbb4-930d-4125-9125-efabe84f0571","signal_json_url":"https://onlylabs.fyi/signals/0ac7dbb4-930d-4125-9125-efabe84f0571/signal.json","text":"job_opened · Infrastructure Security Engineer (Secret + Clearance) · signal_desk=hiring · occurred_at=2026-05-27T17:34:11.592+00:00 · url=https://jobs.ashbyhq.com/cohere/e48c5456-6ff5-4d4b-a632-df0a3dc497ab · data_radar_lanes=Infrastructure, Safety and policy · data_radar_terms=infra, infrastructure, security · data_radar_reason=Cohere has a job signal matching infrastructure, safety and policy. · raw={\"location\":\"Toronto\",\"team\":\"Security\",\"ats\":\"ashby\"}"},{"ref":"E33","kind":"event","title":"Manager, Security Engineering","date":"2026-05-27T17:33:58.088+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/1599cc22-313e-49c9-ac69-b365f5f8f227","signal_url":"https://onlylabs.fyi/signals/59f4db83-269f-4f1a-b109-322749d136fb","signal_json_url":"https://onlylabs.fyi/signals/59f4db83-269f-4f1a-b109-322749d136fb/signal.json","text":"job_opened · Manager, Security Engineering · signal_desk=hiring · occurred_at=2026-05-27T17:33:58.088+00:00 · url=https://jobs.ashbyhq.com/cohere/1599cc22-313e-49c9-ac69-b365f5f8f227 · data_radar_lanes=Safety and policy · data_radar_terms=security · data_radar_reason=Cohere has a job signal matching safety and policy. · raw={\"location\":\"United States\",\"team\":\"Security\",\"ats\":\"ashby\"}"},{"ref":"E34","kind":"event","title":"Senior Manager, Inside Sales","date":"2026-05-27T15:32:46.07+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/f66e8a98-2f34-441f-a66d-62d4c0273411","signal_url":"https://onlylabs.fyi/signals/306d289d-a4d9-4355-9d5a-08d2e4ed017c","signal_json_url":"https://onlylabs.fyi/signals/306d289d-a4d9-4355-9d5a-08d2e4ed017c/signal.json","text":"job_opened · Senior Manager, Inside Sales · signal_desk=hiring · occurred_at=2026-05-27T15:32:46.07+00:00 · url=https://jobs.ashbyhq.com/cohere/f66e8a98-2f34-441f-a66d-62d4c0273411 · data_radar_lanes=Product and customer · data_radar_terms=sales · data_radar_reason=Cohere has a job signal matching product and customer. · raw={\"location\":\"Toronto\",\"team\":\"Revenue\",\"ats\":\"ashby\"}"},{"ref":"E35","kind":"event","title":"Staff UX 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signal_desk=releases · occurred_at=2026-02-13T11:38:57+00:00 · url=https://huggingface.co/CohereLabs/tiny-aya-water · hf_downloads=779 · hf_likes=37 · hf_params=3349227520 · pipeline=text-generation · license=cc-by-nc-4.0 · raw={\"derived_reason\":\"first-party-finetune\"}"},{"ref":"E38","kind":"event","title":"Account Executive","date":"2026-05-26T04:02:48.192+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/ad4309df-9239-4b59-a79b-c7bee617b224","signal_url":"https://onlylabs.fyi/signals/62f64c3b-8c30-41dc-86a5-54dfb93890c8","signal_json_url":"https://onlylabs.fyi/signals/62f64c3b-8c30-41dc-86a5-54dfb93890c8/signal.json","text":"job_opened · Account Executive · signal_desk=hiring · occurred_at=2026-05-26T04:02:48.192+00:00 · url=https://jobs.ashbyhq.com/cohere/ad4309df-9239-4b59-a79b-c7bee617b224 · raw={\"location\":\"Singapore\",\"team\":\"Revenue\",\"ats\":\"ashby\"}"},{"ref":"E39","kind":"event","title":"Senior Manager, People Operations ","date":"2026-05-25T15:27:02.861+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/06d46bfd-1aa3-4778-b5dc-119c1525f237","signal_url":"https://onlylabs.fyi/signals/ddb6acea-9baf-4f3e-99b7-1b54428443a2","signal_json_url":"https://onlylabs.fyi/signals/ddb6acea-9baf-4f3e-99b7-1b54428443a2/signal.json","text":"job_opened · Senior Manager, People Operations  · signal_desk=hiring · occurred_at=2026-05-25T15:27:02.861+00:00 · url=https://jobs.ashbyhq.com/cohere/06d46bfd-1aa3-4778-b5dc-119c1525f237 · raw={\"location\":\"Europe\",\"team\":\"People Operations\",\"ats\":\"ashby\"}"},{"ref":"E40","kind":"event","title":"cohere-ai/cohere-python 7.0.0","date":"2026-05-22T18:44:39+00:00","date_source":"source","source_url":"https://github.com/cohere-ai/cohere-python/releases/tag/7.0.0","signal_url":"https://onlylabs.fyi/signals/1696ddd1-bb62-4f2c-9006-b0aead7eabe0","signal_json_url":"https://onlylabs.fyi/signals/1696ddd1-bb62-4f2c-9006-b0aead7eabe0/signal.json","text":"release · cohere-ai/cohere-python 7.0.0 · signal_desk=releases · occurred_at=2026-05-22T18:44:39+00:00 · url=https://github.com/cohere-ai/cohere-python/releases/tag/7.0.0 · raw={\"repo\":\"cohere-ai/cohere-python\"}"},{"ref":"E41","kind":"event","title":"cohere-ai/cohere-developer-experience python@7.0.0","date":"2026-05-22T18:30:44+00:00","date_source":"source","source_url":"https://github.com/cohere-ai/cohere-developer-experience/releases/tag/python%407.0.0","signal_url":"https://onlylabs.fyi/signals/7e144e34-ab96-4353-b748-237ff4a303f8","signal_json_url":"https://onlylabs.fyi/signals/7e144e34-ab96-4353-b748-237ff4a303f8/signal.json","text":"release · cohere-ai/cohere-developer-experience python@7.0.0 · signal_desk=releases · occurred_at=2026-05-22T18:30:44+00:00 · url=https://github.com/cohere-ai/cohere-developer-experience/releases/tag/python%407.0.0 · raw={\"repo\":\"cohere-ai/cohere-developer-experience\"}"},{"ref":"E42","kind":"event","title":"Head of Solutions Architecture","date":"2026-05-21T21:50:03.146+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/4fedfdfc-3c41-4b77-8c66-c17b4ed2aaeb","signal_url":"https://onlylabs.fyi/signals/9d736c39-6ec2-49f7-8068-7312ae28e436","signal_json_url":"https://onlylabs.fyi/signals/9d736c39-6ec2-49f7-8068-7312ae28e436/signal.json","text":"job_opened · Head of Solutions Architecture · signal_desk=hiring · occurred_at=2026-05-21T21:50:03.146+00:00 · url=https://jobs.ashbyhq.com/cohere/4fedfdfc-3c41-4b77-8c66-c17b4ed2aaeb · data_radar_lanes=Product and customer · data_radar_terms=solutions · data_radar_reason=Cohere has a job signal matching product and 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2026)","date":"2026-05-20T20:47:26.039+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/c73a21dd-167b-48f1-ad59-beb08db37858","signal_url":"https://onlylabs.fyi/signals/b640cdf7-a281-49d9-aa9e-b98fd160e543","signal_json_url":"https://onlylabs.fyi/signals/b640cdf7-a281-49d9-aa9e-b98fd160e543/signal.json","text":"job_opened · Sales Development Intern (Summer, Fall 2026) · signal_desk=hiring · occurred_at=2026-05-20T20:47:26.039+00:00 · url=https://jobs.ashbyhq.com/cohere/c73a21dd-167b-48f1-ad59-beb08db37858 · data_radar_lanes=Product and customer · data_radar_terms=sales · data_radar_reason=Cohere has a job signal matching product and customer. · raw={\"location\":\"Korea\",\"team\":\"Internships\",\"ats\":\"ashby\"}"},{"ref":"E45","kind":"event","title":"Recruitment Coordinator","date":"2026-05-20T17:37:41.614+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/9d5397c6-27c1-41ec-b685-1db83ea941c3","signal_url":"https://onlylabs.fyi/signals/0148bff2-dccf-4e59-a0f9-9957b5ba35a0","signal_json_url":"https://onlylabs.fyi/signals/0148bff2-dccf-4e59-a0f9-9957b5ba35a0/signal.json","text":"job_opened · Recruitment Coordinator · signal_desk=hiring · occurred_at=2026-05-20T17:37:41.614+00:00 · url=https://jobs.ashbyhq.com/cohere/9d5397c6-27c1-41ec-b685-1db83ea941c3 · raw={\"location\":\"Toronto\",\"team\":\"Talent Acquisition\",\"ats\":\"ashby\"}"},{"ref":"E46","kind":"event","title":"Editorial Director","date":"2026-05-20T14:37:08.752+00:00","date_source":"source","source_url":"https://jobs.ashbyhq.com/cohere/1193a17f-1f4c-4f54-ad24-cbd9fe499b5e","signal_url":"https://onlylabs.fyi/signals/fc7cb71d-8219-4f19-b63d-fa2504a2de1c","signal_json_url":"https://onlylabs.fyi/signals/fc7cb71d-8219-4f19-b63d-fa2504a2de1c/signal.json","text":"job_opened · Editorial Director · signal_desk=hiring · occurred_at=2026-05-20T14:37:08.752+00:00 · url=https://jobs.ashbyhq.com/cohere/1193a17f-1f4c-4f54-ad24-cbd9fe499b5e · raw={\"location\":\"Toronto\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E47","kind":"event","title":"Head of Brand Marketing","date":"2026-05-20T14:36:45.691+00:00","date_source":"source","source_url":"https://jobs.ashbyhq.com/cohere/a401b374-0636-405e-b0d1-a1e9f57ad616","signal_url":"https://onlylabs.fyi/signals/2653c526-c01e-401a-807a-0ca3a63805e3","signal_json_url":"https://onlylabs.fyi/signals/2653c526-c01e-401a-807a-0ca3a63805e3/signal.json","text":"job_opened · Head of Brand Marketing · signal_desk=hiring · occurred_at=2026-05-20T14:36:45.691+00:00 · url=https://jobs.ashbyhq.com/cohere/a401b374-0636-405e-b0d1-a1e9f57ad616 · raw={\"location\":\"Toronto\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E48","kind":"event","title":"Senior Full-Stack Senior Engineer (Front-End Leaning)","date":"2026-05-19T15:27:09.182+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/98c7d271-a756-407a-b21a-402e77f2f7cb","signal_url":"https://onlylabs.fyi/signals/1eeebb9d-b1d9-4fcb-acaa-cde4530ac99b","signal_json_url":"https://onlylabs.fyi/signals/1eeebb9d-b1d9-4fcb-acaa-cde4530ac99b/signal.json","text":"job_opened · Senior Full-Stack Senior Engineer (Front-End Leaning) · signal_desk=hiring · occurred_at=2026-05-19T15:27:09.182+00:00 · url=https://jobs.ashbyhq.com/cohere/98c7d271-a756-407a-b21a-402e77f2f7cb · data_radar_lanes=Infrastructure · data_radar_terms=platform · data_radar_reason=Cohere has a job signal matching 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Infrastructure","date":"2026-05-14T19:24:12.21+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/cohere/6aa3cb2b-ee8b-4c92-b505-3a7509f80d7f","signal_url":"https://onlylabs.fyi/signals/501a2f4b-ce4e-4afe-b936-b857d9ff1260","signal_json_url":"https://onlylabs.fyi/signals/501a2f4b-ce4e-4afe-b936-b857d9ff1260/signal.json","text":"job_opened · Software Engineer, Data Infrastructure · signal_desk=hiring · occurred_at=2026-05-14T19:24:12.21+00:00 · url=https://jobs.ashbyhq.com/cohere/6aa3cb2b-ee8b-4c92-b505-3a7509f80d7f · data_radar_lanes=Data demand, Infrastructure · data_radar_terms=data, infra, infrastructure, platform · data_radar_reason=Cohere has a job signal matching data demand, infrastructure. · raw={\"location\":\"New York\",\"team\":\"Agentic Platform\",\"ats\":\"ashby\"}"},{"ref":"E52","kind":"event","title":"Technical Program Manager, Product 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