[{"data":1,"prerenderedAt":316},["ShallowReactive",2],{"categories-init":3,"stack-n8n-ai-automation":4},true,{"stack_id":5,"slug":6,"name":7,"tagline":8,"long_description":9,"key_features":10,"use_cases":17,"pros":23,"cons":28,"cover_image_url":32,"scores":33,"options":42,"additions":196,"option_groups":197,"multi_select_option_types":198,"tools_by_category":199,"related_stacks":254,"faqs":279,"pricing":295,"system_requirements":32,"experience_level":311,"project_type":261,"stack_type_slug":262,"stack_type_icon_url":263,"published_date":105,"last_updated_date":32,"seo_meta":312},90,"n8n-ai-automation","n8n + AI Automation","n8n visual workflow builder connected to Claude or another LLM for AI-powered automation.","n8n with AI automation connects n8n's **visual workflow builder** to large language models like Claude to create intelligent automation pipelines. Workflows can classify incoming emails, extract structured data from documents, draft responses, summarize content, or route tasks based on AI judgment, all configured visually without writing prompt code from scratch.\n\nn8n's HTTP Request node calls LLM APIs, while its built-in nodes handle triggering (webhooks, schedules, Gmail, Slack) and downstream actions (updating databases, sending notifications, creating records in other tools). The AI step sits in the middle of a multi-step workflow, turning **unstructured inputs into structured outputs** that the rest of the pipeline can act on.\n\nThis stack is ideal for automators, operations teams, and indie developers who want to add AI judgment to **repetitive content and data processing workflows** without building a custom application.",[11,12,13,14,15,16],"n8n visual node editor for building multi-step AI workflows without code","LLM API integration via HTTP node or dedicated AI nodes","Trigger from email, webhook, schedule, or dozens of native integrations","Chain AI processing with downstream database writes, Slack messages, or API calls","AI nodes for text classification, extraction, summarization, and generation","Run on n8n Cloud or self-hosted for data privacy",[18,19,20,21,22],"Classifying and routing customer support emails automatically","Extracting structured data from incoming PDF invoices or forms","Generating weekly summaries from data pulled from multiple sources","AI-assisted content moderation pipelines","Drafting first-pass responses that humans review before sending",[24,25,26,27],"No code required for most AI workflow patterns","n8n connects LLMs to hundreds of tools without custom integrations","Self-hosted option keeps sensitive data off third-party AI cloud logs","Faster iteration than building a Python script for each automation",[29,30,31],"LLM API costs add up for high-volume workflows","Complex prompt engineering still requires experimentation outside the visual editor","n8n AI nodes are evolving rapidly, so workflows may need updates as the tool matures",null,{"popularity":34,"learning_curve":36,"flexibility":38,"performance":39,"portability":41},{"score":35,"reasoning":32},4,{"score":37,"reasoning":32},2,{"score":35,"reasoning":32},{"score":40,"reasoning":32},3,{"score":35,"reasoning":32},{"database":43,"orm":47,"authentication":51,"analytics":55,"coding_agent":59,"llm":63,"language":172,"frontend_framework":176,"cms":180,"hosting":184,"reverse_proxy":188,"self_hosted_paas":192},{"tools":44,"descriptions":45,"aliases":46,"see_all":32},[],{},{},{"tools":48,"descriptions":49,"aliases":50,"see_all":32},[],{},{},{"tools":52,"descriptions":53,"aliases":54,"see_all":32},[],{},{},{"tools":56,"descriptions":57,"aliases":58,"see_all":32},[],{},{},{"tools":60,"descriptions":61,"aliases":62,"see_all":32},[],{},{},{"tools":64,"descriptions":164,"aliases":168,"see_all":169},[65,106,128],{"tool_id":66,"name":67,"slug":68,"tooltip_description":69,"logo_url":70,"logo_bg":71,"pricing_model":72,"learning_curve_score":37,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":78,"subcategory":82,"categories":86,"subcategories":89,"flexibility_score":35,"performance_score":76,"portability_score":40,"is_featured":91,"tags":92,"score_reasonings":98,"published_date":104,"last_updated_date":105},153,"Claude","claude","Anthropic's family of AI models, from fast Haiku to frontier Fable, available through the Claude API, Amazon Bedrock, Google Vertex AI, Microsoft Foundry, and claude.ai, known for long context, tool use, and extended thinking.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fclaude.svg","dark",{"slug":73,"display_name":74,"description":75},"usage_based","Usage-Based","Pricing scales with consumption: API calls, data volume, compute time, or similar metered units.",5,"open",{"category_id":79,"name":80,"slug":81},19,"LLM","llm",{"subcategory_id":83,"name":84,"slug":85},51,"Proprietary","proprietary",[87],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},0,[90],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},false,[93],{"tag_id":94,"name":95,"slug":96,"tag_type":97},40,"Web","web","platform",{"learning_curve":99,"flexibility":100,"performance":101,"popularity":102,"portability":103},"The Anthropic API follows the standard messages array pattern familiar from other LLM providers. Python and TypeScript SDKs are well-documented. Basic integration takes under an hour.","Supports tool use, vision, extended thinking, prompt caching, batch processing, streaming, and system prompts. No fine-tuning and no image output limits the ceiling. Multi-cloud availability adds operational flexibility.","Claude 4 Opus and Sonnet 4 are at the frontier of reasoning and coding benchmarks as of 2025. Extended thinking mode significantly improves accuracy on hard problems. Competitive with GPT-4o and Gemini Ultra across most evaluation suites.","Claude is one of the two or three most-referenced LLMs in developer communities worldwide. Massive adoption via Claude.ai, API, Bedrock, and Vertex AI. Powers Claude Code, Cursor (optional model), and many third-party AI products.","Anthropic API uses its own message format and tool-calling schema — not OpenAI-compatible. Migrating to\u002Ffrom Claude requires prompt and schema rewrites. Available on Bedrock and Vertex AI reduces infrastructure lock-in but does not eliminate model lock-in.","2026-05-29","2026-09-27",{"tool_id":107,"name":108,"slug":109,"tooltip_description":110,"logo_url":111,"logo_bg":112,"pricing_model":113,"learning_curve_score":37,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":114,"subcategory":115,"categories":116,"subcategories":118,"flexibility_score":40,"performance_score":76,"portability_score":37,"is_featured":91,"tags":120,"score_reasonings":122,"published_date":104,"last_updated_date":105},155,"OpenAI","openai","OpenAI's API platform for the GPT-6 family (Astra, Sol, Luna), Codex coding models, real-time voice, image generation, and embeddings, with tool use, structured outputs, and fine-tuning.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fopenai.svg","white",{"slug":73,"display_name":74,"description":75},{"category_id":79,"name":80,"slug":81},{"subcategory_id":83,"name":84,"slug":85},[117],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},[119],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},[121],{"tag_id":94,"name":95,"slug":96,"tag_type":97},{"performance":123,"popularity":124,"portability":125,"learning_curve":126,"flexibility":127},"Leads or co-leads industry benchmarks across coding (SWE-bench), reasoning (MATH, GPQA), and instruction-following. o3 and GPT-5 are best-in-class for their respective task types as of 2026.","The most-used LLM API in the world by developer count, third-party integrations, and mindshare. ChatGPT's cultural reach directly drives API adoption. Virtually every developer tool, no-code platform, and enterprise software suite lists OpenAI as a primary integration.","Entirely cloud-bound; no self-hosted option. The API is proprietary — migrating to a different provider requires rewriting prompt logic, tool schemas, and API call structure. Azure and Bedrock availability reduces cloud lock-in but not provider lock-in.","The Chat Completions API is one of the most beginner-friendly interfaces in software — a single POST request with a messages array returns a completion. Official SDKs for Python and Node ship with comprehensive docs and hundreds of cookbook examples. The main learning curve is choosing the right model family and managing costs rather than the API mechanics themselves.","OpenAI offers genuine breadth — five model families, fine-tuning (supervised + reinforcement), function calling, structured outputs, and real-time audio. However, all inference is cloud-only and proprietary; you cannot swap the underlying model weights, run locally, or choose your hardware. Compared to open-weight alternatives, customisation stops at fine-tuning.",{"tool_id":129,"name":130,"slug":131,"tooltip_description":132,"logo_url":133,"logo_bg":112,"pricing_model":134,"learning_curve_score":37,"popularity_score":35,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":138,"subcategory":139,"categories":143,"subcategories":145,"flexibility_score":76,"performance_score":35,"portability_score":76,"is_featured":3,"tags":147,"score_reasonings":158,"published_date":104,"last_updated_date":105},163,"Mistral","mistral","Europe's leading open-weight LLM family, from small edge models to a frontier MoE flagship, available through Mistral's API, its Vibe assistant, the major clouds, or self-hosted.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fmistral.svg",{"slug":135,"display_name":136,"description":137},"freemium","Freemium","A free tier is available; additional features, usage limits, or managed hosting require a paid plan.",{"category_id":79,"name":80,"slug":81},{"subcategory_id":140,"name":141,"slug":142},52,"Open-weight","open-weight",[144],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},[146],{"subcategory_id":140,"name":141,"slug":142,"category_id":79,"is_primary":3,"display_order":88},[148,153,157],{"tag_id":149,"name":150,"slug":151,"tag_type":152},11,"Open Source","open-source","feature",{"tag_id":154,"name":155,"slug":156,"tag_type":152},12,"Self-hostable","self-hostable",{"tag_id":94,"name":95,"slug":96,"tag_type":97},{"learning_curve":159,"flexibility":160,"performance":161,"popularity":162,"portability":163},"The la Plateforme API is OpenAI-compatible, so any developer already familiar with the OpenAI Python SDK can switch with a one-line URL change. Running open-weight models locally via Ollama is also beginner-friendly. The main complexity is the breadth of model choices and understanding trade-offs between MoE and dense architectures.","Unmatched spectrum — from 3B edge models to 675B MoE frontier models; open-weight for full fine-tuning and self-hosting; API for managed access; third-party cloud for enterprise compliance. Codestral covers code, Mathstral covers math, Pixtral covers vision. Configurable reasoning effort in Small 4. Few LLM families offer this range.","Mixtral MoE models punch well above their weight in inference efficiency. Mistral Large 3 is competitive with frontier models on benchmarks. However, on raw capability benchmarks the top Mistral models are generally tier-2 behind GPT-4o and Claude Opus. The efficiency advantage is real and often decisive for cost-sensitive deployments.","Mistral 7B and Mixtral 8x7B were among the most downloaded open-weight models of 2023-2024. The lab is widely recognised as Europe's premier LLM provider, has $14B valuation, and models are available on every major cloud. Strong in enterprise and research circles.","Open-weight models are maximally portable — download once, run anywhere, no ongoing API dependency. Apache 2.0 permits commercial use and modification. Proprietary models (Mistral Large, Pixtral Large) carry some lock-in, but the API is OpenAI-compatible so switching costs are low.",{"claude":165,"openai":166,"mistral":167},"Claude is the default pick for most teams: strong instruction-following and long-context handling for multi-step workflow logic, with per-token pricing across several model tiers depending on how much capability a given step needs.","OpenAI's GPT models are the most widely integrated LLM across n8n's community nodes and general automation tooling, with a range of model tiers from cost-efficient to flagship reasoning, all billed per token.","Mistral is the pick when cost or data control matters most: it has a free tier for experimentation and open-weight models that can be self-hosted entirely, avoiding both per-token API costs and sending data to a third-party API.",{},{"kind":170,"slug":81,"name":80,"href":171},"category","\u002Ftools\u002Fcategories\u002Fllm",{"tools":173,"descriptions":174,"aliases":175,"see_all":32},[],{},{},{"tools":177,"descriptions":178,"aliases":179,"see_all":32},[],{},{},{"tools":181,"descriptions":182,"aliases":183,"see_all":32},[],{},{},{"tools":185,"descriptions":186,"aliases":187,"see_all":32},[],{},{},{"tools":189,"descriptions":190,"aliases":191,"see_all":32},[],{},{},{"tools":193,"descriptions":194,"aliases":195,"see_all":32},[],{},{},{},{},[],{"Automation & Integration":200,"LLM":242},[201],{"tool_id":202,"name":203,"slug":203,"tooltip_description":204,"logo_url":205,"logo_bg":71,"pricing_model":206,"learning_curve_score":40,"popularity_score":35,"hosting_assignment_type":207,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":208,"subcategory":32,"categories":212,"subcategories":214,"flexibility_score":76,"performance_score":35,"portability_score":35,"is_featured":3,"tags":215,"score_reasonings":236,"published_date":104,"last_updated_date":105},22,"n8n","Fair-code workflow automation tool with a visual node-based editor, 500+ integrations, and code nodes. Self-hostable alternative to Zapier and Make, also offered as n8n Cloud.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fn8n.svg",{"slug":135,"display_name":136,"description":137},"self_hostable",{"category_id":209,"name":210,"slug":211},10,"Automation & Integration","automation-integration",[213],{"category_id":209,"name":210,"slug":211,"is_primary":3,"display_order":88},[],[216,217,218,222,226,231],{"tag_id":149,"name":150,"slug":151,"tag_type":152},{"tag_id":154,"name":155,"slug":156,"tag_type":152},{"tag_id":219,"name":220,"slug":221,"tag_type":152},13,"Free Tier","free-tier",{"tag_id":223,"name":224,"slug":225,"tag_type":152},17,"No-code","no-code",{"tag_id":227,"name":228,"slug":229,"tag_type":230},33,"Workflow Automation","workflow-automation","use_case",{"tag_id":232,"name":233,"slug":234,"tag_type":235},44,"Event-driven","event-driven","paradigm",{"learning_curve":237,"performance":238,"flexibility":239,"portability":240,"popularity":241},"Visual workflow canvas is intuitive; advanced node expressions and APIs take more time.","Workflow execution is efficient; throughput scales with self-hosted resources.","Custom nodes via code editor, self-hostable, and API-accessible for any workflow imaginable.","Open source; JSON workflow export and self-hosting make migration feasible.","Very popular among technical users for self-hosted automation; large open-source community.",[243],{"tool_id":66,"name":67,"slug":68,"tooltip_description":69,"logo_url":70,"logo_bg":71,"pricing_model":244,"learning_curve_score":37,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":245,"subcategory":246,"categories":247,"subcategories":249,"flexibility_score":35,"performance_score":76,"portability_score":40,"is_featured":91,"tags":251,"score_reasonings":253,"published_date":104,"last_updated_date":105},{"slug":73,"display_name":74,"description":75},{"category_id":79,"name":80,"slug":81},{"subcategory_id":83,"name":84,"slug":85},[248],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},[250],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},[252],{"tag_id":94,"name":95,"slug":96,"tag_type":97},{"learning_curve":99,"flexibility":100,"performance":101,"popularity":102,"portability":103},[255],{"stack_id":256,"slug":257,"name":258,"tagline":259,"experience_level":260,"project_type":261,"stack_type_slug":262,"stack_type_icon_url":263,"score_popularity":40,"score_learning_curve":40,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":264},186,"n8n-streamlit-ai","n8n + Streamlit AI Agent","n8n orchestrates AI workflows surfaced as an interactive Streamlit dashboard.","intermediate","automation","project","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Fproject.svg",[265,269,273,274,278],{"tool_id":35,"slug":266,"name":267,"logo_url":268,"logo_bg":71},"python","Python","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpython.svg",{"tool_id":94,"slug":270,"name":271,"logo_url":272,"logo_bg":71},"postgresql","PostgreSQL","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpostgresql.svg",{"tool_id":202,"slug":203,"name":203,"logo_url":205,"logo_bg":71},{"tool_id":76,"slug":275,"name":276,"logo_url":277,"logo_bg":71},"streamlit","Streamlit","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fstreamlit.svg",{"tool_id":66,"slug":68,"name":67,"logo_url":70,"logo_bg":71},[280,283,286,289,292],{"question":281,"answer":282},"Should I use Claude, OpenAI, or Mistral for the AI steps?","In n8n the choice is a chat-model sub-node plugged into an AI node, so it is cheap to change: swap the Anthropic, OpenAI, or Mistral Cloud model node and re-enter credentials, and the rest of the workflow stays as it is. Claude is the default because classification and extraction steps reward careful instruction-following, and long context helps when a step reads a full email thread or a multi-page document. OpenAI makes sense when you rely on community nodes or templates built around it. Mistral is the privacy and cost pick: its open-weight models can run on your own hardware through n8n's Ollama model node, so a self-hosted n8n plus a local model keeps every document inside your network. Mixing is common too: a small, cheap model for routing and a stronger one only for the steps that write text a person will read.",{"question":284,"answer":285},"Should I use n8n's built-in AI nodes or call the LLM API with the HTTP Request node?","Start with the built-in nodes. The AI Agent, Basic LLM Chain, Text Classifier, Information Extractor, and Summarization Chain nodes cover most of this stack's use cases, handle credentials and chat-model swaps for you, and produce output the next node can read without hand-written parsing. The HTTP Request node is the fallback for a provider feature n8n's nodes don't expose yet, such as a newly released model parameter or a batch endpoint. The cost of going that route is that you own the request body, the response parsing, and any change the provider makes to its API, which is exactly the maintenance the built-in nodes absorb.",{"question":287,"answer":288},"How do I get reliable structured output from the AI step into the rest of the workflow?","This is the gotcha that bites most AI workflows: an LLM returns text, but the next node (a database write, an IF branch, a CRM update) needs predictable fields. Use the Information Extractor node with a defined schema, or attach a Structured Output Parser to a chain or agent node, rather than asking for JSON in the prompt and hoping. Then plan for the occasional miss: enable Retry On Fail on the AI node, and set an Error Trigger workflow that parks failed items somewhere a person will see them instead of letting a half-parsed record flow downstream. For anything customer-facing, such as drafted replies, keep a human approval step before the send.",{"question":290,"answer":291},"What drives cost as the number of workflow runs grows?","Token spend, almost always. n8n itself is flat: self-hosted execution is unlimited, and n8n Cloud counts one execution per workflow run no matter how many nodes it contains, so a 12-step AI workflow costs the same as a 2-step one there. The LLM bill instead grows with every item processed and every token in the prompt. The main levers are filtering before the AI step (an IF node that skips emails that don't need classifying), trimming what gets sent (the relevant fields, not the whole record), and using a cheaper model tier for routing and tagging steps. The Pricing section covers the per-provider figures.",{"question":293,"answer":294},"How is this different from the n8n + Streamlit AI Agent stack?","This stack is headless: workflows start from events (an incoming email, a webhook, a schedule) and end in other tools, with no user interface of its own. The n8n + Streamlit AI Agent stack adds a Python front end where people type a request and get an answer back, with n8n running the logic behind a webhook. Pick this one when the AI step should happen in the background without anyone asking for it; pick the Streamlit variant when people need to query the workflow on demand. The n8n workflows themselves carry over either way, so starting here and adding a front end later is a small step.",{"summary":296,"starting_cost_label":297,"has_free_tier":3,"line_items":298},"n8n is free to self-host. LLM cost depends entirely on usage and provider. Mistral has a free experimentation tier and self-hostable open-weight models at no API cost, while Claude and OpenAI are pay-per-token with pricing that scales with model choice and volume. Most prototyping runs a few dollars a month; production workflows at scale are what actually drives cost here.","Free to start (self-hosted n8n + free-tier LLM)",[299,303,307],{"label":300,"cost":301,"note":302},"Core tools (self-hosted n8n)","Free (open source)","n8n is free to self-host.",{"label":304,"cost":305,"note":306},"Mistral (free tier or self-hosted)","Free","Open-weight models can run with no per-token API cost.",{"label":308,"cost":309,"note":310},"Claude \u002F OpenAI","Usage-based, per token","Pricing scales with model tier and volume.","beginner",{"title":313,"description":314,"og_image":32,"canonical":315},"n8n + AI Automation: Tools, Pricing & How to Deploy | Tekyous","n8n visual workflow builder connected to Claude or another LLM for AI-powered automation. Compare n8n + AI Automation tools, pricing & how to deploy on Tekyous.","https:\u002F\u002Ftekyous.dev\u002Fstacks\u002Fn8n-ai-automation",1790518879560]