{"slug": "how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway", "title": "How to Use T3 Code With Claude Code and an Open-Source LLM Gateway", "summary": "A developer describes setting up T3 Code with Claude Code and the open-source LLM gateway Lynkr to create a layered stack for coding agents. The architecture separates UX, coding behavior, and model traffic control, addressing cost, reliability, and flexibility issues that arise when agents are wired directly to a single provider. Lynkr sits under Claude Code to handle routing, caching, and provider switching for tool-heavy workflows.", "body_md": "If I were setting up **T3 Code** for serious daily use, the stack I would want looks like this:\n\n```\nT3 Code\n   ↓\nClaude Code\n   ↓\nLynkr\n   ↓\nAnthropic / OpenAI / Ollama / OpenRouter / Bedrock / Azure / Databricks\n```\n\nThat flow is interesting because each layer is doing a different job:\n\nThat separation is the whole point.\n\nT3 Code gives me the UX I want.\n\nClaude Code gives me the coding behavior I want.\n\nLynkr gives me control over how model traffic actually gets handled.\n\nThat is a much better stack than treating the model layer as an afterthought.\n\nI also recorded a short walkthrough of this setup in action:\n\nIf you want the faster visual version before reading the rest, start there. The architecture is the same:\n\n```\nT3 Code\n   ↓\nClaude Code\n   ↓\nLynkr\n   ↓\nYour actual model/provider\n```\n\nT3 Code is interesting because it is not trying to become a new model or a new lab-specific harness.\n\nIt is building a better way to *work with* coding agents people already use.\n\nThat is a smarter product decision than trying to replace everything at once.\n\nIts current support includes:\n\nThat means the value of T3 Code is not “one more coding assistant.”\n\nIt is more like:\n\nThat makes a lot of sense.\n\nBut once you pick **Claude Code** as the coding agent inside that stack, the next problem becomes obvious:\n\n**the model layer under Claude Code matters just as much as the top-level UX.**\n\nBecause once the agent is doing real work, cost and reliability stop being invisible plumbing.\n\nThey become part of the product experience.\n\nClaude Code is a good example because it exposes the problem very clearly.\n\nA real Claude Code session does not look like a single “generate code” call.\n\nIt looks more like:\n\nThat creates a traffic pattern that is very different from plain chat:\n\nThis is exactly why coding-agent workflows need a stronger model layer than “just point it directly at one provider.”\n\nOnce Claude Code is being used as an actual coding agent, the model path underneath it becomes infrastructure.\n\nAnd infrastructure decisions compound.\n\nDirect setup is fine for testing.\n\nBut it gets worse as the workflow becomes more serious.\n\nIf Claude Code is always wired straight to one provider path, you get a few problems:\n\nThat is usually false.\n\nSome steps are lightweight:\n\nSome steps are genuinely expensive:\n\nIf those all hit the same expensive path, you overpay.\n\nCoding agents retry all the time.\n\nThat is not a bug. That is how they work.\n\nBut retries mean the same or almost-the-same context gets resent over and over.\n\nWithout a caching layer or routing control, you keep paying full price for repeated work.\n\nThe expensive part is often not the user’s prompt.\n\nIt is everything around it:\n\nThat is where a lot of token waste hides.\n\nMaybe today you want Claude for everything.\n\nLater maybe you want:\n\nIf the setup is too tightly wired, those changes become more painful than they should be.\n\nLatency spikes, rate limits, auth weirdness, provider outages, degraded outputs — eventually you hit all of them.\n\nIf there is no gateway layer, every one of those issues becomes a client-side problem.\n\nThat is exactly the kind of thing I would rather solve once in the model layer.\n\nThis is the mental model that makes sense to me.\n\nThat is a clean stack.\n\nThe interface stays separate from the agent.\n\nThe agent stays separate from the gateway.\n\nThe gateway stays separate from the providers.\n\nThat separation is valuable because it lets each layer evolve independently.\n\nLynkr is an open-source **LLM gateway** built for coding assistants, MCP-heavy workflows, and tool-heavy traffic.\n\nThat last part matters.\n\nA lot of model-routing products talk about general-purpose requests. But coding traffic is different. It is noisier, more repetitive, and much more likely to carry large tool payloads.\n\nThat is why the fit is real here.\n\nThe role of Lynkr in this stack is not to replace Claude Code.\n\nIt is to sit **under Claude Code** and decide how model traffic should actually be handled.\n\nThat gives you a few levers that matter a lot in coding workflows.\n\nThe biggest mistake people make with coding agents is asking the wrong question.\n\nThey ask:\n\n“Which is the best coding model?”\n\nThe more useful question is:\n\n“Which parts of my coding workflow actually deserve the expensive model?”\n\nThat is what a gateway lets you answer.\n\nFor example:\n\nThat is a much better economic model than treating every Claude Code turn as if it deserves maximum spend.\n\nAnd once that logic sits in the gateway, you do not need to keep rebuilding it at the app layer.\n\nCoding agents repeat themselves constantly.\n\nThe same instructions, the same repo background, similar prompts, similar recovery steps, similar tool outputs — they come up again and again.\n\nThat means a caching layer is not a “nice optimization.”\n\nIt is one of the biggest obvious wins in the stack.\n\nLynkr’s current benchmark claims are the part that stand out here:\n\nThat is exactly the kind of traffic Claude Code creates during real multi-step work.\n\nThe point is not just lower cost.\n\nThe point is lower cost *and* lower latency on repeated work.\n\nThat compounds very quickly.\n\nThis is one of the most under-discussed parts of coding-agent economics.\n\nPeople spend a lot of time comparing model prices, but a huge amount of waste comes from the payload shape itself.\n\nIn coding workflows, the model is often seeing:\n\nThat means reducing payload size is often just as important as picking the right provider.\n\nThis is why gateway-level optimization makes sense.\n\nIt is solving a real problem in the actual traffic pattern, not just shuffling providers around.\n\nThis is maybe the biggest architectural reason I like this stack.\n\nIf T3 Code points to Claude Code, and Claude Code points to Lynkr, then the top-level workflow can remain stable while the backend policy changes underneath.\n\nThat means I can change:\n\n…without having to rethink the interface and workflow every time.\n\nThat is a better long-term design.\n\nThe UI layer should not be where I want model policy to live.\n\nThere are plenty of steps in a coding workflow that can be handled locally or by a cheaper model path.\n\nThere are also plenty of steps where I want a stronger cloud model.\n\nA gateway makes that hybrid model much easier.\n\nFor example:\n\nThat kind of setup is a lot harder to maintain cleanly when every client is wired directly.\n\nThe point is not that T3 Code itself becomes the gateway.\n\nThe point is that the stack stays layered:\n\n```\nT3 Code\n   ↓\nClaude Code\n   ↓\nLynkr\n   ↓\nAnthropic / OpenAI / Ollama / OpenRouter / Bedrock / Azure / Databricks\n```\n\nThat gives you:\n\nThat is the shape I would trust more over time.\n\nIf you are trying T3 Code casually, none of this matters much.\n\nBut if you are actually using it for repeated coding workflows, then it starts to matter fast.\n\nBecause daily coding-agent usage means:\n\nThat is when the gateway stops being optional architecture theory and starts becoming the practical layer that controls cost and reliability.\n\nIf I were using **T3 Code with Claude Code**, I would not want Claude Code wired directly to one backend forever.\n\nI would want:\n\nThat feels like the right stack for where coding tools are going.\n\nBetter UX at the top.\n\nBetter agent behavior in the middle.\n\nBetter economics and control underneath.\n\nIf you want to check the projects:", "url": "https://wpnews.pro/news/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway", "canonical_source": "https://dev.to/lynkr/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway-2aek", "published_at": "2026-06-25 07:55:29+00:00", "updated_at": "2026-06-25 08:13:29.250055+00:00", "lang": "en", "topics": ["developer-tools", "large-language-models", "ai-agents", "ai-infrastructure"], "entities": ["T3 Code", "Claude Code", "Lynkr", "Anthropic", "OpenAI", "Ollama", "OpenRouter", "Bedrock"], "alternates": {"html": "https://wpnews.pro/news/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway", "markdown": "https://wpnews.pro/news/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway.md", "text": "https://wpnews.pro/news/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway.txt", "jsonld": "https://wpnews.pro/news/how-to-use-t3-code-with-claude-code-and-an-open-source-llm-gateway.jsonld"}}