Curated GitHub repositories every AI agent should know.
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Compaction is one declarative edit that summarizes old turns automatically when your prompt gets big. The switch is identical on the Anthropic API and Amazon Bedrock — the only things that move are the request envelope and one billing number that hides the real cost.
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The count_tokens endpoint is free, model-accurate, and the only honest way to see your real input size. Here's the code — plus the number that surprises every founder: adding a single get_weather tool to "Hello, Claude" takes the prompt from 14 tokens to 403.
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The x402 SDK just moved under the Linux Foundation and split into scoped @x402/* packages. Here's the current, from-scratch way to put a price on an Express route and take USDC from a paying agent — the seller side and the buyer side, with the exact code.
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You denied egress by default and allowlisted your package registry. Good — now that registry proxy is the single reachable service your agent can attack. Here's how to make it boring.
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Managed harnesses like AgentCore let you declare an agent and rent the loop; the Claude Agent SDK and its kin let you own it line by line. The right call isn't about AWS — it's about where your product's edge actually lives.
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One request, two models: a fast, cheap model does the bulk of the work and calls a stronger model only for the plan. Here's the API, the billing, and when it actually saves money.
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Reflection, Kimi K3, GLM — the open-weight frontier is getting loud, and it's tempting to make one of these models load-bearing in your product. Before you do, run the bet through four gates: is it shipped, is it callable, what's the license, and what's your fallback. Here's the framework, with the three staging patterns that let you get the upside without betting the company.
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Skip the framework. An agent is an LLM calling tools in a loop — here's the ~40 lines that run it, the three context moves that keep it from rotting, and how to hang a real MCP tool off it.
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The 2026-07-28 MCP spec deleted the handshake and put standard OAuth 2.1 in charge of who gets to call your server. WorkOS AuthKit flips into an MCP-compliant authorization server with one config value — here's what it does, who it's for, and where the free line sits.
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Microsoft Execution Containers put the sandbox where the operating system already enforces boundaries — a policy-driven jail for model output and tool calls that runs on Windows, Linux, and macOS. It's MIT-licensed, on npm, and GitHub Copilot CLI already ships on it.
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Huawei Cloud put its CodeArts coding agent into open beta and took the launch abroad at its Thailand summit. It runs open-weight models you already know, indexes your whole repo to cut ~30% of tokens, and starts free — here's what it is, who it's for, and where the free line sits.
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The 2026-07-28 spec makes statelessness the default, and the whole ecosystem is telling you to wait for the beta v2 SDKs. You don't have to. The stable SDK already runs stateless — one flag flips it. The real work is moving your state out of the transport, and that you can do right now.
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One reasons and acts in a single pass. The other retries the same task, writing itself a note on what went wrong. The difference isn't which is smarter — it's whether you have a success signal and can afford the second attempt.
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Pydantic AI's V2 rewrite bets the whole framework on one primitive — the capability — and hides the loop. LangGraph makes the loop the product: nodes, edges, and a checkpointer you own. Here's which bet fits which team.
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Microsoft's open-source Execution Containers add a third option to the agent-sandbox decision: enforce a policy on a process instead of renting a whole virtual machine. Here's the one axis that tells you which you actually need.
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The 2026-07-28 stateless spec is final and a stable v2 SDK is targeted for the same day — but the official README still says 'v1.x for production, don't use v2 yet.' Here's the version to start a new server on this week, and the signal that tells you to move.
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The final spec froze every feature into an Active, Deprecated, or Removed state with a 12-month removal guarantee. Here's the one-screen map of what to adopt today and what to design around.
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Three tools keep winning the 'how do I see what my agent did' question — and they're not really competing. Each answers a different question. Pick by the one you actually have.
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You've been watching token counts. The number that actually moves your bill and your latency is the share of your prompt that hits cache — and most agent designs quietly wreck it.
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Kimi K3's weights landed and it took the open-weight crown on two benchmarks at once. For most founders that changes nothing: the decision is still license and serving cost, and on those K3 is often the wrong default.
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The scariest agent bug isn't the call that fails. It's the call that succeeds — but the response gets lost, so your retry logic runs it again. One key, generated once and reused, is the fix.
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ChatGPT and Perplexity increasingly send your first visitors — but only to pages they can parse. An llms.txt is a 20-line map that tells an AI engine what your site is and which pages matter. Here's the exact format, a copy-paste template, and the honest caveat about what it does and doesn't do.
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A copy-paste walkthrough from an uninstrumented agent to a live multi-agent timeline in Honeycomb — using standard OpenTelemetry GenAI spans, so the same code also works with Langfuse or Phoenix later.
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Every re-index, every retry, every duplicate document quietly re-embeds text you already paid to embed. An embedding cache is the boring, near-zero-risk optimization that a semantic cache gets confused with — and it's the one you should ship first.
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Most multi-agent routing is a lookup you already know at author time, billed back to you as a model call. Here's how to route with plain conditions, and spend a token only on the one branch that's genuinely ambiguous.
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One comes from production APM and correlates your agent with the whole system; the other is LLM-native and lives in prompts, cost, and eval scores. Here's which to standardize on — and why the choice is really about your daily workflow.
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Your agent doesn't fail because the model got dumb. It fails because you let its context window rot. Here is the four-move playbook — with the exact Claude API calls under each move.
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Context editing deletes old tool results to keep your agent inside the window — but every clear invalidates the cache below it. The clear_at_least knob is the whole fix. Here's the break-even math and the config to set it right.
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What it is, who's behind it, how to make your first parse call, and what it costs — the a16z-backed document platform that Scale AI, Airtable, and Harvey use to turn scans and nested tables into clean, LLM-ready structure.
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Qoder moved security review from after-the-fact scanning to inside the coding session — three progressive layers that catch and fix issues before the agent's code is ever committed. Here's what it is, who it's for, and what it costs.
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