Curated GitHub repositories every AI agent should know.
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Managed Agents bill on two axes — tokens and wall-clock session time — and half the cost tricks you use everywhere else are switched off here. Here's the meter, the exceptions, and the one lever that still works.
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Flipping inference_geo to "us" pins where the model runs and adds 10% to every token — but it does not, by itself, pin where your data is stored. Those are two different knobs, and founders keep flipping the wrong one.
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The free floor moved twice this quarter: Codex is now $0 on any ChatGPT account, and Google pulled Gemini CLI's free login on June 18. Here's the honest decision for a solo founder — Codex vs Kimi Code vs Claude Code vs Antigravity — what each actually costs, and the catch in every 'free.'
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Send every agent call to the cheapest model that can do the job, and escalate only when a validator says the answer isn't good enough.
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When a tool call fails, the two big APIs want you to say so in completely different ways. Anthropic has a dedicated is_error flag; OpenAI has no error field at all — you put the failure in the ordinary output string. Get this one detail wrong and your agent either 400s or silently trusts a broken result.
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Three ways to run an agent that lives longer than one request — and they disagree on one axis: how much of the loop you write yourself. The right pick follows how much control you want and whether the agent must run anywhere but Cloudflare.
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Don't pick one Claude model for your agent — pick three, route by how hard and how frequent each step is, and do it before Sonnet 5's promo pricing expires on August 31.
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The client is Apache-2.0 and self-hostable; the brain is still OpenAI's. Here's what `@openai/codex-security` actually does, the exact commands to run your first scan, and the one flag that decides whether founders can trust it in CI.
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v2.0.0 shipped with the 2026-07-28 spec and split `@modelcontextprotocol/sdk` into nine subpackages. The split isn't bookkeeping — it's the packaging finally matching a stateless world. Run the codemod, pick two or three packages, delete the fat import.
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There's no converter button. Classic ran your config; AgentCore runs your code. Here's the concrete port map — reuse the Lambdas and Knowledge Base, rewrite the orchestration — with the verified CLI and SDK calls, ARM64 gotcha included.
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Anthropic ships two agent-memory primitives with nearly identical names. One is an interface you back yourself; the other is managed, versioned state you rent. The deciding question isn't which remembers better — it's who runs your agent loop and who should own the bytes.
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Both give an agent memory that survives across sessions. One is a primitive you write to; the other is a layer that decides what to remember for you. That single difference — who does the extraction — is the whole decision, and it's the one the comparison tables never name.
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Three genuinely self-hostable eval-and-tracing platforms, three different licenses. The choice that decides your lock-in isn't a feature — it's the LICENSE file. Here's who picks which.
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A single standard parameter now sets reasoning effort across OpenAI, Anthropic, xAI, and Fireworks. It's portable. It is not equivalent — 'medium' means a fixed gear on one provider and half your token budget on another.
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The technique that cuts RAG retrieval failures by two-thirds isn't one trick — it's four, stacked. Here's the whole build: contextualize each chunk, index it two ways, fuse the rankings, and rerank. With code.
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A copy-paste walkthrough: the Cloudflare Agents SDK puts each agent in its own Durable Object — its own compute plus its own SQLite file — so memory lives inside the agent at the edge, with zero infrastructure to run.
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You can't compute recall@k or MRR without labeled (question, relevant-chunk) pairs — so bootstrap them from your own chunks with an LLM, then score your retriever in ~15 lines of numpy.
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V4 Flash 0731 shipped July 31 as an OpenAI-compatible model: two lines to point your agent at it, one extra_body flag to turn thinking on or off, and one gotcha in the 384K-token output ceiling. Python, Node, and curl.
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Simile just raised $200M at $2B to sell simulated customers. You can build a rough, honest version this afternoon — good enough to kill a bad pricing page before real users ever see it, as long as you calibrate it and never trust it as a verdict.
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Project Think is Cloudflare's opinionated base class for long-running agents: durable turns that survive an eviction, sub-agents with their own SQLite, and a code sandbox — wired together. Here's the whole loop, from empty folder to a turn that resumes after a crash.
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Microsoft made Foundry's hosted agents generally available — and the interesting part isn't the runtime. It's that the old 'which framework?' decision is finally decoupled from 'where does it run?', and every deployed agent now gets its own Entra identity. Here's what actually changed for a solo builder, what it costs, and where the lock-in hides.
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Google's Agents CLI shipped August 3. Here's the whole loop — install, scaffold, run locally, evaluate, deploy, publish — with the real commands, so you can take an ADK agent from an empty folder to a Google Cloud runtime in one sitting.
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Two things collided this month. On July 31 DeepSeek shipped V4 Flash 0731 — an open-weight model that beats its own Pro on agent benchmarks at $0.14/$0.28. On August 31 Claude Sonnet 5's $2/$10 introductory price expires and jumps 50%. If bulk agent work is your biggest line item, this is the decision to make before the cliff.
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Four open-source tools now give Claude Code, Codex, and Cursor one shared memory. They don't disagree on recall — they disagree on what your agent's memory *is*: files you own, a tool your agents call, a local service, or a second-brain agent.
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The August 4 build extends sandbox credential masking from environment variables to files on Linux and WSL — a sandboxed command reads a decoy copy while the proxy swaps in the real secret on egress. Here's the mechanism, the one setting it depends on, and where it quietly falls back to a hard deny.
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Three experimental endpoints — generate, improve, and templatize a prompt — return an error after August 17, and the legacy Workbench that held your saved prompts and evals goes with them. Here's what to export today and a copy-paste replacement that no vendor can deprecate.
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Every 'give your agent memory' course collapses three different problems into one word. They aren't the same problem, and they don't use the same code. Here are the three tiers, the one call that wires each, and the rule for when a fact should climb from one tier to the next.
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Once an agent's memory store is large, the question stops being what to keep and becomes what to surface right now. Three signals compete for that decision — and using any one alone breaks in a predictable way.
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Wiring the three memory layers is the easy part. Keeping them healthy over weeks of real traffic is where agents fall over. Here are the four ways memory rots — unbounded growth, stale retrieval, no forgetting, and poisoning — and the specific fix for each.
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Agents that run for hours need retries and checkpoints that survive a crash or a deploy. Temporal gives you that with a cluster to run; Hatchet gives you the same on the Postgres you already have.
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