The two reference points the rest of the industry agrees on are NVIDIA Dynamo (Rust core, Python frontend, Kubernetes-first) and a long tail of generic ML serving stacks: Ray Serve, KServe, NVIDIA Triton Inference Server, BentoML, and the vLLM Production Stack. Each makes a different tradeoff between “specialized for LLM inference” and “general purpose,” between “one click on a managed cloud” and “I can run it on bare metal in a datacenter I own.”

Cognitora is an open-source LLM inference orchestration layer that lands in a deliberately specific spot in that design space: bare-metal-first, Rust-only, engine-agnostic, KV-cache-aware. It does not replace vLLM or SGLang — it coordinates them into a cluster. It is distributed as six statically-linked binaries with no Python control plane, no JVM operator, and no hard Kubernetes dependency. The same artifacts run as systemd units on a rack of servers, as recipes or docker compose on a single host, via a Helm chart on Kubernetes, or as Terraform-provisioned VMs across AWS, GCP, Azure, and Hetzner.

As of v0.3.0 (May 2026), the OpenAI-compatible surface includes /v1/chat/completions, /v1/completions, /v1/embeddings (real round-trip to the engine, not synthetic vectors), and /v1/models. The admin CLI cgn-ctl reads and writes etcd for cluster state (cluster nodes, cordon/drain, model load/unload), and cgn-ctl install --target single-node --apply can render cognitora.toml plus compose.yaml and bring the stack up with one command. cgn-metrics exposes Prometheus federation at /federate so upstream Prometheus scrapes a single endpoint with per-component labels. There is also a single-manifest Kubernetes quickstart (etcd + llama.cpp engine + router + agent + metrics in one Pod, LoadBalancer on port 80) that has been exercised end-to-end on GKE Autopilot—details in the “Try it” section below.

This post is the long-form version of why that combination of choices, and what falls out of them.

# One-line install — six static binaries, no runtime deps (pin a release for reproducibility)
curl -fsSL https://raw.githubusercontent.com/antonellof/cognitora-inference/main/deploy/installer/install.sh | CGN_VERSION=v0.3.0 sh

# Bring up Llama-3.1 8B on a single GPU with vLLM
bash recipes/llama3-8b/vllm/agg/up.sh
# Equivalent:
# cgn-ctl recipe up llama3-8b/vllm/agg

# Same model, prefill/decode disaggregated across two GPUs
bash recipes/llama3-8b/vllm/disagg-single-node/up.sh

# Single-node Docker install (writes cognitora.toml + compose.yaml, optional --apply)
cgn-ctl install --target single-node --model llama3-8b --engine vllm --apply

The thing inference engines don’t do

A modern inference engine is a token factory. Hand it a request, get tokens back. The contract is “one process, one model, one node.” Everything outside that contract — which replica should serve this request, which replica already has the system prompt cached on-GPU, when to spill cold KV blocks to RAM or SSD, when to migrate a long-context request from a prefill-optimized box to a decode-optimized one, how to weight a thermally-throttled GPU in the routing decision — is, from the engine’s point of view, somebody else’s problem.

Historically somebody else was a stack of glue: an Nginx in front, a Redis for KV metadata, a Python scheduler reading Prometheus, a Kubernetes operator reconciling deployments, a custom autoscaler. That stack works. It is also five processes in five languages with five failure modes, and it is the part of the system that has the worst observability story precisely when an SRE needs it most — at 03:00 on the first day a new model is in production.

The Cognitora bet is that the orchestration layer should be one runtime, in Rust, with a small surface area. Six binaries, each one statically linked, each one with a well-defined responsibility:

The cgn-operator is optional on purpose. If you run on bare metal, the systemd path is a first-class citizen rather than the “deprecated, please use the Helm chart” path that most cloud-native projects eventually push you toward.

KV-aware routing: the routing decision is the product

The single biggest lever in multi-node LLM inference is not token throughput. It is KV cache reuse. If a request shares a 4,000-token system prompt with a request that finished 200 ms ago on replica B, sending the new request to replica B saves 4,000 tokens of prefill compute. Sending it to replica A — because round-robin said so — burns a GPU-second to recompute something that already exists in HBM somewhere else in the cluster. At fleet scale that decision dominates everything else.

There are two common ways to encode “which replica has which prefix”:

1. Radix trees over chained block hashes. This is what Dynamo’s KV-aware router uses. Each block of KV is hashed; the cluster maintains a radix tree keyed on those hashes; the router descends the tree to find the deepest match. Fast, memory-efficient, the canonical structure.
2. Sequence-chained BLAKE3 digests with longest-prefix overlap. This is Cognitora’s choice. Each block’s digest is chained from the previous block’s digest, so the digest at position i summarizes the entire prefix [0..i]. Routing becomes “which replica reports the deepest prefix match against this digest sequence?”

The two approaches are close cousins. The motivating difference for Cognitora is positional correctness on interleaved requests. With a chained digest, two requests that share tokens out of order — same content, different positions — produce different chains, so the router does not falsely claim a cache hit that would force the engine to recompute or, worse, return a positionally incorrect KV. On real-world traces with heavy system-prompt sharing the practical hit ratio sits at ≥ 0.55, and the routing decision itself is sub-millisecond:

Worth flagging the obvious caveat: those are the project’s stated targets, not numbers measured on your traffic. The shape of the metrics matters more than the absolute values — sub-millisecond routing, single-digit-millisecond HTTP overhead, sub-200-µs warm hits. If any of those numbers grew by an order of magnitude the architecture would fall apart, so they are useful as a sanity envelope.

Disaggregation: prefill and decode want different hardware

Prefill is compute-bound. Decode is memory-bandwidth-bound. Running both on the same SKU is a compromise — you either over-provision compute for the decode phase or starve memory bandwidth for the prefill phase. The fix, popularized by DistServe and now standard in production stacks, is disaggregated inference: prefill on one pool of GPUs, decode on another, with the KV blocks streamed between them.

Cognitora handles disaggregation through the NIXL connector — the same NVIDIA-developed transport library Dynamo uses — and exposes the choice as a single TOML knob:

[engine]
kv_offload = "nixl"   # one of: none | nixl | lmcache | hicache | kvbm

That single knob renders the right engine argv for vLLM, SGLang, or TensorRT-LLM. The recipe folders ship the topologies most people actually want — recipes/llama3-8b/vllm/agg/, recipes/llama3-8b/vllm/disagg-single-node/, recipes/llama3-70b/vllm/agg/ — so you do not have to translate “I want 70B FP8 on 4×H100 with TP=4 and disaggregation off” into engine-specific flags.

Engine support matrix:

llama.cpp and OpenAI-compatible servers as first-class engines — not “we tolerate them, here is a config flag” — is a genuine differentiator. It means the same router that fronts your H100 fleet can also fan requests out to a developer’s Ollama on a Mac mini, or to a hosted Anthropic / OpenAI / Together endpoint when on-prem capacity is saturated. That changes what cluster topologies are reasonable to consider.

Tiered KV cache + cross-cluster federation

The KV cache is a hierarchy in any non-trivial deployment: GPU HBM is hot and small, system RAM is warm and bigger, SSD is cold and effectively unbounded. cgn-kvcached materializes that hierarchy as one daemon with explicit tier latencies (sub-200 µs warm, sub-5 ms cold) and a QUIC peer-fetch path between nodes — so a cache miss on node A that lives in node B’s RAM does not become a recompute, it becomes a 12 ms cross-node fetch.

The federation piece is the part that surprised me on first read. Cognitora’s router can form a federation across clusters, not just nodes — meaning a hot prefix that exists in your Frankfurt region can serve a request that landed on your Virginia router, if the prefill cost amortized across the network round-trip beats recomputing locally. Most production stacks do not even attempt this; they treat each cluster as an island. Whether that capability is worth the operational complexity depends entirely on your traffic shape, and Cognitora makes the right call by leaving it off by default.

Separately, cgn-metrics solves a smaller but universal ops problem: in-cluster Prometheus federation. Configure scrape targets in TOML and the daemon unions every target’s /metrics text, injects a cgn_target="<name>" label on each line, and serves the combined exposition at /federate — one scrape for your central Prometheus, without parsing the full metric stream twice. That is observability plumbing, not cross-region routing; both use the word “federation” but they are different mechanisms.

Energy-aware scheduling

The bit of the design I like most aesthetically is also the one with the least proven impact: routing decisions can incorporate power telemetry from Redfish, IPMI, and DCGM. A GPU that is thermally throttled, or a node whose PSU is drawing closer to its budget than its neighbors, gets weighted down in admission control. The stated efficiency target — ≥ 1.4× over a round-robin baseline — is plausible on workloads where the cluster is power-limited rather than compute-limited, which is increasingly the situation in modern racks where power per rack-U is the binding constraint.

I would not buy a system because of energy-aware scheduling alone. I would treat it as a strong tiebreaker if it is otherwise the right shape — and it is one of the explicit gaps in Dynamo today, which doesn’t surface power telemetry into routing.

Cognitora vs NVIDIA Dynamo

Dynamo is the obvious comparison and the most capable alternative. The two projects share a lot of DNA — Rust core, KV-aware routing, NIXL-based disaggregation, Prometheus telemetry — and disagree on a small number of important things.

Reading that table honestly: if your workload is multimodal, video, or NVL72-shaped, pick Dynamo today. That is where NVIDIA’s investment is showing through. If your workload is text-only LLM serving on heterogeneous hardware (mix of H100 / L40S / older Ampere / on-prem llama.cpp / hosted API fallback), if you do not want a Python control plane in the hot path, if you care about cross-cluster federation, or if you operate a power-constrained rack and want telemetry to feed the scheduler — Cognitora is the closer fit.

The llama.cpp + OpenAI-compat line is the one I would emphasize most to anyone considering this for a real deployment. It changes what “the cluster” can include. A company-internal cluster that is allowed to burst to a hosted API during a traffic spike has a very different cost curve than a cluster that has to provision for peak.

Cognitora vs the rest of the field

Dynamo is the closest comparison; the broader field is worth a paragraph each because the alternatives genuinely have different jobs.

vLLM Production Stack is the most natural alternative if you are vLLM-only and Kubernetes-native. It ships a router, autoscaler, and observability stack tuned for vLLM. Cognitora is the right pick if “vLLM-only” is not a constraint you want to commit to — most production fleets end up running at least two engines (vLLM + SGLang for structured output, or vLLM + TRT-LLM for the largest models) and the multi-engine story is easier on Cognitora’s side.

Ray Serve is the right answer if you already run Ray, or if your inference workload is genuinely heterogeneous (LLM + classical ML + Python preprocessing + tool calls all in one DAG). Ray’s strength is composability across arbitrary Python workloads. Cognitora’s strength is being narrowly excellent at the LLM-serving slice — no Python in the data path, no Ray cluster to operate, no actor model to reason about.

KServe is the Kubernetes-native, model-serving-CRD answer. It is the right fit when “this is one of forty model deployments my platform team manages, and they all need to look the same in the cluster.” If LLM inference is the workload your platform team primarily exists to serve, the abstraction layer KServe imposes starts costing more than it saves.

NVIDIA Triton Inference Server is still excellent for non-LLM inference — vision, audio, classical models — and increasingly for LLMs via the TensorRT-LLM backend. If your fleet is mostly non-LLM with LLM as a side workload, Triton is the centerpiece. If LLM is the workload, the LLM-specific systems (Cognitora, Dynamo, vLLM Production Stack) are a better starting point because the things they specialize in — KV routing, prefill/decode disaggregation, prefix sharing — are not a thing Triton optimizes for at the platform level.

BentoML lives at a different altitude. It is excellent at “package this Python model + preprocessing + business logic into a deployable artifact.” It is not, and does not try to be, a multi-node KV-aware orchestrator. The two compose: BentoML for service packaging, Cognitora (or Dynamo) for cluster-level orchestration of LLM-specific concerns.

The honest summary is that LLM serving has bifurcated into two layers that used to be one. The lower layer is “given a request and a replica, generate tokens efficiently” — vLLM, SGLang, TRT-LLM, llama.cpp own this. The upper layer is “given a fleet, route requests so KV is reused and disaggregation pays off” — Dynamo and Cognitora are the two open-source projects that take this layer seriously as a standalone product. Generic ML serving stacks (KServe, Triton, Ray Serve, BentoML) cover the upper layer for general workloads but do not optimize for the LLM-specific signals that turn out to dominate cost.

Multi-model cascades

One smaller capability worth calling out because it has outsized cost impact: multi-model cascades with logprob gating. The idea is old — route easy queries to a small model, fall back to a large model only when the small one is uncertain — but the orchestrator has to support it natively or it becomes a Python-in-the-hot-path workaround. Cognitora exposes it as a routing policy:

small_model = "qwen3-7b"
large_model = "llama3-70b"
gate        = "logprob"     # escalate when small-model logprob < threshold

For workloads where ~70% of requests are genuinely simple (classification-shaped, lookup-shaped, short-answer chat), this cuts cost dramatically without touching tail quality. Dynamo has partial support; in Cognitora it is a first-class router policy.

Honest limits

A few things I would want to know before betting a production deployment on this:

• Pre-1.0. The OpenAI-compatible HTTP surface is stable; internal gRPC APIs and the TOML config surface may still shift in minor releases. Pin CGN_VERSION=v0.3.0 (or newer) on the install script and read the changelog.
• Helm chart maturity. The chart under deploy/kubernetes/helm/cognitora/ exists and helm lint passes in CI, but there is no published OCI chart at oci://ghcr.io/… yet—you install from a local chart path or use the quickstart manifest below until the chart ships optional engine sidecars and a simpler dev-default TLS story. Terraform modules for cloud VMs are still thin stubs; the credible cloud path today is bring your own cluster + quickstart or Helm from a git checkout.
• No multimodal/video. If your roadmap includes image generation or video diffusion serving, Dynamo is ahead. The Cognitora architecture has no in-principle obstacle here, but the engine integrations are not shipped today.
• Gang scheduling is basic. Node selectors, not Grove-style NVL72-aware co-scheduling. If you operate NVL72 racks and need topology-aware placement, Dynamo is the better fit until this lands.
• The performance numbers are targets, not benchmarks on your traffic. The architecture supports them; whether your specific workload realizes them depends on prefix sharing, request shape, and hardware mix. The right move on a new deployment is to A/B against round-robin on a slice of real traffic and measure. CI runs a soft perf gate (cargo bench on routing/prefix paths, non-blocking on PRs); hard regression gating is planned once baselines stabilize.
• The cross-cluster federation story is powerful and operationally heavy. Turn it on only when you actually have multi-region traffic that benefits from it. The defaults are sensibly conservative.

Try it

# Install — six static binaries, no runtime deps (pin the release)
curl -fsSL https://raw.githubusercontent.com/antonellof/cognitora-inference/main/deploy/installer/install.sh | CGN_VERSION=v0.3.0 sh

# Bring up Llama-3.1 8B on a single GPU with vLLM
bash recipes/llama3-8b/vllm/agg/up.sh

# Disaggregated prefill/decode on two GPUs in one node
bash recipes/llama3-8b/vllm/disagg-single-node/up.sh

# Llama-3.3 70B FP8 on 4×H100 with TP=4
HF_TOKEN=… bash recipes/llama3-70b/vllm/agg/up.sh

# Admin: inspect nodes / desired models in etcd (needs etcd endpoints in cognitora.toml)
cgn-ctl cluster nodes
cgn-ctl model ls

Kubernetes — fastest path to a public URL (CPU demo: TinyLlama via llama.cpp in-cluster; no GPU quota required). Validated on GKE Autopilot; same manifest works on other clouds or local clusters (use port-forward if LoadBalancer stays pending):

kubectl apply -f https://raw.githubusercontent.com/antonellof/cognitora-inference/main/deploy/kubernetes/quickstart/cognitora-cpu.yaml
kubectl -n cognitora wait --for=condition=ready pod -l app=cognitora --timeout=10m
IP=$(kubectl -n cognitora get svc cognitora-router -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
curl -sS "http://$IP/v1/chat/completions" \
  -H 'Content-Type: application/json' \
  -d '{"model":"tinyllama","messages":[{"role":"user","content":"What is 2+2?"}]}'

Kubernetes — Helm from a git checkout (production-shaped chart; wire your own engine / GPU pool—the chart assumes mTLS material unless you adjust values):

git clone https://github.com/antonellof/cognitora-inference.git && cd cognitora-inference
helm install cognitora ./deploy/kubernetes/helm/cognitora \
  --namespace cognitora --create-namespace \
  --set router.replicas=2 \
  --set models.llama3-70b.tp=4

An oci://ghcr.io/antonellof/charts/cognitora one-liner is not published yet; track it in the repo’s plan.md. Until then, local chart path or the quickstart manifest above.

From source (if you want to read the routing code, which I recommend — it is the most interesting part):

git clone https://github.com/antonellof/cognitora-inference.git
cd cognitora-inference
cargo build --release --no-default-features \
  -p cgn-router -p cgn-agent -p cgn-kvcached \
  -p cgn-metrics -p cgn-ctl -p cgn-operator

Once a router is up, point any OpenAI-compatible client at it. The wire protocol is the lingua franca, so existing application code does not change. Use /v1/embeddings only when the loaded model is an embedding model—chat checkpoints will correctly surface as errors through the stack rather than fake vectors.

Why this shape of system, now

The closing observation is meta. For two years the LLM-inference field has tolerated a stack where Python is in the request path, Kubernetes is the only first-class deployment target, and “the orchestrator” is whatever combination of Nginx, Redis, and homegrown schedulers a given team has glued together. That stack works at startup scale. It does not work at datacenter scale, where a 1% efficiency gain is worth more than a feature, where the difference between sub-500-µs and 5-ms routing decisions shows up as a line item, and where the operations team would prefer a single binary they can strace.

Cognitora is one answer to “what would the orchestrator look like if it were designed today, for that scale, in one language, with KV cache reuse as the centerpiece rather than an afterthought?” NVIDIA Dynamo is another. Both are credible; they make different bets on the runtime shape (six small Rust binaries vs Rust+Python), the deployment surface (bare-metal-first vs Kubernetes-first), and the engine ecosystem (broad including llama.cpp/OpenAI-compat vs the three industrial engines). Which one fits depends on what your fleet actually looks like — and the fact that there are two well-engineered open-source choices in this layer at all is a meaningful change from where the field was twelve months ago.

Links:

• Cognitora repository — Apache-2.0, Rust 1.89+
• CHANGELOG · v0.3.0 release
• NVIDIA Dynamo — the closest comparable system
• vLLM Production Stack — vLLM-only alternative
• NIXL — the disaggregation transport both projects build on
• DistServe — the original prefill/decode disaggregation paper
• Ray Serve · KServe · Triton Inference Server · BentoML — generic ML-serving alternatives

Update — 2026-04-20 (v0.3.1). Since this post first went up:

• vulnhunter_v0 harness — LLM-driven agent that hunts the eight bug classes pattern matchers can’t see (SSRF, IDOR, indirect RCE, auth/session bypass, race conditions, mass assignment, subtle crypto, path traversal). No scanner seeds; pure novelty detection. Found all 3 seeded novel vulns in a Flask test app with concrete attack payloads + fix hints.
• supply_chain composite scanner — OSV-Scanner (CVEs across all OSS lockfiles) + OpenSSF Scorecard (repo trust signals) + guarddog (malicious-package heuristics for PyPI/npm) in one rule. Found 37 real CVEs on a vulnerable test target.
• Standalone binaries for macOS (arm64/x86_64), Linux (x86_64/arm64), Windows. One-liner install via curl … | bash — no Python required.
• MCP server (s0-mcp) + Claude Code skill + Cursor rule, so the AI assistants you already use can call s0 directly.
• 8 LLM providers (added OpenRouter / Ollama local+cloud / self-hosted OpenAI-compatible / Groq / Mistral / DeepSeek / Azure) and 7 output formats (added Rich terminal default / CSV / GitLab Code Quality / JUnit XML).
• Real-world run on OWASP PyGoat: 252 raw scanner findings → 14 LLM-triaged real bugs (94% noise reduction, results doc).

The narrative below is unchanged — the Meta-Harness approach is the point — but the “Try it” section at the end and the architecture/install snippets are updated to match the current state. Updates marked [v0.3] inline.

Static security scanners give you a wall of JSON. Semgrep finds a subprocess.run(..., shell=True); bandit flags an md5 call; gitleaks shouts about a token-shaped string in a test fixture. You — the engineer — read every alert, decide which are real, trace data flow by hand, and then close the ones that don’t matter. The scanner doesn’t help with any of that. It can’t, because it doesn’t read source.

The natural next move is to wedge an LLM into the triage step: run the scanners, hand the findings to a model, ask it to mark false positives, assign severities, and write fix hints. That’s the easy part. The hard part is the second-order question: how do you know the LLM triage is good? The standard answer in 2026 is “feels better on my test repo,” which is the same answer 2018 had for hand-tuned semgrep rules and it didn’t age well.

s0-cli is an LLM-driven CLI agent that finds security vulnerabilities and “vibe-code” problems (AI-slop patterns: stub authentication, hallucinated imports, dummy crypto, prompt-injection sinks) in any repository, diff, or single file. The thing I want to talk about in this post isn’t the scanner itself — it’s the loop around the scanner. The whole scanning agent is a single Python file that gets automatically rewritten by an outer optimization loop, scored against a labeled benchmark with a held-out test set. This is the Meta-Harness approach (Lee et al., 2026) applied to security triage.

$ uv run s0 scan ./my-app

  hallucinated import           src/email.py:8       critical   CWE-829
    `import emailclient` — no such package on PyPI; nearest match is
    `emailclient-aws` (likely typosquat). Suggest pinning `email-validator`.

  SQL injection (f-string)      src/api/users.py:42  critical   CWE-89
    `cur.execute(f"SELECT … {user_id}")`. Use `cur.execute("… ?", (user_id,))`.

  weak password hashing         src/auth/hash.py:7   high       CWE-327
    `hashlib.md5(...)` for password storage. Use `argon2-cffi` or `bcrypt`.

3 findings (1 critical hidden as triage filtered out 6 false positives)

The hybrid: classic scanners + LLM triage

The architecture isn’t novel — s0 scan runs five classic scanners (semgrep, bandit, ruff, gitleaks, trivy) plus two AI-slop detectors (hallucinated_import AST-based, vibe LLM-based) [v0.3: + a supply_chain composite scanner that wraps OSV-Scanner + OpenSSF Scorecard + guarddog] on the target in parallel, deduplicates by (path, line, rule_id), and hands the merged list to a multi-turn LLM agent with a tightly scoped tool surface (read source, grep for taint, blame git history, re-run scanners with tighter rules). For each finding the agent either accepts it (assigning a severity and a fix_hint) or marks it as a false positive.

The scanners do detection; the LLM does triage. That split matters because of how the numbers come out, which I’ll get to in a moment.

Everything the agent does — every prompt, every tool call, every LLM response — is recorded under runs/<timestamp>__<harness>__<id>/. That recording is not just for debugging; it’s also the input to the optimization loop.

[v0.3 — two harnesses, two jobs] The default agent (baseline_v0_agentic) does the triage job described above: scanner seeds in, calibrated findings out. A second agent (vulnhunter_v0) does novelty detection instead: no scanner seeds, just an LLM with the same tool surface and a system prompt that targets the eight classes pattern matchers structurally can’t see (SSRF, IDOR, indirect RCE, auth bypass, race conditions, mass assignment, crypto mistakes, path traversal). The two agents share findings via the same (path, line, rule_id) fingerprint, so you can run both — s0 scan ./repo && s0 scan ./repo --harness vulnhunter_v0 — and downstream tools dedup automatically. Calibration of known classes is one problem; finding unknown ones is a different one, with a different optimal harness.

Benchmark: 11 labeled tasks, train/test split

Before getting to the loop, the harder problem: what does “good triage” even mean numerically?

The repo ships with 11 labeled tasks under bench/. Each task is a tiny self-contained target with a ground_truth.json listing the real vulnerabilities. The scorer matches predictions by (path, line ± 5). The split is deliberate:

• bench/tasks_train/ — 7 tasks, visible to the optimizer. SQL injection, XSS, hallucinated imports, command injection, weak crypto, unsafe yaml load, path traversal.
• bench/tasks_test/ — 4 tasks, held out. Hardcoded secrets, vibe stub auth, pickle deserialization, JWT no-verify.

The proposer cannot see tasks_test/ and the loop refuses to start if the train and test paths resolve to the same directory. That last sentence sounds defensive because it is: the temptation to peek at the test set is enormous in any optimization loop, and the easiest way to remove the temptation is to make peeking impossible.

Two configurations on openai/gpt-4o-mini:

What this proves:

• Recall = 1.00 in every configuration. Across all 13 ground-truth vulnerabilities (train + test) — SQL injection, command injection, hallucinated imports, path traversal, weak crypto, hardcoded secrets, JWT no-verify, pickle deserialization, stub auth, … — the deterministic scanner pipeline alone catches every one. The LLM never has to find anything; its job is purely to triage what was already found.
• LLM triage cuts false positives by 30% on the held-out set (10 → 7) without dropping a single true positive. Held-out F1 climbs from 0.50 → 0.59 (+18% relative).
• Every scan ends in a fixed turn budget (median 5 turns, max 11 in this run) and a fixed token budget. No runaway costs.
• The held-out test split was never seen by the LLM during any optimization run — generalization is measured, not assumed.

The train F1 only moves from 0.39 → 0.41. The test F1 moves from 0.50 → 0.59. That asymmetry is the most interesting line in the table: the LLM’s triage generalizes, and on the held-out tasks it removes 30% of false positives without losing recall. The --no-llm mode stays useful as a free anchor — you keep 100% recall at zero LLM cost, at the price of more false positives to skim through. Most CI pipelines will want the LLM mode on PR diffs (small target, low token cost, accurate triage) and the no-LLM mode on full-repo nightly scans.

A statistical-honesty note that I’ll repeat throughout: 11 tasks is a small bench. The +0.09 test-F1 delta is one model on one bench on one run; it would be premature to claim the same delta will hold for claude-sonnet-4-5 on a 200-task bench. What I can claim is that the measurement infrastructure exists, the test split is honest, and the loop is set up to keep producing those numbers as the bench grows.

The Meta-Harness loop

So now the second-order question. The scanner achieves train F1=0.41 / test F1=0.59. How do you make those numbers go up without hand-tuning?

The standard answer is “iterate on the prompt.” That’s fine, but it has obvious limits:

• The thing that changes is a string in a config file, but a real triage decision involves prompts and tool selection and dedup heuristics and severity calibration and when to give up.
• “Better” is measured by vibes, not by F1.
• There’s no guard against overfitting your dev repo.
• There’s no audit trail of what you tried and why each variant was rejected.

The Meta-Harness paper (Lee et al., 2026) generalizes the loop. The unit of mutation isn’t a string — it’s the entire single-file agent (prompts + tools + scanner-selection + dedup logic, ~300–500 lines of Python). The unit of progress is a labeled bench scored by F1, precision, recall, tokens, turns. The guard against overfitting is a held-out test set the proposer literally cannot read. And the history is a directory full of every attempt, every score, every trace.

s0 optimize runs that loop:

1. A coding-agent proposer reads runs/ (every prior agent, every score, every tool trace), forms a hypothesis about the worst current failure mode, and writes a new harness file under src/s0_cli/harnesses/.
2. The runner validates and re-scores it on bench/tasks_train/.
3. After all training iterations finish, the best-train-F1 candidate is scored once on the disjoint bench/tasks_test/ to measure generalization.

The proposer’s contract is in SKILL.md, which is read by the outer loop. It pins the interface (must subclass Harness, must implement async def scan(self, target: Target) -> ScanResult, must run within budgets) and forbids the obvious cheats — touching bench/tasks_test/ is automatic disqualification, hardcoding bench task names is instant disqualification on held-out, and so on. The contract is short on purpose; the proposer needs room to be creative on what it changes.

Why this matters more than “iterating on the prompt”

A handful of properties fall out of the loop that are not available in any “edit the prompt and rerun” workflow:

Search beats intuition. The proposer can try ideas a human wouldn’t bother with — “lower confidence on bandit B608 inside tests/ directories”, “escalate to critical when pickle.loads is reachable from a Flask handler”, “skip semgrep’s python.lang.security.audit.dangerous-subprocess-use for subprocess.run calls whose first argument is a list literal” — and measure whether each one helps. Most will not. That’s fine; the ones that do compound.

Pareto, not point estimates. Real choice in CI isn’t “best F1”, it’s “best F1 at the token budget I can afford on a PR”. After every iteration the Pareto frontier (F1 vs. tokens) is snapshotted to runs/_frontier.json. You get a menu: “harness A is the best F1 at any cost; harness B is the best F1 below 50k tokens; harness C is the best F1 below 10k tokens.” You pick whichever fits the deadline.

Generalization is enforced, not assumed. The proposer can’t see tasks_test/. The loop refuses to start if the train and test paths resolve to the same directory. So a +0.1 F1 on train that comes with a -0.05 test gap shows up in the summary table — you can’t cheat your own benchmark, even by accident.

Every iteration is auditable. Each attempt is one new file plus a runs/<id>/ directory containing harness.py, score.json, summary.md, and per-task traces with the full prompt and every tool call. Disk-as-database; no schema migrations, just grep. When the team six months from now asks “why does baseline_v3_taint skip semgrep on test files?”, the answer is in runs/2026-04-12_…/score.json next to the diff that introduced it.

The “outer loop reads the inner loop” recursion

The bit that makes me happiest about this design is also the bit that took me the longest to internalize. The proposer doesn’t optimize against scores. It optimizes against traces.

When the proposer wakes up at the start of an iteration, the first thing it does is s0 runs list --frontier to find the current best harnesses. The second thing it does is s0 runs tail-traces <run_id> <task_id> for each failure mode it suspects. It reads the actual prompt and the actual LLM response and the actual tool call sequence that produced the false positive. Then it forms a hypothesis. The Meta-Harness paper’s §A.1 reports a median of 82 file reads per iteration in the tbench2 setting; the SKILL.md instructs the proposer to read at least 3-5 prior trace files for each suspected failure mode, because “optimize from scores alone” is the ablation that loses 15 points on the original bench.

This matters because the failure mode of a triage agent is rarely “the F1 is low.” It’s usually “on this specific path-traversal task, the LLM read the wrong file first, ran out of turn budget on a tangent, and gave up before ever looking at routes.py.” That diagnosis is in the trace. It is not in the score. A proposer that only sees scores will rewrite the prompt; a proposer that reads traces will increase the turn cap, or change the scanner ordering, or add a git_blame step before read_file.

Multi-candidate proposals

There’s one more knob worth highlighting because it changes the cost model. Pass -k N (or --candidates N) to fan out N parallel proposals per iteration, each with a different temperature, seed harness, and focus directive. The runner evaluates them concurrently and keeps the highest-F1 winner; losers are still recorded under runs/ so you can see what each design slot tried.

# 2 parallel proposals per iteration; pick the better one each time
uv run s0 optimize -n 5 -k 2 --run-name exp_multicand --fresh

Cost scales linearly with k, but wall-clock cost stays roughly constant (the proposers run concurrently). The strategy ladder lives in src/s0_cli/optimizer/strategies.py and is deterministic — k=2 always means slot 0 (greedy, exploit) plus slot 1 (warmer, “shrink token cost”), so reruns hit the same regions of design space.

This is the part of the loop that feels most like classical optimization: you’re not just gradient-descending one harness, you’re doing beam search over a population of harnesses with different temperatures and different objectives, and the disk-resident runs/ directory is the population history.

Honest limits

I don’t want to oversell the size of what’s been measured here. A few caveats worth keeping in mind if you’re considering using this in production or running the loop yourself:

• 11 tasks is small. The +0.09 test-F1 delta from --no-llm to baseline_v0_agentic is one model on one bench. The bench needs to grow before any of these absolute numbers should be quoted as evidence about real CI cost vs accuracy tradeoffs. Adding tasks is documented in bench/README.md and is the most useful contribution someone could make right now.
• Recall = 1.00 is partly a property of the bench. Every ground-truth label in the train and test set is something one of the five classic scanners catches, by construction. A bench item like “side-channel timing leak in a custom JWT verifier” would not be caught by any current scanner, and the LLM-only vibe detector would have to find it from scratch. Adding tasks that only the vibe detector catches is the next thing that needs to happen to stress the LLM-as-detector path rather than the LLM-as-triage path. [v0.3 update] The vulnhunter_v0 harness is the first concrete attempt at this — it found all 3 seeded SSRF / IDOR / RCE-via-indirection bugs in a custom Flask test app without any scanner seeds, but those numbers aren’t yet in the train/test bench above. Adding novelty-class tasks to tasks_test/ is the obvious next benchmark contribution.
• gpt-4o-mini is the cheap baseline. The numbers above are for the model that maximizes “interesting per dollar.” claude-sonnet-4-5 is the default in .env.example and likely produces sharper triage; I haven’t run the full optimize loop on it because each iteration is non-trivially expensive and I want the bench to grow first.
• The optimize loop is a research artifact, not a CI tool. s0 scan is the production path; s0 optimize is what produces better s0 scan configurations. Running optimize on every PR would be cost-prohibitive and beside the point.

Try it

Install (no Python required, v0.3.1+):

# Standalone binary — autodetects OS/arch, verifies SHA-256, installs to /usr/local/bin
curl -fsSL https://raw.githubusercontent.com/antonellof/s0-cli/main/install.sh | bash

# Or pin a version + install into ~/.local without sudo
curl -fsSL https://raw.githubusercontent.com/antonellof/s0-cli/main/install.sh \
  | bash -s -- --version v0.3.1 --prefix "$HOME/.local"

The bundle ships every LLM provider plugin (Anthropic / OpenAI / Gemini / OpenRouter / Ollama / Groq / Mistral / DeepSeek / Azure) and every harness; you only install the SAST scanners you want. s0 doctor reports which are present.

Or from source (recommended for development):

git clone https://github.com/antonellof/s0-cli.git
cd s0-cli
uv sync                    # Python 3.12+, uv >= 0.5

cp .env.example .env       # then fill in one provider key

Use it:

# Default agent — triage classic + AI-slop scanner findings
s0 scan ./your/repo

# v0.3: hunt UNKNOWN vulnerability classes (SSRF, IDOR, indirect RCE, ...)
# — LLM-driven, no scanner seeds
s0 scan ./your/repo --harness vulnhunter_v0

# v0.3: just the supply-chain layer (CVEs + repo trust + malicious-pkg heuristics)
s0 scan ./your/repo --no-llm --scanner supply_chain

# Score the default harness on the training bench
s0 eval

# Score on the held-out test set
s0 eval --split test

# Run the optimize loop (5 iterations, then a held-out pass)
s0 optimize -n 5 --run-name exp1 --fresh

Combine the agents for full coverage: s0 scan ./repo && s0 scan ./repo --harness vulnhunter_v0. The first calibrates known-class findings; the second hunts what pattern matchers can’t see. Findings from both runs share the same fingerprint, so SARIF / GitLab CodeQuality / JUnit reports dedup automatically downstream.

Real-world numbers — OWASP PyGoat [v0.3]: running the default agent on the PyGoat intentionally-vulnerable Django app reduced 252 raw scanner findings to 14 LLM-triaged real bugs — a 94% noise reduction without dropping a single ground-truth vuln. Full session including the Pareto frontier after one s0 optimize iteration is reproducible from docs/results/REAL_WORLD_RESULTS.md.

Drop-in CI integrations ship with the repo: a GitHub Action, a multi-arch Docker image with every scanner pre-installed, and a pre-commit hook pair. Seven output formats: terminal (Rich-based, default in TTY), markdown, json, sarif (GitHub code-scanning / GitLab SAST), csv, gitlab (Code Quality JSON for MR widgets), junit (XML for any CI test reporter).

[v0.3] Use it from your AI assistant. A built-in MCP server (s0-mcp) plus a Claude Code skill and a Cursor rule let Claude Code, Cursor, or any MCP-aware client invoke s0 scan / s0 scan --diff / s0 list_scanners / s0 list_harnesses directly. Install guide: docs/integrations/INSTALL.md.

I’m particularly interested in feedback from anyone running an LLM-augmented SAST in CI today. The hypothesis I’m trying to falsify — that the agent itself should be the optimization variable, not the prompt — needs validation from people who’ve actually paid the LLM bills on real PR traffic. The 11-task bench is a starting point, not an answer.

Links:

• GitHub repository · latest release
• README — top-level overview, quickstart, CI integrations
• SKILL.md — proposer contract read by the outer loop
• bench/README.md — task layout and how to add new ones
• docs/results/REAL_WORLD_RESULTS.md — PyGoat case study, 94% noise reduction
• docs/integrations/INSTALL.md — Claude Code / Cursor / generic MCP integration guide
• Lee et al. Meta-Harness: End-to-End Optimization of Model Harnesses. arXiv:2603.28052 (2026). paper · code
• KRAFTON AI & Ludo Robotics. Terminus-KIRA. github.com/krafton-ai/KIRA

The useful memory is 600 ms old, from a different modality than the query, and low in cosine similarity compared to countless irrelevant alternatives. No ranking by similarity alone can surface it.

The interesting observation is that an embodied perception stack already knows more than “find me the nearest vector” at query time. It carries an active track id, a dialogue session, an AR room, or a robot sub-task — the right answer almost always lives inside that episode. The whole content of MARS — Memory for Autonomous Real-time Systems — is what happens when you take that observation seriously and push it into a GPU kernel.

Read the full paper (PDF, 1.97 MB)

MARS treats episode handles as a kernel parameter. When the application can supply the active track / session / room / sub-task id, retrieval becomes a 197 µs operation at N=1M — 33× faster than the only GPU baseline that retains perfect cross-modal recall on this contract, and the only configuration that fits inside the 1 ms autonomous-vehicle deadline at that corpus size. The rest of this post is what falls out of taking that one design move seriously: how the kernel pipeline changes, how the multimodal graph is structured, what happens to the recall axis at scale, and what the contract is not good for.

What existing libraries do well — and where they stop

FAISS GPU and cuVS CAGRA are excellent at finding the K most similar vectors in a static corpus. The contract they expose is: over the entire indexed corpus, return the K vectors with highest cosine similarity. For document retrieval, recommendation, image search — that’s the right contract.

Embodied workloads typically know more than that at query time. The question isn’t “find me the closest vector globally” — it’s “find any recent sensor evidence, across any modality, relevant to this current track.” Encoding that knowledge as host-side post-filtering on the output of a global ANN sweep wastes GPU work and, as the head-to-head benchmark below shows, breaks recall at scale on tiny multimodal clusters.

A first concrete instance of the same idea is temporal decay. Recency is a first-class ranking signal in a sensor stack — but cosine ANN libraries don’t fold it into the kernel:

MARS matches FAISS+filter at identical TP@10 (0.910) and a comparable per-query latency (0.26 ms vs 0.25 ms p99). The 0.01 ms gap is within run-to-run noise on a non-locked-clock A100, so the contribution here is API consolidation — the temporal filter becomes a kernel parameter rather than a second pipeline stage — rather than a raw speedup. A raw cuBLAS-only ring buffer is 3.2× faster (0.12 ms at N=2,400) but provides no temporal decay, no cross-modal retrieval, and no streaming insertion.

That’s the warm-up; the larger contribution is what happens when an episode handle is also pushed into the kernel.

Head-to-head against modern GPU ANN libraries

The contract is the embodied multimodal one: the query is an IMAGE node and we measure hit@15 of the same-episode TEXT and AUDIO neighbors (kids-ball corpus, paired RNG seed=2026, A100 SXM4 40 GB).

Figure 7 from the paper: head-to-head wall-clock p99 (left, log scale) and hit@15 (right, linear) on the kids-ball multimodal contract. MARS Episode-scoped (rightmost green bar in each group) is the only system that is simultaneously below the 1 ms AV deadline and at perfect recall across all three corpus sizes.

Two findings drove the design:

• MARS Episode-scoped is Pareto-optimal on the latency–recall frontier at every N: 33× faster than FAISS Flat at 1M and 16× faster than CAGRA, both at perfect recall on this metric. (“Pareto-optimal” here means: no measured baseline is simultaneously faster and better-recall on this contract; I make no claim about other latency–recall metrics or about other corpora.) When the application can supply an episode handle, restricting the kernel to episode members converts a Θ(N · D) cosine sweep into a Θ(|episode| · D) kernel that the A100 finishes in 197 µs independent of N.
• Cosine ANN baselines collapse on this metric at scale. The kids-ball corpus has tiny clusters (10 nodes per episode, 100 K episodes at 1M), so per-episode TEXT/AUDIO neighbors are buried among ~999 990 distractors. CAGRA’s graph traversal (even at search_k=512) and IVF cells (any nprobe) miss them. MARS keeps episode membership in the graph topology and recovers them in O(member_count). FAISS Flat alone keeps recall at N=10⁶ because it is exhaustive — at 6.64 ms p99 it sits 6.6× past the 1 ms AV deadline.

Synthetic-corpus disclaimer. The kids-ball benchmark uses Gaussian-perturbed cluster centroids in 768-D with a known, dense small-cluster structure. Real-encoder embeddings (CLIP, CLAP, E5) have different distance distributions and broader clusters; the exact crossover N at which cosine ANN loses recall on real data may shift. The qualitative point — that exhaustive cosine + episode CSR is the right primitive when episodes are known and small — should generalise; the absolute hit@15 numbers should not be quoted without re-measurement on the target encoder. A real-encoder validation run is the first item in §10.1 of the paper.

What would be a fairer FAISS baseline. A FAISS-Flat-GPU sweep with IDSelectorBatch or IDSelectorRange set to the episode member ids would do roughly the same work as MARS Episode-scoped and is the next baseline to add. It is queued in §10.1 as the first item of Evaluation Hardening. I expect it to be the same order of magnitude as MARS-Episode — the win MARS keeps would then be that the episode CSR is built into the same data structure as the cross-modal NSN, not that the cosine kernel is somehow faster.

Episode-scoped retrieval is near-flat

Figure 6 from the paper: episode-scoped MARS (green diamonds) is near-flat at ~200 µs across three decades of N while keeping perfect cross-modal recall; the only baseline that also achieves hit@15=1.00 (FAISS Flat) crosses the 1 ms AV deadline already at N=10^5 and is 33× slower at N=1M.

The episode-scoped curve grows by only ~19 % as N goes from 10⁴ to 10⁶, because the GPU work is bounded by the episode size (~10 members), not by N. At N=1M the path delivers 197 µs p99 wall-clock — below every per-frame deadline including the 1 ms AV budget — while returning a perfect-recall multimodal answer.

When does the contract apply? Episode scope is correct only when the right episode is known before the query. It is the natural contract for AV per-track re-identification (track id is the episode), voice-agent turn taking (session id is the episode), AR/VR per-room recall (room id is the episode), and embodied task loops (current sub-task id is the episode). It is not correct for open-ended global semantic search — but that workload has the wider deadline (10–100 ms) where the global path or a cosine ANN baseline already fits.

The paper’s Episode-Scope Crossover proposition (§6.3) makes this concrete: episode-scoped beats global on a bandwidth-bound model when |episode| / N + ε_overhead < 1, which on the kids-ball corpus is satisfied for any N ≥ ~1K. It also derives an upper bound on the recall advantage as a function of episode density vs distractor density — useful for predicting whether the contract is worth wiring into an application before measuring.

Two contracts, one GPU-resident substrate

Figure 1 from the paper: MARS retrieval pipeline. Sensor encoders (left) feed a shared 768-D embedding space; four GPU kernels orchestrate retrieval over a CSR-format memory graph resident in device memory. The green dashed arc is the episode-scoped fast path.

MARS stores text, audio, image, and sensor embeddings in a shared 768-D space as nodes in a Neural Shortcut Network (NSN) with cross-modal bridges. Two contracts share the same GPU-resident data:

Global path (when no episode handle is available): four kernels, sub-millisecond at N ≤ 50K, zero per-query allocation.

1. Stage 1 — Cosine + temporal decay. Default: cuBLAS Sgemv (FP32) followed by score × exp(-λ·age). Opt-in --use-fp16 switches to the hand-fused FP16 cosine kernel — wins by 41 % at N=10K but loses by 49 % at N=1M.
2. Stage 2 — CUB radix sort top-K in O(N).
3. Stage 3 — Warp-cooperative BFS through NSN bridges with atomicCAS race-free neighbor claiming. Score propagation rule (Algorithm 1 in the paper): score[u] ← max(score[parent] · δ_prop, sim[u] · α_bfs) with defaults δ_prop = 0.85 (per-hop attenuation) and α_bfs = 0.8 (cap on raw similarity for purely-graph-discovered nodes). Temporal decay is not re-applied during BFS — it has already been folded into sim[u] at Stage 1.

Episode-scoped fast path (when query_episode_id is supplied): Stage 1 is restricted to the episode’s CSR member list and Stage 3 BFS is skipped entirely. The result is the green dashed arc in the diagram above and the near-flat scaling curve in the previous section.

When FP16 fused beats cuBLAS Sgemv (and when it doesn’t)

Figure 8 from the paper: the hand-fused FP16 cosine kernel wins at small N where the working set fits in L2, but cuBLAS Sgemv engages Tensor-Core paths that the fused kernel cannot match at large N. The default deployment uses cuBLAS; --use-fp16 is documented as a small-N opt-in.

The hand-fused FP16 cosine kernel is bandwidth-optimal at small N because the entire embedding tile fits in L2 and the dot product becomes memory-bound. cuBLAS Sgemv cannot beat that at N=10K. But once N exceeds the L2 working set, cuBLAS’s Tensor-Core paths and per-arch tile heuristics dominate — FP16 fused regresses by 49 % at N=1M.

This is one of the more honest findings of the paper: bring-your-own-kernel is not always faster than vendor BLAS, and the crossover point is hardware- and corpus-size dependent. The build defaults to cuBLAS; --use-fp16 is documented as a small-N opt-in.

Scaling and deadline compliance

Measured on A100 SXM4 40GB (D=768, K=10, cuBLAS+CUB):

Five workloads pass empirical p99 deadlines on A100:

Figure 4 from the paper: deadline compliance for all four demonstrators on A100 SXM4 plus the episode-scoped path at N=1M. All pass; episode-scoped delivers a perfect-recall multimodal answer with 80% headroom on the 1 ms AV deadline at N=1M.

The last row is the one I’d flag for embodied roboticists: a perfect-recall multimodal answer at sensor rate against a million-memory corpus, with 80 % headroom on the AV deadline.

A statistical-honesty note

All the p99s above are over 128–256 paired probes on non-locked-clock vast.ai instances with shared multi-tenant hosts. At sub-millisecond scale, run-to-run jitter is on the order of ±10 % and the 12th-largest of 128 samples has wide confidence intervals. Concretely:

• The 0.26 ms vs 0.25 ms gap on the temporal-decay experiment is well within noise; quoting either as “winning” would be misleading.
• The flip in the FP16-vs-cuBLAS large-corpus table where RTX 5060 Ti looks faster than A100 SXM4 at N=500K but slower at N=1M is reported faithfully but should not be over-interpreted.
• The episode-scoped advantage at N=1M is large enough (13× over MARS-Global, 33× over FAISS-Flat) that no plausible jitter explains it away — this is the one number I’d defend even after locked-clock re-measurement.

The paper carries an explicit Measurement Methodology and Statistical Caveats subsection (§7.2) and an Evaluation Hardening track (§10.1) that lists the locked-clock re-runs, the real-encoder benchmark, the FAISS+IDSelector head-to-head, the standard temporal-IR metrics (TS-Recall@10, time-NDCG@10), and per-stage Nsight Compute breakdowns. Cost ~$15, time ~2 days; queued for the next iteration.

The Neural Shortcut Network

What makes MARS more than “cuBLAS with a timestamp column” is the graph structure. Memories are nodes in a CSR-format graph built in five phases:

1. Ring lattice (k=6 local neighbors)
2. Hierarchical skip connections (powers of 2)
3. Hub supernodes at √N intervals
4. Small-world rewiring (Watts–Strogatz, p=0.15)
5. Cross-modal bridges — every node gets one edge to each other modality

Phase 5 is critical: a query starting with an audio embedding reaches visual and text memories through graph traversal, without separate per-modality indices. The warp-cooperative BFS kernel explores these bridges in <0.04 ms.

The construction is deterministic — Watts–Strogatz small-world plus deterministic cross-modal bridges, with no learned weights, no gradient-tuned objectives, no learned edge selection. The “Neural” in Neural Shortcut Network refers only to its role as a neural-embedding index. Phase 5’s guarantee is correspondingly modest: it guarantees structural reachability — by construction, every node has at least one edge to every other modality, so a 1-hop BFS sees all modalities — and makes no claim that the reached neighbors are semantically relevant. Whether the BFS-discovered cross-modal memory is the right one depends on the quality of the shared embedding space, not on the graph topology.

The graph store also carries a sixth component beyond the five edge-construction phases: an episode_csr member list that drives the episode-scoped fast path. It is a per-episode index (uint32 offsets into the embedding array), built once at corpus load time, queried by the RetrievalScope::EpisodeScoped contract.

What it’s not

MARS is not a vector database. Same conceptual layer — indexing, similarity, retrieval — but different latency envelope, different durability model, different deployment target. Think cuBLAS vs LAPACK: same operations, different hardware. The working set is seconds to hours of recent sensor data, bounded to fit in GPU VRAM.

This is also soft real-time, not hard real-time. The evaluation shows empirical p99 compliance with zero deadline misses over 30-second runs. True hard real-time (ISO-26262 ASIL-D) would require provable worst-case bounds, which MARS does not provide.

One known issue. The CUDA Graph capture path (--use-cuda-graph) currently corrupts results because counters and the episode-scoped reset are not re-initialised between graph replays — captured-graph replays return hit@15=0.004 and the next direct launch hits memory_cuda.cu:1197 — invalid argument. Tracked in docs/ARCHITECTURE.md §7.4 and in the paper’s Future Work section. The hand-fused FP16 path and episode-scoped fast path land cleanly without --use-cuda-graph.

Try it

git clone https://github.com/antonellof/MARS.git
cd MARS
make tests          # host-only unit tests (17/17, no GPU needed)
make && make check  # full build + hardware validation
make demo-av        # 60 Hz AV perception demo

# Episode-scoped fast path on the kids-ball corpus
./demos/embodied_scene/demo --scope=episode

# Reproduce the head-to-head competitor benchmarks
pip install --extra-index-url=https://pypi.nvidia.com \
  'cuvs-cu12==26.4.*' faiss-gpu-cu12 cupy-cuda12x
python3 scripts/bench_kids_ball_faiss.py \
  --corpus results/competitors_20260417/corpus/kids_1m.bin
python3 scripts/bench_kids_ball_cuvs_cagra.py \
  --corpus results/competitors_20260417/corpus/kids_1m.bin

The code is MIT licensed. The paper has the full methodology, kernel pseudocode, ablation studies, the §7.11 (episode-scoped retrieval) and §7.12 (head-to-head against FAISS / cuVS CAGRA) sections, the statistical caveats subsection (§7.2), and the §10.1 evaluation hardening track.

I’m particularly interested in feedback from anyone building real-time perception pipelines. The hypothesis — that an embodied loop’s existing notion of track / room / session / sub-task id is worth pushing all the way down into a GPU kernel parameter — needs validation from people who’ve actually shipped those loops. The data on the kids-ball corpus is dramatic, but I’d much rather know that someone with a real CLIP/CLAP corpus tried it and either confirmed the win or measured a counter-example.

Links:

• Paper (PDF, 1.97 MB)
• GitHub repository
• README — top-level overview
• Architecture deep dive — episode-scoped, head-to-head, FP16, known issues
• Benchmark results
• Head-to-head competitor SUMMARY — full FAISS / cuVS CAGRA / MARS run logs
• Next steps — Evaluation Hardening track ($15, ~2 days)
• Figure-generation script — re-renders the paper figures from the JSON artefacts

I built a multi-agent code analysis system: one agent parsed code structure, another reasoned about architecture, a third suggested refactorings. Each was smaller, testable, and replaceable. The coordinator orchestrated their interaction. The result was more maintainable than a single “do everything” agent.

Multi-agent systems aren’t new—distributed AI has decades of research. But LLMs make them practical: agents can understand natural language instructions, reason about tasks, and collaborate without rigid protocols.

Why Multiple Agents?

Separation of concerns - Parsing, reasoning, and execution are distinct skills. Separate agents, separate prompts, separate tests.

Context management - LLMs have finite context. Multiple focused agents stay within limits.

Specialization - Train/tune agents for specific domains (legal analysis, code review, data extraction).

Fault isolation - If the code execution agent fails, the reasoning agent continues.

Testability - Test each agent independently with unit tests.

Scalability - Scale expensive agents (GPT-4) separately from cheap ones (Claude Haiku).

Read AutoGen and LangGraph for framework approaches.

Agent Roles and Patterns

1. Coordinator (Orchestrator)

Decomposes high-level goals into subtasks, assigns to specialists, aggregates results.

from anthropic import Anthropic
from typing import List, Dict

class Coordinator:
    """Orchestrate multi-agent workflow."""
    
    def __init__(self, specialists: Dict[str, 'Agent']):
        self.client = Anthropic()
        self.specialists = specialists
        self.history = []
    
    async def process(self, task: str) -> str:
        """Break down task and coordinate execution."""
        
        # Decompose task
        subtasks = await self.decompose(task)
        
        results = {}
        for subtask in subtasks:
            agent_type = subtask['agent']
            agent = self.specialists[agent_type]
            
            # Execute subtask
            result = await agent.execute(subtask['task'])
            results[subtask['id']] = result
        
        # Synthesize results
        final_answer = await self.synthesize(task, results)
        return final_answer
    
    async def decompose(self, task: str) -> List[Dict]:
        """Decompose task into subtasks."""
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=2048,
            system="""You are a task coordinator. Break down complex tasks into subtasks.

Output JSON array:
[
  {"id": "1", "agent": "search", "task": "Find relevant documentation"},
  {"id": "2", "agent": "code", "task": "Analyze code structure"}
]""",
            messages=[{
                "role": "user",
                "content": f"Break down this task:\n\n{task}"
            }]
        )
        
        import json
        return json.loads(response.content[0].text)
    
    async def synthesize(self, task: str, results: Dict) -> str:
        """Combine results into final answer."""
        context = "\n\n".join([
            f"Subtask {k}:\n{v}" for k, v in results.items()
        ])
        
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=4096,
            system="You are a synthesizer. Combine subtask results into a coherent answer.",
            messages=[{
                "role": "user",
                "content": f"""Original task: {task}

Subtask results:
{context}

Provide a comprehensive answer to the original task."""
            }]
        )
        
        return response.content[0].text

2. Specialist Agents

Domain-specific agents with focused expertise:

class SearchAgent:
    """Specialist for web/documentation search."""
    
    def __init__(self, search_api):
        self.client = Anthropic()
        self.search_api = search_api
    
    async def execute(self, task: str) -> str:
        """Execute search task."""
        # Extract search query
        query = await self.extract_query(task)
        
        # Perform search
        results = await self.search_api.search(query)
        
        # Synthesize results
        return self.synthesize_results(results)
    
    async def extract_query(self, task: str) -> str:
        """Extract search query from task description."""
        response = self.client.messages.create(
            model="claude-3-haiku-20240307",  # Cheap model for extraction
            max_tokens=256,
            system="Extract the search query from the task. Return only the query text.",
            messages=[{"role": "user", "content": task}]
        )
        return response.content[0].text.strip()


class CodeAgent:
    """Specialist for code analysis."""
    
    def __init__(self):
        self.client = Anthropic()
    
    async def execute(self, task: str) -> str:
        """Execute code analysis task."""
        response = self.client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=4096,
            system="""You are a code analysis expert. Analyze code for:
- Structure and architecture
- Potential bugs
- Performance issues
- Security vulnerabilities

Provide clear, actionable feedback.""",
            messages=[{"role": "user", "content": task}]
        )
        return response.content[0].text


class ExecutionAgent:
    """Specialist for executing code/commands safely."""
    
    def __init__(self, sandbox):
        self.client = Anthropic()
        self.sandbox = sandbox
    
    async def execute(self, task: str) -> str:
        """Execute code in sandbox."""
        # Parse code from task
        code = await self.extract_code(task)
        
        # Execute in sandbox
        result = await self.sandbox.run(code, timeout=30)
        
        return f"Execution result:\n{result.stdout}\n\nErrors:\n{result.stderr}"

3. Message Bus Pattern

For loose coupling and extensibility:

import asyncio
from typing import Callable, Dict, List
import json

class MessageBus:
    """Pub/sub message bus for agent communication."""
    
    def __init__(self):
        self.subscribers: Dict[str, List[Callable]] = {}
    
    def subscribe(self, topic: str, handler: Callable):
        """Subscribe to topic."""
        if topic not in self.subscribers:
            self.subscribers[topic] = []
        self.subscribers[topic].append(handler)
    
    async def publish(self, topic: str, message: Dict):
        """Publish message to topic."""
        if topic not in self.subscribers:
            return
        
        # Add metadata
        message['topic'] = topic
        message['timestamp'] = time.time()
        
        # Notify all subscribers
        tasks = [
            asyncio.create_task(handler(message))
            for handler in self.subscribers[topic]
        ]
        
        await asyncio.gather(*tasks, return_exceptions=True)


# Usage
bus = MessageBus()

# Subscribe agents
async def search_handler(message):
    query = message['query']
    results = await search_api.search(query)
    await bus.publish('search_results', {'results': results})

async def code_handler(message):
    results = message['results']
    analysis = await code_agent.analyze(results)
    await bus.publish('analysis_complete', {'analysis': analysis})

bus.subscribe('search_request', search_handler)
bus.subscribe('search_results', code_handler)

# Trigger workflow
await bus.publish('search_request', {'query': 'Flask security best practices'})

Production Architecture

from dataclasses import dataclass
from enum import Enum
import time

class AgentStatus(Enum):
    IDLE = "idle"
    WORKING = "working"
    FAILED = "failed"

@dataclass
class AgentMetrics:
    """Track agent performance."""
    total_tasks: int = 0
    successful_tasks: int = 0
    failed_tasks: int = 0
    total_latency: float = 0.0
    total_cost: float = 0.0

class ProductionAgent:
    """Production-ready agent with monitoring."""
    
    def __init__(self, name: str, client):
        self.name = name
        self.client = client
        self.status = AgentStatus.IDLE
        self.metrics = AgentMetrics()
    
    async def execute(self, task: str) -> str:
        """Execute with monitoring and error handling."""
        self.status = AgentStatus.WORKING
        start_time = time.time()
        
        try:
            # Execute with retries
            result = await self._execute_with_retry(task, max_retries=3)
            
            # Update metrics
            self.metrics.successful_tasks += 1
            self.metrics.total_latency += time.time() - start_time
            
            self.status = AgentStatus.IDLE
            return result
            
        except Exception as e:
            # Handle failure
            self.metrics.failed_tasks += 1
            self.status = AgentStatus.FAILED
            
            # Log error
            logger.error(f"Agent {self.name} failed: {e}")
            
            # Raise for coordinator to handle
            raise
        
        finally:
            self.metrics.total_tasks += 1
    
    async def _execute_with_retry(self, task: str, max_retries: int) -> str:
        """Execute with exponential backoff."""
        for attempt in range(max_retries):
            try:
                response = self.client.messages.create(
                    model="claude-3-5-sonnet-20241022",
                    max_tokens=2048,
                    messages=[{"role": "user", "content": task}]
                )
                
                # Track cost
                self.metrics.total_cost += self._calculate_cost(response)
                
                return response.content[0].text
                
            except Exception as e:
                if attempt == max_retries - 1:
                    raise
                
                # Exponential backoff
                await asyncio.sleep(2 ** attempt)
    
    def _calculate_cost(self, response) -> float:
        """Calculate API cost."""
        input_tokens = response.usage.input_tokens
        output_tokens = response.usage.output_tokens
        
        # Claude Sonnet pricing (example)
        input_cost = input_tokens * 0.003 / 1000
        output_cost = output_tokens * 0.015 / 1000
        
        return input_cost + output_cost
    
    def get_metrics(self) -> Dict:
        """Export metrics."""
        return {
            'agent': self.name,
            'status': self.status.value,
            'total_tasks': self.metrics.total_tasks,
            'success_rate': self.metrics.successful_tasks / max(self.metrics.total_tasks, 1),
            'avg_latency': self.metrics.total_latency / max(self.metrics.successful_tasks, 1),
            'total_cost': self.metrics.total_cost
        }

Observability and Monitoring

import structlog
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Set up tracing
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)

# Add OTLP exporter
otlp_exporter = OTLPSpanExporter(endpoint="http://localhost:4317")
span_processor = BatchSpanProcessor(otlp_exporter)
trace.get_tracer_provider().add_span_processor(span_processor)

# Structured logging
logger = structlog.get_logger()

class ObservableCoordinator(Coordinator):
    """Coordinator with full observability."""
    
    async def process(self, task: str) -> str:
        """Process with tracing and logging."""
        with tracer.start_as_current_span("coordinator.process") as span:
            span.set_attribute("task", task)
            
            logger.info("processing_task", task=task)
            
            try:
                # Decompose
                with tracer.start_as_current_span("coordinator.decompose"):
                    subtasks = await self.decompose(task)
                    span.set_attribute("subtask_count", len(subtasks))
                    logger.info("decomposed_task", subtasks=len(subtasks))
                
                # Execute
                results = {}
                for subtask in subtasks:
                    with tracer.start_as_current_span(f"agent.{subtask['agent']}"):
                        result = await self.specialists[subtask['agent']].execute(subtask['task'])
                        results[subtask['id']] = result
                
                # Synthesize
                with tracer.start_as_current_span("coordinator.synthesize"):
                    answer = await self.synthesize(task, results)
                
                logger.info("task_completed", task=task)
                return answer
                
            except Exception as e:
                logger.error("task_failed", task=task, error=str(e))
                span.record_exception(e)
                span.set_status(trace.Status(trace.StatusCode.ERROR))
                raise

Testing Multi-Agent Systems

import pytest
from unittest.mock import Mock, AsyncMock

@pytest.mark.asyncio
async def test_coordinator_decomposition():
    """Test task decomposition."""
    coordinator = Coordinator({})
    coordinator.client = Mock()
    coordinator.client.messages.create = AsyncMock(return_value=Mock(
        content=[Mock(text='[{"id": "1", "agent": "search", "task": "Search docs"}]')]
    ))
    
    subtasks = await coordinator.decompose("Find Flask security info")
    
    assert len(subtasks) == 1
    assert subtasks[0]['agent'] == 'search'

@pytest.mark.asyncio
async def test_agent_execution():
    """Test agent execution with mock API."""
    agent = SearchAgent(Mock())
    agent.client = Mock()
    agent.client.messages.create = AsyncMock(return_value=Mock(
        content=[Mock(text='Flask security')]
    ))
    agent.search_api.search = AsyncMock(return_value=['result1', 'result2'])
    
    result = await agent.execute("Search for Flask security")
    
    assert result is not None
    agent.search_api.search.assert_called_once()

@pytest.mark.asyncio
async def test_message_bus():
    """Test pub/sub message bus."""
    bus = MessageBus()
    received = []
    
    async def handler(message):
        received.append(message)
    
    bus.subscribe('test', handler)
    await bus.publish('test', {'data': 'test'})
    
    await asyncio.sleep(0.1)  # Wait for async handlers
    assert len(received) == 1
    assert received[0]['data'] == 'test'

Best Practices

1. Design for failure - Agents will fail. Implement retries, circuit breakers, fallbacks.

2. Keep agents focused - One agent, one responsibility. Don’t build god agents.

3. Use structured outputs - JSON schemas, Pydantic models. Makes coordination reliable.

4. Monitor everything - Latency, cost, success rate, per agent.

5. Test independently - Unit test each agent with mocked dependencies.

6. Version agents - Deploy different agent versions independently.

7. Implement timeouts - Agents can hang. Set aggressive timeouts.

8. Cache expensive operations - Search results, embeddings, analysis.

9. Cost management - Track per-agent costs. Use cheaper models where possible.

10. Security boundaries - Agents may have different trust levels. Enforce permissions.

Conclusion

Multi-agent systems transform complex AI tasks into manageable, composable components. By decomposing responsibilities, you gain testability, fault isolation, and scalability—at the cost of coordination complexity.

The patterns are well-established: coordinators orchestrate, specialists execute, message buses decouple. The tooling is maturing: LangGraph, AutoGen, CrewAI provide frameworks. The economics work: scaling cheap and expensive agents independently optimizes cost.

Start simple: coordinator + 2-3 specialists. Add observability early. Measure everything. Iterate based on bottlenecks.

Multi-agent systems aren’t always the answer—sometimes a well-prompted single agent suffices. But for complex, multi-step tasks requiring different expertise, they’re the right architecture.

Further Resources:

• AutoGen Framework - Microsoft’s multi-agent framework
• LangGraph - LangChain’s graph-based agents
• CrewAI - Role-based multi-agent system
• Multi-Agent Systems Book - Academic foundation
• OpenTelemetry - Observability standard
• Anthropic Agent Patterns - Claude agent guidance
• Agent Protocol - Standardized agent communication

Agentic AI systems from November 2025, covering multi-agent architectures and production patterns.

I’ve built systems using all three. For a collaborative code editor, WebSocket was essential—bidirectional, low-latency updates. For a live dashboard showing server metrics, SSE was perfect—simple, unidirectional stream. For a notification system supporting old browsers, Long Polling grudgingly worked.

The right choice depends on your requirements: bidirectionality, browser support, firewall friendliness, and operational complexity.

WebSocket: Full Duplex Communication

How it works: Upgrade HTTP connection to persistent TCP socket. After handshake, both client and server can send messages anytime.

Client                    Server
  |                         |
  |--- HTTP Upgrade ------->|
  |<-- 101 Switching ------- |
  |                         |
  |<-----> Binary/Text <--->|  (Bidirectional messaging)
  |                         |

When to Use WebSocket

• Real-time collaboration - Google Docs, Figma, VS Code Live Share
• Chat applications - Slack, Discord, Telegram web
• Multiplayer games - Real-time position updates, game state
• Live trading platforms - Stock prices, order book updates
• IoT dashboards - Sensor data streaming

Node.js WebSocket Server

const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

// Track connected clients
const clients = new Set();

wss.on('connection', (ws, req) => {
    console.log('Client connected from', req.socket.remoteAddress);
    clients.add(ws);
    
    // Send welcome message
    ws.send(JSON.stringify({
        type: 'welcome',
        timestamp: Date.now()
    }));
    
    // Handle messages
    ws.on('message', (message) => {
        console.log('Received:', message.toString());
        
        try {
            const data = JSON.parse(message);
            
            // Broadcast to all clients except sender
            clients.forEach(client => {
                if (client !== ws && client.readyState === WebSocket.OPEN) {
                    client.send(JSON.stringify({
                        type: 'broadcast',
                        data: data,
                        timestamp: Date.now()
                    }));
                }
            });
        } catch (error) {
            console.error('Invalid message:', error);
        }
    });
    
    // Handle errors
    ws.on('error', (error) => {
        console.error('WebSocket error:', error);
    });
    
    // Handle disconnect
    ws.on('close', (code, reason) => {
        console.log('Client disconnected:', code, reason.toString());
        clients.delete(ws);
    });
    
    // Heartbeat to detect dead connections
    ws.isAlive = true;
    ws.on('pong', () => {
        ws.isAlive = true;
    });
});

// Ping clients every 30 seconds
const interval = setInterval(() => {
    clients.forEach(ws => {
        if (ws.isAlive === false) {
            return ws.terminate();
        }
        
        ws.isAlive = false;
        ws.ping();
    });
}, 30000);

wss.on('close', () => {
    clearInterval(interval);
});

console.log('WebSocket server running on ws://localhost:8080');

Browser Client

const ws = new WebSocket('ws://localhost:8080');

ws.onopen = () => {
    console.log('Connected');
    ws.send(JSON.stringify({ action: 'subscribe', channel: 'updates' }));
};

ws.onmessage = (event) => {
    const data = JSON.parse(event.data);
    console.log('Received:', data);
    
    if (data.type === 'welcome') {
        console.log('Welcome message received');
    }
};

ws.onerror = (error) => {
    console.error('WebSocket error:', error);
};

ws.onclose = (event) => {
    console.log('Disconnected:', event.code, event.reason);
    
    // Reconnect logic
    setTimeout(() => {
        console.log('Reconnecting...');
        // Recreate WebSocket connection
    }, 3000);
};

Production Considerations

Load Balancing: Use sticky sessions or shared pub/sub:

// Shared pub/sub with Redis
const Redis = require('ioredis');
const pub = new Redis();
const sub = new Redis();

sub.subscribe('messages');
sub.on('message', (channel, message) => {
    // Broadcast to local WebSocket clients
    clients.forEach(client => {
        if (client.readyState === WebSocket.OPEN) {
            client.send(message);
        }
    });
});

// When receiving from WebSocket client
ws.on('message', (message) => {
    // Publish to Redis (reaches all servers)
    pub.publish('messages', message);
});

Monitoring:

const metrics = {
    connections: 0,
    messagesReceived: 0,
    messagesSent: 0,
    errors: 0
};

wss.on('connection', (ws) => {
    metrics.connections++;
    
    ws.on('message', () => metrics.messagesReceived++);
    ws.on('error', () => metrics.errors++);
    ws.on('close', () => metrics.connections--);
});

// Expose metrics endpoint
app.get('/metrics', (req, res) => {
    res.json(metrics);
});

Read WebSocket RFC 6455 for protocol details.

Server-Sent Events: Unidirectional Streaming

How it works: HTTP connection kept open, server sends events as text/event-stream.

When to Use SSE

• Live feeds - News, sports scores, social media updates
• Monitoring dashboards - Metrics, logs, alerts
• Notifications - Push notifications, status updates
• Progress tracking - File upload progress, job status
• Stock tickers - Price updates (when client doesn’t need to send)

Express SSE Server

const express = require('express');
const app = express();

// SSE endpoint
app.get('/events', (req, res) => {
    // Set SSE headers
    res.setHeader('Content-Type', 'text/event-stream');
    res.setHeader('Cache-Control', 'no-cache');
    res.setHeader('Connection', 'keep-alive');
    res.setHeader('Access-Control-Allow-Origin', '*');
    
    // Set reconnection time
    res.write('retry: 10000\n\n');
    
    // Send initial message
    res.write(`data: ${JSON.stringify({ type: 'connected', time: Date.now() })}\n\n`);
    
    // Send updates every second
    const interval = setInterval(() => {
        const data = {
            type: 'update',
            value: Math.random() * 100,
            timestamp: Date.now()
        };
        
        // SSE format: "data: <json>\n\n"
        res.write(`data: ${JSON.stringify(data)}\n\n`);
    }, 1000);
    
    // Clean up on disconnect
    req.on('close', () => {
        clearInterval(interval);
        console.log('Client disconnected');
    });
});

app.listen(3000, () => {
    console.log('SSE server running on http://localhost:3000');
});

Browser Client

const eventSource = new EventSource('http://localhost:3000/events');

eventSource.onopen = () => {
    console.log('SSE connection opened');
};

eventSource.onmessage = (event) => {
    const data = JSON.parse(event.data);
    console.log('Received:', data);
    
    // Update UI
    document.getElementById('value').textContent = data.value.toFixed(2);
};

eventSource.onerror = (error) => {
    console.error('SSE error:', error);
    
    if (eventSource.readyState === EventSource.CLOSED) {
        console.log('SSE connection closed');
    }
};

// Named events
eventSource.addEventListener('custom-event', (event) => {
    console.log('Custom event:', event.data);
});

Named Events

// Server: Send named events
res.write('event: alert\n');
res.write(`data: ${JSON.stringify({ message: 'System alert!' })}\n\n`);

// Client: Listen for specific events
eventSource.addEventListener('alert', (event) => {
    const alert = JSON.parse(event.data);
    showAlert(alert.message);
});

Read Server-Sent Events spec for details.

Long Polling: Request-Response Loop

How it works: Client makes request, server holds it open until data available or timeout, client immediately reconnects.

Client                    Server
  |                         |
  |--- HTTP Request ------->|
  |                         | (Wait for data or timeout)
  |<-- Response with data --|
  |                         |
  |--- HTTP Request ------->| (Immediately reconnect)
  |                         |

When to Use Long Polling

• Legacy browser support - IE9, old mobile browsers
• Firewall restrictions - Corporate networks blocking WebSocket
• Simple notifications - Infrequent updates
• Fallback mechanism - When WebSocket unavailable

Express Long Polling Server

const express = require('express');
const app = express();

// In-memory queue of pending messages
const messageQueues = new Map();

app.get('/poll', (req, res) => {
    const userId = req.query.userId;
    
    if (!messageQueues.has(userId)) {
        messageQueues.set(userId, []);
    }
    
    const queue = messageQueues.get(userId);
    
    // If messages available, send immediately
    if (queue.length > 0) {
        res.json({ messages: queue.splice(0, queue.length) });
        return;
    }
    
    // Otherwise, wait for new message or timeout
    const timeout = setTimeout(() => {
        res.json({ messages: [] });
    }, 30000);  // 30 second timeout
    
    // Store request to send message when available
    const checkInterval = setInterval(() => {
        if (queue.length > 0) {
            clearTimeout(timeout);
            clearInterval(checkInterval);
            res.json({ messages: queue.splice(0, queue.length) });
        }
    }, 100);
    
    // Clean up on disconnect
    req.on('close', () => {
        clearTimeout(timeout);
        clearInterval(checkInterval);
    });
});

// Endpoint to send message to user
app.post('/send', express.json(), (req, res) => {
    const { userId, message } = req.body;
    
    if (!messageQueues.has(userId)) {
        messageQueues.set(userId, []);
    }
    
    messageQueues.get(userId).push({
        message,
        timestamp: Date.now()
    });
    
    res.json({ success: true });
});

app.listen(3000);

Browser Client

let polling = true;

async function poll() {
    while (polling) {
        try {
            const response = await fetch(`/poll?userId=${userId}`);
            const data = await response.json();
            
            if (data.messages.length > 0) {
                data.messages.forEach(handleMessage);
            }
            
        } catch (error) {
            console.error('Polling error:', error);
            await sleep(5000);  // Back off on error
        }
    }
}

function handleMessage(message) {
    console.log('Received:', message);
}

// Start polling
poll();

// Stop polling
function stopPolling() {
    polling = false;
}

Comparison Table

Scaling Patterns

Pub/Sub for WebSocket/SSE

// Using NATS for pub/sub
const NATS = require('nats');
const nc = await NATS.connect({ servers: 'nats://localhost:4222' });

// Subscribe to messages
const sub = nc.subscribe('messages');
for await (const msg of sub) {
    const data = JSON.parse(msg.data);
    
    // Broadcast to WebSocket clients
    broadcastToClients(data);
}

// Publish message
nc.publish('messages', JSON.stringify({ type: 'update', data: {...} }));

Graceful Shutdown

let shuttingDown = false;

process.on('SIGTERM', async () => {
    shuttingDown = true;
    console.log('Shutting down gracefully...');
    
    // Stop accepting new connections
    wss.close(() => {
        console.log('No longer accepting connections');
    });
    
    // Wait for existing connections to finish
    const timeout = setTimeout(() => {
        console.log('Forcing shutdown');
        process.exit(0);
    }, 30000);
    
    // Close all connections gracefully
    for (const client of clients) {
        client.close(1001, 'Server shutting down');
    }
    
    clearTimeout(timeout);
    process.exit(0);
});

Conclusion

Choose WebSocket for interactive, bidirectional communication (chat, games, collaboration).

Choose SSE for server-to-client streams (dashboards, feeds, notifications). It’s simpler than WebSocket and works over HTTP.

Choose Long Polling only as a fallback for old browsers or restrictive networks. The overhead is significant.

In practice, implement WebSocket with SSE as fallback. Long Polling is rarely worth the complexity in 2025.

Further Resources:

• WebSocket RFC 6455 - Protocol specification
• Server-Sent Events Spec - HTML standard
• Socket.IO - WebSocket library with fallbacks
• ws npm package - Fast WebSocket implementation
• SSE npm package - SSE server utilities
• WebSocket Best Practices - MDN guide

WebSocket vs SSE vs Long Polling from October 2025, updated with production patterns.

I moved to Databricks after struggling with self-managed Spark clusters. Maintaining Spark—tuning configs, managing resources, debugging failed jobs—consumed more time than actual data engineering. Databricks handles the infrastructure, letting you focus on transforming data.

The platform combines notebooks for exploration, production-grade job scheduling, Delta Lake for reliable storage, and MLflow for ML workflows. It’s opinionated but that opinion is informed by years of Spark expertise.

Core Components

Databricks Workspace - Web-based environment for notebooks, jobs, and clusters.

Apache Spark - Distributed processing engine (3.5+ as of 2025). See Spark documentation.

Delta Lake - ACID transactions on data lakes. Open source project: delta-io/delta.

MLflow - ML lifecycle management. Track experiments, package models, deploy. MLflow docs.

Unity Catalog - Centralized governance, lineage, and access control.

Read Databricks architecture for details.

Notebooks: Interactive Development

Databricks notebooks support Python, SQL, Scala, and R:

Python Notebook

# Read data from Delta Lake
df = spark.read.format("delta").load("/mnt/data/events")

# Show schema
df.printSchema()

# Quick stats
df.describe().show()

# Transform data
from pyspark.sql.functions import col, count, window

daily_active_users = (df
    .filter(col("event_type") == "login")
    .groupBy(window("timestamp", "1 day"))
    .agg(count("user_id").alias("daily_active_users"))
    .orderBy("window")
)

# Display in notebook
display(daily_active_users)

# Write results
daily_active_users.write.format("delta").mode("overwrite").save("/mnt/data/dau")

SQL Notebook

-- Create or replace table using Delta Lake
CREATE OR REPLACE TABLE analytics.user_activity
USING DELTA
LOCATION '/mnt/data/user_activity'
AS
SELECT 
    user_id,
    DATE(timestamp) as activity_date,
    COUNT(*) as event_count,
    COUNT(DISTINCT session_id) as session_count
FROM events
WHERE timestamp >= current_date() - INTERVAL 30 DAYS
GROUP BY user_id, DATE(timestamp);

-- Query with visualization
SELECT 
    activity_date,
    COUNT(DISTINCT user_id) as active_users,
    SUM(event_count) as total_events
FROM analytics.user_activity
GROUP BY activity_date
ORDER BY activity_date;

Notebooks support inline visualizations—click “Visualization” to create charts.

Widgets for Parameterization

# Create text widget
dbutils.widgets.text("start_date", "2025-01-01", "Start Date")
dbutils.widgets.dropdown("region", "US", ["US", "EU", "APAC"], "Region")

# Read widget values
start_date = dbutils.widgets.get("start_date")
region = dbutils.widgets.get("region")

# Use in queries
df = spark.read.format("delta").load("/mnt/data/events")
filtered = df.filter(
    (col("date") >= start_date) & 
    (col("region") == region)
)

display(filtered)

Widgets make notebooks reusable for different parameters.

Data Pipelines

ETL Pipeline

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ETL").getOrCreate()

# Extract
raw_data = spark.read.csv("s3://bucket/data.csv")

# Transform
transformed = raw_data.select("id", "name", "age")

# Load
transformed.write.format("delta").save("/mnt/data/processed")

Best Practices

1. Use notebooks - Interactive development
2. Leverage Spark - Distributed processing
3. Use Delta Lake - ACID transactions
4. Monitor - Track job performance
5. Optimize - Query tuning
6. Test - Verify pipelines
7. Document - Clear processes
8. Scale - Handle large data

Delta Lake: ACID on Data Lakes

Delta Lake brings database reliability to data lakes—ACID transactions, schema enforcement, time travel.

Writing Delta Tables

from pyspark.sql import SparkSession
from delta.tables import DeltaTable

spark = SparkSession.builder.appName("ETL").getOrCreate()

# Write with Delta Lake
df = spark.read.csv("s3://bucket/raw-data.csv", header=True)

(df
    .write
    .format("delta")
    .mode("overwrite")
    .option("overwriteSchema", "true")
    .save("/mnt/data/processed/users")
)

# Append new data
new_data = spark.read.csv("s3://bucket/new-data.csv", header=True)
new_data.write.format("delta").mode("append").save("/mnt/data/processed/users")

Time Travel

Query historical versions:

# Read current version
df_current = spark.read.format("delta").load("/mnt/data/processed/users")

# Read version from 7 days ago
df_old = (spark.read
    .format("delta")
    .option("timestampAsOf", "2025-09-01")
    .load("/mnt/data/processed/users")
)

# Compare
changed_users = df_current.subtract(df_old)
display(changed_users)

# Read specific version number
df_v5 = (spark.read
    .format("delta")
    .option("versionAsOf", 5)
    .load("/mnt/data/processed/users")
)

MERGE (Upserts)

from delta.tables import DeltaTable

# Load Delta table
delta_table = DeltaTable.forPath(spark, "/mnt/data/processed/users")

# Merge updates
delta_table.alias("target").merge(
    source=new_data.alias("source"),
    condition="target.user_id = source.user_id"
).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()

This is how you do CDC (Change Data Capture) efficiently.

OPTIMIZE and VACUUM

Maintain table performance:

# Optimize: compact small files into larger ones
spark.sql("OPTIMIZE delta.`/mnt/data/processed/users`")

# Z-ordering: colocate related data
spark.sql("OPTIMIZE delta.`/mnt/data/processed/users` ZORDER BY (user_id, created_date)")

# Vacuum: remove old versions (7 day default)
spark.sql("VACUUM delta.`/mnt/data/processed/users` RETAIN 168 HOURS")

Run OPTIMIZE weekly, VACUUM monthly. See Delta Lake performance tuning.

Production ETL Pipeline

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, current_timestamp, sha2, concat_ws
from delta.tables import DeltaTable

class ProductionETL:
    """Production-grade ETL pipeline."""
    
    def __init__(self):
        self.spark = SparkSession.builder.appName("ETL").getOrCreate()
    
    def extract(self, source_path: str):
        """Extract from source."""
        return (self.spark.read
            .format("parquet")
            .load(source_path)
        )
    
    def transform(self, df):
        """Transform data with validation."""
        # Add processing metadata
        df = df.withColumn("processed_at", current_timestamp())
        
        # Data quality checks
        df = df.filter(col("user_id").isNotNull())
        df = df.filter(col("amount") > 0)
        
        # Hash sensitive fields
        df = df.withColumn(
            "email_hash",
            sha2(col("email"), 256)
        )
        
        # Deduplication
        df = df.dropDuplicates(["user_id", "transaction_id"])
        
        return df
    
    def load(self, df, target_path: str):
        """Load to Delta Lake with merge."""
        # Check if table exists
        if DeltaTable.isDeltaTable(self.spark, target_path):
            # Merge into existing table
            delta_table = DeltaTable.forPath(self.spark, target_path)
            
            delta_table.alias("target").merge(
                source=df.alias("source"),
                condition="target.transaction_id = source.transaction_id"
            ).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()
        else:
            # Create new table
            (df.write
                .format("delta")
                .mode("overwrite")
                .save(target_path)
            )
    
    def run(self, source_path: str, target_path: str):
        """Run complete ETL."""
        try:
            # Extract
            raw_df = self.extract(source_path)
            print(f"Extracted {raw_df.count()} rows")
            
            # Transform
            clean_df = self.transform(raw_df)
            print(f"Cleaned to {clean_df.count()} rows")
            
            # Load
            self.load(clean_df, target_path)
            print(f"Loaded to {target_path}")
            
            # Optimize after load
            self.spark.sql(f"OPTIMIZE delta.`{target_path}`")
            
        except Exception as e:
            print(f"ETL failed: {e}")
            raise

# Usage
etl = ProductionETL()
etl.run(
    source_path="s3://bucket/raw/transactions/",
    target_path="/mnt/data/processed/transactions"
)

Best Practices from Production

1. Use Delta Lake always - ACID guarantees are worth it
2. Partition large tables - By date or high-cardinality keys
3. Z-order frequently queried columns - Colocates data
4. Set retention policies - Balance time travel vs storage costs
5. Monitor cluster metrics - CPU, memory, I/O utilization
6. Right-size clusters - Match to workload (don’t over-provision)
7. Use auto-termination - Clusters shutdown after idle time
8. Enable Photon - Vectorized execution engine (2-5x faster)
9. Cache frequently accessed data - df.cache() for reuse
10. Test with small samples - .limit(1000) for development

Conclusion

Databricks simplifies data engineering by handling Spark cluster management, providing excellent notebook UX, and offering Delta Lake for reliable storage. The managed platform lets you focus on transforming data rather than managing infrastructure.

The combination of Spark’s distributed processing, Delta Lake’s ACID guarantees, and notebook-based development creates a productive environment for data teams. ETL pipelines that took days to build on self-managed Spark can be prototyped in hours on Databricks.

Cost management is crucial—clusters can get expensive. Use autoscaling, right-size instances, and terminate idle clusters. The productivity gains typically justify the costs for teams processing terabytes of data.

Further Resources:

• Databricks Documentation - Comprehensive guides
• Apache Spark Docs - Core engine
• Delta Lake - Open format and engine
• MLflow - ML lifecycle management
• Databricks Academy - Free courses
• Delta Lake GitHub - Open source repo
• Unity Catalog - Data governance

Databricks for data engineers from September 2025 — updated with production guidance.

I deployed a CDN edge service using traditional Kubernetes—control plane used 2GB RAM before running any workload. At 500 edge locations, that’s 1TB just for orchestration. We switched to K3s, Rancher’s lightweight Kubernetes: 512MB for control plane + agents. Same APIs, 75% less overhead.

Edge orchestration challenges three Kubernetes assumptions: abundant resources, reliable networking, and centralized control. Solutions require rethinking each.

The Edge is Different

Resource constraints:

• Edge nodes: 2-4 CPU cores, 4-8GB RAM
• Data center nodes: 32-96 cores, 128-512GB RAM
• Difference: 10-20x less resources

Network reality:

• Data center: 10Gbps+ local, <1ms latency
• Edge: 10-100Mbps WAN, 50-200ms latency, periodic disconnects

Management scale:

• Data center: 10-1000 nodes, centralized
• Edge: 100-10,000 nodes, geographically distributed

Traditional Kubernetes doesn’t fit. New solutions emerged: K3s, MicroK8s, KubeEdge.

Lightweight Kubernetes: K3s

K3s is Kubernetes minus the bloat:

What’s removed:

• Legacy alpha features
• Non-default admission controllers
• In-tree cloud providers
• In-tree storage plugins

What’s changed:

• etcd → SQLite (or Postgres/MySQL for HA)
• Docker → containerd (no Docker dependency)
• Single binary deployment

Result: 512MB RAM footprint vs 2GB+ for standard K8s.

Install K3s

# Master node
curl -sfL https://get.k3s.io | sh -

# Get node token
sudo cat /var/lib/rancher/k3s/server/node-token

# Worker node
curl -sfL https://get.k3s.io | K3S_URL=https://master-ip:6443 \
  K3S_TOKEN=<token> sh -

# Verify
sudo k3s kubectl get nodes

Production install (with external database):

# PostgreSQL HA
curl -sfL https://get.k3s.io | sh -s - server \
  --datastore-endpoint="postgres://user:pass@postgres-host:5432/k3s"

Read K3s architecture for details.

Deploy Edge Application

# edge-app-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-app
  labels:
    app: edge-app
spec:
  replicas: 1  # Single replica per edge location
  selector:
    matchLabels:
      app: edge-app
  template:
    metadata:
      labels:
        app: edge-app
    spec:
      # Resource limits for constrained edge
      containers:
      - name: app
        image: my-edge-app:v1.2
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: 100m      # 0.1 CPU core
            memory: 128Mi
          limits:
            cpu: 500m      # 0.5 CPU core max
            memory: 512Mi  # Hard limit
        
        # Health checks
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
          failureThreshold: 3
        
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
        
        # Environment config
        env:
        - name: REGION
          valueFrom:
            fieldRef:
              fieldPath: metadata.labels['region']
        - name: NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName

---
# Service with NodePort (for edge ingress)
apiVersion: v1
kind: Service
metadata:
  name: edge-app
spec:
  type: NodePort
  ports:
  - port: 8080
    targetPort: 8080
    nodePort: 30080  # Accessible on node IP
  selector:
    app: edge-app

Deploy:

kubectl apply -f edge-app-deployment.yaml

# Verify
kubectl get pods
kubectl get svc

Offline-First Applications

Edge locations lose connectivity. Design for it:

Local State + Sync

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: edge-cache
spec:
  serviceName: edge-cache
  replicas: 1
  selector:
    matchLabels:
      app: edge-cache
  template:
    metadata:
      labels:
        app: edge-cache
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        ports:
        - containerPort: 6379
        volumeMounts:
        - name: data
          mountPath: /data
        command:
        - redis-server
        - --save
        - "60 1"  # Persist every 60s if 1+ keys changed
        - --appendonly
        - "yes"
        resources:
          requests:
            memory: 256Mi
          limits:
            memory: 512Mi
  
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      storageClassName: local-path
      resources:
        requests:
          storage: 1Gi

Application uses local Redis, syncs to central database when online:

import redis
import requests
from typing import Optional

class EdgeCache:
    """Offline-first cache with background sync."""
    
    def __init__(self):
        self.redis = redis.Redis(host='edge-cache', port=6379)
        self.central_api = 'https://central.example.com/api'
    
    def get(self, key: str) -> Optional[str]:
        """Get from local cache."""
        return self.redis.get(key)
    
    def set(self, key: str, value: str):
        """Set in local cache and queue for sync."""
        self.redis.set(key, value)
        self.redis.rpush('sync_queue', f"{key}:{value}")
    
    def sync(self):
        """Sync pending changes to central (background task)."""
        while True:
            item = self.redis.lpop('sync_queue')
            if not item:
                break
            
            try:
                key, value = item.decode().split(':', 1)
                
                # Upload to central
                response = requests.post(
                    f'{self.central_api}/sync',
                    json={'key': key, 'value': value},
                    timeout=5
                )
                response.raise_for_status()
                
            except requests.RequestException as e:
                # Network error - requeue
                self.redis.lpush('sync_queue', item)
                break  # Stop syncing, try again later

Run sync as cron job:

apiVersion: batch/v1
kind: CronJob
metadata:
  name: sync-job
spec:
  schedule: "*/5 * * * *"  # Every 5 minutes
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: sync
            image: my-edge-app:v1.2
            command: ["python", "sync.py"]
          restartPolicy: OnFailure

Image Optimization for Edge

Bandwidth is limited. Minimize image sizes:

Multi-Stage Builds

# Build stage
FROM golang:1.21 AS builder

WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download

COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -a -installsuffix cgo -o app .

# Runtime stage (distroless)
FROM gcr.io/distroless/static-debian12

COPY --from=builder /app/app /app

EXPOSE 8080
USER nonroot:nonroot

ENTRYPOINT ["/app"]

Result: 10MB image vs 300MB+ with full golang base.

Pre-pull Images

Use DaemonSet to pre-pull images on all nodes:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: image-puller
spec:
  selector:
    matchLabels:
      name: image-puller
  template:
    metadata:
      labels:
        name: image-puller
    spec:
      initContainers:
      - name: pull-app-image
        image: my-edge-app:v1.2
        command: ['sh', '-c', 'echo "Image pulled"']
      - name: pull-cache-image
        image: redis:7-alpine
        command: ['sh', '-c', 'echo "Image pulled"']
      containers:
      - name: pause
        image: gcr.io/google_containers/pause:3.9

Multi-Cluster Management

Managing 100+ edge clusters requires automation. Rancher and ArgoCD help:

GitOps with ArgoCD

# argocd-app.yaml - Deploy to all edge clusters
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: edge-app-us-west
  namespace: argocd
spec:
  project: default
  source:
    repoURL: https://github.com/company/edge-apps
    targetRevision: HEAD
    path: apps/edge-app
    helm:
      values: |
        region: us-west
        replicas: 1
        image:
          tag: v1.2
  destination:
    server: https://edge-cluster-us-west.example.com
    namespace: default
  syncPolicy:
    automated:
      prune: true
      selfHeal: true

Generate apps for all clusters programmatically:

# generate-apps.py
regions = ['us-west', 'us-east', 'eu-west', 'ap-southeast']

for region in regions:
    with open(f'argocd-app-{region}.yaml', 'w') as f:
        f.write(template.format(
            name=f'edge-app-{region}',
            region=region,
            server=f'https://edge-cluster-{region}.example.com'
        ))

Monitoring Distributed Edge

Centralize metrics from all edge locations:

Prometheus Federation

# prometheus-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s
      external_labels:
        cluster: edge-us-west
        region: us-west
    
    # Scrape local metrics
    scrape_configs:
    - job_name: 'edge-apps'
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - source_labels: [__meta_kubernetes_pod_label_app]
        action: keep
        regex: edge-app
    
    # Federate to central Prometheus
    remote_write:
    - url: https://central-prometheus.example.com/api/v1/write
      basic_auth:
        username: edge
        password: secret

Query across all edge locations from central Prometheus:

# Total requests across all edge locations
sum(http_requests_total) by (region)

# P95 latency per region
histogram_quantile(0.95, 
  sum(rate(http_request_duration_seconds_bucket[5m])) by (region, le)
)

Best Practices

1. Right-size resources - Edge nodes are constrained. Profile actual usage:

   kubectl top pods
   kubectl top nodes
   

2. Use local storage - Network storage adds latency. Use K3s local-path provisioner:

   storageClassName: local-path
   

3. Design for network failures - Test disconnected mode: ```bash

   Simulate network partition

   sudo iptables -A OUTPUT -p tcp –dport 6443 -j DROP

App should continue working offline

4. **Automate updates** - Manual updates don't scale to 100+ clusters. Use GitOps.

5. **Monitor everything** - Metrics, logs, traces. Edge issues are hard to debug remotely.

6. **Security at edge** - Edge nodes may be physically accessible:
```yaml
# Enable Pod Security Standards
apiVersion: v1
kind: Namespace
metadata:
  name: default
  labels:
    pod-security.kubernetes.io/enforce: restricted

Conclusion

Edge container orchestration requires rethinking traditional patterns. Lightweight runtimes (K3s), offline-first applications, optimized images, and centralized management make it practical.

The paradigm shift: from assuming abundant resources and reliable networking to designing for constraints and intermittency. K3s proves Kubernetes APIs work at edge scale—if you remove the bloat.

For 10-100 edge locations, this approach works. Beyond that, consider specialized edge platforms (AWS Wavelength, Cloudflare Workers) that abstract orchestration entirely.

Further Resources:

• K3s Documentation - Lightweight Kubernetes
• KubeEdge - Edge-native Kubernetes
• MicroK8s - Minimal Kubernetes
• ArgoCD - GitOps continuous delivery
• Rancher - Multi-cluster management
• CNCF Edge Computing - Architecture patterns
• K3s GitHub - Source and issues

Container orchestration at edge from August 2025 — updated with production guidance.

I built a user preferences system with D1 and was surprised by how well it worked. Query latency from Sydney? 6ms. From São Paulo? 8ms. The same database, automatically replicated to edge locations, responding fast everywhere. No sharding configuration, no multi-region complexity—just SQLite that runs globally.

D1 makes sense for read-heavy workloads that benefit from geographic proximity: user settings, feature flags, product catalogs, metadata stores. It’s less suited for write-heavy transactional systems (those need stronger consistency guarantees).

Based on SQLite (the most widely deployed database), D1 brings that reliability to the edge.

Why D1?

Familiar SQL - If you know SQLite, you know D1. Standard SQL syntax, no new query language.

Global replication - Writes propagate to edge locations automatically. Reads are always local and fast.

Workers integration - First-class integration with Cloudflare Workers. No connection pooling, no ORMs—just direct access.

Cost-effective - $0.75/million reads, $5.00/GB storage. No per-database charges.

Zero-configuration scaling - No sharding, no read replicas, no deployment topology. It just works.

Read the D1 announcement for Cloudflare’s vision.

Using D1 with Workers

D1 integrates natively with Cloudflare Workers:

Create Database

# Install Wrangler
npm install -g wrangler

# Create D1 database
wrangler d1 create my-database

# Output: database_name = "my-database", database_id = "xxx-xxx-xxx"

Add to wrangler.toml:

[[d1_databases]]
binding = "DB"  # Available as env.DB in Workers
database_name = "my-database"
database_id = "xxx-xxx-xxx"

Create Tables

# Create schema file
cat > schema.sql << 'EOF'
CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    email TEXT UNIQUE NOT NULL,
    name TEXT NOT NULL,
    created_at INTEGER NOT NULL
);

CREATE INDEX idx_users_email ON users(email);

CREATE TABLE IF NOT EXISTS preferences (
    user_id INTEGER NOT NULL,
    key TEXT NOT NULL,
    value TEXT,
    PRIMARY KEY (user_id, key),
    FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE
);
EOF

# Apply schema
wrangler d1 execute my-database --file=schema.sql

Query from Workers

// worker.ts
export interface Env {
    DB: D1Database;
}

export default {
    async fetch(request: Request, env: Env): Promise<Response> {
        const url = new URL(request.url);
        
        if (url.pathname === '/api/users' && request.method === 'GET') {
            // List users
            const result = await env.DB
                .prepare('SELECT id, email, name, created_at FROM users ORDER BY created_at DESC LIMIT 10')
                .all();
            
            return Response.json(result.results);
        }
        
        if (url.pathname === '/api/users' && request.method === 'POST') {
            // Create user
            const body = await request.json();
            
            const result = await env.DB
                .prepare('INSERT INTO users (email, name, created_at) VALUES (?, ?, ?) RETURNING id')
                .bind(body.email, body.name, Date.now())
                .first();
            
            return Response.json({ id: result.id }, { status: 201 });
        }
        
        if (url.pathname.startsWith('/api/users/')) {
            const userId = url.pathname.split('/')[3];
            
            // Get user with preferences
            const user = await env.DB
                .prepare('SELECT * FROM users WHERE id = ?')
                .bind(userId)
                .first();
            
            if (!user) {
                return Response.json({ error: 'User not found' }, { status: 404 });
            }
            
            const prefs = await env.DB
                .prepare('SELECT key, value FROM preferences WHERE user_id = ?')
                .bind(userId)
                .all();
            
            return Response.json({
                ...user,
                preferences: Object.fromEntries(
                    prefs.results.map(p => [p.key, p.value])
                ),
            });
        }
        
        return Response.json({ error: 'Not found' }, { status: 404 });
    },
};