Why NovaFabric? A plain-English guide

Who this is for: engineers who have heard "AI observability" but aren't sure why they need something more — or what a reproducibility layer actually buys them day-to-day.

What you will learn

Everything below runs locally, offline, with no accounts and no telemetry. NovaFabric is local-first, open-source (Apache-2.0), and has shipped v0.1 through v0.9. Where a capability is design intent rather than code, this guide labels it PLANNED — it never claims a roadmap item as done.

The core problem: AI runs have no memory of themselves

When a traditional program runs, you have logs, stack traces, and (usually) deterministic behavior. When an AI agent or model workload runs, you get output — and then it's gone.

Two weeks later someone asks:

Without infrastructure, the answer to all three is: "I don't know."

The relevant standards for answering these questions already exist — OpenTelemetry GenAI semantic conventions for model calls, OpenLineage for pipeline lineage, MCP for tool calls, in-toto and SLSA for build provenance — but they live as fragments. No single tool unifies them into a developer-friendly replay fabric for complete AI systems.

That is what NovaFabric does. Every run becomes a Run Capsule — a portable, schema-valid, secret-redacted record of exactly what happened, written on both success and failure, that you own and can read months later. The product thesis in one line:

Tracing tells you what happened. NovaFabric tells you whether a past run can be safely replayed, compared, and proven.

The five primitives at a glance

Primitive What it is Since
Asset Registry Local SQLite registry of versioned assets (name@version), pinned to a git SHA, with a six-state lifecycle v0.1
Run Capsule The unit of capture: a ULID-named directory holding every observable fact of one execution v0.2
Replay Re-execute or inspect a capsule under control, in four honest modes: forensic, mocked, semantic, exact v0.3
Lineage A directed provenance graph (SQLite) derived from capsules: provenance, blast-radius, replay-chain, time-travel v0.4
Evidence Bundle A signed, self-contained ZIP an auditor can verify offline with only sha256sum + an ed25519 verifier v0.4

Cryptographic sealing is part of the Evidence Bundle / trust layer, not a sixth primitive. The strategic verb chain across all five is Capture → Seal → Replay → Diff → Audit.

Five things you can do that you couldn't do before

1. Capture — "What exactly happened?"

The situation: Your IoT anomaly-detection agent files a report: "vibration sensor spiked at 14:32, probable bearing failure." Three months later, maintenance disputes it. What code ran? What data did the model actually see? What did it output before writing the report?

Without NovaFabric: Gone. You have the report file and nothing else.

With NovaFabric:

nova capture python workloads/iot_detective/agent.py

No code changes are required. nova capture injects a sitecustomize.py over PYTHONPATH and installs monkey-patches that record the full run as a capsule: model name, temperature, top_p, seed, every prompt sent, every response received, every tool call and its result, wall-clock timing, and exit code. Model calls are written in OpenTelemetry GenAI semantic-convention form (gen_ai.*). A capsule is written even on failure (with status: failure and an error block), and even if no AI SDK is present.

A capsule is a directory identified by a ULID (a time-sortable ID). Inside it you'll find, among other files:

File Contents
capsule.yaml The manifest
trace.jsonl OpenTelemetry spans
model-calls.jsonl LLM calls in OTel GenAI semconv
tool-calls.jsonl Tool invocations and results
env.lock Python version, packages, safe env vars, OS/arch/CPU/mem, GPU presence
redaction-proof.json Proof that a secret scan ran before the capsule was finalized

Validate and read it:

nova validate capsules/01KR9Q2AD…       # check the capsule is well-formed
cat capsules/01KR9Q2AD…/model-calls.jsonl  # read the recorded LLM calls

A secret scanner redacts in place ([REDACTED:rule-id]) and writes redaction-proof.json before finalize. A capsule missing that proof is invalid to nova validate and cannot be exported — redaction is enforced, not optional.

Three months later, you open the capsule and read exactly what happened.

2. Diff — "Did the agent's behavior change?"

The situation: You updated the hpc-analyst system prompt to be more concise. Did the actual diagnoses change? Are the tool calls the same? Does it still cite the right log lines?

Without NovaFabric: You'd have to run it again and eyeball the output manually.

With NovaFabric:

nova diff capsules/01KR9Q2A…  capsules/01KRB4F7…

nova diff aligns the model and tool calls across the two runs and reports a structured comparison:

model:          same     (qwen3:35b)
tools_called:   same     (read_log, write_diagnosis)
tool_args:      CHANGED — read_log now reads 40 lines, was 100
output_length:  CHANGED — 312 tokens → 180 tokens
classification: same     (OOM)

You can see immediately whether the prompt change caused a behavioral regression — even if the final label looks the same.

Wire it into CI as a gate:

nova diff --assert-no-regressions capsules/baseline… capsules/candidate…
# exits non-zero (1) on a regression, so the pipeline fails

This is the "worked yesterday, fails today" flaky-agent problem turned into a check your CI can enforce.

3. Replay — "Can I reproduce this result?"

The situation: The HPC team disputes a diagnosis from last Tuesday. They say the agent hallucinated a line number. You want to prove the output is reproducible — or find out if something drifted.

Without NovaFabric: You'd have to hope the same log file is still there, hope the model hasn't changed, and re-run manually.

With NovaFabric: Replay has four honest, falsifiable modes — pick the one that matches what you're trying to establish:

Mode What it does Use it for
forensic Read-only inspection, no subprocess, no network Audit / post-incident
mocked Re-spawns the command; LLM calls served from the capsule cache; tool calls gated by a safety ladder CI / regression
semantic Re-executes and judges meaning, not tokens (0.0–1.0 similarity) Drifting remote LLMs
exact Byte-exact, requires a deterministic env and per-call seed Local / on-prem / compliance

For the incident above, start read-only:

nova replay --mode forensic capsules/01KR9Q2AD…

This inspects the exact same inputs stored in the capsule — same log content, same model config, same recorded tool responses — with no subprocess and no network. To re-drive the command with cached LLM responses, use --mode mocked. If the agent produces the same diagnosis, it's reproducible; if not, something drifted (a model update, a tool behavior change, or non-determinism).

Honesty note: NovaFabric does not claim byte-exact replay of remote LLM calls. exact mode is for deterministic local/on-prem execution; for drifting remote models, semantic mode judges meaning instead of tokens.

A replay is itself a new capsule — so you can nova diff the original against the replay to see precisely what moved.

4. Lineage — "Which runs are affected by this data problem?"

The situation: You discover that slurm-logs/job-42819-oom@v1 contained truncated output — the last 200 lines were missing. Which agent runs read that file? Which diagnoses might be wrong?

Without NovaFabric: You'd grep through logs hoping something is recorded, or just assume everything is suspect.

With NovaFabric: Each agent declares what it consumed, and NovaFabric builds a provenance graph from those declarations:

# inside hpc_analyst/agent.py
record_consumed("slurm-logs/job-42819-oom@v1")

The lineage graph is a SQLite cache derived from each capsule's lineage.jsonl, with mechanical edge types (consumed, produced_by, replayed_from) and two confidence levels (observed at runtime vs. inferred from structure). Query the blast radius — every run downstream of the bad file:

nova lineage blast-radius slurm-logs/job-42819-oom@v1
# → run:01KR9Q2AD…  run:01KRB4F7…  run:01KRC3X9…

You now re-run exactly those capsules with the corrected log — no guesswork, no "assume everything is suspect." Related queries: provenance (ancestors), replay-chain, and time-travel.

NovaFabric also emits OpenLineage 2.0.2 events (START / COMPLETE / FAIL), so the same lineage can flow into Marquez, Atlan, or OpenMetadata.

5. Evidence Bundle — "Prove this was done correctly"

The situation: An auditor asks you to demonstrate that your ML experiment reviewer agent actually read the baseline CSV before making a deployment recommendation — and that the output hasn't been tampered with since.

With NovaFabric:

nova export-evidence capsules/01KRB4F7… --output bundle.zip --key ed25519.pem

--output/-o and --key (a PEM-encoded ed25519 private key) are both required — the command exits non-zero without them.

This produces a signed, self-contained ZIP that embeds the capsule, a lineage subgraph, in-toto DSSE attestations, ed25519 signatures, and vendored JSON schemas. The key property: it is verifiable with only sha256sum plus an ed25519 verifier — no NovaFabric runtime required — so an auditor can check the signature and read the exact chain of events entirely offline.

Scope caveat (honest): an Evidence Bundle attests that a capsule is unmodified since signing. It does not certify regulatory compliance or vouch for the content itself. Corrected 2026-07-30: RFC 3161 trusted timestamps and an append-only Merkle transparency log are not merely planned — NovaSeal (trust/novaseal/, ADR-0041/ADR-0070) implements both, plus DSSE signing, and is experimental and opt-in on the nova capture / nova verify path (tests/seal/ passes, including a p99 latency gate). Ed25519-signed Evidence Bundles plus in-toto DSSE attestations remain the stable default; NovaSeal is the richer, still-evolving layer on top, not a separate unbuilt service.

How it fits together

         nova capture
              │
              ▼
         [Run Capsule]  ←── portable, redacted record of one run
              │
       ┌──────┼───────────────┐
       ▼      ▼               ▼
   validate  diff          replay
              │               │
              │         [replay capsule]  ←── itself diffable
              │
         lineage (from lineage.jsonl)
              │
              ▼
      [lineage graph]  ←── which runs consumed which assets
              │
              ▼
      nova export-evidence
              │
              ▼
     [Evidence Bundle]  ←── signed ZIP, verifiable offline

The design invariant behind this picture: the capsule is the source of truth. The Asset Registry, the lineage graph, and (in server mode) the metadata database are all derived, rebuildable indexes — you can throw them away and rebuild them from capsules.

Capture approaches

You don't have to change your agent code to use NovaFabric. There are four ways to capture, all shipped:

Approach When to use Example
nova capture You control how the process starts nova capture python agent.py
nova api-proxy Agent already running as a service, or non-Python transparent HTTP proxy, URL-classified against a vendored provider registry
nova mcp-proxy Agent uses MCP tools over stdio nova mcp-proxy -- python mcp_server.py
In-process hooks / @novafabric.agent Jupyter notebook, embedded agent decorate the function or install hooks in-process

Under the hood, nova capture covers both SDK-level hooks (OpenAI, Anthropic) and wire-level hooks (httpx, requests, aiohttp, urllib3, Bedrock, and MCP) — so capture works whether or not your code uses a vendor SDK directly. Third-party plugins are auto-discovered via the novafabric.hooks entry-point group.

Where NovaFabric runs

The same five primitives work from a laptop to a cluster:

Corrected 2026-07-30 — this used to be accurate, it no longer is. The cluster-scale collector, parent/child capsules for distributed runs, an object capsule store, a production metadata store with row-level security, lineage-at-scale, and a live topology dashboard are shipped, as experimental (not planned-only): Collector (Go + Python, Phase 2, v0.14.3), parent/child capsules (Phase 3, v0.15.0), Object Capsule Store (Phase 4, v0.14.5), Metadata DB with Postgres RLS (Phase 5, v0.14.6), and all four lineage-at-scale backends — KuzuDB, Postgres, Apache AGE, JanusGraph — (Phase 6, complete as of v0.70.0). nova serve --topology (the live topology dashboard, ADR-0068) is also shipped experimental. What is genuinely still design intent only: cross-cluster federation and full cross-org identity (Phase 6 of design/architecture/cluster-scale.md's federation layer, not to be confused with the lineage-backend Phase 6 above). See docs/tutorials/cluster-scale.md for the full, corrected status table.

The testbench in one sentence

The nova-testbench runs four real agents continuously — HPC failure analyst, ML experiment reviewer, IoT anomaly detector, incident triage — so that live capsules, real diffs, and a growing lineage graph are always available to explore. It is the simplest proof that the whole pipeline works end to end.

make nova-loop       # run all agents in a continuous loop
make nova-dashboard  # open the local dashboard

When does NovaFabric pay off?

The system earns its value the first time you hit one of these:

Question Command Payoff
"Why did this agent output change?" nova diff Structured, aligned comparison in seconds
"Which runs must I re-validate after a data update?" nova lineage blast-radius Exact downstream set, not "everything is suspect"
"Can I reproduce this result from months ago?" nova replay --mode forensic Read-only re-drive against the stored inputs
"Prove to the auditor what happened." nova export-evidence Signed ZIP, verifiable offline with sha256sum + a verifier

Without NovaFabric, each of those questions costs hours of forensic work — if it's answerable at all.

Next steps