Flybrain Connectome

flybrain-connectome

v3.01.2 · 2026-10-26 · 116 functional units · 270 module profiles · 64 gates · EPE 全栈 + AIBOM 供应链披露 + 收据完整性门禁 + Gate 60/61/62/63/64/65/66 + fail-loud 纪律 + 版本单一真源 + Xu 2026 树突计算前沿对照 + Xu 2026 双层认知计算参考实现（tools/dendritic_rule_engine.py + examples/07_dendritic_rule_cache_demo.ipynb）+ E4 区室边界 + S1 外部验证 + S2 lesion cascade + S3 统计强化 + 纪律有效性量化（catch-rate 100%，W1–W7 全部关闭）+ Gate 63 research_claims 顶层统计完整性 + 作者署名规范化（P-3 / P-4 → Li Xing + ORCID）+ bioRxiv 投稿准备包 + flybrain demo 一条命令 + 5 分钟上手 + flybrain audit CLI + JOSS 投稿包 + MCP 生态提交包 + 9 篇论文 DOI 补齐 + P1-3 HTTP/MCP 契约统一（Gate 66 · HTTP DISPATCH 从 12→47 全覆盖 MCP）+ P1-1 LangGraph runnable 后端（--runnable 生成含 MockLLM + MemorySaver checkpointing + OTel trace 的可执行 Python 脚本）

The 89% problem

Deloitte's 2026 agent-pilot survey found 89% of enterprise agent pilots never reach production. Teradata: 78% have pilots, only 14% scaled. The failure pattern repeats across industries in three words:

"跑不稳、管不住、查不清" — unstable, ungovernable, unauditable.

flybrain-connectome addresses the third word. Every agent edge in a generated topology traces back to a real neuron or synapse class in the Drosophila male brain connectome, serialized as W3C PROV-JSON — the same format EU AI Act auditors expect. Your agent swarm isn't a black box; it's a verifiable structure with biological provenance.

5-minute quickstart — from zero to an interactive D3 topology graph in five minutes, no data download.

Auditable Agent Topology — 可审计的 Agent 拓扑生成与溯源, grounded in the real Drosophila male-brain connectome (MaleCNS v1.0 — 166,700 neurons / 124,177,617 synapses, CC-BY).

flybrain-connectome turns a real, published brain wiring diagram into task-tailored multi-agent topologies you can instantiate as agent swarms — and it can prove, for every edge, which real neuron / synapse class it came from. The motif provenance is serialized as W3C PROV-JSON, the same standard EU AI Act auditors expect.

5-module × 8-agent topology (predictive_coding_v2 blueprint) — the same graph you get from flybrain demo. Try it live →

It is a developer toolkit and research artifact, not a trained model and not a simulation of a fly. Read the honest boundaries before you quote it.

Repo status: PUBLIC since 2026-10-18. History was audited and cleaned before publicization (see CHANGELOG v1.3.0 for the original Tier-2 IP leak and docs/D3_REPOSITORY_PUBLICITY_DECISION_2026-10-18.md for the 16-step publicization runbook that was executed). The Tier-1 code is MIT-licensed and reachable at https://github.com/lm203688/flybrain-connectome. Tier-2 raw data (MaleCNS 5.63 GB, C. elegans Witvliet 2020, Drosophila larva edges.csv, Petkova 2025 zebrafish) is fetched via the documented data pipeline — see §"Requirements" and the S1 external-validation script.

What it is / what it is NOT

The killer offline feature: generate needs no data — it composes topologies from a 7 KB derived motif snapshot. The connectome data is only needed if you want to query the real brain.

Honest status of the unit library

The 116 functional units are split cleanly by how their evidence comes together. This is the number to remember when reading any downstream claim:

The 68.1 % bucket is not a defect and not a hidden error. It is a comparative-neuroscience scaffold: bird song, mammalian cortical layers, fish Mauthner, cephalopod vertical lobe, Aplysia and leech CPGs, insect mushroom-body — all are wired into the library with named references and can be composed like the anchored ones, but they are not measured in a real connectome the way the 37 anchored units are. Reading the library correctly means reading those two numbers together, not the 116 by itself.

How to use this — tiered by what you can actually get today

Rather than a single "this is a X" pitch, the useful things this project does break down into three tiers. Which one you actually need depends on what you are building.

Tier 1 — usable today, offline, no data download. Neuron-circuit search across 3 species × 4 axes (system / function / species / provenance); compose a virtual-organism topology from any subset of units; run the four canonical DEMOs (mammal, insect, electrosensory, swim) end-to-end. This is the developer tool the project actually is. flybrain search, flybrain compose, flybrain generate cover it.

Tier 2 — for people designing AI-agent scaffolds. The composed topologies trace back to real circuitry via W3C PROV-JSON, which means a downstream agent framework (LangGraph / CrewAI / AutoGen / SwarmLabs) can pick up a starting topology and audit where each wiring decision came from. This is not a general agent infra — it is a biology-prior layer you plug into one.

Tier 3 — what this project explicitly does not do. It does not beat biology at anything (Phase 3 measured negative and is not claimed). It does not replace any EM reconstruction pipeline. It does not ship a trained model or a spiking simulator. If someone reads a v1.x changelog entry that promises any of those, the honest answer is "that was aspirational; the withdrawal is documented in docs/PROJECT_REALITY_ASSESSMENT.md".

The four moats (measured, not marketed)

We ran the experiments. Two of the "obvious" advantages turned out to be false under controlled testing, and we say so.

What we explicitly do not claim

Honesty is a feature. The reproducible experiment scripts live in integrations/swarmlabs/diagnostics/ and the reports in the repo root (SNN_ENERGY_REPORT.md, MULTI_READOUT_REPORT.md, FAULT_ISOLATION_REPORT.md).

Install

The toolkit needs one runtime dependency (numpy, because the connectome engine imports it at module level) plus optional extras for provenance and columnar IO. No other third-party code is required. Pick either setup:

Repo status: PUBLIC since 2026-10-18. No authentication required — the clone commands below work directly. History was audited and cleaned before publicization (see CHANGELOG v1.3.0 for the original Tier-2 IP leak and docs/D3_REPOSITORY_PUBLICITY_DECISION_2026-10-18.md for the 16-step publicization runbook).

A. Clone and run (recommended, verified end-to-end)

git clone https://github.com/lm203688/flybrain-connectome.git
cd flybrain-connectome
python flybrain_cli.py --help

B. Editable install (puts flybrain on your PATH)

git clone https://github.com/lm203688/flybrain-connectome.git
cd flybrain-connectome
pip install -e .
flybrain info

pip install -e . is editable: the flybrain entry point maps back to the checkout, which is precisely what lets it find the toolkit. A plain pip install . ships only the launcher, so point it at a checkout explicitly:

pip install .
export FLYBRAIN_HOME=/path/to/flybrain-connectome
flybrain info

This path is verified in CI, not just documented. The package job in .github/workflows/ci.yml runs pip install ".[prov]", invokes the console script, resolves the toolkit via FLYBRAIN_HOME, generates an organism, and asserts the artifact's invariants — so the install instructions cannot silently rot.

C. Optional: the standardized provenance bundle

Structural provenance (motif_provenance, with the snapshot sha256) is always recorded. The standardized W3C PROV-JSON bundle (prov_json) is serialized by the optional dataprov package:

pip install dataprov                     # or: pip install "flybrain-connectome[prov]" (once on PyPI)

Without it the toolkit still runs: prov_json becomes null and get_provenance_bundle answers with prov_available: false plus the reason, rather than crashing. Both arms are asserted by scripts/check_degraded_mode.py — absent must degrade, present must actually produce the bundle.

Always check what you got

flybrain info

reports the resolved toolkit root, the motif snapshot, the data dir, API-key status, and how many tools are exposed. Run it first whenever something looks wrong — it turns "ImportError" into a named, fixable problem.

To query the real connectome you also need the data

MaleCNS v1.0 is CC-BY, ~5.63 GB, and deliberately not in this repo:

export FLYBRAIN_DATA_DIR=/path/to/your/male_cns

generate and the bundled motif snapshot need no data at all.

Quickstart

Every flybrain <cmd> below also works as python flybrain_cli.py <cmd> (setup A), so you can try the project without installing anything.

0. Sanity-check the install

flybrain info
# -> toolkit root [ok] · motif snapshot [ok, 7363 bytes] · tools exposed: 36

1. Serve the connectome (optional — only for querying real data)

flybrain serve --port 8799
# or: python flybrain-connectome-mcp/run_api.py

2. Query the real brain

export FLYBRAIN_API_KEY=<your-key>   # set in config.json or env

flybrain query pair_synapses --args '{"pre":10540,"post":12569}'
# -> 711   (the real APL → DPM connection)

flybrain query connectome_overview
# -> {"total_synapses": 124177617, "annotated_neurons": 211577, ...}

3. Pull the derived motif document

flybrain motifs --out motifs.json      # live pull from the service
# offline snapshot (no server, no data): flybrain-connectome-mcp/motifs/swarm_agent_topology_motifs.json

4. Generate a synthetic bio-topology (offline, no data)

flybrain generate --blueprint predictive_coding_v2 --modules 5 --agents-per-module 8
# -> flybrain-connectome-mcp/generated_organisms/<name>.{json,html} + flybrain-connectome-mcp/swarmlabs/<name>.swarm.{json,yaml}

5. Read the C. elegans developmental axis

flybrain stages         # L1 x4 / L2 / L3 / adult x2, measured per animal
flybrain stages --caveat            # why the L1 columns are a floor
flybrain stages --unit cel_amphid_chemosensory_unit

6. Read the larva connectome cluster ledger

flybrain larva-clusters             # network totals + per-unit coverage
flybrain larva-clusters --top 20    # the 20 biggest clusters
flybrain larva-clusters --unannotated
flybrain larva-clusters --caveat    # what "covered" does and does not mean

7. Read the MaleCNS male-dimorphic ledger

flybrain composition               # can declared combinations actually form?
flybrain costs                      # latency/energy per connection type
flybrain function                   # coverage by FUNCTION, not by species
flybrain function --vision          # only what PROJECT_VISION requires
flybrain dimorphism                 # 2,368 cells nobody has looked at
flybrain dimorphism --provenance    # ... with the curator note behind each sex label
flybrain dimorphism --top 20 --internal
flybrain dimorphism --caveat        # what "male-specific" does not mean here

8. Verify everything still works

flybrain verify        # needs FLYBRAIN_DATA_DIR set to your data

9. Profile a topology (offline, no data)

The topology-aware layer turns "what shape is this organism?" into a callable, reproducible answer — density, degree spread, reciprocity, modularity Q, transitivity, spectral radius (power iteration, no numpy), feedback-edge fraction, a directed 3-node motif census, and a deterministic structural signature. It is honest by construction: not_measured names what a structural profile cannot speak to (dynamics, energy, task performance).

flybrain generate --blueprint predictive_coding_v2 --modules 5 --agents-per-module 6 --name demo
flybrain profile generated_organisms/demo.json
flybrain profile generated_organisms/demo.json --json          # full profile
flybrain profile generated_organisms/demo.json --compare generated_organisms/other.json

Self-validated by Gate 15 (scripts/check_topology_profile.py).

Repository layout

flybrain-connectome/
├── flybrain_cli.py                 ★ developer CLI (serve/query/motifs/generate/verify/info)
├── pyproject.toml                  zero deps, `flybrain` console script
├── LICENSE · CITATION.cff · CHANGELOG.md
├── flybrain-connectome-mcp/
│   ├── mcp_connectome/             query engine (core) + 47-tool MCP + HTTP API (:8799)
│   ├── motifs/                     drop-in client + derived motif snapshot
│   ├── synthetic_bio_topology.py   ★ C1 generator (motif → agent graph + config + PROV)
│   ├── _verify.py                  end-to-end smoke test (70/70)
│   └── flybrain-connectome-mcp/
│       ├── generated_organisms/     example artifacts (json/html + swarm configs)
│       └── swarmlabs/               swarm config exports
├── integrations/swarmlabs/         consistency checkers + diagnostics (reproducible experiments)
├── scripts/                        run_gates.py · check_no_secrets.py · check_docs_consistency.py
│                                   check_mcp_tools.py · check_degraded_mode.py
│                                   check_generated_artifact.py · gh_publish.py
├── .github/workflows/ci.yml        the same gates, on py3.10/3.12/3.13 + packaged-CLI job
└── docs/                           arxiv_paper.md · mcp_setup.md · module_factory.md

HTTP API reference

Service: flybrain-connectome-mcp/mcp_connectome/api_server.py.

Auth: Authorization: Bearer <key> or X-API-Key: <key>. Keys come from config.json (api_keys) or env FLYBRAIN_API_KEY. If none is configured the server generates a temporary per-boot key and warns loudly — it never falls back to a key committed to source.

The 47 tools (real names, do not invent — verified against GET /tools by scripts/check_docs_consistency.py):

Real-connectome queries — the 11 marked ⚠ need FLYBRAIN_DATA_DIR:

connectome_overview ⚠ · neuron_info ⚠ · pair_synapses ⚠ · neuron_outputs ⚠ · neuron_inputs ⚠ · degree ⚠ · search_neurons ⚠ · superclass_projection ⚠ · hub_targets ⚠ · list_superclasses ⚠ · pathway ⚠ · export_swarm_motifs

Generator surface — offline-capable, served from the motif snapshot:

list_blueprints · list_modules · list_bio_motifs · batch_generate_topologies · get_provenance_bundle · list_stacked_blueprints · get_stacked_feasibility · list_neuropils · get_neuropil_detail · get_module · check_module_compatibility · compose_virtual_organism · list_virtual_organisms · list_presets · get_preset · apply_preset · organism_to_swarmlabs · get_module_agent_roles · save_market_organism · load_market_organism · list_market_organisms · rate_market_organism · search_market_organisms · delete_market_organism · list_combination_rules · validate_combination · expand_organism · get_multiplier_metrics · profile_topology · profile_health

Functional-unit composition library — the composable layer above neuron types:

list_functional_units · get_functional_unit · list_connection_types · list_evidence_ledger · compose_from_units

Synthetic organism schema

synthetic_bio_topology.design_organism(spec) returns:

{
  "organism": {
    "nodes": [ {"id": "m0_rep", "role": "tissue_rep", "module": 0,
                "profile": "memory", "ref_superclass": "CB", ...} ],
    "edges": [ {"from": "m0_rep", "to": "m0_c0", "weight": -0.31,
                "kind": "inhibitory", "module": 0, "ref_body": 10540} ]
  },
  "metrics": {
    "n_nodes": 29, "n_edges": 18, "avg_degree": 1.24,
    "inhibition_ratio": 0.11, "n_modules": 4,
    "intra_module_edges": 14, "inter_module_edges": 4,
    "modularity_est": 0.778, "profile_breakdown": { ... }
  },
  "blueprint": "predictive_coding_v2",
  "motif_provenance": {            // 3-stage traceability
    "mode": "local_snapshot",
    "motif_synapse_stats": { ... },     // % of the 124M-synapse dataset
    "motif_query": "SELECT ...",        // runnable provenance query
    "blueprint_to_motif": { ... }
  },
  "prov_json": { ... }             // W3C PROV-JSON bundle (schema v1.2.1)
}

The W3C PROV-JSON (prov_json) bundle records entity / activity / agent nodes with wasDerivedFrom / wasAssociatedWith / wasAttributedTo edges, attributing every topology to FlyCellAtlas and the flybrain:dataset_anchor (MaleCNS v1.0). It is consumable by yProv4ML, MLflow2PROV, and MIT Sloan provenance tooling.

Blueprint & module library

25 blueprints across three categories, composed from 270 modules: 10 cognitive-function modules (sensory / memory / motor / integration / attention / working_memory / reward / punishment / habit / metacognition) + 190 cross-species neuron types spanning cortical pyramidal (L2/3 CX1/CX5, L4b/L4c, L5 basal tuft, L6 reticulo-thalamic, L7 temporal, Mayeret) + intermediate interneurons (Calbindin/LAMP/NPH/NPF/VLAC/SST-dendritic/ SST-somatic/KCN, plus PV/SST/VIP/chandelier/basket) + modulatory systems (dopaminergic A9/A11/VMC/VTA/SNC, serotonergic dorsal/ventral/median raphe, noradrenergic LC, cholinergic MT/horizontal, histaminergic TM, orexin) + retinal (bipolar cone/rod, amacrine ACL1/ACL2, midget/PV ganglion, horizontal) + spinal/cerebellar (α/γ motor, Icc/Ih/Ihp interneurons, glycinergic, nociceptive, Purkinje/granule/stellate, inferior olive) + thalamic/basal ganglia (LGN/MD/VA/VL/VPL/VPM/RTN, STN, red nucleus, striatal D1/D2 MSN) + limbic/edge (hippocampal CA1/CA2/CA3/CA4/granule/subiculum, amygdala BLA/LA, septum, entorhinal L2/L3, grid cell) + special senses (pineal, melanopsin, olfactory, vestibular, cochlear outer hair)

• reactive astrocyte

• four clades whose neurons have no vertebrate-cortex counterpart (35 further types, each carrying its own biological_reference):

• insect (14) — Kenyon cell / mushroom-body output / PPL1-PAM dopaminergic / APL, antennal-lobe projection and local neurons, protocerebral bridge / ellipsoid body / fan-shaped body / noduli / ring neurons, s-LNv and lateral clock neurons, descending neuron
• bird (6) — HVC, RA, LMAN, Area X, DLM, NIf (the song system)
• fish (8) — Mauthner cell, reticulospinal neuron, lateral-line hair cell, ampullary and tuberous electroreceptors, ELL pyramidal cell, electrosensory inhibitory interneuron, electric-organ pacemaker neuron
• cephalopod (7) — vertical-lobe large / amacrine / small cells, superior frontal lobe cell, chromatophore motor neuron, arm motor neuron, arm sensory afferent

• a second non-cortical batch: rhythm, state and error (24 further types, v1.6.0):

• segmental / peripheral oscillators (9) — leech swim-CPG interneuron and body-wall motor neuron with its mechanosensory T cell, ascidian larval motor neuron and ocellus photoreceptor, Aplysia sensory / motor / serotonergic facilitating neurons, buccal ganglion CPG neuron
• infrared and amphibian (5) — trigeminal infrared afferent, infrared tectal map neuron, multimodal optic-tectum neuron, tectal T5-type prey detector, tongue motor neuron
• error teaching and state (6) — climbing fibre, cerebellar Golgi cell, vestibular nucleus neuron, pre-Bötzinger complex neuron, phrenic motor neuron, VLPO neuron
• navigation and active sensing (4) — head-direction cell, place cell, barrel cortex neuron, whisker motor neuron

Every blueprint traces to one of 40 topology motifs (3 original MaleCNS-extracted + 37 conceptual patterns spanning anatomical / functional / plasticity / specialized categories) or is honestly marked motif: none (synthetic orchestration, no bio-prior) — 16 bio-prior, 9 not:

• 5 basic — flat / hub / ring / layered / random reference topologies
• 16 biological — e.g. predictive_coding_v2 (recurrent_feedback+reward), metacognitive_control (inhibitory_hub), habit_consolidation (recurrent_feedback), hierarchical_deep (reservoir_smallworld) …
• 4 synthetic — rl_actor_critic, curiosity_driven, cognitive_control, multi_motif_blend (honestly motif: none)

Nested composition a+(b+c) is parsed as isomorphic to a+b+c. Full list:

from flybrain_connectome_mcp.synthetic_bio_topology import BLUEPRINTS  # (in-repo path)
# or after pip install: import synthetic_bio_topology as c1; c1.BLUEPRINTS

Machine-readable equivalents (same data, over HTTP/MCP): list_blueprints, list_modules, list_bio_motifs.

Functional-unit & connection-type library

The neuron list above is the vocabulary. It is not the unit of composition: a single neuron type does nothing on its own, and wiring two of them together means nothing. What composes is a function with a known interface. So the library is split into two tiers, and the upper tier is the one you actually build from:

• 116 functional units — each is ONE function (phototransduction, action selection, theta pacing, gliotransmission, sparse mushroom-body coding, ring-attractor heading, song timing, Mauthner escape, active electrolocation, camouflage, leech swim rhythm, infrared thermal imaging, climbing-fibre error teaching, sleep/wake switching …) delivered by a group of neuron populations (1–6 of them, taken from the 254-module vocabulary) wired into an internal sub-circuit, and exposing typed ports (light in → high_res out; candidates + value in → selected_action out). Composition: 40 mammal-cortex-shaped + 16 cross-clade (insect / bird / fish / cephalopod, v1.4.1) + 16 rhythm/state/error and active-sensing (annelid, ascidian, mollusc, pit viper, frog, cerebellum, brainstem, hippocampus, barrel cortex, v1.6.0)
  • 10 Drosophila-shaped (optic lobe, giant-fiber escape, male courtship & song, lateral horn, v1.8.0 — every one of them fully anchored, see below).
  • 5 Drosophila-shaped (thermal preference, feeding circuit, leg motor, multimodal LAL, sleep memory, v1.10.0 — first batch anchored, see below).
  • 4 C. elegans connectome-derived (forward locomotion command, touch-avoidance reflex, amphid chemosensory, nociceptive avoidance — v2.27.0, see below; each also carries a L1 → L2 → L3 → adult axis, v2.30.0).
  • 8 Drosophila larva connectome-derived (larv_olfactory_associative_learning_unit, larv_somatosensory_escape_unit, larv_gustatory_feeding_program_unit, larv_descending_motor_command_unit, larv_mushroom_body_feedback_unit — v2.28.0, third connectome — plus larv_thermosensory_relay_unit, larv_visual_relay_unit, larv_respiratory_relay_unit — v2.29.0, one unit per traced sensory channel, see below).
• 66 connection types — each is one connection FORM, constrained on the port types it may join, in six families: micro (axo-dendritic / axo-somatic / axo-axonic / dendro-dendritic … point-to-point contact sites) · circuit (feedforward / feedback / lateral / mutual / recurrent / disinhibition / winner-take-all / global inhibitory gain control / synfire chain / efference-copy cancellation / half-centre oscillation / grid phase precession …) · modulatory (diffuse / volume / retrograde / state gating) · electrical (gap junction / ephaptic / mixed electro-chemical / tripartite astrocyte / plateau-potential bistability) · (STDP / LTP-LTD / short-term / homeostatic scaling / modulator-gated / repeated-activation LTS / serotonergic presynaptic facilitation / short-term habituation / coincidence detection) · (topographic mapping / divergent fan-out / convergent fan-in / corollary discharge / global broadcast / sparse expansion / command-neuron recruitment / bistable state switch / thermal cross-modal convergence).

So the algebra is virtual organism = units (nodes) + connection types (typed edges), and every edge is mechanically checkable because both endpoints declare port types:

unit A .out_port[type]  --connection_type(from_port, to_port)-->  unit B .in_port[type]

compose_from_units enforces port-type compatibility, required-port satisfaction, external drivability/readability of the whole organism, and reports feedback loops as warnings (an intentional feedback loop is a first-class citizen here). An unsatisfied required input on the organism boundary is the organism's external input, not a defect — pass allow_boundary_inputs: false to validate an interior sub-chain instead.

Four canonical chains ship as fixtures and are re-validated by Gate 9 on every run — --demo mammal (retina → thalamus → cortex → basal ganglia → motor thalamus), --demo insect (antennal lobe → mushroom body sparse code → learned valence → central complex heading/steering, clock-biased), --demo electrosensory (EOD pacemaker → efference-copy cancellation → electrolocation, plus a passive-electric cue into the Mauthner escape command) and --demo swim (leech half-centre swim CPG → ascidian larval oscillator → frog reticulospinal escape swim). --demo with no argument runs all four.

Developmental axis (v2.30.0) — the same four circuits at L1 / L2 / L3 / adult

The Witvliet 2020 series is eight reconstructions, not two: four L1 animals, one L2, one L3 and two adults. The library used to carry only the two adults. All eight are now loaded and measured per animal. Each of the four C. elegans units above carries the result in provenance.developmental_stages; run flybrain stages to print it.

The four circuits, measured at every stage (synapses per animal):

Those four rows do not add up (v2.34.0). The circuits are not disjoint cell-class sets — AIA sits in all four, AIB/AIZ/AVB/PV in two or three — so an edge like AIZ → AIB is internal wiring for two circuits at once and gets counted by both. Adding the column overstates the wiring that is actually internal to these circuits by ~1.27×. Both figures are now reported per stage:

Use union for "how much of this animal's wiring sits inside these circuits"; use the sum only when the circuits are counted individually. The extractor now asserts that the per-circuit rows reconstruct the sum, and refuses to write when they disagree.

One circuit is measured wider than the unit it attaches to (v2.35.0). PROFILES declares 11 cell classes for the touch-avoidance circuit, PLM among them, but extract_cel_profiles.py only emits a member profile for a declared class that actually has an edge inside the module — and in the adult reconstruction PLM has none, so the v2.27 unit ships 10 members and cel.plm does not exist anywhere in this library. Merged across all eight reconstructions PLM does have edges, so this series measures 11. Both are correct; the unit name is identical, which is exactly why the gap had no name and no guard. The artifact now records it as declared_vs_shipped and the extractor refuses to write if a unit ever ships a class the series cannot measure. Practical consequence: compare the touch-avoidance stage numbers against the unit by that one class, not directly.

Across all 16 measurements the four circuits are present in every animal at every stage (6–37 edges at L1), so what development adds is synapse weight, not new topology.

The boundary that matters here. A side result — 271 edge pairs appear in L1 and not in the adults — is not reported as transient developing synapses. A single L1 file carries 566–795 distinct edges against 1,107 for a single L2 file, so "seen at L1" is confounded with reconstruction depth. Only the claim that survives that confound is published; the caveat field of cel_development_stages.json states this in the artifact itself. As of v2.34.0 that 271 is computed (stage_diffs.l1_only_keys, alongside l1_union_keys 1,188, adult_union_keys 1,980, shared_keys 917, adult_only_keys 1,063) rather than written into the caveat as a literal that could not be re-derived if the source changed.

Larva cluster ledger (v2.31.0) — how much of the larva network the units cover

The eight larva units were built by aggregating nodes on (cell_type, annotations). That left one question unanswered: how much of the 352,611-synapse larva network do they actually span? flybrain larva-clusters answers it by walking all 116,922 edges.

A synapse counts as covered only when both endpoints' (cell_type, modality bucket) resolve to a larv.* profile that at least one unit declares as a member. A synapse is frontier when neither endpoint does.

Per unit, at both-endpoint resolution:

Three cell classes sit entirely outside every unit: the unlabelled class (346 cells), MB-FFN and pre-DN-SEZ.

_unannotated in a profile id does not mean "unannotated" (v2.36.0). 15 of the 43 profile classes carry that suffix, and it is bucket()'s modality-recognition fallback, not an annotation verdict: a cell whose annotations string is literally KC contains no modality keyword, so it lands in the fallback bucket while being fully named. 13 of those 15 classes hold annotated cells — 1,686 of them, including larv.kc_unannotated (144 cells, all annotated KC), larv.mb_fbn_unannotated (100, all annotated like CN-10; FAN-2) and larv.mb_ffn_unannotated (54, all FFN-23 and siblings). The earlier line in this README that read the frontier list as an annotation verdict was wrong on exactly this point: MB-FFN's 54 cells all carry names.

The network-wide figure is unaffected — 1,560 cells (52.77%) genuinely have no official annotation, because it reads the annotations column directly and never touches the bucket. What was wrong was the reading of the names. The ids are unchanged (40 profile keys, gate 9 and every published artifact index them by name); the two notions are now measured apart in annotation_identity and carried on each unit as member_id_caveat.

The per-unit rows are not additive either (v2.34.0). Nineteen larv.* profiles are declared by more than one unit — larv.lhn_unannotated, larv.lhn_mechano, larv.kc_unannotated and friends — so one synapse legitimately counts toward each of them. The eight rows sum to 319,064 against a covered total of 271,956 (1.17×). Use the union figure for network coverage and the per-unit rows only to compare units with one another; the shared-profile list is in unit_attribution and flybrain larva-clusters --caveat prints it. The extractor asserts that the sum can only exceed the covered total, that the coverage partition closes, and that per-cluster internal + out reconstructs the network.

The headcount was the wrong measure. The larva network has 1,560 cells with no official annotation — 52.77% of its neurons, and the number that made the network look mostly uncharacterised. They touch 212,542 synapses, 60.28% of the total, because the units do declare the unannotated populations as filler roles. Coverage and characterisation are different things, and the ledger holds them apart on purpose: a unit can span three quarters of the larva synapses while its unannotated members still have no known function. Cluster 0 — the authors' unassigned bucket, 364 cells — is the single largest holder of synapses in the whole network (51,211) and is not given a function here.

Male dimorphic ledger (v2.32.0, audited v2.33.0) — 2,368 cells inside the male connectome

MaleCNS v1.0 is a male reconstruction, and its annotation table carries a dimorphism column. Nobody in this library had ever read it: the 58 anchored profiles were built from superclass / type, and type is not sex-aware. flybrain dimorphism streams all 151,856,684 weight rows and asks, for every edge, whether at least one endpoint is one of the 2,368 bodies the column flags.

The gap between those last two rows is the finding. The male population is not an island: it is a thin, widely-connected subset that mostly talks to universal cells, and only about one in nine of the synapses it touches sits between two of its own cells. A "male circuit" declared on these numbers would have to be the 1.28% — which is smaller than the measurement noise of the surrounding network. So no circuit is declared: FUNCTIONAL_UNITS stays at 115, and 29 units carry the population's distance in provenance.male_dimorphic.

The heaviest type is TmY21 (372 bodies, 732,527 synapses, 0.590% of the network). The heaviest both-ends pair is TmY21 → LoVP92 (442 edges / 2,721 synapses), then AN09B017b → mAL_m1, then pC1_2a → mAL_m8.

How big is "big" here (v2.33.0). v2.32.0 quoted those totals with nothing to compare them against, which is how oviIN's two bodies / 196,360 synapses came to look like an error — it is 10× the "1k–10k per neuron" folk rule. It is not an error. The per-body distribution in this connectome is heavy-tailed:

So oviIN sits at the 99.98th percentile — the top of a long tail, not an outlier — while TmY21, the heaviest type by total, sits at only the 72nd percentile per body. Cell count and per-cell strength are different things and the ledger now carries both.

Three things the type names will not tell you (v2.33.0). First, the annotation table carries three independent type axes — type (MaleCNS, 11,751 classes), flywireType (8,199) and hemibrainType (4,495) — and type disagrees with flywireType on 33.08% of bodies. TmY21 in this ledger is the class FlyWire calls Tm36; the alias travels with every top type. All 5,413 anchored type names were checked to sit on the type axis, and the script refuses to write if that ever stops being true. Second, each type now carries the curator's own matchingNotes sentence behind the sex label (oviIN: "more extensive dendrites in male") plus its trace grade — 150 of 479 types have such a note and 327 are only Roughly traced. Third, the per-type totals are now checked on every run: each pair is charged once per dimorphic endpoint, so they must sum to touching + both-ends (14,966,115), and the extractor refuses to write if they do not. They did not in v2.32.0 — internal pairs were counted four times, which inflated every per-type figure by 3× the internal total.

What this ledger cannot say. MaleCNS contains no female reconstruction, so a dimorphism label is the curator's annotation, not a comparison made here. Nothing in it supports "larger in males", and the artifact says so in its own caveat field. Two counters are also kept separate on purpose: 194 dimorphic types are listed in an anchored profile's own type list (someone looked at them), while all 479 sit inside some anchored ^XXX family rule — family membership is not tracing.

Connection cost axis (v2.38.0) — what each connection costs, and what we cannot say

PROJECT_VISION.md claims the generator can optimise any goal against the biological library as prior. Phase 3 tested that and found the optimiser could only win on structure (−84% wire cost) while the headline computation claim measured negative, 0/7 topologies beating random.

The cause was in the vocabulary rather than in the optimiser. The 66 connection types carried delay_class with three symbolic values — immediate / short / long — and a parameters block with no units. With latency a category and energy absent, the only axis left to trade was structure. flybrain costs adds the numbers where a citation exists, and unknown where one does not.

16 of 66 types carry a latency; 50 do not, and that split is the finding. Every type with a number is an actual contact site (micro 9/10, electrical 3/7, modulatory 4/6). All 20 circuit motifs, 13 macro geometries and 10 plasticity rules have none, because a single-contact delay is not a defined quantity for a pattern — compose the members' delays and the motif's cost follows. Energy is present for all 66, because energy composes over a motif while latency does not. That asymmetry is the design.

21 types carry no latency by rule. They are flagged abstraction_only — a winner-take-all or a half-centre oscillator is a design pattern, not a biological contact, and an autapse is chemically a chemical synapse but as a design pattern has no delay literature can supply. Their delay_class is kept under designed_cost so an assumption is never summed with a measurement. Latency is a range, not a point: the spread is the trade, since choosing a fast synapse trades against reliability.

Does the axis actually steer an optimiser? (v2.39.0) Data nobody optimises over is decoration, so cost_aware_optimization_experiment.py runs the same GA twice — same space, same budget, same evaluation seed — changing only the objective, and asks whether the latency penalty actually lowers latency.

At unit granularity it cannot be tested, and that is a measured fact: only 14 of 115 units have every connection type on the latency axis. The other 101 carry unknown-latency types with a median unknown share of 100%, so a 5–6 unit organism almost always contains one and has no latency figure at all.

At connection-type granularity it works. With a fixed 6-edge budget:

A 99% latency cut bought with no extra wire and less energy — a Pareto improvement, because the fast types (electrical, 0.05–0.2 ms) are also the cheap ones. Across four parameter settings: k=6 gives −98.9% twice identically, k=8 gives −51.2%, and k=10 gives +73% — the axis stops working, because 16 latency-bearing types cannot fill a 10-type candidate from the fast end. The capacity boundary is in the artifact rather than averaged away.

Three measurement holes were found by the optimiser, all of them mine: treating unknown as 0 ms (so the cheapest way to win was to select what cannot be measured — reported as a bogus −99.9%); charging it the slowest latency instead (GA learned to add unmeasured types, and latency rose 16–19% across three runs); and a floating candidate size (GA shrank every candidate to one type and reported a 99.9% win by deleting the topology). The fix in all three is the same: an unmeasured type is incomparable, not cheap and not expensive, so topologies containing one are left out of the latency ranking, and the candidate size is asserted rather than assumed.

GATE 12 enforces this. It fails when a design abstraction gains a latency, when a range is malformed, when the fastest electrical is not strictly faster than the slowest chemical (the trade the axis exists to express), when a graded value cites nothing, or when any type loses its energy entry.

Function axis (v2.37.0) — coverage by function, not by species

The library is organised by taxon. Its 116 units carry 48 distinct system values and 60+ distinct function values, and their names are anatomical: cortical_, retinal_, larv_, cel_, frog_, birdsong_. That is a catalogue of species circuits, not a function library, and PROJECT_VISION.md asks for the second kind — it names the set the generator grows toward.

flybrain function measures the distance. Every unit is tagged with the function categories its own capability / task_hint_pool fields declare; nothing is inferred from description prose, and no unit is added, renamed or reclassified.

Of the 15 categories PROJECT_VISION requires:

The honest measure is taxon breadth, and it is the library's biggest gap. covered counts units, so a category carried by a single species reads as covered. Splitting it by how many taxa actually carry each required category:

Only 1 of the 15 required categories — associative_learn — spans 3 or more taxa. The other 14 are single-taxon implementations.

GATE 11 derives that split from the matrix and fails if it is hand-written or silently improves. The library can generate 115 circuits; it cannot yet generalise a function across species, which is what a morphology generator needs. Closing it means new units with real evidence — roughly 14 more, each with literature verification, a JEV run and a profile registration.

Metacognition, the last missing category (v2.41.0)

A sweep of all 116 units for confidence, uncertainty, metacognit, precision and attentional in their name, function and description returned nothing — the gap was real, and the 48 untagged units could not fill it (their capabilities are mechanism verbs like oscillate and reflex). So it needed a unit: prefrontal_metacognitive_unit, with six profiles.

The literature separates two networks rather than one "confidence centre": dACC and insula carry objective uncertainty and correlate negatively with reported confidence; VMPFC and pgACC carry the subjective sense and correlate positively; and the lateral frontal pole maps a private estimate onto a public report — a stage separable from confidence itself. JEV returned PARTIAL (lit 0.76, model 0.56, score 4): well documented, not computationally validated. Two fail-closed gates fired on the way in (no JEV verdict, then members not in MODULE_PROFILES) and neither was bypassed.

The honest summary of this axis: only 2 of 24 categories are carried by three or more taxa (associative_learn, credit_assign), and 41 of 116 units belong to no category at all. Cross-taxon coverage is this library's weakest dimension, and that is now a number rather than an impression. A tag means the unit declares a mechanism implementing the category — it is a coverage label, not a claim that the unit performs the function well.

Keyword repair and match evidence (v2.42.0) — the matrix had to be believed

v2.42.0 caught two categories reporting 0 units while the library already declared them. homeostasis' keyword list was homeostasis, thermoregulation, circadian_rhythm, fluid_balance — and the library writes that concept as circadian_pace, oscillate_circadian, circadian_track, sleep_wake, metabolic_support. Same story for neuromodulation, whose six tokens matched nothing while twelve units declared it as modulate, modulation, peptide_release, calcium_wave. Both read missing, so the work queue said "build these" when the library already had them.

The fix was to add the library's actual words, not to loosen the matcher. Two candidate tokens were rejected after probing every capability string, and the rejections are recorded in function_coverage.json so they stay visible:

Each tag now carries its firing token: match_evidence[unit] = ["cap <- token", …]. GATE 11 re-derives all 213 pairs from the units' own capability / task_hint_pool fields and refuses to write any tag it cannot reproduce. A fault-injection test that gave spinal_cpg_unit a fabricated rhythm_generate <- circadian evidence entry made the gate fail with the tag was not earned. That is the difference between a coverage matrix and a coincidence report — the matrix had overstated itself three times before this gate existed.

Composition semantics (v2.40.0) — can the library be assembled?

composable_with decides which units may be wired together, and until v2.40.0 nothing checked it. Gates 1–12 verified units are well formed, that function coverage is measured, that latencies are cited — none of them asked whether a declared combination can actually form.

Of 163 declarations across 115 units:

Usable combinations: 23 mutual pairs plus 28 one-way-but-reachable. For example amygdala_fear_unit exposes out: limbic.fear while hypothalamic_arousal_unit accepts only arousal.state / rhythm.circadian — no overlap, so that composition cannot be built.

Fixed in v2.41.0. The builder no longer takes a declaration at face value: suggest_composition now emits an edge only when the other unit declared it back or the port types actually meet, and records everything it dropped in skipped_edges with the reason — so a discarded composition is visible rather than silent. strict=False restores the old behaviour. On the memory query: strict keeps 2 edges and drops 1 with reason: ports_cannot_meet_and_not_reciprocated; legacy kept 3.

GATE 13 enforces it behaviourally, not by grepping source: it calls suggest_composition and checks that strict never returns more edges than legacy and that nothing appears in both the kept and skipped lists.

What is clean: internal_wiring has 0 dangling role references, and 115/115 units expose both an input and an output port.

The gate also had to survive bad data: its first version crashed on a composable_with entry naming a unit that does not exist, and a gate that dies on one bad reference cannot report the rest. With that fixed, injecting both a dangling role reference and a dangling target reports both and exits 1.

GATE 11 keeps it honest. It is the first gate that asks whether the library covers what the vision requires. It does not fail for having gaps — the gap list is the work queue. It fails when a gap becomes invisible: the summary disagrees with the matrix, a category names a unit the library does not have, tagged + untagged stops equalling the library size, or a cross_species claim contradicts its own count.

MaleCNS embodiment (v1.7.0, densified v1.8.0)

Everything above is literature-curated, which left the library unable to answer the first question any reader asks: does any of this correspond to the fly brain it is named after? functional_units/malecns_anchors.json (built by scripts/build_malecns_anchors.py) answers it, for the part where an answer is honest.

• Presence. 37 of 116 units have member populations that resolve to real annotated MaleCNS v1.0 cell types. Across the whole library that is 58 of 204 neuron populations covering 107,558 real cells: the kc role of the mushroom-body unit is 4,064 real Kenyon cells over 15 real KC types (KCg-m, KCab-m, KCa'b'-ap2 …), its input role is 583 real antennal-lobe projection neurons over 145 types (DA1_lPN, VM5d_adPN …), its gaba role is the 2 real APL cells. Matching is by an explicit name rule plus the dataset's own superclass — a name rule alone would have swallowed the clock neurons DN1a/DN1pA and the endocrine DNES1-3 into the descending-neuron anchor — and the complete match list is written into the artifact, so a wrong rule is auditable by reading JSON rather than by trusting a script.
• Measured structure instead of assumed structure. A single pass over the real synapse table produces the complete 27×27 superclass matrix and the anchored-cell-type matrix, independently cross-checked against connectivity_stats.json: both agree on 124,177,617 synapses over 25,582,938 edges. Each declared unit edge therefore carries the real measured synapse count between its two populations' actual cell types — antennal-lobe PN → Kenyon cell measures 378,315 synapses over 21,658 connected type pairs, Kenyon cell → mushroom-body output neuron measures 463,640, Kenyon cell → APL measures 210,352 — and in v1.8.0 the ten new Drosophila-shaped units went through the same test before their wiring was written: T4/T5 → lobula-plate tangential cell measures 296,217, medulla intrinsic → T4/T5 measures 1,924,544, PN → lateral horn measures 391,155, giant fiber → TTMn measures the famous 90 (over the real mixed electrical-chemical synapse), pC1 → pIP measures 1,973, AMMC → pIP measures 90.
• Support tier at type-pair resolution (v2.2.0). The profile-level count is still the contradiction gate, but it is no longer the whole verdict: every declared edge is additionally walked across the 5,413×5,413 anchored type-pair matrix, counting how many individual (from_type, to_type) pairs carry real synapses. That separates a pathway spread over many cell types from a profile-level non-zero carried by one lucky pair, and it surfaced six FRAGILE edges that the cell-type-set verdict had treated as fine: giant_fiber_escape_unit GF → TTMn (4 pairs, 1 live: DNp01 → TTMn = 90) and GF → PSI (1 pair: = 16); (240 pairs, 1 live: = 19); (145 pairs, 1 live: = 2); and both edges of ( = 16, = 3). (≥2 live pairs), (exactly 1), . FRAGILE is a , not a new contradiction — those edges do have measured support, they just should not be cited as strong evidence, and Gate 10 re-derives every tier from the raw matrix so a hand-written tier cannot survive.

Honest boundary. These units are curated from comparative-neuroscience literature with a named reference each (provenance.extracted_from_malecns is false for all 97) — they are NOT extracted from MaleCNS v1.0, unlike the three original fly motifs. Member ratios are composition weights, not measured cell counts. provenance.embodied_in_malecns marks the 31 units whose populations do resolve to real annotated cells. Embodiment proves the parts exist and that a declared edge is not contradicted; it does not prove the declared microcircuit or its connection form. Treat the layer as a composable scaffold with literature provenance, not as a measured cell assembly.

Machine-readable equivalents: list_functional_units, get_functional_unit, list_connection_types, compose_from_units. Regenerate + verify with python scripts/gen_functional_units.py and scripts/check_functional_units.py (Gate 9, which fails the build if the artifacts drift from the generator, if a member neuron does not exist in the vocabulary, or if the composer stops rejecting malformed wiring). The embodiment layer is rebuilt with python scripts/build_malecns_anchors.py and locked by scripts/check_embodiment.py (Gate 10, which recomputes every measured count from the matrices, re-derives every support tier from the raw type-pair matrix, re-injects nine defects every run, and refuses to let an unacknowledged contradiction or a hand-typed reason ship).

Science & paper

The methodology, the four moats, the six falsified mechanisms, and the two negative results are written up in docs/arxiv_paper.md (preprint-ready).

Research self-verification discipline (S6–S9)

This project follows four scientific self-verification disciplines that are uncommon in open-source tooling but essential for research integrity:

• S6 · Control group scoreability — every control experiment must verify that the control arm has scoreable samples in every task category. If a category is structurally unscoreable for the control arm, it must be excluded or reported separately. (Caught a false +33.4pp result in P1.)

• S7 · Resolution pre-check — before starting a scan experiment, verify that the target resolution matches the parameter-space geometric limit (clamp_density = n_nodes / (n * (n-1))). (Caught a silent clamp in R2.)

• S8 · Mechanism falsification as output — when a mechanism hypothesis is falsified, the result must include a structured falsified_mechanisms list (mechanism, hypothesis, test, result, verdict). Six mechanisms have been tested and falsified (P1 feature addition, P2 task alignment, P3 edge density, P1 correction, R3 monotonic gain, H-A critical edge sparsity).

• S9 · Perturbation analysis over correlation — when correlation R² < 0.05, perturbation data must exist and contain boundary signals. Correlation alone is insufficient for mechanism claims.

Xu 2026 dendritic computation — pattern reference (v3.00.3)

Xu, N., et al. (Science, 2026-10-16, DOI: 10.1126/science.adz5323) demonstrated with optogenetic causal evidence that apical dendrites encode abstract rules, cell bodies encode sensory input, and their nonlinear integration produces decision output. This is external theoretical support for our L3 boundary — it does not change the project's architecture.

Reference-only implementation (does NOT alter core combinatorial algebra):

Explicitly not done (see memo §2.3): no subcellular compartments in the 116-unit core, no cross-species transfer claim from mouse auditory cortex to Drosophila MaleCNS, no neuromorphic implementation. Xu 2026 is a pattern reference for few-shot hypothesis caching, not a new architecture. These disciplines are enforced by Gate 30 (scripts/check_llm_orchestrator.py), which independently recomputes claim metadata from experiment data and refuses to let an unverified claim pass.

Reproducible negative results

The project maintains a public research_claims.json with six claims (R1–R6), each containing: statement, quantitative prediction, falsification condition, reproduction command, sample size, random seed, boundary statement, and falsified mechanisms. Current status: 1 supported after metric fix, 3 complete, 2 at boundary (partial-falsification and anchor-gap, both terminal per PR-2b precedent). The research claim system is itself gated (Gate 30) to prevent silent drift.

Key references:

• MaleCNS v1.0 — Cell, 2026-09-03 (CC-BY). The 166,700-neuron male-brain connectome this project is grounded in. Obtainable from male-cns.janelia.org/download / gs://flyem-male-cns/v1.0/; queryable as neuPrint dataset male-cns:v1.0.
• Friston (2008) Neuropsychologia vs Fodor (1983) The Modularity of Mind — the massive-modularity debate our fault-isolation claim sits within.
• Double dissociation as the cognitive-neuroscience gold standard (Geddes 2014; Glenberg 2014; Campanella 2022; Vaidya PNAS 2010).
• dataprov (RI-SE, W3C PROV) — the provenance serialization we adopt.

Citing this work: see CITATION.cff (GitHub renders a "Cite this repository" button from it). Release-by-release changes, including which claims were retracted, are in CHANGELOG.md.

Using it from an MCP client: see docs/mcp_setup.md. Module variants, SwarmLabs integration, and the organism market (v1.1.0): see docs/module_factory.md. for client configs, the 47-tool reference, and troubleshooting.

Development & testing

One command runs every offline gate

The same set CI runs, so "green locally" and "green in CI" cannot diverge:

python scripts/run_gates.py              # 64 gates, no dataset needed
python scripts/run_gates.py --quick      # the 53 gates that don't need numpy / a server
python scripts/run_gates.py --with-data  # 64 gates: + `flybrain verify` (70 assertions)

Makefile shortcuts (v2.43.0)

make help          # all targets
make setup         # create .venv, install [dev]
make lint          # ruff check + black --check
make format        # ruff --fix + black
make typecheck     # mypy on shipped code
make test          # pytest --cov
make gates         # = python scripts/run_gates.py
make gates-data    # + flybrain verify
make smoke         # generate a smoke organism
make build         # build dist/{sdist,wheel}
make publish       # twine upload

Docker (v2.43.0)

Two flavors in one image. The dataset is never baked in — mount it:

docker build -t flybrain-connectome .

# MCP stdio server
docker run --rm flybrain-connectome

# HTTP API on :8799
docker run --rm -p 8799:8799 \
  -v "$(pwd)/male_cns:/data/male_cns:ro" \
  -e FLYBRAIN_DATA_DIR=/data/male_cns \
  flybrain-connectome serve-http

docker-compose.yml also ships a dev / api / mcp / gates service matrix — one command per use case:

docker compose up --build dev      # shell into the dev container
docker compose run --rm gates      # offline CI-equivalent gates

Dev container / Codespaces

Open in VS Code or Codespaces; .devcontainer.json installs everything and runs make gates on entry.

Pre-commit

pre-commit install       # one time
pre-commit run --all-files

Hooks: ruff + ruff-format + black + trailing-whitespace + end-of-file-fixer + check-yaml/toml/json + check-merge-conflict + debug-statements + detect-secrets (against .secrets.baseline).

Individually: each gate

# credential gate — MUST pass before any push (see CHANGELOG 1.0.0)
python scripts/check_no_secrets.py

# docs-vs-code drift gate: tool count/names, versions, schema, blueprint count
python scripts/check_docs_consistency.py

# every advertised MCP tool must actually respond (needs FLYBRAIN_DATA_DIR)
python scripts/check_mcp_tools.py

# a missing OPTIONAL dependency must degrade, not crash — and must not
# silently skip producing provenance when the dependency IS installed
python scripts/check_degraded_mode.py

# the generated artifact is structurally real (not just "exit code 0")
python scripts/check_generated_artifact.py --dir <out-dir> --name <organism>

# end-to-end smoke test (needs FLYBRAIN_DATA_DIR -> your data)
flybrain verify

# consistency between this repo and any downstream consumer
python integrations/swarmlabs/check_swarmlabs_sync.py

# reproducible experiments
python integrations/swarmlabs/diagnostics/fault_isolation_experiment.py --quick
python integrations/swarmlabs/diagnostics/snn_energy_experiment.py
python integrations/swarmlabs/diagnostics/multi_readout_experiment.py

Individually:

# credential gate — MUST pass before any push (see CHANGELOG 1.0.0)
python scripts/check_no_secrets.py

# docs-vs-code drift gate: tool count/names, versions, schema, blueprint count
python scripts/check_docs_consistency.py

# every advertised MCP tool must actually respond (needs FLYBRAIN_DATA_DIR)
python scripts/check_mcp_tools.py

# a missing OPTIONAL dependency must degrade, not crash — and must not
# silently skip producing provenance when the dependency IS installed
python scripts/check_degraded_mode.py

# the generated artifact is structurally real (not just "exit code 0")
python scripts/check_generated_artifact.py --dir <out-dir> --name <organism>

# end-to-end smoke test (needs FLYBRAIN_DATA_DIR -> your data)
flybrain verify

# consistency between this repo and any downstream consumer
python integrations/swarmlabs/check_swarmlabs_sync.py

# reproducible experiments
python integrations/swarmlabs/diagnostics/fault_isolation_experiment.py --quick
python integrations/swarmlabs/diagnostics/snn_energy_experiment.py
python integrations/swarmlabs/diagnostics/multi_readout_experiment.py

scripts/check_mcp_tools.py --offline skips the 11 data-backed tools, which is what CI does (the dataset is never downloaded in CI). So CI proves the generator surface and the packaging, while the data path is proved by flybrain verify on a machine that has the connectome.

License & data provenance

• Code: MIT — see LICENSE. Repo is PUBLIC since 2026-10-18. See LICENSE-CLAUSE.md for the 3-tier split and the re-publicization history.
• Connectome data: MaleCNS v1.0, CC-BY. The 5.63 GB dataset is not included in this repository. Obtain it from the original publication and set FLYBRAIN_DATA_DIR. All numbers in this project are derived from that real dataset; nothing is synthetically fabricated to look impressive.

"Virtual organism" is a software-layer analogy: we reuse real topology principles (motif shapes, recurrence rates, inhibition patterns), not real cells. It is not a wet-lab construct and is not claimed to be one.