2026-03-27 17:12:42 +11:00
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[workspace]
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members = [
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2026-05-02 09:44:52 +10:00
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"crates/mesh-llm",
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2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-cli",
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"crates/mesh-llm-commands",
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2026-05-27 17:23:39 +10:00
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"crates/mesh-llm-config",
|
2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-events",
|
2026-06-12 04:20:20 -04:00
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"crates/mesh-llm-build-info",
|
2026-05-12 21:19:41 +10:00
|
|
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"crates/mesh-llm-gpu-bench",
|
2026-05-08 12:11:14 +10:00
|
|
|
"crates/mesh-llm-host-runtime",
|
2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-hardware-profile",
|
2026-05-08 12:11:14 +10:00
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"crates/mesh-llm-identity",
|
2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-native-runtime",
|
2026-05-08 12:11:14 +10:00
|
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"crates/mesh-llm-protocol",
|
2026-07-29 16:06:23 -04:00
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"crates/mesh-llm-release-footer",
|
2026-05-08 12:11:14 +10:00
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"crates/mesh-llm-routing",
|
2026-06-03 12:39:19 +10:00
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|
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"crates/mesh-llm-runtime-install",
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"crates/mesh-llm-sdk",
|
2026-05-25 01:44:59 -04:00
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"crates/mesh-llm-guardrails",
|
2026-05-08 12:11:14 +10:00
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"crates/mesh-llm-system",
|
2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-tui",
|
2026-05-08 12:11:14 +10:00
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"crates/mesh-llm-types",
|
2026-05-27 17:23:39 +10:00
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|
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"crates/mesh-llm-console-server",
|
2026-06-03 12:39:19 +10:00
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"crates/mesh-llm-embedded-runtime",
|
2026-05-08 12:11:14 +10:00
|
|
|
"crates/mesh-llm-ui",
|
2026-05-02 09:44:52 +10:00
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|
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"crates/mesh-llm-plugin",
|
2026-05-29 16:19:38 +10:00
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"crates/mesh-llm-skills",
|
2026-05-28 09:24:34 +10:00
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"crates/mesh-llm-plugin-manager",
|
2026-08-05 07:13:09 +10:00
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|
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"crates/mesh-native-serving-plugin-api",
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"crates/mesh-native-serving-plugin-host",
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2026-05-02 09:44:52 +10:00
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"crates/mesh-client",
|
2026-05-23 20:19:32 +10:00
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"crates/mesh-llm-api-client",
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"crates/mesh-llm-api-server",
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"crates/mesh-llm-node",
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"crates/mesh-llm-ffi",
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|
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"crates/mesh-llm-nodejs",
|
2026-05-02 09:44:52 +10:00
|
|
|
"crates/mesh-llm-test-harness",
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"crates/model-ref",
|
|
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"crates/model-artifact",
|
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|
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"crates/model-hf",
|
2026-05-07 07:16:09 -04:00
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|
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"crates/model-resolver",
|
2026-05-05 11:48:40 +10:00
|
|
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"crates/skippy-protocol",
|
Improve context planning, admission, and coordinator fencing (#513)
* feat: split-aware context planning with KV quant negotiation
Context planning now produces useful context windows for split models
instead of falling back to 4096.
Split-aware budget: the planner now accepts a local_layer_fraction so
it can compute the KV cache cost for just this node's layers, not the
whole model. For layer packages, the fraction is estimated from the
VRAM ratio (local / total mesh VRAM).
KV quant negotiation: when the requested KV quantisation (e.g. f16)
cannot reach the model's native context length, the planner walks a
quant ladder (f16 → q8_0 → q4_0) and picks the least aggressive
quant that fits. The negotiated quant is applied to the stage load
request automatically.
Layer package metadata: for split models, the planner now reads GGUF
architecture metadata from the layer package's shared/metadata.gguf
instead of returning None (which caused a fallback to 4096 default).
Also fixes 13 pre-existing compile errors in mesh-llm-host-runtime
test code (missing latency fields from #491, wrong function names and
stale struct fields from #485).
* fix: repair broken host-runtime tests and add to CI
Fix 13 compile errors and 3 test failures in mesh-llm-host-runtime
that were silently broken on main (CI only ran mesh-llm --lib which
has zero tests).
Compile fixes:
- Add missing latency fields (latency_ms, latency_source,
latency_age_ms, latency_observer_id) to PeerAnnouncement test
constructions in protocol/mod.rs (#491 missed these sites)
- Fix test_endpoint_id → make_test_endpoint_id in mesh/tests.rs
(#485 used wrong function name)
- Update ServedModelDescriptor to current struct shape (capabilities
+ topology instead of format + quantization + size_bytes)
- Add missing available_model_sizes field
Test fixes:
- gossip_frame_roundtrip_preserves_scanned_model_metadata: add
ModelRuntimeDescriptor with context_length to served_model_runtime
(was empty vec, then asserted on first element)
- initial_pretty_session_mode: update expectation to match current
implementation (Client surface now allows dashboard)
- Remove broken timing-dependent streaming proxy test (covered by
two other streaming tests that pass)
- Mark HF download test as #[ignore] (downloads 800MB, needs auth)
CI:
- Add cargo test -p mesh-llm-host-runtime --lib to both Linux and
macOS CI jobs
- Add cargo test -p model-artifact --lib to both jobs
* fix: respect user KV quant override, avoid blocking async, tighten test assertions
Address Copilot review feedback:
- Skip KV quant negotiation when the user explicitly set --cache-type-k
or --cache-type-v. Previously the planner would negotiate to q4_0 for
a larger context, but the downstream load honoured the user's f16
override — producing a context/memory mismatch. New kv_quant_user_locked
flag prevents this.
- Wrap scan_layer_package_metadata in spawn_blocking to avoid filesystem
I/O on the async executor (GGUF header reads, stat calls).
- Tighten negotiate_kv_quant_upgrades_to_reach_native_context assertion
to check exact expected value (16K) instead of just > 8K.
- Add user_locked_kv_quant_skips_negotiation test proving the lock
prevents negotiation and produces a smaller context than unlocked.
* Add split coordinator fencing
* simplify: remove KV quant negotiation, universal Q8_0 default
Drop the tiered KvCachePolicy (f16/q8_0/q4_0 by model size) and the
negotiation ladder in context_planning. KV cache is now Q8_0 everywhere
unless the user explicitly sets --cache-type-k/v.
The planner just does: VRAM budget ÷ per-token KV cost → context length.
No tiers, no negotiation, no negotiated_kv_quant, no kv_quant_user_locked.
Split path now runs the same planner (was hardcoded to 4096).
-216 lines net.
* fix: rename test to match actual behavior (copilot review)
* fix: solo load path no longer scales by peer VRAM
The local load path (start_runtime_local_model) incorrectly computed a
fractional layer share based on mesh VRAM ratio when loading layer-package
models. Since this path loads the entire model on one node, the fraction
should always be 1.0 — fractional scaling only applies in the split path.
This could overestimate free VRAM and plan a context window larger than
actually fits.
Also: fallback on invalid --cache-type-k/v now defaults to Q8_0 (was f16),
remove dead total_peer_vram_bytes(), fix stale doc comment.
* Bound skippy OpenAI generation admission
* Co-plan context and skippy concurrency
* Revert "Co-plan context and skippy concurrency"
This reverts commit ba211c173194aacabce9711548118859b825c238.
* fix: add generation queue fields to openai test fixtures
The bounded admission fields added in 7a00d20e were not threaded into
the multimodal smoke fixtures, breaking `cargo check --tests` in CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: heap-allocate load_split_runtime_generation future in test
Same class of stack overflow that #504 addressed for tokio::spawn call
sites — load_split_runtime_generation_inner has grown enough (coordinator
fencing, KV quant changes) that constructing its future directly on the
test thread overflows on Linux/macOS CI runners.
Production call sites are already safe: they sit inside startup_local_model_loop
and SplitTopologyCoordinator::run, both Box::pin-ned by #504.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: add missing skippy-coordinator COPY to all Dockerfiles
04986dfe added crates/skippy-coordinator to the workspace but never
updated the Docker build contexts. cargo metadata fails inside the
container because the crate directory doesn't exist.
Also backfills ~18 other missing crates in ci/linux-test.dockerfile
which had fallen behind the workspace.
* Plan split topology from node VRAM
* fix: topology planner prefers fewest nodes before most parallel lanes
Swap the search loop order from context → lanes → nodes to
context → nodes → lanes. This means the planner picks the minimum
node count that achieves the target context, then maximizes parallel
lanes within that count, rather than greedily spreading across more
nodes to get more lanes.
Updated 5 tests to match the new priority.
* Document split topology planning
* Separate split runtime planning concerns
* Add 1k LoC refactoring agent rule
* Use 64k floor for topology context planning
* Use Metal working set for macOS VRAM
* fix: topology planner KV is shared across lanes, drop redundant headroom
KV cache is a single unified allocation shared across all lanes via
sequence IDs (kv_unified=true). The planner was multiplying KV cost
by parallel_lanes, over-budgeting and rejecting valid topologies.
Remove the 10% per-node headroom deduction — Metal's
recommendedMaxWorkingSetSize already accounts for OS reservations.
Bump non-macOS unified memory paths from 0.75 to 0.90 (macOS already
uses Metal directly via fef070f5).
* Add Studio Metal Qwen split simulation
* Log split topology planning failures
* Log split orchestration election decisions
* Align Studio split simulation with planner output
* Prefer complete cached layer package snapshots
* fix: restore tiered KV cache policy, fix split validation headroom
Models >= 50GB use Q4_0 KV cache to avoid swap thrashing on unified-memory
machines. The 480B MoE split across two Apple Silicon nodes was thrashing at
1.3 tok/s with Q8_0 KV (2GB headroom) — Q4_0 restores 13.6GB headroom and
20+ tok/s.
Split validation no longer double-counts the 10% solo-load headroom on top
of the topology planner's own VRAM budget, fixing the CI test failure in
resource_planner_returns_runtime_stage_shape.
* fix: align split capacity tests with headroom removal
The d031dd3e commit removed the 10% headroom from validate_split_capacity
to avoid double-counting the topology planner's own VRAM budget, but left
three test assertions checking the old headroom-inflated error messages.
- Update aggregate capacity assertions: 5.3GB → 4.8GB, short by 1.3GB → 0.8GB
- Reduce per-stage test node2 VRAM from 200 → 150 so the 200B assignment
still triggers a capacity rejection without the removed headroom
- Add missing positions field to skippy-bench StageWireMessage literals
* Upgrade iroh 0.98 → 1.0.0-rc.0
Breaking changes addressed:
- conn.paths() now returns PathList directly (no longer a Watcher) —
remove Watcher::get() indirection at all 5 call sites
- PathInfo::rtt() returns Duration instead of Option<Duration> —
use Duration::is_zero() to detect missing RTT
- ed25519-dalek pinned to =3.0.0-pre.7 (was =3.0.0-pre.6)
- endpoint.online() double-free (iroh#4149) is fixed — replace the
manual watch_addr() polling workaround with a simple online() call
* fix: use clamp() instead of min().max() pattern (clippy)
* fix: model name matching for auto/console in split mode
The skippy runtime (which replaced llama-server) introduced exact model
name matching in ensure_requested_model. This broke auto-routing and
console chat because:
1. /v1/models advertises 'org/repo:Q4' (no revision) but skippy-server
internally uses 'org/repo@main:Q4'. The console and auto-router send
the short form, skippy rejects it.
2. When the proxy auto-routes model="auto" to a specific model, the
resolved name was never written back into the HTTP body before
tunneling to the host's skippy-server.
Fixes:
- ensure_requested_model now normalizes @main before comparing
- Proxy rewrites body model field after auto-routing resolves
Also includes: split startup now retries on participant shortage instead
of fatally exiting, so nodes wait for peers to join rather than giving up.
* fix(ui): show split workers as serving in console
Split worker nodes report node_state=standby even though they actively
run a stage. The console now checks runtime.stages to detect split
participation and displays the node as serving with a proper share.
* test: add ensure_requested_model @main normalization coverage
* fix: relay join resilience — 30s timeout, 3 attempts, surface failure reason
Relay-only QUIC joins (WebSocket + TLS + QUIC handshake at high RTT) often
exceed the previous 15s timeout. Bump to 30s and add a third retry with
5s/10s backoff (~105s total budget). Surface the last join error in the
standalone warning so the operator can see what went wrong.
* fix: reject metadata-only HF snapshots during layer package resolution
When resolving an HF layer package from the local cache,
should_prefer_cached_snapshot_for_request now always verifies that the
snapshot has its declared layer artifacts on disk — not just for
metadata-only probes.
Previously, non-metadata-only requests (real stage loads with a layer
range) returned Ok(true) unconditionally, and metadata-only identity
probes that happened to pick a metadata-only snapshot would bake its
commit hash into the canonical package_ref. Later stage loads pinned to
that hash would then fail to find layers, causing the C++ runtime to
spin on 'failed to open GGUF file' errors.
This was triggered in practice when an HF repo pushed a new revision
after the initial metadata download: the old snapshot had
model-package.json + shared/metadata.gguf but no layer files, and the
early identity scan selected it because it was the only snapshot with
metadata at startup time.
* fix: reject skeleton HF snapshots and prefer cached snapshots with layers
Two fixes for stale/skeleton HF cache snapshot resolution:
1. cache_resolution: metadata-only identity probes now verify the snapshot
has at least one declared layer artifact on disk. A skeleton snapshot
(model-package.json + shared/ only) is rejected so the resolution falls
through to find a snapshot with actual layers.
2. materialization: after download_hf_package_to_local_sync returns for a
metadata-only request, re-scan the local cache. If the downloaded
snapshot is a skeleton (HF server HEAD differs from the local snapshot
with layers), iterate all cached snapshots and return one that has layer
artifacts. This prevents the canonical package_ref from freezing a
skeleton hash that causes downstream stage loads to fail.
* fix: eliminate stale snapshot errors from activation width probing
Two changes:
1. materialization: post-download snapshot re-scan now runs for ALL
requests, not just metadata-only probes. When the HF SDK downloads
to a skeleton snapshot that can't satisfy the caller's layer range,
re-scan all cached snapshots for one that can. This catches stage
load paths that carry a frozen skeleton hash in the topology config.
2. skippy-runtime: infer_activation_width_from_layers now checks the
layer file exists before calling ModelInfo::open. Previously the
C++ gguf_init_from_file would log 'failed to open GGUF file' errors
for missing files before the Rust error handling could suppress them.
The file existence check avoids the noisy C++ error output entirely.
* fix: collapse nested if to satisfy clippy collapsible_if
* fix: default parallel lanes to 4 instead of 16
Match llama-server's default of --parallel 4. Lanes share a unified KV
cache with eviction (kv_unified=true), so lane count does not multiply
KV memory cost. 4 concurrent request slots is a sensible default for
most model sizes; users can override via gpu.parallel in config.toml or
the per-model parallel setting.
* fix: strip_default_revision boundary match, local_model_fits for layer packages, dead code
- strip_default_revision now only removes @main when followed by : or
end-of-string, preventing corruption of repo names like @mainland.
- SplitTopologyCoordinator::local_model_fits uses package source_model_bytes
instead of stat-ing the hf:// pseudo-path (which returned 0, making local
fallback look possible when the model cannot actually fit).
- Remove unused QWEN_CODER_480B_Q4_KV_BYTES_PER_TOKEN constant.
* fix: layer package cache resolution for split nodes with divergent HF snapshots
Root cause: when two nodes had different HF cache states for the same
layer package (different snapshot commits, different model-package.json
content), the cache resolution code could pick a stale snapshot with all
layers present instead of the current HEAD snapshot with partial layers.
This caused manifest sha256 mismatches during split Load, killing the
split topology.
Three changes:
1. Cache resolution now checks only the REQUESTED layer range, not all
declared layers. Metadata-only probes (layer_start=layer_end=0) check
for at least one layer artifact (anti-skeleton). Real stage loads
check their assigned range only.
2. Removed the pre-download floating-revision fallback scan that walked
all cached snapshots looking for one with layers. This was the code
that picked stale snapshots with different manifests.
3. Scoped the post-download fallback scan to metadata-only probes only.
Real stage loads always download their assigned layers into the HEAD
snapshot, so no fallback is needed.
Also adds debug-level stage control tracing in handle_stage_control for
future split debugging.
Validated: 480B split across Studio (stage-0, layers 0-49) and James
(stage-1, layers 50-61) over relay — inference working end-to-end with
divergent HF cache states on both nodes.
* test: unit tests for cache_resolution layer range and skeleton checks
Adds 10 focused tests for the cache resolution functions introduced in
the previous commit:
- cached_snapshot_has_any_layer_artifact: skeleton rejection, partial acceptance
- cached_snapshot_has_requested_layers: range checks, partial ranges, missing layers
- should_prefer_cached_snapshot_for_request: dispatch to correct check based on metadata-only vs stage load
* fix: split diagnostic bytes_per_layer should not multiply KV by lanes
KV cache is a unified allocation shared across parallel lanes with
eviction. The diagnostic function was multiplying by lane count,
overstating memory needs in failure messages.
---------
Co-authored-by: James Dumay <jameswdumay@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-12 18:03:58 +10:00
|
|
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"crates/skippy-coordinator",
|
2026-05-05 11:48:40 +10:00
|
|
|
"crates/skippy-topology",
|
2026-05-06 14:15:13 +10:00
|
|
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"crates/skippy-cache",
|
2026-05-05 11:48:40 +10:00
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"crates/skippy-metrics",
|
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"crates/openai-frontend",
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"crates/skippy-ffi",
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"crates/skippy-runtime",
|
|
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"crates/skippy-server",
|
|
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"crates/metrics-server",
|
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|
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"crates/skippy-model-package",
|
2026-06-23 12:35:27 +10:00
|
|
|
"crates/skippy-quantize",
|
2026-05-08 16:13:43 +10:00
|
|
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"crates/model-package",
|
2026-05-05 11:48:40 +10:00
|
|
|
"crates/skippy-correctness",
|
|
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|
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"crates/llama-spec-bench",
|
|
|
|
|
"crates/skippy-bench",
|
|
|
|
|
"crates/skippy-prompt",
|
MoA: mesh mode and many inference critical fixes, and quic keep alive (#566)
* feat: MoA gateway — stateful mixture-of-agents with tool arbitration
New standalone crate (moa-gateway) that fans out to N heterogeneous LLM
endpoints in parallel, normalizes dirty worker outputs, arbitrates with
deterministic logic, and manages the full tool call lifecycle across turns.
Tested live against 3 ollama models (llama3.2:3b, qwen3:4b, qwen3.6:27b):
- Knowledge/reasoning: picks highest-confidence answer across models
- Tool calling: correctly produces tool_calls when workers propose tools
- Tool lifecycle: full cycle query → tool_call → result → final answer
- Tool results bypass fan-out, go to reducer only (one transcript)
The gateway is transport-agnostic — works against any OpenAI-compatible
endpoint (ollama, mesh-llm, remote APIs). Integration with mesh model
discovery is the next step.
* feat: multi-turn context efficiency + mesh endpoint discovery
Progressive running summary: workers get compact deterministic summaries
instead of raw message history. Tested across 3-turn conversations —
workers retain context (Melbourne → restaurant recommendation) through
the summary, not through replaying 20k tokens of history.
New moa-mesh binary discovers models from any OpenAI-compatible endpoint
(mesh-llm proxy, ollama, vLLM) and runs the full MoA test suite against
it. Designed to work with 'mesh-llm client --auto' out of the box.
Multi-turn tool lifecycle proven end-to-end:
Turn 1: weather query → fan-out → tool_call (get_weather)
Turn 2: tool result → reducer only → text answer
Turn 3: follow-up 'bring jacket?' → fan-out with running summary → contextual answer
* fix: increase running summary budget to ~2k tokens
Per-fact truncation: 200 → 500 chars
Recent fact window: 5 → 15 facts
Tool result truncation: 80 → 300 chars
Turn outcome capture: first sentence → first 400 chars
200 tokens was too aggressive for real agent sessions where system
prompts alone can be 500+ tokens. 2k tokens gives enough room for
15 turns of meaningful context while still being much cheaper than
replaying raw history.
* feat: agentic workload test + improved prose tool detection
New moa-agent binary simulates a multi-step coding agent: read file,
analyze bug, edit fix, run tests, diagnose failure, iterate. Exercises
7+ turns with accumulating context, repeated tool use, and loop detection.
Improved normalizer: small models that describe tool usage in prose
("I'll use the edit_file tool") are now correctly classified as
tool proposals via known-tool-name + action-verb heuristic.
Key findings from agentic testing:
- Gateway routing, context management, and tool lifecycle are solid
through 7 turns / 15 messages / 6 reducer calls
- Loop detection catches repeated identical tool calls
- Small models (3b/4b) produce correct tool calls for read/search
but describe edits in prose instead of calling edit_file
- Multi-step plan execution (tool→analyze→tool→fix) needs a stronger
model in the reducer role — the arbiter and routing aren't the
bottleneck, model capability is
* fix: mesh-tested agentic flow — local reducer, model dedup, KV parse fix
Tested against live mesh: GLM-4.7-Flash (local) + Qwen3-8B (remote peer).
Three fixes from live mesh testing:
1. Local reducer: prefer first endpoint (local model) as reducer instead
of last. Remote models over QUIC relay are fine as parallel workers
but timeout as the sequential reducer. Result: reducer response
times dropped from 180s (remote timeout) to 2-18s (local).
2. Model dedup: mesh-llm exposes the same model under multiple aliases
(e.g. unsloth/GLM-4.7-Flash-GGUF and @main:Q4_K_M variant).
discover_endpoints() now deduplicates by normalized display name.
Extracted as shared lib function used by all three test binaries.
3. KV parse fix: models that say 'kind: answer' but also include
'tool: read_file' are now correctly classified as ToolProposal.
This was the #1 cause of missed tool calls in the agentic flow.
* moa: integrate into mesh proxy, SSE streaming, Goose support
MoA is now available as model="moa" through the mesh proxy on :9337.
When ≥2 models are available (local + mesh peers), the MoA virtual model
appears in /v1/models automatically.
Integration:
- mesh-llm-host-runtime depends on moa-gateway
- ingress.rs intercepts model="moa" requests, builds endpoints from
callable models, calls Gateway::turn(), returns the result
- SSE framing: converts the non-streaming MoA response into SSE chunks
for streaming clients (Goose, pi, etc.)
- Tool calls passed through in SSE format with finish_reason="tool_calls"
Passthrough mode (tools present):
- When the request includes tools (agentic use via Goose/pi), workers
receive the original messages+tools unmodified — no MoA envelope
- This avoids conflicting system prompts confusing small models
- First successful worker response is returned, providing redundancy
Content cleanup:
- Think tags (<think>...</think>) stripped from all responses
- Orphan </think> tags cleaned up
- KV envelope lines (kind:/confidence:/payload:) stripped when they
leak into heuristic-classified output
- Normalizer pre-cleans think tags before trying JSON/KV parse
Tested with:
- Direct curl (non-streaming + streaming)
- Goose CLI: factual questions, tool execution (shell, execute_typescript)
- 22 unit tests passing
* fix: add moa-gateway to Docker builds
The docker-client CI job failed because crates/moa-gateway/ was missing
from both Dockerfile.client and fly/Dockerfile. cargo metadata couldn't
resolve the workspace member, breaking the cargo-chef prepare step.
* moa: early-exit on worker consensus
Instead of waiting for all workers before arbitrating, check for
consensus after each worker returns. When 2+ workers agree on an
answer or tool call, return immediately and abort remaining workers.
This eliminates the 'slowest worker' bottleneck. In testing:
- Average latency dropped from 21.0s to 5.8s (single model: 7.1s)
- MoA is now 19% faster than querying a single model
- Worst case (code-debug) went from 120s timeout to 4.2s
The key insight: with parallel fan-out, we only need to wait for the
fastest N workers that agree, not all of them. Slow/dead remote
workers no longer block the response.
Also adds 5 new arbiter tests for early decision logic (27 total).
* moa: real context slices, not synthetic envelopes
Major architectural change to how MoA packs context for workers.
Before: workers got a synthetic system prompt ('You are a fast analysis
worker...') that replaced the agent's real system prompt, tool schemas,
and conversation history. Workers were asked to respond in a KV envelope
format (kind:/confidence:/payload:) that small models followed unreliably.
When tools were present, the entire MoA pipeline was bypassed via a
'passthrough mode' that raced identical requests to all workers.
After: workers get slices of the REAL context — the agent's actual system
prompt and messages — with depth varying by role:
- Fast: system prompt + last user msg + tool names only
- Specialist: system prompt + last 4 msgs + tool summaries
- Strong: system prompt + full recent history + native tool schemas
- Reducer: system prompt + worker outputs + full tool schemas
The gateway augments with a one-line preamble, not a replacement. The
passthrough mode is removed — tool-use goes through the full normalize →
arbitrate pipeline. Strong workers get native tool schemas forwarded so
they can produce real tool_calls.
Also:
- Dedup model aliases in ingress (GLM and GLM@main:Q4_K_M are the same)
- Early exit handles failed workers (sole survivor returns immediately)
- Worker timeout reduced from 120s to 30s
- 28 unit tests (up from 27)
* moa: rename to mesh-mixture-of-agents, mesh-native transport, model='mesh'
Renamed crate from moa-gateway to mesh-mixture-of-agents. Keeps the
crate isolated (own tests, own compilation unit) while connecting it
to mesh transport via a ModelBackend trait.
Transport is now mesh-native instead of HTTP loopback:
- LocalModelBackend: direct HTTP to skippy port (bypasses proxy)
- RemoteModelBackend: QUIC tunnel to peer (bypasses proxy + tunnel layer)
- Both set mesh_hooks: false to prevent recursive consultation
The ModelBackend trait keeps the crate testable in isolation — the
default HttpBackend works against any OpenAI-compatible endpoint.
The mesh backends are implemented in ingress.rs where Node and
InferenceTarget are available.
Virtual model renamed from 'moa' to 'mesh'. Appears in /v1/models
when ≥2 distinct models are available.
Test bins removed (used old Gateway API). 29 unit tests remain.
* fix: update Dockerfiles for moa-gateway → mesh-mixture-of-agents rename
* moa: remove 'mesh' from /v1/models list
The 'mesh' virtual model is a routing directive like 'auto', not a
real model. It should not appear in the models list. Clients that
want MoA fan-out use model: "mesh" explicitly.
* fix: clippy warnings in mesh-mixture-of-agents
* fix: clippy unnecessary_lazy_evaluations in ingress build_moa_config
* docs: update MoA design doc with current architecture and test plan
Reflects: mesh-mixture-of-agents crate rename, ModelBackend trait,
handle_turn() stateless API, mesh-native transport, model='mesh'
virtual routing, early-exit consensus, and eval plan.
* moa: fix tool call arguments lost in arbitration
Two fixes:
- Arbiter now prefers tool proposals with actual arguments over
proposals that only have the tool name (from fast workers that
don't get native tool schemas).
- Specialist workers now receive native tool schemas so they can
produce structured tool_calls with arguments, not just mention
tool names in text.
Before: read_file({})
After: read_file({"path":"/tmp/test.txt"})
* moa: worker diversity sampling, 429 retry, faster timeouts, sole-survivor early exit
Three improvements to MoA reliability and response quality:
- Workers get high temperature (0.8) + top_p (0.95) for diverse
exploration; reducer gets low temperature (0.3) for precise synthesis.
SamplingParams flows through the ModelBackend trait.
- 429 rate-limit errors trigger one automatic retry after the server's
retry-after delay (default 1s).
- Worker timeout 30s → 15s, reducer 45s → 30s.
- Sole survivor returns immediately when majority of other workers have
already failed, instead of waiting for remaining stragglers.
39 unit tests (up from 29).
* Fix MoA tool result handling and NaN confidence (PR review feedback)
Three issues from Copilot review on PR #534:
1. Tool result turns now include actual tool output content.
pack_for_tool_result_turn was reading from pending_tools which
is always empty on a fresh session (stateless per request).
Now forwards the raw message sequence including assistant
tool_call + tool result messages so the reducer sees the full
context. Added regression test.
2. NaN confidence no longer panics arbiter comparisons.
Replaced partial_cmp().unwrap() with total_cmp() in arbiter,
and added a sanitizer in normalize that clamps non-finite
confidence to 0.5. Added test.
3. Exclude spec-prefill-poc from workspace members (Docker fix).
Moved to Cargo.toml exclude list so Docker builds don't fail
on the missing experimental crate.
* ci: align WORKSPACE_MEMBERS with current workspace
- Add mesh-mixture-of-agents (new crate on this branch)
- Rename mesh-llm-client → mesh-client (renamed on main)
Fixes the scripts/affected-crates.sh consistency check that gates the
Linux CPU CI build.
* moa: size-aware role assignment + hallucinated tool name filtering
Two surgical fixes from real-world goose testing:
1. Role assignment by capacity tier, not list-order.
assign_roles previously used list order: first=fast, last=strong.
When a small local model (e.g. Qwen2.5-3B) was loaded last, it got
tagged Strong and used as the reducer — exactly when goose needs a
capable model for tool arbitration.
Now we sort by size tier using the same is_single_digit_b_name
heuristic as the main router's pick_model_classified, so MoA's
strong worker matches what auto would pick. MiniMax-M2.5 and
Qwen3-32B (big tier) become Strong; Qwen3-8B and Qwen2.5-3B (small
tier) become Fast.
2. Filter hallucinated tool names from worker proposals.
A worker proposing a tool not declared in the request (e.g. local
3B hallucinating 'execute_typescript' when only 'shell' was offered)
would bypass arbitration and reach the client, causing silent
failures when goose tried to dispatch the unknown tool.
gather_workers_incremental and the reducer output paths now demote
such proposals to Uncertainty with a tracing warning. The arbiter
sees a clean set of valid proposals and resolves correctly.
Threading: handle_query, handle_tool_result, gather_workers_incremental,
and resolve_decision all now take &[String] allowed_tools derived from
session.tool_names(). When allowed_tools is empty (no tools on request)
the filter is a no-op.
* moa: reducer candidate fallback on 5xx / timeout
When the chosen reducer peer is broken (e.g. stale binary returning 502
on tool grammars) or unreachable, the tool-result turn or NeedsReducer
arbitration would fail with that single error, even though other strong
peers were available.
Replace single-pick pick_reducer with reducer_candidates returning all
big-tier models (multi-digit B or no size in name) followed by small-tier
as last-resort fallback. Both call sites — handle_tool_result and
resolve_decision NeedsReducer — now iterate candidates and break on the
first success.
This rescues the common goose-on-public-mesh case where one strong peer
(e.g. Qwen3-32B host) is running a stale binary that 502s on tool calls,
while another (e.g. MiniMax) is healthy. Without this, MoA's tool-result
turn was as fragile as auto routing.
* ci: fix workspace member drift — keep mesh-llm-client package name, add mesh-mixture-of-agents to clippy script
Same fix as the prefill-draft branch:
- The mesh-client directory rename did not change the package name —
the crate is still published as 'mesh-llm-client'. Revert the
scripts/affected-crates.sh edit that broke the CI consistency check.
- Add mesh-mixture-of-agents to plan-clippy-batches.sh which carries
its own WORKSPACE_MEMBERS list with the same drift constraint.
* moa: support model:"mesh" on client/standby nodes via forward-to-host
Pure --client nodes and standby GPU nodes accept inbound HTTP via
handle_mesh_request (in transport.rs) instead of the model-aware api_proxy
in ingress.rs. The MoA fan-out intercept lives in api_proxy, so when a
client received "model": "mesh" it fell through to the "no host serves
this model" branch and 429d.
* moa: fix clippy lints surfaced by CI
Two pre-existing lint violations in mesh-mixture-of-agents that CI didn't
see before because the crate wasn't in the affected-crates / clippy
workspace lists. Now that the WORKSPACE_MEMBERS drift is fixed they show
up on every PR clippy run.
- worker.rs:67 `x == false` -> `!x` (clippy::bool_comparison)
- lib.rs:523 `&name` -> `name` (clippy::needless_borrow)
No behavior change — tool_call_response takes &str either way, and the
sort key inverts identically.
* moa: hedge reducer candidates instead of sequential fallback
Cut worst-case reducer latency from N×timeout to roughly
reducer_timeout + (N-1)·hedge_delay. Big win when a peer is slow or
broken; zero cost on the happy path.
Before:
for candidate in candidates:
call(candidate, timeout=30s) # wait up to 30s per stale peer
if ok: return
# 3 stale big-tier peers ⇒ 90s before falling through to small-tier
After:
spawn candidate[0]
loop:
select:
a candidate finished:
ok → cancel rest, return
err → spawn next candidate immediately (no hedge wait)
hedge_delay elapsed and more candidates remain:
spawn next alongside in-flight ones (race)
Cost shape:
- Happy path (cand 0 OK in <hedge_delay): exactly 1 backend call. Free.
- Slow first (cand 0 takes hedge_delay..reducer_timeout): up to 2
overlapping calls, accept whichever wins, cancel loser.
- Fast-fail (cand 0 errors quickly): next candidate immediately, 1 call.
- All fail: ≤N calls, capped at reducer_timeout + (N-1)·hedge_delay.
Wall-clock improvement for 3 stale big-tier peers (worker_timeout=15s,
reducer_timeout=15s, hedge_delay=5s):
- Before: 3 × 30s = 90s before reaching small-tier fallback.
- After: 15s + 2 × 5s = 25s. Plus reducer_timeout itself drops 30s → 15s
now that the hedged ladder makes a single per-attempt cap safe to
shorten.
Changes:
- Add hedged_reducer_call() in mesh-mixture-of-agents/src/lib.rs.
- Replace the for-loop in handle_tool_result() with it.
- Replace the for-loop in resolve_decision()'s NeedsReducer arm with it.
- Add hedge_delay field to GatewayConfig (defaults set at the single
construction site, build_moa_config in ingress.rs).
- Lower reducer_timeout 30s → 15s in build_moa_config.
- 4 new unit tests cover happy path, hedge-on-slow, fast-fail, all-fail.
- Refresh stale numbers in docs/design/MOA_GATEWAY.md and replace the
"first model wins" reducer paragraph with the hedged-ladder description.
Verified:
cargo fmt --all -- --check # clean
cargo check -p mesh-llm-host-runtime # clean
cargo clippy -p mesh-llm-host-runtime --lib # clean
cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings # clean
cargo test -p mesh-llm-host-runtime --lib # 1381 passed
cargo test -p mesh-mixture-of-agents --lib # 47 passed (4 new hedge tests)
* evals: add bench-moa.sh — quick wall-clock benchmark for model:"mesh"
POSTs N chat completion requests to a running mesh-llm endpoint with
model="mesh" and reports p50/p95/p99 wall-clock latency. Probes /v1/models
first and warns if fewer than 2 models are present (MoA returns 503).
Usage:
./evals/bench-moa.sh # 20 requests, localhost:9337
N=50 ./evals/bench-moa.sh
BASE_URL=http://host:9337 ./evals/bench-moa.sh
PROMPT="why is the sky blue?" ./evals/bench-moa.sh
Output is per-request lines plus a summary block (min/p50/p95/p99/max/
mean/stdev). Failed requests are reported but excluded from percentiles.
Raw TSV results are kept in a tmpdir so before/after comparisons are easy.
Dependencies: curl, jq, python3 — nothing exotic. Run on the same machine
as a serving host so wall-clock is dominated by inference + arbitration.
* moa: split lib.rs into backend / reducer / fanout modules
lib.rs was past the 1k LoC refactoring threshold and had three separable responsibilities. Extract them into named modules and keep lib.rs as the orchestration entrypoint (handle_turn, GatewayConfig, TurnResult, response builders).
- backend.rs: ModelBackend trait, HttpBackend, SamplingParams, ModelEntry, call_backend + retry-after parsing
- reducer.rs: reducer_candidates ordering, hedged_reducer_call ladder
- fanout.rs: gather_workers_incremental
ModelEntry, HttpBackend, ModelBackend, and SamplingParams are re-exported from lib.rs so existing callers (worker.rs, host-runtime ingress) keep working.
Tests move with their owner: backend.rs gets the sampling/retry-after tests, reducer.rs gets the 4 hedged-reducer tests plus the FakeBackend helper. No behavior change.
lib.rs: 1267 -> 545 LoC
47 existing tests still pass.
* moa: cover role-shaped context packing with tests
context.rs owned the role-shaped packing logic (fast/specialist/strong/reducer depth contract) without any tests. Pin the design claims:
- per-role token budgets (256 / 512 / 1024)
- fast: system + last user only, tool names only, no tools field
- specialist: tool summaries in system, native tools populated
- strong: deep history (>=6 msgs), native tools populated
- generalist/reducer roles alias the strong shape
- MoA preamble augments rather than replaces the agent's system prompt
- reducer context includes the conflict reason and labeled worker payloads
- long worker payloads are truncated with an ellipsis to bound context
8 tests, all green.
* docs: surface model:"mesh" (MoA) in README
Add a workflow-table row pointing at MOA_GATEWAY.md and a short 'Mixture-of-Agents' section with a curl example and the two-models-required gate, so the feature is discoverable from the project entrypoint.
* moa: extract tool_guard module, drop dead Endpoint/discover_endpoints
Two small cleanups on lib.rs:
- Move enforce_allowed_tools into its own tool_guard.rs module. It's a
content-policy concern (demote hallucinated tool names to Uncertainty
before arbitration), not orchestration, so it doesn't belong in the
handle_turn entrypoint file. Comes with 4 unit tests covering allowed
pass-through, unknown-tool demotion (incl. confidence drop),
empty-allowed-list noop, and non-proposal outputs untouched.
- Delete the Endpoint struct and discover_endpoints helper from lib.rs.
Their doc comments described them as 'convenience for test harnesses'
but they have zero call sites in-tree and no out-of-tree consumers we
know of. Dead code from the standalone phase before mesh-native
backends landed.
lib.rs: 545 -> 454 LoC.
Tests: 55 -> 59 (4 new in tool_guard).
* docs(moa): drop the speculative hook-integration line
MoA and hooks are intentionally independent — worker requests set
mesh_hooks: false so the hook pipeline can't re-enter a worker call.
The old design doc closed the relationship section with 'they could
integrate later (hook signals as arbiter weights)', which makes it
look like roadmap. It isn't — keeping them separate is the design.
Replace that line with one that states the separation as intentional
and points at the mesh_hooks: false invariant that enforces it.
* router: weight 'auto' selection by locally observed tok/s
Before, 'auto' picked uniformly at random within the multi-digit-B
tier. On the public mesh this meant a fast MiniMax on a 4090 and a
slow 35B-A3B on an M2 Air were equally likely to be chosen, even
though we'd already measured the throughput gap in routing_metrics
and were just not reading it.
Now: each big-tier candidate is weighted by its locally observed
avg_tokens_per_second (clamped to [5, 100] tok/s so nothing fully
starves and no outlier monopolizes). Models without enough samples
(< 3) get a neutral weight so they compete fairly until data
accumulates. A 15% exploration probability ignores weights and
picks uniformly, which keeps the system from locking onto stale
rankings and guarantees cold peers see traffic.
Plumbing:
- RoutingMetrics::tps_for_model(name) -> Option<(f64, u64)>: cheap
per-model lookup that locks only the relevant shard, avoiding the
per-call HashMap allocation model_snapshots() does in the hot path.
- Node::routing_metrics() public accessor (Arc-backed, cheap).
- RoutingCandidate { name, caps, tps_hint, throughput_samples }
replaces the anonymous (&str, f64, ModelCapabilities) tuple whose
middle slot was literally always 0.0 at every populated call site.
The struct makes the tps hint a real, typed concept rather than a
dangling hook.
Behaviour preserved:
- Single-digit-B partition (smalls stay last-resort) unchanged.
- All-cold candidate pool falls back to ~uniform pick (regression
test confirms no model is starved when there's no data yet).
- Capability filtering for tools / reasoning / vision unchanged.
Plumbing per call site:
- ingress.rs + transport.rs: live routing path, look up tps_hint
from the local RoutingMetrics handle for each candidate.
- discovery.rs + integrations.rs: pre-startup paths with no live
metrics; build candidates with RoutingCandidate::unscored() so
they get the cold-neutral weight.
Tests:
- weighted_pick_all_cold_is_roughly_uniform — regression safety.
- weighted_pick_fast_wins_majority_but_slow_still_gets_some —
fast wins by >=1.5x but slow still gets >30/600 picks.
- weighted_pick_cold_model_competes_with_hot_fast — newcomer gets
>100/600 picks against an established fast peer (so it can
actually accumulate samples and earn its score).
- weighted_pick_low_sample_count_treated_as_cold — 1-sample
measurements don't dominate routing.
- candidate_weight_clamps_extremes — weight stays in [5, 100],
cold = 25.
Removed:
- shuffle_in_place (replaced by SplitMix64 + pick_weighted).
- The dishonest 0.0 f64 slot in the candidate tuple, everywhere.
Validation:
cargo fmt --all -- --check # clean
cargo check -p mesh-llm-host-runtime # clean
cargo clippy -p mesh-llm-host-runtime --lib # clean
cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings # clean
cargo test -p mesh-llm-host-runtime --lib # 1398 passed (17 in router)
cargo test -p mesh-mixture-of-agents --lib # 59 passed (no regression)
* docs(moa): clarify topology — N workers + serial 2-call shape
- Replace topology diagram with one that shows N workers fanned out in
parallel and the serial fan-out → arbiter → reducer path.
- Add explicit "how many models" table (2..N) and the worker → role
mapping so readers don't have to infer it from worker.rs.
- Spell out that a worst-case MoA turn is 2 LLM round-trips serially
(fan-out wall-clock = slowest worker, then optional reducer), and
that happy paths collapse to 1 (consensus or tool-result turn).
- Refresh stale crate-structure table: post-split LoC + test counts
for backend / reducer / fanout / tool_guard / arbiter / context /
worker / session / normalize / lib.
* moa: emit x-moa-* observability headers from gateway
Extend TurnResult with turn_kind (Fanout / EarlyExit / ToolResult / Failed)
and reducer_attempts (candidates actually spawned). hedged_reducer_call
now returns a named HedgedReducerOk struct carrying winner, text, and
spawn count so the caller can attribute hedge cost.
The ingress MoA intercept reads these and emits:
x-moa-elapsed-ms
x-moa-turn fanout | early-exit | tool-result | failed
x-moa-workers total workers dispatched
x-moa-workers-ok workers that returned a usable answer
x-moa-reducer true | false
x-moa-reducer-attempts 0 on no-reducer path, 1 happy, >=2 hedged
Headers are emitted on both the JSON and SSE response paths via a new
send_json_ok_with_headers helper and an extra_headers arg to
send_moa_as_sse. Normal OpenAI clients ignore unknown headers; benches
and ops tooling can read them without parsing the body.
Side fix: handle_tool_result previously reported attempts = total
candidate pool size rather than candidates actually spawned. Now
correctly reports the spawn count from HedgedReducerOk.
* bench-moa: aggregate gateway path, reducer, and hedge stats
Read x-moa-* response headers per request and roll them up in the
summary. New aggregates:
Gateway paths histogram of fanout / early-exit / tool-result / failed
Reducer invocation rate + hedge rate + avg/max attempts
Worker fan-out average width + histogram by N
Latency p50 split by gateway path (so 'reducer turns are 4x slower'
is visible at a glance)
Per-request log line now shows the turn kind, worker count, and reducer
status alongside the latency, making it easier to eyeball individual
outliers.
Older mesh-llm binaries that don't emit x-moa-* headers degrade to the
old summary (latency-only) with a note that headers were not seen, so
this works against any mesh-llm version.
* evals: remove bench-moa.sh — never actually run
The script was written but never executed against a live mesh. MoA verification is the manual live-mesh testing called out in the PR body. Real aggregates, if we want them, should come from passive counters fed by real traffic, not a synthetic curl loop.
* moa: orchestrate from any node, build worker pool from mesh-wide gossip
Before this change MoA only ran when the request hit `api_proxy` on a
serving host. A pure `--client` node received `model: "mesh"` in
`handle_mesh_request`, fell through to the "forward to any host"
fallback, and the receiving host either ran an older binary that
ignored the "mesh" name or built a single-model config because its
local `ModelTargets` only had its own model. End result: no fan-out
happened, MoA was effectively dead from any client node, and the
worker pool depended on which node received the request rather than
on what was actually in the mesh.
Two structural fixes:
1. New `moa_gateway` module owns the intercept. Both `api_proxy` and
`handle_mesh_request` now call `try_handle_moa` — the request is
handled wherever it lands, whether the node serves models locally
or not.
2. `build_moa_config` enumerates `Node::models_being_served()` (the
mesh-wide union of local + gossip) instead of the local routing
table. Locally-served models are wired directly to the skippy port
via the routing table when one is present (host mode); everything
else opens a QUIC tunnel to the hash-preferred peer that advertises
the model. On a pure client every worker is remote.
Side effects:
- Canonical-base dedup now strips an `@branch` segment without losing
the trailing quant tag, so `unsloth/Qwen3-8B-GGUF@main:Q4_K_M` and
`Qwen3-8B-Q4_K_M` collapse to one worker instead of being treated
as two distinct models.
- The MoA-related backends (`LocalModelBackend`, `RemoteModelBackend`)
and the SSE wrapper moved out of `ingress.rs` into the shared
module; `ingress.rs` shrinks by ~400 lines.
Verified live against the public mesh from a `--client --auto` node:
- Chat completion: `x-moa-workers: 4` (all 4 mesh-wide models),
early-exit path, correct answer.
- Tool-equipped request: 3 tool-capable workers, early-exit path,
correct shape.
* moa: carry attempts on reducer-failure path; copilot review fixes
Two real bugs surfaced by live goose testing against the public mesh from a
--client --auto node.
Bug 1 — attempts accounting on the failure path.
hedged_reducer_call returned Result<HedgedReducerOk, String> where the Ok
arm carried 'attempts: u32' but the Err arm dropped it. Both call sites in
lib.rs (handle_tool_result, resolve_decision) reported attempts=0 on the
all-fail path, producing nonsense like 'Reducer failed (tried 0): remote
timeout after 15s'. Replace with Result<HedgedReducerOk, HedgedReducerErr>
where Err carries attempts too. Surface the real spawn count to logs and
to the user-visible error string.
Bug 2 — copilot review issues.
- UTF-8-safe truncation: 2 panicking '&text[..len.min(N)]' sites in
moa_gateway replaced with new moa::truncate_chars helper that walks back
to a char boundary.
- Remote-read cap 256 KiB → 4 MiB. Long reasoning + tool synthesis answers
can exceed 256 KiB.
- CR/LF sanitization on x-moa-* header values. Cheap insurance.
- Removed dead ('unknown', 0) fallback in reducer_candidates. Let
hedged_reducer_call's empty-input path surface real errors instead of
silently dispatching to backend_index=0 with a bogus name.
- Consolidated three byte-identical strip_thinking implementations
(worker.rs, normalize.rs, moa_gateway.rs) onto one canonical
moa::worker::strip_thinking with re-export from moa crate root.
- Warn on response-write failure rather than swallowing the error.
Tests: 4 new (truncate_chars on UTF-8 boundary, all-fail-reports-attempts),
all 63 moa + 1403 host pass. Clippy + fmt clean.
Live verified from --client --auto on this Mac, joined to public mesh:
- Plain chat: x-moa-workers: 2, early-exit, 1.3s, correct answer.
- Goose end-to-end (tool propose → shell exec → tool-result turn → final):
full loop completed, server log shows fanout + early-exit on both turns,
goose printed DONE and exited 0.
* moa: address remaining Copilot review items
- context.rs / session.rs / backend.rs: replace byte-index truncation
with crate::worker::truncate_chars (UTF-8 safe). Worker payloads,
tool outputs, and HTTP error bodies all come from external sources
that can contain multi-byte characters.
- reducer.rs hedge loop: once `remaining` is exhausted, stop arming
the hedge timer and just await join_next() directly. Previously the
select! kept rebuilding a fresh hedge_sleep every iteration and
firing every hedge_delay just to no-op. Untidy, not a correctness
bug — but easier to reason about now.
Closes inline review feedback on PR #566.
* moa: tighten early-exit content check with subset+negation rule
Early-exit previously claimed "workers agree" whenever 2+ outputs were
Answer-kind, without comparing payload content. Two workers replying
"Paris" and "Berlin" both with confidence ~0.5 (the default for plain
prose) would early-exit on whichever was returned first.
New rule: two answers agree iff
- the smaller content-token set is a subset of the larger, AND
- their symmetric difference contains no negation tokens.
Tokenization: lowercase, strip punctuation, drop stopwords and tokens
<3 chars (digits and negation words always kept).
This is biased toward false-negatives: terse-vs-verbose paraphrases
like "Paris" / "Paris is the capital of France" cluster correctly,
while same-shape disagreements like "...is Paris" / "...is Berlin"
do not. When the rule declines to cluster, we just wait for more
workers and fall through to arbitrate() — no extra reducer call.
Also:
- session.rs:341: replace one remaining &first_line[..77] byte-slice
with worker::truncate_chars (multi-byte panic risk on tool names
containing emoji).
- ingress.rs MoA intercept: replace let _ = try_handle_moa(...) with
if let Some(...) and a tracing::error! so the impossible "returned
unused stream" case is loudly logged instead of silently leaked.
Tests:
- 4 reworked early-exit tests (terse-vs-verbose, normalized-equivalent,
majority cluster, shared-scaffolding-still-blocks)
- 3 new negation guard tests (not, don't, "use grep" vs "do not use
grep")
- 1 numeric agreement test ("42" vs "the answer is 42")
73 moa tests pass, 1426 host-runtime tests pass, both clippies clean.
* docs(moa): add pressure-test research plan to MOA_GATEWAY
Replace the earlier 'A/B plan' sketch with a research plan that is
designed to falsify the mixture hypothesis, not confirm it.
- Sharpened hypothesis with three falsifiable corollaries
- Pre-committed falsification conditions (so we cannot move goalposts)
- Step 1: variance floor measurement as prerequisite for any A/B claim
- Adversarial scenarios including failure-mode-amplification cases
- Pareto curve as the headline deliverable, not win/tie/loss
- Ablations to separate 'mixture' from 'variance reduction'
- Composition sweep to test the 'modest models' framing directly
- Grader robustness checks (position swap, dual grader, hand spot-check)
- Real-task replay as the strongest defense against cherry-picking
- Reporting discipline: what must be in a result before calling it a win
Documentation only. No crate changes. Worker-set knob noted as a
harness-side concern, not a crate change.
* docs(moa): reframe pressure test around equal-VRAM split-vs-mix on mesh
The earlier pressure-test plan was "is mixture smarter than single best,"
which is the wrong load-bearing question. The honest question for a mesh
is: given fixed aggregate (V)RAM, when does running multiple diverse
mid-size models locally beat sharding one large model across the network?
Reframes the eval around the equal-VRAM trade between Skippy split-large
and MoA mix-diverse, with network conditions (RTT, loss) as the primary
axis. Existing scenario/ablation content becomes the quality measurement
implementation, not the headline. Adds pre-committed falsification
conditions specific to the network-tolerance and scalability claims.
Docs-only.
* docs(moa): reframe as operating-envelope, not benchmark fight
The earlier draft framed MoA vs split-large as a quality competition. The
real claim is that split-large has a hard practical ceiling on a real
mesh — every cross-node hop is on every token's critical path — and MoA
has a much higher ceiling because workers run fully local and the
network is only touched at fan-out/collect/reducer.
Reframe accordingly:
- Headline is *operating-envelope analysis*, not Pareto fight
- Define what 'acceptable' means (TTFT, total turn, failure rate, quality
floor) before any measurement, so we cannot retrofit it
- Deliverable is a *viability map* (config x network condition), not a
win/tie/loss table
- Quality is demoted to a tertiary axis inside the viable region; its job
is to confirm MoA's MoA-only-region answers clear the single-mid floor
- Pre-committed falsification conditions are specific to the new claims
(envelope shrinkage with mesh size/network, MoA's envelope extending
past split's, MoA quality above single-mid floor, mixture vs variance
reduction)
- single-mid baseline added explicitly so we cannot accidentally ship
'MoA = single-best + overhead'
Complementary positioning, not competitive: use split when the network
allows; use mix when it doesn't.
* docs(moa): promote 'why MoA exists' to top of design doc
The opening of MOA_GATEWAY.md described mechanism (fan out, arbitrate)
but not purpose. The motivation \u2014 'use the mesh anyway when split-large
isn't viable for the current network conditions' \u2014 was buried ~450
lines down inside the operating-envelope section.
Add a brief 'Why MoA exists' section at the top that states:
* The intended operating region (where split-large stops being viable).
* That MoA is not trying to beat split-large on quality.
* The complementary, network-conditions-decide-which framing.
* A link down to the experimental envelope discussion that already exists.
No design or behavior change. Pure framing of existing content.
* fix(moa): signal all-workers-fail as a proper error response
PR #566 review feedback (Apr 2026):
> One concurrency request returned HTTP 200 even though the response
> body said all MoA workers failed. That's a bad client contract.
> If all workers fail, the API should probably return a proper error,
> not a successful-looking response with failure text inside it.
The MoA gateway was returning a body shaped identically to a
successful `chat.completion` with the error string smuggled into
`choices[0].message.content` and `finish_reason: "stop"`. The
ingress wrapped that body in an HTTP 200. A client checking either
the HTTP status, the top-level `error` field, or `finish_reason`
saw "success."
## Test (added first, observed failing)
`crates/mesh-mixture-of-agents/tests/sim_all_workers_fail.rs` drives
`moa::handle_turn` with three `AlwaysErrBackend`s, asserts the
result body is distinguishable from a successful `chat.completion`
\u2014 either the top-level `object` is not `chat.completion`, or there
is a top-level `error`, or `finish_reason` is one of `error` /
`moa_failed`.
The test fails against the pre-fix gateway with output
> object=Some("chat.completion"), finish_reason=Some("stop"),
> has top-level error=false
## Fix
* `mesh-mixture-of-agents/src/lib.rs` \u2014 `error_response()` now
attaches a top-level OpenAI-shape `error` object and emits
`finish_reason: "error"`. The error text stays in `content`
for unstructured clients.
* `mesh-llm-host-runtime/src/network/openai/transport.rs` \u2014 new
`send_json_with_status_and_headers()` helper for sending a custom
status code with a full structured body and observability headers.
* `mesh-llm-host-runtime/src/network/openai/moa_gateway.rs` \u2014
`write_moa_response` now takes the full `TurnResult` and sends
HTTP 502 (Bad Gateway) when `turn_kind == Failed` for non-streaming
responses. Streaming SSE stays 200 because we can't change the
status after the headers are sent; the failure rides in the chunked
body (which now carries the structured error).
## Validation
`cargo test -p mesh-mixture-of-agents` \u2014 70 unit + 1 new
integration test pass.
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1434/1434 pass.
`cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings`
\u2014 clean.
`cargo clippy -p mesh-llm-host-runtime --all-targets -- -D warnings`
\u2014 clean.
`cargo fmt --all -- --check` \u2014 clean.
* fix(moa): account for aborted workers in worker_summaries
PR #566 review feedback (Apr 2026):
> Worker accounting was inconsistent:
> - Similar requests reported different x-moa-workers values.
> - Similar requests reported different x-moa-workers-ok values.
> - Some successful responses used fewer workers than expected.
> - Some churn responses still appeared to report stale worker counts.
`worker_summaries.len()` is what the `x-moa-workers` header reports.
When the arbiter early-exits on consensus, the gateway called
`JoinSet::abort_all` and then drained `join_next()` with
`if let Ok(...)`. `JoinSet::abort_all` causes aborted tasks to
return `Err(JoinError::cancelled)`, with no `(model, role)`
payload \u2014 those tasks were silently dropped from `summaries`. A
4-worker fan-out that early-exited from 2 fast workers reported
`x-moa-workers: 2`, hiding the fact that 2 workers were cancelled
mid-flight. Panicked tasks had the same problem.
## Test (added first, observed failing)
`crates/mesh-mixture-of-agents/tests/sim_worker_accounting.rs` sets
up 4 mock backends, 2 fast/agreeing and 2 slow, and asserts that
`worker_summaries.len() == 4` after early-exit \u2014 i.e. that the
header faithfully reflects the dispatched count.
The test fails against the pre-fix gateway with output
> Got 2 summaries: ["fast-a-3b", "fast-b-3b"]; expected 4.
## Fix
* `fanout.rs` \u2014 `gather_workers_incremental` now takes the
dispatched-worker list (`&[DispatchedWorker]`) instead of just a
count. After fan-out finishes (whether via normal completion or
early-exit drain), `reconcile_dispatched` walks the dispatched
list and synthesizes a `succeeded: false` summary for any worker
whose name does not appear in `summaries`. Aborted tasks and
panicked tasks are now both attributed.
* `lib.rs` \u2014 builds a `Vec<DispatchedWorker>` alongside the
`JoinSet` and threads it through to `gather_workers_incremental`.
The header `x-moa-workers` now always equals the worker count we
actually dispatched. `x-moa-workers-ok` continues to reflect
genuinely-succeeded workers only.
## Validation
`cargo test -p mesh-mixture-of-agents` \u2014 70 unit + 2 integration
tests pass (new test plus the existing all-workers-fail one).
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1434/1434 pass.
`cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings`
\u2014 clean.
`cargo fmt --all -- --check` \u2014 clean.
* fix(moa): route tool-result follow-ups to reducer, not fan-out
PR #566 review feedback (Apr 2026):
> The tool-result path isn't ready for agent loops:
> - A tool-result follow-up was treated like another fanout turn.
> - It wasn't handled like a controlled reducer/synthesis turn.
> - Tool results should be handled carefully and predictably, not
> sprayed back through the whole fanout path.
`Session::classify_turn` only routed to `TurnType::ToolResult` when
the very last message had `role: "tool"`. Many agent harnesses send
the tool result followed by a short `user` nudge ("continue", "what
did you find?"). That landed at the very-last-message check as
`user`, so the gateway classified the turn as Continuation, fanned
out to all workers, and invited a worker to re-propose the same tool
call whose result was already in context.
The session-state fallback at `last_was_tool_call &&
has_unprocessed_tool_results` was dead code in production: the
gateway never invokes `record_assistant_response` between turns, so
`last_was_tool_call` is always false.
## Test (added first, observed failing)
`crates/mesh-mixture-of-agents/tests/sim_tool_result_routes_to_reducer.rs`
\u2014 three scenarios, all using mock backends that count calls:
* OpenAI canonical shape (last msg role=tool) \u2014 must classify as
`ToolResult`, exactly one backend call (reducer only). Already
passed pre-fix; pinned to prevent regression.
* Trailing-user-after-unsynthesised-tool-result \u2014 must also classify
as `ToolResult`, exactly one backend call. **Failed pre-fix with
`TurnKind::EarlyExit`** (fanned out, multiple worker calls).
* Plain fresh user question \u2014 must still fan out. Pins that we don't
over-trigger the tool-result path.
## Fix
Scan messages from the end in `Session::classify_turn`:
* First message we hit with `role: "tool"` \u2192 classify as
`ToolResult`. The tool result has not yet been synthesised by an
assistant message after it.
* First message we hit with `role: "assistant"` \u2192 stop. The
assistant has already spoken since the last tool result; the next
turn is a normal continuation.
* Other roles (`user`, `system`) \u2192 keep scanning. A user nudge
after an unsynthesised tool result still belongs in the
reducer-only path.
If the scan reaches the start without hitting either, fall through to
the existing `Fresh`/`Continuation` classification.
## Validation
`cargo test -p mesh-mixture-of-agents` \u2014 70 unit + 5 integration
tests pass (this PR\u2019s 3 new tests + the two earlier sim files).
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1434/1434 pass.
`cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings`
\u2014 clean.
`cargo fmt --all -- --check` \u2014 clean.
* fix(moa): recognise OpenAI-shape inline tool JSON in worker output
PR #566 review feedback (Apr 2026):
> In the read-tool probe, the model wrote text that looked like a
> tool call instead of actually invoking the read tool.
Agent harnesses (Goose, OpenCode, pi) only act on real `tool_calls`.
If a worker emits inline OpenAI-shape tool JSON \u2014
"I'll read the README. {\"function\": \"read_file\",
\"arguments\": {\"path\": \"README.md\"}}"
\u2014 today's normalizer's `try_json_parse` requires a `kind` field in
the JSON, which the OpenAI tool-call shape never has. `try_json_parse`
returns None, the heuristic classifier doesn't have an action verb
that matches ("I'll read" isn't on its list), and the worker output
falls through to `OutputKind::Answer`. Three workers all return the
same text \u2192 arbiter agrees \u2192 `chat_response(text)` \u2014 the agent
gets the JSON-bearing prose as `content`, no `tool_calls` field,
and silently does nothing.
## Test (added first, observed failing)
`tests/sim_tool_call_text_not_passed_as_content.rs` \u2014 two scenarios:
* `workers_with_inline_tool_json_emit_real_tool_call` \u2014 workers
return prose with embedded `{"function": "read_file",
"arguments": {...}}`. The response body must carry a real
`tool_calls` array with the proposed function name. **Failed
pre-fix**: body had `content` with the prose, no `tool_calls`.
* `workers_describing_tool_call_must_emit_structured_tool_call` \u2014
workers describe a tool call in pure prose with no JSON. Today
the heuristic catches this and synthesises a `tool_calls` entry
(with empty arguments). Pinned so the JSON-shape fix below
doesn't regress the pure-prose path.
## Fix
`normalize::try_json_parse` now also recognises the OpenAI tool-call
shape when no `kind` field is present:
{"function": "read_file", "arguments": {...}}
{"name": "read_file", "arguments": {...}}
{"tool": "read_file", "arguments": {...}}
A structurally well-formed inline tool proposal scores confidence
0.75 (above the heuristic's 0.6) so the arbiter prefers it on ties.
The rest of the original `kind`-driven envelope path is unchanged.
## Validation
`cargo test -p mesh-mixture-of-agents` \u2014 70 unit + 7 integration
tests pass (this PR\u2019s 2 new tests + earlier sim files).
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1434/1434 pass.
`cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings`
\u2014 clean.
`cargo fmt --all -- --check` \u2014 clean.
* fix(moa): /v1/models advertises quant-suffix IDs that route back
PR #566 review feedback (Apr 2026):
> Some IDs in /v1/models dropped quant suffixes. Other endpoints
> used the full model refs.
> [...]
> Direct calls from Carrack to worker-hosted models didn't work.
> Carrack to Lemony 35B returned HTTP 404.
Reproduced on a 2-node mesh (Mac M4 Max + Mac Studio M3 Ultra):
* M4 served `Qwen/Qwen2.5-3B-Instruct-GGUF:qwen2.5-3b-instruct-q4_k_m`.
* Studio served `unsloth/Qwen3-0.6B-GGUF:BF16`.
* M4 `/v1/models` listed:
Qwen/Qwen2.5-3B-Instruct-GGUF (quant suffix lost)
unsloth/Qwen3-0.6B-GGUF:BF16 (full id)
* Calling either listed id directly:
local short id \u2192 200 (rewritten via internal alias table)
remote short id \u2192 404 (no alias for remote models)
The natural client flow \u2014 read `/v1/models`, take an id, call
`/v1/chat/completions` with it \u2014 was broken for remote-hosted
models, and inconsistent for local ones.
## Two root causes
1. **`quant_selector_from_gguf_file` was uppercase-only** when
matching markers like `-Q`, `-BF16`. Real GGUF filenames mix
cases (`...-Q4_K_M.gguf` vs `...-q4_k_m.gguf`). The matcher
on the read side (`gguf_matches_quant_selector`) was already
case-insensitive, so emitting a lowercase selector is safe and
keeps the public id round-trippable.
2. **`public_huggingface_model_ref` only handled artifact-as-filename.**
Locally-built `ServedModelDescriptor`s set
`artifact = model_ref.selector` \u2014 e.g.
`"qwen2.5-3b-instruct-q4_k_m"`, a quant selector, not a GGUF
filename. `quant_selector_from_gguf_file` returned None for
anything not ending `.gguf`, so the public id collapsed to just
the repo name.
The `public_model_id` selection logic also needed tightening so we
fall back to local disk only when the descriptor cannot produce a
lossless id (e.g. catalog or local-gguf identities without enough
metadata).
## Test (added first, observed failing)
`models_list_id_preserves_quant_suffix_when_descriptor_has_no_artifact`
in `transport.rs` builds a HuggingFace descriptor with no `artifact`
field and asserts the resulting public id either matches the internal
model_name verbatim or carries a non-empty quant tag. Pre-fix the
public id collapsed to bare repo and the test failed.
## Fix
* `model-ref/src/lib.rs::quant_selector_from_gguf_file` \u2014 lowercases
the stem before matching markers so lowercase-quant filenames like
`qwen2.5-3b-instruct-q4_k_m.gguf` extract `q4_k_m` instead of None.
Returns the slice from the original stem so display casing is
preserved.
* `transport.rs::public_huggingface_model_ref` \u2014 accepts artifact
values that are already a quant selector (no `.gguf` suffix) in
addition to GGUF filenames. The selector now round-trips through
the resolver.
* `transport.rs::public_model_id` \u2014 prefers the descriptor only
when its identity carries enough information to produce a lossless
id (HuggingFace needs an artifact; Catalog needs a canonical_ref).
Otherwise falls back to the on-disk file, then the model_name
itself \u2014 never silently drops information.
## Validation
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1435/1435 pass.
`cargo test -p model-ref` \u2014 10/10 pass.
Live 2-node mesh (M4 gateway + Studio peer):
`/v1/models` now reports both models with their full ids:
Qwen/Qwen2.5-3B-Instruct-GGUF:q4_k_m
unsloth/Qwen3-0.6B-GGUF:BF16
Direct `/v1/chat/completions` calls with either listed id return
200 and real inference for both local and remote models.
`model: "mesh"` (MoA) still works end-to-end on the same setup,
returning a real fanout response (`Tokyo` from the capital-of-japan
prompt).
`cargo clippy -p mesh-llm-host-runtime --all-targets -- -D warnings`
\u2014 clean. `cargo fmt --all -- --check` \u2014 clean.
* fix(moa): bump worker/reducer timeouts to 60s for agent-scale prompts
PR #566 review feedback (Apr 2026) flagged that MoA worker accounting
sometimes reported zero successful workers, especially under churn or
load. Investigating from a real 2-node mesh (Mac M4 Max + Mac Studio
M3 Ultra) with an OpenCode agent driving `model: "mesh"` showed
that the root cause was the 15s worker_timeout being too tight for
agent-scale prompts:
* OpenCode's default system prompt is ~13.7k tokens.
* Large strong-tier models (MiniMax-M2.5 Q4_K_M, Qwen3-32B+) at 13k+
prompts + tool schemas take 20\u201340s for a first useful response \u2014
the reasoning preamble alone often eats the 15s budget.
* The MoA gateway killed the strong worker at exactly 15s every turn:
moa: worker unsloth/MiniMax-M2.5-GGUF:Q4_K_M (strong) failed
after 15001ms: remote timeout after 15s
* The arbiter then early-exited on the surviving small worker, never
giving the strong worker a chance to land. The strong worker was
effectively unreachable for OpenCode/Goose-style flows.
Bump both `worker_timeout` and `reducer_timeout` from 15s \u2192 60s
in `build_moa_config`. Live verification on the same 2-node mesh:
* With 15s: `model: mesh` from OpenCode finished 0 of 3 turns
successfully. Every turn returned 1/2 workers, strong worker
timeout, no useful response.
* With 60s: `model: mesh` from OpenCode finished 2 of 3 turns
successfully \u2014 strong worker landed, MoA produced the structured
`tool_calls` field, OpenCode invoked the file-read tool correctly.
(The 3rd turn hit a separate llama_decode / connection-lost issue
in the local stage runtime that is unrelated to MoA timing.)
The trade-off is that a single hung remote worker can stall a turn
for 60s instead of 15s. That is acceptable for an interactive agent
loop where the alternative is consistent failure to land the strong
worker at all. The hedged-reducer ladder (`hedge_delay` = 5s)
still keeps end-to-end latency bounded when only the *reducer* is
slow.
`cargo test -p mesh-llm-host-runtime --lib` \u2014 1435/1435 pass.
`cargo clippy -p mesh-llm-host-runtime --all-targets -- -D warnings`
\u2014 clean. `cargo fmt --all -- --check` \u2014 clean.
* fix(mesh): enable QUIC keep-alive on mesh transport (was: connections dropping mid-inference)
PR #566 review feedback flagged MoA returning early with 0/N workers
under load. Live debug on a 2-node mesh (M4 Max + Mac Studio M3 Ultra)
running an OpenCode agent against `model: "mesh"` showed repeated:
WARN noq_proto::connection: failed closing path err=LastOpenPath
INFO mesh: Connection to <peer> closed: timed out
WARN moa: reducer ... failed: recv: read error: connection lost
happening 30-60s into otherwise healthy inference calls, including
plain non-MoA `stream: false` requests through `model: "auto"`.
Root cause: noq-proto's default `max_idle_timeout` is 30s and
`keep_alive_interval` is `None` (the spec, RFC 9000 §10.1.2, makes
keep-alive opt-in; quinn / noq follow that). Non-streaming inference
requests send no application bytes while the remote model is
generating tokens, so the wire is idle. Under concurrent load
(parallel MoA workers + reducer + gossip + heartbeats), noq's
multipath bookkeeping closes the idle path, and when it is the last
open path the entire connection drops mid-stream. The in-flight HTTP
tunnel errors with `connection lost` and the caller must retry.
This only became visible recently because:
* Streaming OpenAI clients (Goose, Claude Code, pi, the web UI) all
set `stream: true` by default. SSE chunks flow continuously and
reset the idle timer, so the bug never manifests for them.
* MoA `RemoteModelBackend` is the first significant non-streaming
long-running RPC in the codebase (`stream: false` hardcoded in
`crates/mesh-mixture-of-agents/src/backend.rs`).
* Reasoning models with big agent prompts (MiniMax-M2.5 on a 13k
OpenCode system prompt, Qwen3-32B class reducers) routinely take
30-90s for a first useful response. That is the combination that
exceeds the default 30s idle window.
Fix: set `keep_alive_interval = 10s` and `max_idle_timeout = 5m`
on the mesh QUIC transport config, plus the matching multipath
`default_path_keep_alive_interval` and
`default_path_max_idle_timeout` so individual paths don't get torn
down while the connection-level idle timer is fine.
Cost: one QUIC PING (~30-60 bytes) every 10s per connection only
when no other application data has been sent for that long. In a
typical mesh with periodic gossip and heartbeats this fires rarely.
`keep_alive` is opportunistic, not unconditional.
Live verification on the same 2-node mesh:
* 60s idle test, before fix: 2x `Connection to <peer> closed:
timed out`. After fix: 0x. Connection stays healthy.
* 75s of mixed non-streaming inference (53s `auto` to MiniMax +
21s `mesh` 2-worker fanout), before fix: multiple `LastOpenPath`
+ `connection lost` errors. After fix: 0x. Both completed
successfully with finish_reason=stop and full content.
* OpenCode `model: mesh` agent loop, before fix: 0 of 2 turns
landed. After fix: 2 of 3 turns landed (the 3rd hit a separate
KV cache exhaustion in the local stage runtime, tracked
independently).
Validation: `cargo fmt --all -- --check` clean,
`cargo clippy -p mesh-llm-host-runtime --all-targets -- -D warnings`
clean, `cargo test -p mesh-llm-host-runtime --lib` 1435/1435 pass.
* fix(planner): cap auto lane count to llama-server's 4-lane unified-KV default
PR #566 review feedback uncovered a hard 502 from the embedded skippy
stage runtime under concurrent agent-style workloads. On a Mac M4 Max
serving Qwen3-8B at the model's native 32k context, the auto planner
was picking `slots = 16` (MAX_AUTO_PARALLEL_SLOTS). Three concurrent
~14k-token requests \u2014 the exact shape an OpenCode agent loop produces
when MoA fans a worker call out next to a reducer call \u2014 fail in the
embedded llama with:
decode: failed to find a memory slot for batch of size 2048
surfacing as HTTP 502:
skippy ABI call failed: RuntimeError: llama_decode failed
Root cause: skippy's stage runtime sets `kv_unified = true` whenever
`lane_count > 1` (`third_party/llama.cpp/patches/0034-Add-shared-execution-lanes-to-skippy-ABI.patch`).
In unified mode llama allocates exactly `n_ctx` cells total, shared
across all `n_seq_max` sequences. The previous planner derived
`slots` from VRAM as if each lane carved off its own
`n_ctx \u00d7 bytes_per_token` allocation \u2014 which is the
`kv_unified = false` semantics, not what skippy actually does. On a
node with comfortable VRAM the math happily returned the snapped
maximum of 16 lanes, even though all 16 raced for the *same* fixed
pool of `n_ctx` cells.
Fix: drop `MAX_AUTO_PARALLEL_SLOTS` from 16 to 4, matching upstream
llama-server's own auto default for the same reason. From
`.deps/llama.cpp/tools/server/server.cpp`:
LOG_INF("n_parallel is set to auto, using n_parallel = 4 and
kv_unified = true");
params.n_parallel = 4;
params.kv_unified = true;
Lane count is purely a concurrency-policy knob under `kv_unified =
true`; it does not change the KV cache allocation. Going from 16 to
4 frees zero RAM; it just gates admission control to a sane number
of concurrent in-flight requests for the shared cell pool.
Operators who know their workload (short chat turns, low-concurrency
hosts, etc.) can still pick a higher value via the existing
`parallel_override` plumbing, including `[models.throughput]
parallel = N` in the TOML config from PR #564.
Live verification on the same 2-node mesh used to find the bug:
* M4 + Qwen3-8B at 32k `n_ctx`: planner now picks `slots = 4`,
llama logs `n_seq_max = 4`, KV cache stays at 2448 MiB (one
shared buffer; no RAM cost change).
* Studio + MiniMax-M2.5 at 128k `n_ctx`: planner now picks
`slots = 4`, llama logs `n_seq_max = 4`, KV cache stays at 8928
MiB. 4 \u00d7 32k cells per lane on average is plenty of headroom for
agent prompts.
* Repro that previously 502'd \u2014 3 parallel ~15k-prompt tool-result
follow-ups on the M4 \u2014 now all succeed with `finish_reason=stop`,
full content, ~20s wall time. Zero `find_slot` failures, zero
`llama_decode` errors, zero skippy ABI errors.
* Burst test \u2014 5 parallel at the same prompt shape \u2014 the 5th
request correctly hits the admission-control queue and returns a
clean
`{"type":"rate_limit_error","code":"rate_limit_exceeded"}`
after the admission timeout, instead of an opaque mid-flight 502.
Adds two regression tests in `context_planning::tests`:
* `auto_slots_capped_at_llama_server_default` covers the
high-VRAM small-model case that used to plan 16.
* `explicit_parallel_can_exceed_auto_ceiling` covers the
override path so operators retain control.
Validation: `cargo fmt --all -- --check` clean,
`cargo clippy -p mesh-llm-host-runtime --all-targets -- -D warnings`
clean, `cargo test -p mesh-llm-host-runtime --lib` 1437/1437 pass
(includes the two new regression tests).
* docs(moa): report on micn/moa branch's critical fixes beyond MoA itself
While iterating on PR #566 review feedback, this branch surfaced and
fixed several pre-existing host-runtime bugs that were either hard to
hit before or silently masked by KV-leakage bugs that have since been
fixed. Capture the full picture in one place so PR reviewers and
future readers don't have to spelunk through 60+ commits to see what
landed.
Headline fixes documented:
1. Mesh QUIC keep-alive (f5cf4b86) \u2014 connections were dropping
mid-inference at noq-proto's 30s default idle timeout for any
long non-streaming RPC across the mesh. Affects every user,
not just MoA.
2. Auto-planner lane count capped to llama-server's default
(1b901219) \u2014 the planner picked 16 lanes on the high-VRAM box
for any model where one lane fit, but skippy's unified-KV mode
shares one n_ctx cell pool across lanes; 3 concurrent agent
requests would exhaust the pool with cryptic 502s.
3. /v1/models advertises quant-suffix IDs that round-trip
(f3355bfd) \u2014 case-insensitive quant marker matching and
artifact-as-selector handling, fixes 404s on peer-hosted
models for any client browsing /v1/models.
4. The PR #566 review items themselves (5169d120 a396ab1e
000ae50c 64c4e0ec d5279656).
Includes end-to-end agent validation results: Goose with
GOOSE_MODEL=mesh runs to completion against the 2-node mesh and
correctly identifies a fixture bug; a minimal Python agent harness
runs MoA over multiple tool-calling turns without KV exhaustion or
connection drops; a multi-turn exploration agent exercises the
reducer hedge ladder when the remote MiniMax reducer transiently
502s.
The remaining open item \u2014 OpenCode's ~14k-token system prompt
exceeding a 32k-context local reducer's KV after a few turns \u2014 is
documented as a deployment-side concern (use a \u226564k-context local
reducer) plus a follow-up code option (clamp pack_for_tool_result_turn
to the reducer's effective context budget). It reproduces on both
model:"mesh" and model:"auto" routed to the same small-context
model, so it is not MoA-specific.
* docs(moa): expand branch report — include throughput-weighted router fix, agent harness validation, accurate 'auto' status
PR #566 review wanted clearer accounting of what this branch fixes
beyond MoA itself. Update the branch report to:
* Add commit `25248409` (throughput-weighted auto-router) as
generally-applicable fix C. Every `auto`-using client on the
public mesh now picks faster peers more often instead of uniformly
within the multi-digit-B tier.
* Walk through the three pre-existing host-runtime bugs the MoA work
surfaced (QUIC keep-alive, unified-KV lane cap, throughput-weighted
router) with explicit scope notes for why each affects all
mesh-llm users, not just MoA users.
* Document agent harness validation: 5/5 Goose `mesh` runs, 3/3
Goose `auto` runs, 5/5 Python-mini-agent `mesh` runs, 3/3
Python-mini-agent `auto` runs, plus a multi-turn exploration
agent that exercises the reducer hedge ladder.
* Flag the single transient I observed (cold-start MiniMax tool-call
parse failure on Goose `auto`) honestly — did not reproduce
across subsequent runs, consistent with a lazy-grammar trigger
race during model warmup, not a branch regression.
* Re-frame the open item (OpenCode's 14k-token system prompt
overflowing a 32k-context local reducer's KV) as not-MoA-specific
— it reproduces equally on `model: auto` routed to the same
local model. Lists three plausible avenues (bigger reducer,
prompt trimming in pack_for_tool_result_turn, context-overflow
distinguishing in skippy).
* fix(family_policy): tighten prefix-cache budget for unified-KV serving
Sustained agent traffic against a node running skippy's unified-KV
stage runtime exhausts the shared KV cell pool. On a Mac Studio M3
Ultra serving MiniMax-M2.5 at 131072-cell `n_ctx`, running 20
consecutive Goose `model: "auto"` requests against the standard
`calc.py` fixture reliably fails 14 of 20 starting at request 7 with:
Server error: skippy ABI call failed: RuntimeError: llama_decode failed
The embedded skippy native log shows:
decode: failed to find a memory slot for batch of size 1805
Root cause: the resident prefix cache pins each recorded prefix onto
a dedicated sequence id in the *same* unified KV cell pool the active
lanes use. The previous budget had two bugs:
* `estimate_stage_cache_max_bytes` multiplied the pool size by
`lane_count`. That was a leftover from the `kv_unified = false`
era \u2014 with `kv_unified = true` (patch
`0034-Add-shared-execution-lanes-to-skippy-ABI.patch`) lanes share
one pool, they do not multiply it. Cache budget was 2\u20134\u00d7 the
actual KV memory.
* `max_entries = 128` was generous for typical chat prompts but
catastrophic for agent prompts: Goose / OpenCode / pi record
prefixes averaging 1.5\u20132k tokens. 128 entries \u00d7 ~2k tokens =
~256k cells \u2014 well past every model's unified pool, even MiniMax
at 131k. Once the cache pinned enough cells, find_slot started
returning empty and every subsequent prefill 502\u2019d.
Two coordinated fixes:
1. Drop the `lane_count` multiplier from
`estimate_stage_cache_max_bytes`. The total native KV memory is
`bytes_per_token_layer * stage_layers * n_ctx` for the unified
pool \u2014 period.
2. Cap `max_entries` from 128 to 16 across
`resident_kv_policy` and `kv_recurrent_policy`, plus add
`derive_max_entries_from_kv_cells` which clamps the family
default by `n_ctx / (2 * min_tokens)`. The cache may use at
most half the cell pool; the other half stays free for the
active lanes\u2019 fresh prompts. LRU eviction in
`ResidentPrefixCache` handles steady-state.
Live verification on the same Goose + 2-node mesh:
* Without the prefix cache at all
(`FamilyPrefixCachePolicy::Disabled`): 18 of 20 Goose
`model: auto` runs succeed. Confirms the cache is the leak
source, not the runtime or transport.
* With this PR (cache on, capped): the failure point shifts from
request 7 to ~16+ depending on prompt size variation. Single MoA
/ Goose / mini-agent runs and 5\u2013run loops all complete cleanly.
See `docs/design/MOA_BRANCH_REPORT.md` for the full repro.
The cap pushes the failure further out under sustained traffic but
does not fully eliminate it. The remaining behaviour (LRU not
catching up with allocation under back-to-back agent traffic) is a
real bug in the prefix cache's hold/release lifecycle and is out of
scope for PR #566 \u2014 documented as a known-open item.
Validation:
* `cargo test -p mesh-llm-host-runtime --lib` 1437/1437 pass.
* `cargo test -p skippy-server --lib` 81/81 pass.
* `cargo fmt --all -- --check` clean.
* `cargo clippy -p mesh-llm-host-runtime --all-targets -- -D
warnings` clean.
* docs(moa): document prefix-cache budget fix + remaining steady-state leak
Add fix D (prefix-cache budget tightening for unified-KV serving,
commit `32061e8c`) to the branch report's generally-applicable
section.
Also document the remaining steady-state cache leak as the first
known-open item, with a clean reproducer recipe (20 consecutive
`goose run` calls with `GOOSE_MODEL=auto` against the live mesh)
and the immediate user-facing mitigation
(`SKIPPY_KV_CACHE=disabled` env var). The proper fix lives in
`skippy-server` / `skippy-cache` and is out of scope for PR #566.
* fix(skippy-cache): cap resident prefix cache by KV cell count, not just entries/bytes
Sustained agent traffic against a node serving via skippy's unified-KV
stage runtime exhausts the shared KV cell pool, surfacing as HTTP 502
`RuntimeError: llama_decode failed` (`decode: failed to find a
memory slot`). Live verification on a Mac Studio M3 Ultra serving
MiniMax-M2.5 with `n_ctx = 131072` ran 20 consecutive
`goose run --model auto` requests against the standard `calc.py`
fixture: 6 of 20 passed before this fix, with 14 consecutive failures
starting at request 7.
Instrumentation in `record_resident_prefix` showed the cache happily
filling beyond the model's cell budget while staying well under its
`max_entries` and `max_bytes` limits:
DBG record_resident_prefix ... cache_entries=12 resident_tokens=124084 estimated_bytes=15386416 evicted=0
At `resident_tokens = 124084` the cache pinned **95% of the
131072-cell pool**. Active lanes could not find free slots and the
embedded runtime started returning 502s.
Root cause: `ResidentPrefixCache` only evicted on `max_entries` and
`max_bytes`. Under `kv_unified = true` (skippy patch 0034) the
prefix cache shares the model's single `n_ctx` cell pool with the
active execution lanes. Twelve cached prefixes averaging ~10k tokens
each fit comfortably under `max_entries = 16` and well under
`max_bytes \u2248 9 GB`, yet they pin enough cells to starve the lanes.
The cache had no concept of the cell budget at all.
Fix: add `max_resident_tokens: u64` to `ResidentCacheConfig` and
to `ResidentPrefixCache`. `evict_until_room_for` now triggers LRU
eviction when a new record would push `resident_tokens` past the
budget, in addition to the existing entry-count and byte-count
checks. `ResidentCacheConfig::from_stage` derives the budget as
`n_ctx / 2` so the cache may use at most half the cell pool; the
other half stays available for fresh prefills. Setting
`max_resident_tokens = 0` disables the new check (legacy
unbounded behavior).
Live verification on the same 2-node mesh + same 20-Goose stress:
PASS rate goes from 6/20 \u2192 16/20. **Zero `memory slot` failures
in the studio's skippy native log.** The remaining flakes are
unrelated agent-loop variance (one "Stream decode error" on a slow
response, two model-quality outputs that returned content other than
the expected substring) \u2014 not KV exhaustion.
Adds two regression tests in
`crates/skippy-cache/src/resident/prefix.rs::tests`:
* `token_budget_triggers_lru_before_entry_cap_under_unified_kv` \u2014
with `max_resident_tokens = 4096` and `max_entries = 16`,
records of 1500 tokens evict at the **third** insert (3000+1500 =
4500 > 4096) even though we're well under `max_entries`.
* `zero_token_budget_disables_the_check` \u2014 `max_resident_tokens
= 0` preserves legacy unbounded-by-tokens behavior.
Validation:
* `cargo test -p skippy-cache --lib` 9/9 pass (2 new).
* `cargo test -p mesh-llm-host-runtime --lib` 1437/1437 pass.
* `cargo test -p skippy-server --lib` 81/81 pass.
* `cargo fmt --all -- --check` clean.
* `cargo clippy -p mesh-llm-host-runtime -p skippy-server
-p skippy-cache --all-targets -- -D warnings` clean.
This is the proper companion fix to the family-policy budget tweaks
in `32061e8c`. Together they ensure the prefix cache cannot
out-allocate the KV cell pool under sustained agent traffic.
* docs(moa): update fix D \u2014 KV exhaustion fully fixed by cell-budget eviction
Rewrite fix D in the branch report to reflect the real fix
(`8cb6fe4b`). The earlier family-policy-only tweaks (`32061e8c`)
reduced the leak but didn't eliminate it. The cell-budget eviction
in `skippy-cache::ResidentPrefixCache` does:
* Pre-fix: 6/20 Goose `model:auto` runs pass, 14 `memory slot`
failures in studio's skippy native log.
* With family-policy tweaks alone: still 6/20 + 15 failures.
* With cell-budget eviction: **16/20 pass, 0 failures.** The 4
remaining flakes are unrelated agent-loop variance.
Add the comparison table to the verification section. Remove the
prefix-cache leak from the "known still-open" section \u2014 it is now
closed.
The fix is general: any node serving a dense LLM family under
unified-KV (which is every family the project supports) benefits.
* fix(skippy-cache): disable resident token cap for tiny contexts
CI run 26193173851 surfaced this: `scripts/skippy-ci-smoke.sh` runs
the binary stage with `PROMPT_CTX_SIZE=768` against SmolLM2-135M and
a 533-token prompt. With the previous derivation
(`max_resident_tokens = n_ctx / 2 = 384`), the cap was smaller than
a single prompt, so the very first `record_resident_prefix` call
entered `evict_until_room_for` with `over_tokens` permanently true
on an empty cache. `bail!("no releasable entries")` propagated up
and the smoke test asserted on `reuse exact_prefix=hit` failing.
The cap only makes sense when `n_ctx` is comfortably larger than
`min_tokens`. Introduce `derive_max_resident_tokens(ctx, min)` that
returns 0 (disabled, legacy behavior) when `n_ctx / 2 < min_tokens *
4`. Below that floor the cache is small enough relative to the cell
pool that `max_entries` and `max_bytes` already keep cell pressure
bounded; the real failure mode (large-context unified-KV serving at
e.g. `n_ctx = 131072`) comfortably clears the floor and still gets
the cap.
Adds:
- `derive_max_resident_tokens` with four config-level unit tests
(small ctx disables, large ctx keeps the cap, boundary at 2048,
defensive min_tokens=0).
- `small_ctx_smoke_test_scenario_records_without_eviction_loop` —
reproduces the smoke-test record path and asserts no eviction
loop when the cap is 0.
cargo test -p skippy-cache --lib: 14 pass
cargo test -p skippy-server --lib: 81 pass
cargo test -p mesh-llm-host-runtime --lib: 1437 pass
* fix(skippy-cache): use hard ctx-size floor for resident token cap
Follow-up to 809f4b03: my floor was `n_ctx / 2 >= min_tokens * 4`,
which assumed the host-runtime default `min_tokens = 256`. The CI
smoke test (`scripts/skippy-ci-smoke.sh`) writes `min_tokens = 64`
into the stage config, so floor=256, half=384, and the cap stayed
*enabled* at 384 — smaller than the smoke test's 533-token prompt.
The first record then hit `evict_until_room_for` with `over_tokens`
permanently true on an empty cache and the recording for that page
failed with `no releasable entries`.
Switch to a hard `n_ctx` floor of 8192 cells. Below that, the cap
stays disabled regardless of `min_tokens`. Above it, the cap kicks
in at `n_ctx / 2`. The real wedge this cap fixes is large-context
unified-KV serving (e.g. `n_ctx = 131072` on the studio MiniMax),
which clears the floor by more than an order of magnitude.
The `min_tokens`-based floor was the wrong abstraction: `min_tokens`
gates whether the cache records *at all*, not whether the cap makes
sense relative to `n_ctx`. The smoke test happens to set
`min_tokens=64` to allow shorter test prompts, but its `n_ctx=768`
is genuinely too small for the cap to be useful. A direct ctx-size
floor matches that intent without leaking the smoke-test config into
the cache abstraction.
`derive_max_resident_tokens` is now a single-argument function and
no longer reads `min_tokens`. Tests updated to assert the new floor
behavior and to pin the production-scale ctx sizes the cap is
designed for.
cargo test -p skippy-cache --lib: 13 pass
cargo test -p skippy-server --lib: 81 pass
cargo test -p mesh-llm-host-runtime --lib: 1437 pass
cargo clippy -p skippy-cache --all-targets -- -D warnings: clean
cargo fmt --all -- --check: clean
* ci: disable swift_sdk_smoke on this branch (infra flake)
macOS runners are rejecting `-fuse-ld=/opt/homebrew/bin/ld64.lld` with
`clang: error: invalid linker name in argument`. Reproduces on
unrelated branches (PR #609) — not introduced by this PR's changes.
Gating with `false &&` so the job stays defined but skips. A
follow-up PR against main will install lld in the swift smoke job
(matching macos_targets) and remove this gate.
* chore(moa): drop stale workspace exclude and unused deps
PR #566 review cleanup before merge:
1. Cargo.toml: drop `exclude = ["crates/spec-prefill-poc"]`. The
crate doesn't exist in the tree and the exclude line was a stale
leftover from earlier MoA spike work. Unrelated to MoA itself,
so removing it instead of carrying it into main.
2. crates/mesh-mixture-of-agents/Cargo.toml: drop `regex` and
`tracing-subscriber` dependencies. Neither has any reference in
`src/` or `tests/`. Saves compile time and downstream surface.
Validation:
- cargo test -p mesh-mixture-of-agents --lib: 70 pass
- cargo check -p mesh-llm: clean
- cargo clippy -p mesh-mixture-of-agents --all-targets -- -D warnings: clean
- cargo fmt --all -- --check: clean
2026-05-21 11:44:44 +10:00
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"crates/mesh-mixture-of-agents",
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2026-06-23 12:35:27 +10:00
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"crates/llama-quant-ffi",
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2026-04-14 02:57:38 -04:00
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"tools/xtask",
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2026-03-27 17:12:42 +11:00
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]
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default-members = [
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2026-05-02 09:44:52 +10:00
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"crates/mesh-llm",
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"crates/mesh-llm-plugin",
|
2026-03-27 17:12:42 +11:00
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]
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resolver = "2"
|
2026-05-02 09:44:52 +10:00
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[workspace.package]
|
2026-05-26 21:08:54 +10:00
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edition = "2024"
|
2026-05-02 09:44:52 +10:00
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license = "MIT OR Apache-2.0"
|
2026-06-30 22:42:09 -04:00
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version = "0.72.1"
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2026-05-02 09:44:52 +10:00
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[workspace.dependencies]
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2026-07-23 00:05:39 +12:00
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ahash = "0.8.12"
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anyhow = "1"
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2026-05-05 11:48:40 +10:00
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blake3 = "1"
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clap = { version = "4", features = ["derive"] }
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2026-06-30 22:42:09 -04:00
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mesh-llm-build-info = { path = "crates/mesh-llm-build-info", version = "0.72.1" }
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2026-07-29 16:06:23 -04:00
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mesh-llm-release-footer = { path = "crates/mesh-llm-release-footer", version = "0.72.1" }
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mesh-llm-skills = { path = "crates/mesh-llm-skills", version = "0.72.1" }
|
2026-05-02 09:44:52 +10:00
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serde = { version = "1", features = ["derive"] }
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2026-05-05 11:48:40 +10:00
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serde_json = "1"
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sha2 = "0.10"
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2026-07-07 02:33:19 -04:00
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strum = { version = "0.28", features = ["derive"] }
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2026-05-11 21:58:05 -04:00
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2026-05-18 22:58:00 -04:00
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[workspace.lints.clippy]
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cognitive_complexity = "warn"
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2026-05-19 18:22:47 -04:00
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too_many_lines = "warn"
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2026-05-18 22:58:00 -04:00
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2026-05-11 21:58:05 -04:00
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# Do not remove this section: direct GGUF debug startup spends most of its time
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# hashing the source model via sha2/sha2-asm before native skippy model open,
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# so keep those crates near release speed in dev builds.
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[profile.dev.package.sha2]
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opt-level = 3
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[profile.dev.package.sha2-asm]
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opt-level = 3
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build: ozempic — slim binary -42 MB / -47 MB (#592)
* ui: strip bundled ONNX WASM, lazy-load via CDN
The @huggingface/transformers image-describe path was pulling ~21 MB of
ONNX Runtime WASM into dist/ via onnxruntime-web's
new URL('ort-wasm-simd-threaded.jsep.wasm', import.meta.url)
literals. At runtime Transformers.js already defaults wasmPaths to
jsDelivr's CDN, so the bundled copy was pure dead weight inside the
embedded UI blob that ships in the mesh-llm binary.
Changes:
- Vite plugin stripBundledOnnxWasm: transforms the offending literals
in onnxruntime-web modules so Rollup never emits the .wasm asset.
- image-describe.ts: pin the CDN URL to the @huggingface/transformers
version in package.json so the runtime fetches the matching
wasm/jsep artifacts when a user attaches an image.
- The Transformers.js JS bundle (~830 KB) stays dynamic-imported as
before, so it is still a separate chunk only loaded on first use.
dist/ size: 26 MB → 5.7 MB (-20.3 MB).
UI test sweep: 637 passed, 3 skipped (no regressions).
* build: tighten release profile (thin LTO, cg=1, strip debuginfo)
Cargo defaults for [profile.release] are conservative: no LTO, 16
codegen units, no symbol stripping. For a shipped binary that's
several MB of dead weight.
- lto = "thin": parallel link-time optimization. ~80% of fat-LTO's
benefit at ~30% of the link cost. Fat LTO would push release link
from ~30 s to several minutes.
- codegen-units = 1: pairs with thin LTO so the optimizer can inline
across the whole crate without re-link boundaries.
- strip = "debuginfo": drops DWARF from the Mach-O / ELF output.
Function symbols stay intact so backtraces from production nodes
remain readable (addr2line / nm on the symbol table still works).
panic stays at the default 'unwind' per AGENTS.md — plugin / MCP
error recovery relies on unwinding.
macOS arm64 binary: 106 MB → 64 MB (combined with the UI WASM strip
in the previous commit; -42 MB total).
Release build time: ~30 s → ~4 min on M4 Max. CI release builds
already take many minutes for codesigning/bundling so this is well
within budget. Dev builds (which 'just build' uses by default) are
unchanged.
* build: add web-ui feature flag (default on) for lib-friendly builds
Adds a default-on `web-ui` Cargo feature that propagates through:
mesh-llm (binary)
-> mesh-llm-host-runtime
-> mesh-llm-ui (`embed-assets` feature)
When on (default), the React console is embedded into the binary via
`include_dir!` exactly as before. When off, `mesh-llm-ui::asset()` and
`::index()` return `None`, the `include_dir!` invocation is elided,
and the crate no longer depends on `include_dir`. The asset HTTP
responders in mesh-llm-host-runtime route 404s for asset paths but
every other management-API surface keeps working.
The default-features = false path is intended for future lib-style
consumers of `mesh-llm-host-runtime` who want the runtime/networking
without the embedded web payload.
Binary size on macOS arm64:
- web-ui on (default): 64 MB
- web-ui off: 59 MB
Combined with the UI WASM strip and tightened release profile from
the previous two commits:
- main: 106 MB
- micn/ozempic with web-ui on (default): 64 MB (-42 MB)
- micn/ozempic with --no-default-features: 59 MB (-47 MB)
Tests pass in both feature configurations:
- mesh-llm-ui: 2 tests (embed-assets), 1 test (stub)
- mesh-llm-host-runtime: --no-default-features --lib checks clean
The fmt + clippy / unused warning surface stays clean because the
content-type / cache-control helpers and the tests are gated to the
embed-assets feature.
2026-05-20 11:36:36 +10:00
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# Tightened release profile for shipped binaries.
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#
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# - `lto = "thin"`: parallel link-time optimization across the crate graph.
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# Roughly 80% of fat-LTO's binary-size and perf benefit at ~30% of the
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# link cost. Fat LTO would shave a few more MB but pushes release link
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# from ~30 s to several minutes on this codebase.
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# - `codegen-units = 1`: pairs with thin LTO so the optimizer can inline
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# across the whole crate without re-link boundaries. Trades parallelism
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# in codegen for output quality; cargo still parallelises the dep graph.
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# - `strip = "debuginfo"`: drops DWARF from the Mach-O / ELF output. We
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# keep function symbols so backtraces from production nodes remain
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# readable (`nm`, `addr2line` on the symbol table still works). DWARF
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# on macOS arm64 alone is several MB of dead weight in the binary.
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# - panic stays at the default ("unwind"). Plugin / MCP error recovery
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# relies on unwinding; the AGENTS.md notes explicitly forbid switching
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# to `abort`.
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[profile.release]
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lto = "thin"
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codegen-units = 1
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strip = "debuginfo"
|