Wreath

Many separate parts, gathered and woven until they hold a single shape.

A Python 3.14-first ASGI framework, PostgreSQL stack, job system, policy engine, and native HTTP server. One package; no mandatory runtime dependencies.

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One system. One obvious home.

A production web service needs more than a router. It needs validation and data access. It also needs identity, policy, background work, observability, and a server. Wreath ships those parts together and names each module after the job it does: wreath.pagination paginates, wreath.jobs runs jobs, and wreath.email sends mail.

The imagery belongs to the name. The API stays literal.

from wreath import Request, Wreath

app = Wreath()

@app.get("/hello/{name}")
async def hello(request: Request, name: str) -> dict:
    return {"hello": name}

wreath run app:app          # native HTTP server
wreath dev app:app          # native server with reload
uvicorn app:app             # or any conforming ASGI server

Wreath is pre-1.0. Shipped surfaces are implemented and tested. Work that is named but not finished lives only in the roadmap.

Choose your route

The published site keeps Browse in its header on every page. Search is Ctrl K or /.

What ships

The full generated inventory says what each part replaces and links to its guide: what you do not have to install.

Native gzip, shaped to the document

Wreath owns both halves of gzip. The encoder and decoder are independent native kernels: they share the RFC 1951/1952 wire format, not one duplex algorithm. When Content-Type is known, JSON, GraphQL, HTML, logs, plain text, and high-entropy JSON take separately compiled policies. The result is still ordinary gzip readable by browsers, zlib, zlib-ng, and libdeflate.

Retired userspace instructions per uncompressed byte at level 6, lower is better. Each cell is Wreath / libdeflate / zlib-ng:

That is fewer encode and decode instructions in every document row. The cache figures are the 500 kB representatives: the high-entropy encoder retires only 4.13 LLC misses/kB, while the regular-document decoder stays at 1.79–2.22 LLC misses/kB with a 97.86–98.17% LLC hit rate. Across the corpus, Wreath's output is 0.33% larger than zlib-ng's and 0.80% larger than libdeflate's—the ratio cost kept beside the instruction win rather than hidden.

The comparison uses repository-native boundaries, application- or stream-owned warm contexts, six document sizes from 10 kB through 1 MB (the high-entropy case is 500 kB), and hardware performance counters on Linux x86-64 / Ryzen 7 7730U. A subsequent cache sweep over 30 JSON, GraphQL, HTML, text, and log documents chose the 32 Ki-entry format hash table and shallower per-format search limits: encode instructions fell 4.79%, L1D misses 12.87%, and LLC misses 52.07% on the same corpus. The shipped balance retires 49.04 encode instructions/byte with 91.62% L1D and 93.92% LLC hit rates; output is 23.71% of input. No elapsed time, cycles, or IPC enters either result. The repository retains the measurement data.

One real request, all the way through

This is not a plaintext race or a synthetic JSON payload. All three configurations render the same operations-intelligence dashboard from one successful request: HTTP policy and sessions, nested typed input, bearer authentication, Cedar, overlapping PostgreSQL and HTTP wire calls, eleven requested rows projected from a sparse 730-day × 48-tenant × 6-measure source, eleven chart paths, temporal, geospatial and vector work, ranked pagination, protobuf and MessagePack exports, an escaped template, compression, and HTML emission.

Retired userspace instructions per request, lower is better. Five samples are too few for a meaningful histogram, and scaling every row independently made the old glyphs impossible to compare. This instead prints the observed range, its width relative to the median, and the median directly:

The median hardware-counter account is:

The denominators matter. An L1D percentage divides L1 data accesses; L1I divides instruction fetches. The two L2 percentages divide, separately, core demand requests and hardware-prefetch requests observed by AMD's L2 events. Neither is “the fraction of the L1 misses above that hit L2”, and combining demand with prefetch into one rate hides which population changed. “All L2 misses / kI” therefore reports both the absolute median and misses per thousand retired instructions. The latter can rise when useful work is deleted: DCZ retires 44% fewer instructions than ordinary Wreath while its roughly fixed cache traffic is divided by a smaller instruction count.

This is why Wreath's lower L2 percentage is not more L2 pressure. The optimized Wreath row records 159,258 total L2 misses per request, versus BlackSheep's 2,592,360—16.28× fewer—even though BlackSheep's demand hit percentage is higher. Moving work toward L1 means shrinking active data/code, eliminating copies, and keeping transient buffers contiguous; it does not mean optimizing the percentage itself. Every stack materializes only the eleven chart rows that reach the response. Wreath additionally reconciles consecutive measures from one series in one bucket walk, borrows exact numeric cells while their request owner stays alive, hashes each dense bucket and measure once into a call-owned lookup plan, and keeps row indices, values, presence bits and the LTTB selection workspace call-owned and contiguous. Coarse temporal counts skip zone reconstruction when the calendar gap alone proves the final bucket is before the end. Sparse-vector indices and values share one compact native allocation, while metrics insert first-seen exact integers directly and pay arbitrary-precision conversion only for duplicate sums. The ranker bounds and transforms its existing embedding in native workspace instead of building a Python score array; template emission borrows safe top-level scalars; and content-format dispatch no longer allocates normalized Python strings. Prepared template fragments can also carry policy-owned provenance, so gzip compresses only their dynamic edges and does not reread the full uncompressed stable suffix; ordinary byte bodies retain the exact comparison and safe full-compression fallback.

Format-aware Wreath retired 28.96× fewer instructions than FastAPI in this run. The dictionary-aware configuration retired 52.08× fewer instructions, removing 3,222,669 instructions per request—a further 44.38% from Wreath's full request path. BlackSheep on Granian was the least instruction-heavy of the three ecosystem stacks, at 28.85× and 51.88× the two Wreath medians. The DCZ response is RFC 9842 dcz: a real response for neighbouring resource 41 is the client-held dictionary for resource 42, whose 35,805-byte body differs at only two bytes. The same configuration falls back to standard concatenated gzip members for ordinary clients, recompressing the 249-byte dynamic prefix while reusing the independently readable stable member. It does not claim that DCZ and gzip are nested in one response.

All five rows use TLS 1.3 over HTTP/1.1 and are medians of five alternating 30/15-request slopes. The unchanged A/A controls differed from the primary medians by 0.58%, 0.90%, 0.14%, 0.44%, and 0.11%, respectively. The harness accepts a DCZ sample only after checking its dictionary hash, decoded response facts, secure transport, and both required Vary fields; it separately exercises and decodes the fragment-gzip fallback before attaching counters.

The ecosystem implementations are not slow pure-Python foils. FastAPI uses Starlette policy middleware, Pydantic, and Uvicorn/uvloop/httptools. Sanic uses its native production server in single-process mode. BlackSheep uses Granian's single-threaded ASGI runtime with uvloop, the faster small-process mode its server documents. Sanic and BlackSheep share one msgspec-typed business kernel; all three use cedarpy, asyncpg, aiohttp, NumPy, Jinja, protobuf, msgspec, and standard gzip. Wreath performs the corresponding transaction through its dependency-free declarative surfaces and native data kernels.

To expose the successful-path application cost layer by layer, a smaller companion request removes the dashboard calculations while keeping the same routing, CORS, validation, auth, Cedar, PostgreSQL, HTTP, and JSON result. The starred cells below are deliberately called out because they are not like-for-like framework feature costs:

The cumulative decomposition shows where work enters:

^* Sanic and BlackSheep share a hand-written, success-path adapter: msgspec decodes the known-good body, the query is converted inline, and bearer "authentication" is an exact comparison with "Bearer user". Wreath's cells exercise its public binder and bearer backend, including structured validation refusals, case-insensitive scheme parsing, duplicate-credential refusal, Identity publication, protected-route resolution, and a proper 401 Bearer challenge. Invalid input is therefore not equivalent: for example, the Sanic adapter answers 500 for a bad query or missing token where Wreath answers 422 or 401, and it accepts a duplicated Authorization field when the first value is valid. Every later cumulative BlackSheep and Sanic cell inherits this shortcut. Those full rows remain an account of the exact successful applications, but the starred increments are not evidence that equivalent framework validation or authentication is cheaper. In the retained Sanic run, the 1,966-instruction authentication increment is also below its available 2,747-instruction A/A resolution and is unresolved.

On this stripped companion, Wreath retired 8.43× fewer instructions than FastAPI and 5.78× fewer than both Sanic and BlackSheep. BlackSheep has the lowest ecosystem route floor; the two lightweight stacks converge once the same Cedar, PostgreSQL and outbound HTTP work dominates. Its purpose is decomposition; the holistic request above is the headline system comparison.

CORS is Starlette's CORSMiddleware, which FastAPI already brings in. It is not padded into the stack as another package. cedarpy 4.8.7 has a stateless public authorization call and no reusable compiled-policy handle. The measured request therefore includes that public lifecycle.

Both clients speak their real wire protocols to the same deterministic, in-process PostgreSQL and HTTP peers. The verifier rejects a sample unless the security, CORS, session, compression, and business response facts agree. Imports, startup, compilation, pool creation, and warm-up cancel through N/N/2 slopes. Server and generator are pinned separately with PYTHONHASHSEED=0. Instructions and the four L1 events share one non-multiplexed perf pass; five AMD component events split demand from prefetch L2 traffic in a second pass. L1 hits are accesses minus misses; aggregate L2 misses are retained only as an absolute and per-instruction normalization, not presented as one hit rate. The result contains no elapsed time, cycles, or IPC. It was recorded on CPython 3.14.7 and Linux x86-64, on a Ryzen 7 7730U. SIMD dispatch can make another architecture retire a different count.

Reproduce it:

uv sync --inexact --group benchmark
uv run python -m benchmarks.bench_holistic_stack_instructions \
  --requests 30 --trials 5 --connections 8 --warmup 16 \
  --output docs/perf/data/e2e-holistic-stack-instructions.json

# The cumulative framework-layer control
uv run python -m benchmarks.bench_e2e_instructions \
  --requests 4000 --trials 5 --connections 32 --warmup 500 \
  --output docs/perf/data/e2e-stack-instructions.json

The repository retains the holistic raw samples, Wreath application, FastAPI application, and counter harness, plus the cumulative control samples.

Install

pip install wreath
# or
uv add wreath

uv add 'wreath[linux]'     # io_uring reactor and native TLS on Linux
uv add 'wreath[h3]'        # HTTP/3; `http3` is an alias

The base wheel includes Wreath's portable C implementation. The framework remains a conforming ASGI application when you use another server.

The engineering rule

Wreath moves repeated work out of requests and moves byte-heavy work into native kernels. It does not call an idea faster because it was rewritten in C. Every performance change starts with a measurement, keeps its controls, and reports the machine and method that produced the result.

The same refusal applies to correctness. Unsupported declarations fail at startup. Hot-path complexity has executable probes. The test runner samples declared controls and asks whether the suite notices when one disappears.

Read the request path for the design and performance for the numbers.

Work on Wreath

uv sync
uv run wreath-check
uv run wreath-check --docs
uv run wreath test

The repository's AGENTS.md is the engineering contract. The agent cookbook turns it into task-shaped routes through the tree.