Flit MCP Server

Flit

Flit is a tiny little physics engine for 3js. It doesn't do much yet! but I believe!

Spheres, collisions, raycasts, gravity, and a spatial-hash broadphase — nothing more, on purpose. All simulation state lives in flat Float32Arrays (structure-of-arrays), so the CPU path doubles as the reference implementation for a future WebGPU compute backend: same buffers, same kernels, no re-architecting.

What's in the box

• World — point-sphere particles, semi-implicit Euler integration, impulse + positional-correction contact solver, infinite ground plane.
• SpatialHash — uniform grid broadphase keyed by Morton (Z-order) cell codes. This is the Euclidean cousin in the LSH family: MinHash buckets documents by Jaccard similarity; this buckets positions so nearby points collide in the same bucket. Morton keys are bijective (no hash-collision pair bloat), invertible (no side table), and locality-ordered for a future sorted-array GPU backend.
• Distance & bit utilities — squared Euclidean/L1/Minkowski/L∞/Hellinger/ chi-square/KL distances (ported from FLANN), Morton (Z-order) keys, float bit-flips for radix sorting, octagonal approximate distance. See THIRD_PARTY_NOTICES.md for provenance and licenses.

For agents

Building a little three.js game or demo? Two ways in:

• Skill: skills/flit/SKILL.md — copy the skills/flit/ directory into your agent's skills path (e.g. .claude/skills/, ~/.code_puppy/skills/). It carries the 30-second integration recipe, the MCP option, measured performance envelope, and contributor rules.
• MCP server (no code needed): npm run mcp, or point your client at it:

{
  "mcpServers": {
    "flit": {
      "command": "npx",
      "args": ["vite-node", "mcp/server.ts"],
      "cwd": "<path-to-this-repo>"
    }
  }
}

Tools: flit_info, flit_reset, flit_spawn (rain/explosion/grid/fountain presets), flit_add_particles, flit_step, flit_state — the last two return flat xyz positions shaped for InstancedMesh syncing.

Usage

npm i flit-physics

import { World } from 'flit-physics';

const world = new World({ restitution: 0.4 }); // gravity and a floor at y=0 included

const ball = world.addParticle({ position: [0, 10, 0], radius: 0.5, mass: 1 });

// fixed timestep, e.g. from your rAF loop
world.step(1 / 60);

// sync to three.js: positions is a live Float32Array, 3 floats per particle
mesh.position.set(
  world.positions[ball * 3],
  world.positions[ball * 3 + 1],
  world.positions[ball * 3 + 2],
);

Develop

npm install
npm test        # vitest
npm run build   # tsc -> dist/
npm run bench   # broadphase + full-step micro-benchmarks
npm run demo    # three.js demo scene (vite dev server)

Benchmarks

Deterministic seeds; numbers from a local dev machine, recorded at commit time (see commit messages for the full series, including rejected designs).

• bench/broadphase.bench.ts — broadphase only, N=4096: xor-hash 3.4–3.7 ms (444 pairs, ~90% collision bloat) → Morton keys with ordered probing 3.5–3.7 ms (234 pairs, exact).
• bench/world.bench.ts — full World.step, N=1024: xor-hash 0.85 ms/step → Morton ordered-probing 0.83 ms/step → 0.93 ms/step with the sequential-impulse velocity solver (4 iterations + LUT friction). Exact keys, real contacts, +11%.
• bench/scaling.bench.ts — two regimes, and one important caveat. Fixed box (density rises): pairs scale ~N² from crowding physics. Scaled box (constant spawn density): flat O(N) ≈ 1.2 ms per 1000 through N=8000 — but only while bodies are scattered. The caveat: with gravity on, everything rains into a dense floor pile over ~2-4s (WARMUP=240 to reproduce), and steady-state piles are contact-solver dominated: ~4.3 ms per 1000 at N=8000 (87k contacts x 4 iterations), putting the 60fps pile budget near 3k bodies. The known fix for piles is island sleeping / agglomeration — parked.
• bench/morton-libs.bench.ts — codec bake-off vs npm libs (npm run bench:libs): ours 3.8 ns/encode, fast-morton MB 26.1, fast-morton LUT 43.8, @thi.ng/morton 539.9. In-house wins; the libs stay as devDependencies purely so the bake-off stays runnable.
• Rejected on measurement (see commits): 63-bit BigInt keys (13× alloc regression); sorted-array + binary-search broadphase (1.4× slower than Map probing in JS — negative result recorded).

Roadmap

• Morton-ordered broadphase cells — done, measured, shipped
• three.js demo scene — npm run demo, 220 balls in a box
• Open issue: settled-pile solver cost — see docs/issues/001-settled-pile-performance.md (self-contained brief with repro, evidence, and definition of done; suitable for an agent or human to pick up)
• WebGPU compute backend once the CPU reference settles

License

MIT — see LICENSE. Third-party portions and their licenses are listed in THIRD_PARTY_NOTICES.md.