intellign 0.1.0

Python SDK and MCP server for the Intellign optimization platform — assignment, scheduling, routing, allocation, and matching problems solved via API

intellign

Python SDK + MCP server for the Intellign optimization platform — describe assignment, scheduling, routing, allocation, or matching problems and get solved rosters back. All computation runs server-side; this package is a typed HTTP client plus an MCP wrapper.

Guides: SDK guide · REST API reference · MCP server · Deployment · Examples

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Install

pip install intellign                  # core SDK (httpx + pydantic)
pip install "intellign[mcp]"           # + intellign-mcp server
pip install "intellign[pandas]"        # + Result.to_dataframe()
pip install "intellign[ical]"          # + Result.export_ical()

Python ≥ 3.10. Get an API key from your Intellign dashboard — ik_live_ (production) or ik_test_ (free sandbox: ≤200 entities/targets, capped solver budget, no quota usage).

Quickstart

from intellign import Client, Problem

client = Client(
    api_key="ik_live_...",
    base_url="https://api.intellign.ai",   # or your deployment / http://localhost:8000
)

nurses = [
    {"id": "n1", "name": "Ada",  "skills": "triage,general"},
    {"id": "n2", "name": "Bola", "skills": "surgery"},
]
clinics = [
    {"id": "c1", "name": "ER",      "required_skills": "triage",  "capacity": 1},
    {"id": "c2", "name": "Theatre", "required_skills": "surgery", "capacity": 1},
]

problem = (
    Problem.assignment(name="Nurse assignment")
    .entities(rows=nurses)
    .targets(rows=clinics)
    .maximize("skill_match", weight=70,
              entity_column="skills", target_column="required_skills")
    .maximize("workload_balance", weight=30, target_column="capacity")
    .require("entity.skills superset_of target.required_skills")   # hard constraint
    .quality("balanced")                                           # fast | balanced | best
)

result = client.submit(problem).wait()
for a in result.assignments:
    print(a["resource_id"], "->", a["target_id"])

df = result.to_dataframe()              # optional pandas extra

Async — identical surface, plus SSE progress streaming:

from intellign import AsyncClient

async with AsyncClient(api_key="ik_live_...") as client:
    job = await client.submit(problem)
    async for event in job.stream_progress():
        print(event.get("current_generation"), event.get("best_fitness"))
    result = await job.result()

What you can do

Capability	SDK entry point	Example
Structured solve (builder)	Problem.assignment()... → client.submit()	examples/01
Upload data once, reference by id	client.upload_dataset("team.csv") → .entities(dataset_id=...)	examples/02
Natural-language solve	client.solve_nl("assign vans to zones...", ds_a, ds_b)	examples/03
Starter templates	client.templates() / client.template(name)	examples/04
Live progress (SSE)	job.stream_progress() (sync or async)	examples/05
Webhooks on completion	client.create_webhook(url) — HMAC-signed deliveries	examples/06
Guided conversational flow	client.create_session() / send_message() / export_session()	examples/07
Robust error handling	typed exceptions, idempotency keys, retries	examples/08
LLM tool access (MCP)	intellign-mcp — stdio + Streamable HTTP	MCP guide

Objectives & constraints

Objectives are named metrics with weights (live catalog: client.capabilities()): skill_match, distance, workload_balance, attribute_match, cost, preference_match — each maximize or minimize.

Constraints use a small formal grammar (invalid expressions are rejected at create time with the reason):

entity.skills superset_of target.required_skills
entity.hours <= target.max_hours          # also >= == < >, or numeric literal
sum(entity.load) <= target.capacity
haversine(entity.location, target.location) <= 25
entity.region in ['north', 'east']

.require(expr) = hard (violation weight 100); .require(expr, hard=False) = soft.

Jobs & results

client.submit() returns a Job: .status(), .wait(timeout=300), .result(), .stream_progress(). Solves are asynchronous server-side — prefer webhooks over polling for production integrations.

Result: .assignments (list of dicts with resource_id/target_id/ score/rationale), .metrics, .best_fitness, .to_dataframe(), .export_ical(path) for scheduling results with start/end timestamps.

Errors, retries, idempotency

Every API failure raises a typed subclass of IntelligError carrying .code, .status_code, and .request_id (quote it in support requests): AuthenticationError, ScopeError, InvalidSpecError, NotFoundError, ConflictError, QuotaError, RateLimitError (.retry_after), SandboxLimitError, SolveFailedError, ServerError.

• The client automatically retries 429/502/503/504 (honoring Retry-After).
• Pass idempotency_key="your-uuid" to create_problem/solve_problem — retries return the original response instead of duplicating work (24 h window).
• Rate limits per key/minute by plan: free 30, pro 120, enterprise 600.

Full error taxonomy and endpoint reference: API.md.

Webhooks

hook = client.create_webhook("https://myapp.com/hooks/intellign")
secret = hook["secret"]        # shown exactly once — store it

Deliveries are POSTs with X-Intellign-Event and X-Intellign-Signature: sha256=<hmac_sha256(secret, raw_body)>; verify with a constant-time compare (working receiver: examples/06_webhooks.py). Failed deliveries retry 3× and everything is inspectable via client.webhook_deliveries(id).

MCP server

Expose Intellign as tools to Claude or any MCP client:

pip install "intellign[mcp]"
INTELLIGN_API_KEY=ik_live_... intellign-mcp                          # stdio (local)
intellign-mcp --transport http --port 8001 --stateless               # hosted, multi-tenant

Tools: create_problem, solve_problem, get_status, get_result, list_templates, get_template. Resources: intellign://schema/spec, intellign://capabilities. Over HTTP each request authenticates with its own Authorization: Bearer ik_... header. Setup + hosting: MCP.md and DEPLOYMENT.md.

Development

python3 -m venv .venv && .venv/bin/pip install -e ".[dev]"
.venv/bin/python -m pytest -q                     # unit suite (offline)

# contract tests against a live server:
INTELLIGN_CONTRACT_BASE_URL=http://localhost:8000 \
INTELLIGN_CONTRACT_API_KEY=ik_... .venv/bin/python -m pytest -m contract -q

.venv/bin/python -m build                         # wheel + sdist into dist/

Layout: src/intellign/ (client, spec models, builders, job/result, errors, mcp/server.py), tests/ (unit, respx-mocked), tests/contract/ (env-gated, live server), examples/ (runnable per capability).

Releasing

CI (.github/workflows/ci.yml) tests every push/PR on Python 3.10–3.12 and builds artifacts. Publishing (publish.yml) uses PyPI Trusted Publishing:

1. Bump src/intellign/_version.py.
2. Commit, tag vX.Y.Z, push the tag.
3. Create a GitHub Release from the tag → workflow builds, verifies the tag matches the package version, and publishes to PyPI via OIDC (no token secrets). One-time setup: add this repo as a trusted publisher on pypi.org (workflow publish.yml, environment pypi) and create the pypi environment in repo settings.

License

MIT