neoxlink 0.6.2

Open-source SDK for LLM-first intent parsing, taxonomy mapping, extraction, and public-data matching pipelines

NEOXLINK-SDK

Bridging the gap between Chat and Transaction — turn fuzzy natural language into Standardized Business Intelligence and executable procurement workflows.

Vision: NEOXLINK-SDK is the operating system for AI commercialization. It closes the last mile between “the model understood the request” and “the business system can act on it” by normalizing intent with the UNSPSC global standard (Code + Name), Structured Preview, human or agent confirmation, durable structured records, and AI Resolve (direct answers or real supply-chain handoff). Agent Interoperability is first-class: integrate directly, run inside Skill runtimes, or expose capabilities via MCP (Model Context Protocol) tools.

中文文档 README_zh.md · UNSPSC 快速查阅（同仓） · MCP 集成说明

System architecture (chat → transaction)

High-level data path from natural language to standardized, actionable records. (Diagram is a logical view; your deployment may split API, matching, and MCP host.)

flowchart LR
  subgraph input [NL_input]
    U[User_or_Agent]
  end
  subgraph sdk [NEOXLINK_SDK]
    P[Parse_and_Structured_Preview]
    C[Confirm_or_policy_gate]
    S[Structured_record]
    M[Match_or_resolve]
  end
  subgraph standard [Business_standard]
    UNS[UNSPSC_Code_plus_Name]
  end
  U --> P
  P --> UNS
  P --> C
  C --> S
  S --> M
  M --> U

For the maintained layering diagram (HTTP client vs local UNSPSC catalog vs orchestration), see docs/wiki/repository-layout.md — it is versioned with the repo and mirrors what CI tests against.

The gap (and how we close it)

Classic chat AI stops at paraphrasing needs. Enterprise procurement, trading, and compliance systems speak codes, constraints, and structured intents — not paragraphs. NEOXLINK-SDK translates messy language into structured business instructions aligned to UNSPSC, then supports Supply-Demand Matching on the same normalized axis.

Dimension	Traditional AI chat	NEOXLINK-SDK
Output	Free-form text	Structured Preview + typed payloads
Taxonomy	Ad-hoc labels	UNSPSC (Code + Name) normalization
Transaction readiness	Low	Parse → confirm → structured DB → resolve / match
Agent integration	Ad-hoc prompts	Skill adapters + MCP tool surface
Matching	Semantic vibes only	Supply-Demand Matching with explicit signals

Features

• UNSPSC-first taxonomy — consistent Code + Name for demand and supply.
• Structured Preview — LLM-refined structure before anything is committed.
• Human / agent confirmation — overrides and policy gates before persistence.
• Structured persistence — records land in a structured pipeline ready for operators.
• AI Resolve — AI-direct answers or routing toward real fulfillment.
• Supply-Demand Matching — staged ProcurementIntentEngine with pluggable data and ranking.
• Agent Interoperability — NeoxlinkSkill, NeoxlinkMCPAdapter, and chain-style orchestration.
• MCP tool exposure — stable tool names such as neoxlink.parse_preview and neoxlink.confirmed_submit.

Core flow

1. Natural language in — buyer, seller, or agent describes the need in plain language.
2. LLM Structured Preview — intent is refined into a preview (including UNSPSC where applicable).
3. User / agent confirm — approve or edit; business truth is explicit.
4. Structured database — confirmed record is stored for downstream systems.
5. AI Resolve — answer, escalate, or connect to real supply / fulfillment.

Quick start

Install

pip install neoxlink
# or, from this repo:
pip install -e .

Minimal Python: SDK + Structured Preview

from neoxlink import SDK

sdk = SDK(
    base_url="https://neoxailink.com",
    api_key="ak_live_xxx",  # your NeoXlink API key
)
draft = sdk.parse_preview(
    "We need urgent packaging compliance consulting for EU retail launch.",
    entry_kind="demand",
)
print(draft.preview.unspsc.code, draft.preview.unspsc.name)

Advanced integrations use neoxlink_sdk directly (NeoXlinkClient, StructuredSubmissionPipeline, ProcurementIntentEngine, NeoxlinkMCPAdapter). See examples/ and the sections below.

Run a local example

pip install -e .
python examples/04_procurement_intent_engine.py

MCP (Model Context Protocol) stdio server

pip install 'neoxlink[mcp]'
export NEOXLINK_API_KEY=your_key
neoxlink-mcp

Point your MCP host (Claude Desktop, Cursor, etc.) at the neoxlink-mcp command, or use the config template in mcp/config.neoxlink.example.json. Optional: NEOXLINK_ENABLE_MATCH=1 to expose neoxlink.match_intent (local matching pipeline; supply your own data source in custom deployments).

Use cases

• Global procurement & sourcing — standardize requisitions and supplier catalogs across regions using UNSPSC.
• Cross-border trade — align multilingual requests with a single commodity and service taxonomy for RFQs and compliance.
• B2B marketplaces & ERP handoff — turn conversational intake into records that downstream systems can ingest.
• Agent products — ship MCP tools or Skill contracts without reinventing procurement ontology.
• Supply-Demand Matching — rank partners with transparent scoring on top of normalized intent.

Architecture highlights (v0.6)

Module	Role
neoxlink_sdk.client.NeoXlinkClient	HTTP client: parse_entry, confirm_entry, resolve_entry, structured_submit.
neoxlink_sdk.pipeline.StructuredSubmissionPipeline	Parse → confirm → resolve orchestration (ParseDraft, ConfirmedEntry, ResolveResult).
neoxlink_sdk.engine.ProcurementIntentEngine	Staged matching: intent → UNSPSC → clarification → retrieval → ranking.
neoxlink_sdk.skill.NeoxlinkSkill	Skill-runtime adapter (preview vs auto-confirm).
neoxlink_sdk.mcp.NeoxlinkMCPAdapter	MCP-friendly tool facade for Agent Interoperability.
neoxlink_sdk.credits	Credit / BYOM metering for metered clients.
neoxlink_sdk.plugins.PluginRegistry	Register model adapters, data sources, ranking strategies.

The in-repo wiki also documents on-disk layout, HTTP vs UNSPSC layers, and running tests (Python 3.11+). Open-source “module one–eight” design remains in REPOSITORY_ARCHITECTURE.md.

Open-source community layout

1. Templates
2. Examples
3. Plugins
4. Contributors
5. Ecosystem
6. Adoption

Governance & scope

• OPEN_SOURCE_SCOPE.md
• REPOSITORY_ARCHITECTURE.md
• CONTRIBUTOR_WORKFLOW.md
• DATA_COLLABORATION_GUIDELINES.md
• PROMPT_COLLABORATION.md
• GOVERNANCE.md

Extended examples

• examples/01_structured_pipeline.py — parse / confirm / resolve
• examples/02_skill_runtime.py — Skill runtime
• examples/03_chain_style.py — chain-style invocation
• examples/04_procurement_intent_engine.py — UNSPSC matching engine
• examples/05_credits_and_byom.py — credits & BYOM
• examples/06_plugin_registry.py — plugins
• examples/07_open_source_pipeline.py — open-source reference pipeline
• examples/08_startup_policy_realworld.py — interactive advisor
• examples/model_apis/ — OpenAI, Anthropic, Gemini, Ollama, router
• neoxlink-mcp + mcp/config.neoxlink.example.json — MCP stdio server for agent hosts

Optional extras for model examples:

pip install -e ".[model_examples]"

Local development

This package targets Python 3.11+ (requires-python in pyproject.toml). Run the test suite with a 3.11+ interpreter (system python3 on some macOS installs is 3.9 and will not load the type annotations used in the code):

python3.11 -m venv .venv
.venv/bin/pip install -e ".[dev]"
.venv/bin/python -m pytest

Community

• community/README.md
• CONTRIBUTING.md
• CODE_OF_CONDUCT.md

License

MIT