Electronics RFQ Agent

AI quoting agent for electronics distributors -- RFQ in, quote out via MCP tools **Your sales engineers are spending 2-4 hours turning RFQ documents into quotes. This does it in seconds.** Electronics RFQ Agent is a Python library and CLI that reads RFQ documents (PDF, Excel, Word), looks up every line item against your ERP catalog, and outputs a draft quote. It connects to SAP, Epicor, Oracle, and Microsoft Dynamics through MCP servers, so it works with Claude, GPT-4, or any agent framework that speaks MCP. - [Install - ](#install)[The problem this solves - ](#the-problem-this-solves)[Quickstart - ](#quickstart)[Commands - ](#commands)[API reference - ](#api-reference)[How it differs from the alternatives - ](#how-it-differs-from-the-alternatives)[ERP support - ](#erp-support)[Benchmarks - ](#benchmarks)[Integration matrix - ](#integration-matrix)[Try it in Docker - ](#try-it-in-docker)[Security - ](#security)[FAQ - ](#faq)[Contributing - ](#contributing)[License ``` `pip install electronics-rfq-agent-cli # or uv add electronics-rfq-agent-cli` ``` ``` `git clone https://github.com/RudrenduPaul/electronics-rfq-agent cd electronics-rfq-agent pip install -e . # or, with uv: uv sync` ``` Parsing RFQ documents (PDF, Excel, Word) calls the Anthropic API, so set`ANTHROPIC_API_KEY`before running anything that touches a real document: ``` `export ANTHROPIC_API_KEY="sk-ant-..."` ``` You don't need this key to run`erfa audit`against an existing quote file, or to explore the CLI with`--help`. Only document parsing calls out to Claude. We were working with electronics distributors who had 3-5 sales engineers spending most of their day on quote entry. Every tool we found was either tied to one specific ERP or required a 6-month integration project. We wanted something that worked with what distributors already had, could be self-hosted (quote data is sensitive), and was actually extensible. The MCP architecture means adding a new ERP is writing one file. The parser handles the document formats distributors actually send: hand-filled PDFs, multi-sheet Excel files, and the occasional scanned table. No ERP system required to try it out; the mock backend ships with 200 realistic electronics parts. You do need`ANTHROPIC_API_KEY`set, since parsing the RFQ document is still a real Claude call: ``` `from electronics_rfq_agent import QuoteAgent from electronics_rfq_agent.mcp.mock import MockERP agent = QuoteAgent(erp=MockERP()) quote = agent.run_sync("path/to/rfq.xlsx") for line in quote.lines: print(f"{line.rfq_line.part_number}: {line.status} @ {line.unit_price}") print(quote.summary())` ``` MockERP applies quantity-based pricing tiers automatically: qty >= 1000 gets 20% off, qty >= 100 gets 10% off, qty >= 10 gets 5% off. List price applies below qty 10. This mirrors real-world volume pricing so benchmarks and integration tests reflect realistic cost curves. ``` `from electronics_rfq_agent import QuoteAgent from electronics_rfq_agent.mcp import EpicorMCP agent = QuoteAgent( erp=EpicorMCP(base_url="https://your-epicor.company.com", api_key="..."), max_concurrent=10, # parallel ERP lookups (default: 10) ) quote = agent.run_sync("rfq_2026_0619.xlsx") print(quote.summary())` ``` `erfa`ships two subcommands. Every flag below is pulled straight from`erfa --help`. ``` `# Generate a quote from an RFQ file against the mock ERP export ANTHROPIC_API_KEY="sk-ant-..." erfa quote rfq.xlsx --mock # Save the quote as JSON for later inspection erfa quote rfq.xlsx --mock --output quote.json # Audit what happened: what was found, substituted, or missing and why erfa audit quote.json` ``` **Audit output example**(real output from`erfa audit docs/example-quote.json`, generated by running the mock ERP's pricing logic against the sample RFQ in`tests/fixtures/sample_rfq.txt`): ``` `Audit Report - Quote df9fd083 RFQ Source : tests/fixtures/sample_rfq.txt Lines : 5 Total : USD 64.04 FOUND (4) ------------------------------------------------------------ L 1 RES-0402-10K-1PCT qty=1000 unit=0.0064 ext=6.40 L 2 CAP-100NF-50V-X7R-0402 qty=500 unit=0.0104 ext=5.20 L 3 IC-LM358-SOIC8 qty=50 unit=0.7101 ext=35.50 L 4 XTAL-16MHZ-SMD qty=25 unit=0.6774 ext=16.94 NOT FOUND (1) ------------------------------------------------------------ L 5 MOSFET-NMOS-20V-3A-SOT23 Part 'MOSFET-NMOS-20V-3A-SOT23' not found in ERP catalog Fill rate: 80% (4 found / 0 substituted / 1 not found)` ``` **Zero-price parts:**If a part exists in the ERP catalog but has a unit price of $0.00, the agent quotes $0 rather than skipping the line, and sets`line.notes`to a message flagging the zero price so you catch it before quoting the customer. Check`line.notes`for any found or substituted line before sending a quote out. The full reference lives in](#license)[docs/api.md: every`QuoteAgent`parameter, the shared ERP connector interface,`Quote`/`QuoteLineItem`field-by-field, and the exception hierarchy. The exports below are what`from electronics_rfq_agent import ...`actually gives you, grepped from`src/electronics_rfq_agent/__init__.py`, not guessed: **SAP note:**pyrfc requires the SAP NetWeaver RFC Library, which is not on PyPI and must be downloaded manually from SAP's support portal (S-user required). See](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/api.md)[docs/erp-setup/sap.mdfor step-by-step instructions. Measured using the in-memory mock backend (200 realistic parts, no ERP system required). Run it yourself: ``` `git clone https://github.com/RudrenduPaul/electronics-rfq-agent cd electronics-rfq-agent uv run python benchmarks/run.py` ``` **ERP lookup latency (100 individual lookups, mock backend):** **Quote assembly time by RFQ size (parser mocked, no Anthropic API call):** *These numbers cover ERP lookup and quote assembly only. The RFQ parser is mocked and no Anthropic API call is made. In production, AI document parsing adds 5-15s per document and real ERP lookups add 100-500ms per line (parallelised at up to`max_concurrent=10`), giving a realistic total of ~15s for a 50-line RFQ. Manual baseline: 2-4 hours.* Electronics RFQ Agent works with any agent framework that supports MCP: ``` `git clone https://github.com/RudrenduPaul/electronics-rfq-agent cd electronics-rfq-agent cp .env.example .env # add your ERP credentials and Anthropic API key docker compose up -d` ``` Your quote data never leaves your environment. - **Vulnerability scanning:**Trivy scans every CI run (HIGH/CRITICAL severity, build fails on unfixed findings), results uploaded to the GitHub Security tab. - **Release signing:**the release workflow signs built wheels and sdists with Sigstore and attaches a CycloneDX SBOM to each GitHub Release once a version is tagged. - **Dependency pinning:**Dependabot keeps GitHub Actions and Python dependencies current (weekly patch updates, monthly Actions updates). - **Disclosure:**](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/erp-setup/sap.md)[SECURITY.md. Report vulnerabilities privately via email or GitHub Security Advisories; 48-hour acknowledgement, 90-day responsible disclosure. **What does Electronics RFQ Agent actually do?**It takes an RFQ document (PDF, Excel, or Word) from a customer, extracts every line item with Claude, looks each part up against your ERP catalog through an MCP connector, and returns a priced draft quote with margin applied. A sales engineer reviews and sends it instead of building it from scratch. **Is it production ready?**It's Beta (v0.2.x). The core library, Epicor, Oracle, and Dynamics connectors are covered by CI (lint, mypy strict, pytest with an 80% coverage gate, Trivy scanning) across Python 3.10-3.12. SAP support is explicitly marked Beta and requires a manual install step (the SAP NetWeaver RFC Library isn't on PyPI). **Do I need an ERP system to try it?**No.`MockERP`ships with 200 realistic electronics parts and applies quantity-based pricing tiers, so you can run the full parse-lookup-quote flow with zero ERP credentials. You do still need`ANTHROPIC_API_KEY`, since document parsing is a real Claude call. Only`erfa audit`(reading an already-saved quote file) works with no key at all. **How is this different from asking ChatGPT to read the RFQ?**A generic chat model can read a document and even guess at prices, but it has no connection to your actual ERP catalog, inventory, or contract pricing, so it's making numbers up. Electronics RFQ Agent looks up every line item against your real catalog data and only quotes what's actually in stock at the actual cost price plus your configured margin. **How is this different from SAP Joule?**Joule is SAP's proprietary AI assistant suite, built into SAP's cloud platform (SAP BTP) and centered on SAP data. Electronics RFQ Agent is MIT-licensed, self-hosted, and works with SAP, Epicor, Oracle, and Dynamics through the same MCP interface. Your quote data never has to leave your environment, and you're not tied to one ERP vendor. **How do I call this from an agent instead of the CLI?**Run`erfa mcp`to launch an MCP stdio server exposing`quote_rfq`,`lookup_part`, and`audit_quote`as typed tools. Point any MCP-compatible client (Claude, GPT-4, Gemini, or a custom agent framework) at`erfa mcp`as the launch command, and it can call those tools directly with structured JSON in and out, no CLI output to parse. **Which document formats does it parse?**PDF (including scanned tables, via Claude's vision capability), Excel (`.xlsx`/`.xls`, searches every worksheet for the line-item header row), and Word (`.docx`, reads all tables). Plain text RFQs work too. **What happens to a line item that isn't in the ERP catalog?**It's marked`not_found`with a note explaining why (rather than silently dropped or guessed at), and it's excluded from the quote total. Run`erfa audit`on the saved quote JSON to see exactly which lines were found, substituted, or missing, and why. **Can I use this commercially?**Yes. It's MIT licensed, so you can use it, modify it, and ship it commercially with no royalty and no obligation to open-source your changes. Attribution via the license file is all that's required. **Is my RFQ and pricing data sent anywhere besides Anthropic?**Only the RFQ document content goes to the Anthropic API for parsing. ERP lookups go directly from your machine to your ERP system over the connector you configure: there's no intermediary service, and nothing is stored outside your own environment unless you choose to persist quote JSON yourself. **Is this published to PyPI?**Yes, as`electronics-rfq-agent-cli`(`pip install electronics-rfq-agent-cli`). The publish pipeline (build, Sigstore signing, SBOM, trusted-publisher upload) lives in`.github/workflows/release.yml`and runs on every`v*`tag push. The Python import path (`electronics_rfq_agent`) and the`erfa`CLI command are unchanged; only the registry-facing package name carries the`-cli`suffix, matching this maintainer's naming convention for CLI tools. - Read](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/SECURITY.md)[CONTRIBUTING.mdbefore opening a PR - Good first issues are labeled in GitHub - ERP adapters live in`electronics_rfq_agent/mcp/`; each is a self-contained file with no changes to core - All PRs require 80% test coverage GitHub Discussions:](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/CONTRIBUTING.md)[Ask questions, share ideasDiscord: coming soon Full docs:](https://github.com/RudrenduPaul/electronics-rfq-agent/discussions)[Getting started·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/getting-started.md)[API reference·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/api.md)[ERP setup: Epicor·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/erp-setup/epicor.md)[SAP·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/erp-setup/sap.md)[Oracle·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/erp-setup/oracle.md)[Dynamics 365·](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/docs/erp-setup/dynamics.md)[Changelog If you use Electronics RFQ Agent in research, please cite: ``` `@software{paul2026electronicsrfq, author = {Paul, Rudrendu and Nandy, Sourav}, title = {Electronics RFQ Agent: AI Quoting Agent for Electronics Distributors}, year = {2026}, url = {https://github.com/RudrenduPaul/electronics-rfq-agent}, license = {MIT} }` ``` This is a web browser that enables your coding agent, such as Claude Code, to visit websites on your behalf and assist you in identifying bugs or creating UI test cases. What Shopify did for ecommerce, Chipp does for AI agents. Build, deploy, and monetize AI agents for your business — no engineering team required. Interact with capabilities of the CRIC Wuye AI platform, an intelligent assistant specifically for the property management industry. AI agents that automate bookkeeping and financial close for SMBs. Unified MCP gateway that loads 150+ AI tools into Claude Desktop via a single Docker command on Windows. Routes tool calls across specialized servers (Business Intelligence, API Hub, Content Automation, Intelligence) with centralized logging and governance. Built for teams without dedicated platform engineers. KLAVE is an MCP server that gives Claude the ability to autonomously + privately negotiate any deal on your behalf...no human back-and-forth required. Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers. 39 tools for the LLM Conveyors AI agent platform. Run Job Hunter, B2B Sales, ATS scoring, resume rendering, and more from any MCP client. Automates marketing operations with AI-powered optimization, real-time analytics, and multi-platform integration. The Most Comprehensive ServiceNow AI Toolkit — 400+ MCP tools, 26 AI capabilities, SDK mode, Direct API mode. Covers ITSM, ITOM, CMDB, HRSD, CSM, SecOps, GRC and 35+ modules. 42-tool MCP server for CRM, relationship intelligence, and multi-agent orchestration. Search contacts, manage deals, analyze relationships, and coordinate AI agents.](https://github.com/RudrenduPaul/electronics-rfq-agent/blob/HEAD/CHANGELOG.md)