AI Skill Hub 强烈推荐:cre-acquisition-orchestrator Agent工作流 是一款优质的Agent工作流。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。
首个开源AI原生框架,专为商业地产(CRE)收购流程设计。集成智能代理编排、工作流自动化和尽职调查功能,通过AI助手自动化复杂的房产交易分析与决策流程,适合地产投资公司和并购专业团队。
cre-acquisition-orchestrator Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
首个开源AI原生框架,专为商业地产(CRE)收购流程设计。集成智能代理编排、工作流自动化和尽职调查功能,通过AI助手自动化复杂的房产交易分析与决策流程,适合地产投资公司和并购专业团队。
cre-acquisition-orchestrator Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:npm 全局安装 npm install -g cre-acquisition-orchestrator # 方式二:npx 直接运行(无需安装) npx cre-acquisition-orchestrator --help # 方式三:项目依赖安装 npm install cre-acquisition-orchestrator # 方式四:从源码运行 git clone https://github.com/ahacker-1/cre-acquisition-orchestrator cd cre-acquisition-orchestrator npm install npm start
# 命令行使用
cre-acquisition-orchestrator --help
# 基本用法
cre-acquisition-orchestrator [options] <input>
# Node.js 代码中使用
const cre_acquisition_orchestrator = require('cre-acquisition-orchestrator');
const result = await cre_acquisition_orchestrator.run(options);
console.log(result);
# cre-acquisition-orchestrator 配置说明 # 查看配置选项 cre-acquisition-orchestrator --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export CRE_ACQUISITION_ORCHESTRATOR_CONFIG="/path/to/config.yml"
An open-source, multi-orchestrator workspace for commercial real estate multifamily acquisitions: drop documents, state the goal, watch 31 AI roles coordinate, and review the acquisition package.
Fastest proof path: run npm run proof, open the local dashboard, and trace one source-backed fact from upload to IC package. Full reviewer script: Public Proof Path.
I've been working on something that I think the CRE industry needs, and I wanted to share where it is now.
A few months ago I wrote about what happens when you point 489 AI agents at a 200-unit multifamily acquisition. That article was the bigger vision. This repo is the engineering behind the practical open-source version: the 31 named AI roles, orchestration logic, domain knowledge files, schemas, local dashboard, deterministic simulation engine, and source-backed review workflow that make the vision usable.
It is not fully production-ready. I want to be direct about that. But what is here is the most in-depth open-source framework I have seen for CRE acquisition orchestration because the category barely exists. There are agent frameworks for coding, customer support, research, and data analysis. There is almost nothing that models how a real multifamily acquisition moves across due diligence, underwriting, financing, legal, and closing while preserving data handoffs, review gates, and investment committee evidence.
The project is local-first: you can run the proof path with no API keys, inspect uploaded tables and source rows, review extracted candidate fields with provenance, approve or waive ambiguous values, and export Markdown/JSON for an investment committee starter package. The dashboard's workflow runtime defaults to live ChatGPT/Codex (with web search on) so the team can pull and cite real market, lender, and environmental data, while the deterministic offline demo stays the no-credential public proof path for tours, screenshots, and CI.
Everything in here - the agent prompts, domain skills, schemas, pipeline architecture, dashboard, and demo artifacts - is yours to use as a starting point. Fork it. Build on it. Adapt it to your own deals, investment thesis, and internal acquisition workflow. If this framework helps even one CRE team rethink how they approach acquisitions, it was worth open-sourcing.
Let's bring this industry into the future.
Disclaimer: This project is a reference architecture and educational framework, not production software for making investment decisions. Nothing here is financial, legal, or investment advice.
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data/status/pipeline-verification-ledger.md.npm run codex:status, npm run codex:smoke, npm run codex:run:full, npm run validate:codex, and npm run eval:live were run and recorded; the full live workflow completed with 21/21 agents passing on first attempt.agentTimeoutMs and per-result timedOut, so smoke/full live runs are covered by the same contract gate.npm run seed:prod-local -- --count 150 creates a sanitized QA-LOCAL-2026-* 150-deal corpus with source documents, extraction artifacts, approved fields, criteria, phase state, checkpoint status, and completed-report artifacts under local data/.npm run test:prod-local-data validates schemas, source-hash provenance, local-only output boundaries, idempotent reseeding, and generated-artifact sensitive-token avoidance.npm run proof regenerates Parkview, starts the dashboard, waits for readiness, and points reviewers to docs/PROOF-PATH.md to trace one source-backed fact from upload to IC package.docs/QA-INVENTORY.md documents routes, roles, modals, buttons, inputs, workflows, and acceptance criteria; docs/QA-BUG-LOG.md records each production-scale QA defect with reproduction evidence, fix, and verification, with 30 Playwright browser tests passing.tesseract.js, with no external OCR service.npm run setup installs and verifies PyMuPDF; npm tracks tesseract.js.fixtures/parsers/scanned-offering-memo-ocr.pdf verifies a true image-only offering memo can extract through the local bridge.npm run setup creates .venv, installs parser dependencies from scripts/requirements.txt, supports read-only --check, and the dashboard parser service prefers the repo virtualenv.diligence.checklistItems candidates with line provenance.npm run verify:v3 runs release checks, root tests, parser/workspace coverage, dashboard typecheck/build, audits, offline eval, production smoke, and full Playwright E2E. CI now runs this gate too.$0 rent no longer deflates in-place averages, content-aware rent-roll/T12 classification, graceful parse_failed for oversized/corrupt inputs, path-redacted parser errors, and an automated npm run test:pile smoke test.config/thresholds.json for dealbreakers and a deal-specific exit cap (fixed a clean deal wrongly marked FAIL).npm run eval scores an 8-deal synthetic benchmark (with npm run eval:offline for the no-API layers) and writes an honest trust report. The live (Codex) layer proves the agents catch narrative risks the deterministic fixture is blind to (red-flag recall 100%, dealbreaker recall 100%, IC verdict 100%, determinable financials 100%), with model-dependent returns (~25%) documented as an honest limit. See Honest Evaluation.diligence.checklistItems candidates with line provenance for operator review, without auto-applying economics.npm run serve serves the built dashboard plus the loopback API/WS together (loopback-default, not multi-tenant); see docs/DEPLOYMENT.md.npm run release:check readiness gate.| Capability | Why It Matters |
|---|---|
| **31-role acquisition team** | The repo models a real acquisition desk with orchestrators, diligence specialists, underwriting, financing, legal, closing, and ingestion roles instead of one generic assistant. |
| **19-section prompt anatomy** | The 21 acquisition specialists follow the 19-section anatomy from [Agent Development](docs/AGENT-DEVELOPMENT.md) (identity, mission, inputs, strategy, outputs, checkpoint/logging/resume protocols, error recovery, dealbreaker detection, confidence scoring, downstream contract, self-review, and self-validation). Orchestrators and ingestion roles use their own purpose-specific templates. |
| **Local source-package review** | Operators can drop deal files into a local workspace, inspect extracted fields, review source provenance, and decide what becomes deal data. |
| **Human approval gate** | The system is designed around operator judgment: candidate fields are accepted, rejected, waived, or left unresolved before workflows consume them. |
| **Strict schema contracts** | Phase outputs, agent findings, checkpoints, document manifests, and events validate against JSON Schema with shared enums and closed objects. |
| **Deterministic Parkview demo** | A complete Austin/Travis County sample run produces populated reports and workpapers with no API keys. |
| **Live Codex runtime (default launch lane)** | Launching a real workflow uses ChatGPT-authenticated Codex CLI execution by default, with web search on so agents cite real facts; the deterministic offline simulation stays the no-credential demo/CI fallback so live AI is never required just to evaluate the system. |
| **Operator dashboard** | The React workspace is one persistent deal space: a lifecycle spine from Intake to IC, the agents at work on the focused stage, a live feed, a command bar to dispatch the team, and IC package assembly - all in one frame. |
| **Public validation harness** | Demo verification, parser tests, workspace tests, schema tests, security assertions, docs drift checks, and browser E2E coverage are part of the repo. |
| **Open, inspectable domain layer** | CRE assumptions live in Markdown skill files and JSON config, so operators can see and change the policy rather than trusting hidden code. |
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pandas, openpyxl, pdfplumber, PyMuPDF)From a fresh clone on Windows:
git clone https://github.com/ahacker-1/cre-acquisition-orchestrator.git
cd cre-acquisition-orchestrator
npm install
npm run setup -- --skip-codex-install --skip-login
npm run proof
Open http://localhost:5173 if the browser does not open automatically. The proof path regenerates deterministic Parkview artifacts, starts the dashboard, waits for the local UI/API to respond, and works even if Codex is missing or login is skipped. npm run setup -- --skip-codex-install --skip-login also prepares the local parser virtual environment used for XLSX/PDF extraction without starting the optional Codex/ChatGPT auth path.
To require a complete live-agent setup during onboarding:
npm run setup -- --require-codex
Check Codex auth status:
npm run codex:status
Expected login output should say Logged in using ChatGPT.
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The public demo is intentionally visual: a first-time visitor should understand the workspace before they read the architecture. The path below starts at the front door, drops a document package into Intake, inspects the uploaded tables field by field, then moves through the persistent deal space - the lifecycle spine, the agents at work, and the IC package - using the deterministic Parkview sample so it populates with no API keys.

| Phase | What Happens | Example Outputs |
|---|---|---|
| 1. Document Intake | Operator uploads source files, classifies document types, and previews parser output. | Source manifest, hashes, extraction candidates, warnings |
| 2. Source Review | Candidate fields are accepted, rejected, or waived before they become underwriting inputs. | Approved rent roll fields, T12 fields, provenance trail |
| 3. Due Diligence | Specialists review rent roll, operating expenses, physical condition, market, title, environment, and tenant credit. | Unit mix, rent roll analysis, OpEx notes, diligence flags |
| 4. Underwriting | The model builder, scenario analyst, and IC memo writer translate inputs into an investment view. | 10-year pro forma, 27-scenario matrix, DSCR, IRR, equity multiple |
| 5. Financing | Lender outreach and quote comparison turn deal metrics into debt strategy. | Loan sizing, lender quote comparison, term-sheet draft |
| 6. Legal | PSA, title/survey, loan documents, insurance, estoppels, and transfer documents move through review. | Legal checklist, estoppel tracker, PSA risk notes, closing conditions |
| 7. Closing | Closing coordinator and funds-flow manager assemble close mechanics. | Closing checklist, prorations, wire schedule, funds-flow workpaper |
| 8. IC Package | The workspace gathers outputs into a decision package. | Markdown package, JSON export, manifest, review trail |
The Parkview sample follows this path end to end with deterministic data so contributors can validate behavior without API keys or private deal files.
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创新的垂直领域AI应用,将智能代理编排与地产业务深度融合。框架设计完整,代码质量良好,但市场成熟度和采用度需观察。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,cre-acquisition-orchestrator Agent工作流 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | cre-acquisition-orchestrator |
| 原始描述 | 开源AI工作流:The first comprehensive open-source AI-native framework for CRE acquisitions, tu。⭐60 · TypeScript |
| Topics | AI代理编排商业地产工作流自动化尽职调查TypeScript |
| GitHub | https://github.com/ahacker-1/cre-acquisition-orchestrator |
| License | Apache-2.0 |
| 语言 | TypeScript |
收录时间:2026-05-18 · 更新时间:2026-05-19 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端