debtstack-ai 0.1.0

Corporate credit data API for AI agents

DebtStack.ai Python SDK

Corporate credit data for AI agents.

Why DebtStack?

Equity data is everywhere. Credit data isn't.

There's no "Yahoo Finance for bonds." Corporate debt structures, guarantor chains, and covenant details are buried in SEC filings—scattered across 10-Ks, 8-Ks, credit agreements, and indentures. An AI agent trying to answer "which telecom companies have leverage above 5x?" would need to read dozens of filings, extract the right numbers, and compute ratios manually.

DebtStack fixes this. We extract, normalize, and serve corporate credit data through an API built for AI agents.

Three Things You Can't Do Elsewhere

1. Cross-Company Credit Queries

# Find distressed telecom companies
GET /v1/companies?sector=Telecommunications&min_leverage=5&sort=-net_leverage_ratio

Screen 177 companies by leverage, coverage ratios, or maturity risk in one call. No filing-by-filing analysis.

2. Pre-Built Entity Relationships

# Who guarantees this bond?
POST /v1/entities/traverse
{"start": {"type": "bond", "id": "893830AK8"}, "relationships": ["guarantees"]}

Guarantor chains, parent-subsidiary hierarchies, structural subordination—mapped and queryable. This data exists nowhere else in machine-readable form.

3. Agent-Ready Speed

< 100ms response time

AI agents chain multiple calls. If each took 30 seconds (reading a filing), a portfolio analysis would take hours. DebtStack returns in milliseconds.

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Installation

pip install debtstack-ai

For LangChain integration:

pip install debtstack-ai[langchain]

Quick Start

from debtstack import DebtStackClient
import asyncio

async def main():
    async with DebtStackClient(api_key="your-api-key") as client:

        # Screen for high-leverage companies
        risky = await client.search_companies(
            sector="Telecommunications",
            min_leverage=4.0,
            fields="ticker,name,net_leverage_ratio,interest_coverage",
            sort="-net_leverage_ratio"
        )

        # Drill into the riskiest one
        ticker = risky["data"][0]["ticker"]
        bonds = await client.search_bonds(ticker=ticker, has_pricing=True)

        # Check guarantor coverage on their notes
        for bond in bonds["data"]:
            guarantors = await client.get_guarantors(bond["cusip"])
            print(f"{bond['name']}: {len(guarantors)} guarantors")

asyncio.run(main())

Synchronous Usage

from debtstack import DebtStackSyncClient

client = DebtStackSyncClient(api_key="your-api-key")
result = client.search_companies(sector="Energy", min_leverage=3.0)

What's In The Data

Coverage	Count
Companies	211 (S&P 100 + NASDAQ 100 + high-yield issuers)
Entities	28,128 (subsidiaries, holdcos, JVs, VIEs)
Debt Instruments	4,496 (bonds, loans, revolvers) with 97% document linkage
Bond Pricing	3,557 bonds with FINRA TRACE pricing (updated 3x daily)
SEC Filing Sections	14,511 (searchable full-text)
Covenants	1,247 structured covenant records

Pre-computed metrics: Leverage ratios, interest coverage, maturity profiles, structural subordination scores.

Relationships: Guarantor chains, issuer-entity links, parent-subsidiary hierarchies.

API Methods

Method	What It Does
search_companies()	Screen by leverage, sector, coverage, risk flags
search_bonds()	Filter by yield, spread, seniority, maturity
resolve_bond()	Look up CUSIP, ISIN, or "RIG 8% 2027"
traverse_entities()	Follow guarantor chains, map corporate structure
search_pricing()	FINRA TRACE bond prices, YTM, spreads
search_documents()	Full-text search across credit agreements, indentures
batch()	Run multiple queries in parallel
get_changes()	Track debt structure changes over time

Examples

Which MAG7 company has the most debt?

result = await client.search_companies(
    ticker="AAPL,MSFT,GOOGL,AMZN,NVDA,META,TSLA",
    fields="ticker,name,total_debt,net_leverage_ratio",
    sort="-total_debt",
    limit=1
)
# Returns structured data in milliseconds, not minutes

Find high-yield bonds trading at a discount

result = await client.search_bonds(
    seniority="senior_unsecured",
    min_ytm=8.0,
    has_pricing=True,
    sort="-pricing.ytm"
)

Who guarantees a specific bond?

guarantors = await client.get_guarantors("893830AK8")
for g in guarantors:
    print(f"{g['name']} ({g['entity_type']}) - {g['jurisdiction']}")

# Output:
# Transocean Ltd. (holdco) - Switzerland
# Transocean Inc. (finco) - Cayman Islands
# Transocean Offshore Deepwater Drilling Inc. (opco) - Delaware
# ... 42 more entities

Search for covenant language

result = await client.search_documents(
    q="maintenance covenant",
    section_type="credit_agreement",
    ticker="CHTR"
)
# Returns matching sections with highlighted snippets

LangChain Integration

from debtstack.langchain import DebtStackToolkit
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
from langchain import hub

toolkit = DebtStackToolkit(api_key="your-api-key")
tools = toolkit.get_tools()

llm = ChatOpenAI(temperature=0, model="gpt-4")
prompt = hub.pull("hwchase17/openai-functions-agent")
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

result = agent_executor.invoke({
    "input": "Which telecom companies are most at risk of default?"
})

MCP Server (Claude Desktop)

Add to ~/.config/claude/mcp.json:

{
    "mcpServers": {
        "debtstack-ai": {
            "command": "python",
            "args": ["-m", "debtstack.mcp_server"],
            "env": {
                "DEBTSTACK_API_KEY": "your-api-key"
            }
        }
    }
}

Then ask Claude:

• "Which energy companies have near-term maturities and high leverage?"
• "Who guarantees the Transocean 8% 2027 notes?"
• "Compare Charter's debt structure to Altice"

Pricing

DebtStack offers usage-based pricing with a free tier to get started.

See debtstack.ai/pricing for details.

Links

• Docs: docs.debtstack.ai
• Discord: discord.gg/debtstack-ai
• Issues: GitHub

License

MIT