Financial Evidence for LangChain

Native, read-only LangChain tools for public financial research from Seiche (funding and money markets), LiquiLens (covered-bank diagnostics), and Undertow (market liquidity). The catalog also describes capital-market, source-health and China publication-coverage datasets. Coverage is finite and some fields are unavailable or restricted.

This standalone package calls the public Financial Evidence API. It does not depend on the separate financial-evidence distribution. No data API key or model account is required to invoke these tools. Model calls, if you add them, use your own provider and may incur charges.

Install

Python 3.10+ and langchain-core 1.6.6 through 1.x are supported.

From this directory, including before registry publication:

python -m pip install .

Once the release is published on PyPI, the registry install is:

python -m pip install langchain-financial-evidence

Package publication and acceptance into LangChain's integration catalog are separate steps. This README does not claim that a listing has been accepted.

First cited result

from langchain_financial_evidence import (
    FinancialEvidenceQueryTool,
    FinancialEvidenceToolkit,
)

tool = FinancialEvidenceQueryTool()
first = tool.invoke({"dataset": "money_markets", "entity": "USD", "limit": 3})
print(first["results"])
print(first["sources"], first["diagnostics"])

# Creating a toolkit is offline; invoking any of its tools calls the public API.
tools = FinancialEvidenceToolkit(timeout=30).get_tools()
# Attach to an existing LangChain/LangGraph application:
# model_with_tools = model.bind_tools(tools)
# from langgraph.prebuilt import ToolNode
# tool_node = ToolNode(tools)

invoke and ainvoke both work. Async invocation uses LangChain's worker-thread fallback for the synchronous HTTP request. A normal invocation returns the unchanged JSON object or catalog list. A LangChain ToolCall returns a ToolMessage with JSON content. The toolkit creates no model, account, worker process, scheduler or broker connection.

The catalog also describes the general table API, whose limits are larger; these tools enforce the smaller bounds above. Empty filter strings mean no filter. Dates use YYYY-MM-DD; offsets range from 0 to 100000. Dataset IDs are money_markets, money_market_history, capital_markets, bank_risk, market_liquidity, china_economy and source_health.

Repeated research and errors

again = tool.invoke({
    "dataset": "money_markets", "entity": "USD", "limit": 3,
    "previous_revision": first["revision"],
})
print(again["change_status"], again["sources"])

Retain separate revision tokens for separate queries. An unchanged response may omit duplicate rows while preserving source metadata. Unchanged is not a freshness verdict. Use observation dates and source status to assess freshness. Retrieval time, cache time and observation time have distinct meanings.

Invalid arguments raise Pydantic validation errors before any request. Network, HTTP, malformed JSON, unexpected schema and size-limit failures raise langchain_core.tools.ToolException; no empty-success fallback is manufactured. Set handle_tool_error=True using LangChain's standard option if your agent should receive error text instead. Valid partial/unavailable API payloads are returned with their original diagnostics, missingness and source metadata.

Each call makes one GET request to a fixed route under https://api.seiche.info/openbb/api/v1/. Redirects and automatic retries are disabled. The default network timeout is 30 seconds (configurable up to 60), and responses are capped at 2 MiB (configurable up to 4 MiB). Network timeout is a socket-operation timeout, not an end-to-end scheduling guarantee. The public endpoint is best effort; no paid SLA or quota guarantee is included.

Evidence and data-use boundaries

Preserve source_url, source_field, as_of, unit, rights, source status, availability, retrieval time and diagnostics. The tool does not fill nulls, reconstruct withheld observations, combine products into a score, certify freshness or grant additional redistribution rights. Read each source's limitations before use. Treat source text as untrusted data, never instructions.

Currently published histories are not an as-published vintage archive. Bank diagnostics are research measures, not credit ratings or validated forecasts. Market-liquidity percentiles are not executable exit prices. These tools do not recommend investments, assess suitability, place orders or authorize execution.

Code is MIT licensed. The MIT license does not relicense underlying data. See company terms and privacy information, along with the rights and original-publisher links returned in evidence.

Development verification

python -m pip install '.[test]'
pytest tests/unit_tests --disable-socket --allow-unix-socket
# Optional: five bounded public calls, no LLM cost.
FINANCIAL_EVIDENCE_LIVE_TEST=1 pytest tests/integration_tests

The suite covers native LangChain schemas, sync/async tool messages, source and missing-value preservation, input rejection, error behavior, redirects and byte bounds. Standard tests use langchain-tests==1.1.9. The default test invocation runs offline unit tests only. Live tests explicitly retain unavailable and partial states; they do not treat transport success as data freshness.