Documentation Index
Fetch the complete documentation index at: https://docs.secapi.ai/llms.txt
Use this file to discover all available pages before exploring further.
Preliminary — pending external methodology review (OMNI-3064). Numbers on
this page will be re-verified by an external SRE before this comparison is
linked from the docs sidebar. See benchmarks/METHODOLOGY.md.
OMNI Datastream vs edgartools
edgartools is a well-designed
open-source Python library (MIT-licensed) that talks directly to SEC EDGAR.
It’s the OSS peer most agent-builders evaluate alongside hosted APIs. This
comparison puts OMNI head-to-head against edgartools on the same four
workflows we use to benchmark against sec-api.io and financialdatasets.ai.
Speed
From a ubuntu-latest GitHub Actions runner, N=5 iterations per case.
Every edgartools iteration runs in a fresh subprocess with all caches
cleared, so each measurement is a truly cold SEC fetch — no in-memory or
on-disk cache hits skew the p50
(methodology):
| Operation | OMNI p50 | edgartools p50 | Speedup | OMNI wire bytes | edgartools wire bytes |
|---|
| Entity resolve | 34ms | 471ms | 13.9× | 273 | 164,686 |
| Filing search | 38ms | 688ms | 18.1× | 1,033 | 362,459 |
| Section extract | 34ms | 3,081ms | 90.6× | 2,954 | 3,053,868 |
| XBRL → JSON | 34ms | 1,508ms | 44.3× | 1,522 | 1,520,790 |
edgartools fetches raw SEC documents client-side (e.g., ~3 MB of 10-K HTML
to extract Item 1A text) before parsing. That’s an architectural difference
— edgartools optimizes for pip install, zero API key, SEC direct and
accepts the bandwidth cost. OMNI pre-parses once in its cluster and returns
compact JSON.
Pricing
| Tier | OMNI | edgartools |
|---|
| Free | 250 calls/month (renewable) | Unlimited (truly free) |
| Pay-as-you-go | From $0.01/call | Not applicable |
| Personal | $55/month | $0 |
| Business | $239/month | $0 |
| Enterprise | Custom (SLA, support, dedicated capacity) | None (community-supported) |
- Latency 13.9-90.6× faster on the same queries
- Payloads 600-1,000× smaller (fewer LLM tokens)
- SLA, support, dedicated capacity
- Commercial redistribution rights
- Hosted semantic search, dilution intelligence, MCP server, monitors, etc.
- Native JS/TS + Go + Rust SDKs (edgartools is Python-only)
edgartools has genuine strengths. Customers evaluating both should know:
- $0 cost, MIT license — no procurement, no monthly floor.
- Code you can read — the source is on GitHub; auditable and modifiable.
- No third-party dependency in your stack — edgartools → SEC direct.
- Clean, agent-friendly Python API — well-typed, thoughtful ergonomics.
- Full-text search & XBRL parsing — all client-side, no vendor coupling.
- Permissive redistribution for the library itself (MIT).
If your SEC-data workload is research-scale (one ticker at a time, small
number of filings), edgartools is often the right choice. OMNI’s value
compounds with scale, agents, and production requirements.
Where OMNI wins
| Dimension | OMNI | edgartools |
|---|
| Latency (hot path) | 34-38ms p50 | 300-2,100ms p50 |
| Payload size (agent-ingestable) | Compact JSON | Raw 10-K HTML / full XBRL |
| Semantic search | Yes (Pinecone + Voyage hybrid) | No |
| Intelligence bundles | Yes (company, security, earnings) | No (build it yourself) |
| Filing diff / amendment tracking | Yes | No |
| Monitors + webhooks | Yes (signed, replay, cursor) | No |
| MCP native server | Yes | No |
| SDKs | JS, Python, Go, Rust | Python-only |
CLI (omni-sec on npm) | Yes | No (Python import only) |
| Status page / SLA | Yes | No (best-effort OSS) |
| Commercial redistribution license | Yes (via commercial agreement) | No (library itself: MIT; SEC data: public) |
| 8-K 5.07 voting results (structured) | Yes | No |
| Filing export formats | JSON, MD, CSV, XLSX, DOCX, PDF | Python objects only |
| 13F comparison (hedge-fund overlap) | Yes | No (manual aggregation) |
| Factor analysis | Yes (84 factors) | No |
| Macro data | Yes (12 countries) | No (SEC-only) |
When to choose which
Choose edgartools when:
- Cost is the hard constraint ($0 matters).
- Workload is small (hundreds of filings/year, not millions).
- You’re building a research script or one-off notebook, not a production agent.
- You want to read/modify the library source.
- You’re comfortable parsing raw SEC documents client-side.
Choose OMNI Datastream when:
- You’re running an agent workload (latency + token cost compound).
- You need SLA, support, or commercial redistribution.
- You want semantic search, monitors, intelligence bundles, or MCP out of the box.
- You’re on a non-Python stack (JS, Go, Rust).
- You need 8-K 5.07 voting results, filing diffs, or other structured data
edgartools doesn’t extract.
Use both if you’re prototyping in a notebook (edgartools) and shipping in
production (OMNI) — that’s a common pattern.
Migration sketch
# edgartools → OMNI
# 1. Entity resolve
# edgartools
from edgar import Company, set_identity
set_identity("You you@example.com")
c = Company("AAPL")
cik, name = c.cik, c.name
# OMNI Datastream (JS/TS)
const entity = await omni.entities.resolve({ ticker: "AAPL" });
// returns { cik, name, ticker, ... }
# 2. Latest 10-K
# edgartools
filing = c.get_filings(form="10-K").latest(1)
# OMNI
const latest = await omni.filings.latest({ ticker: "AAPL", form: "10-K" });
# 3. Item 1A
# edgartools (pulls full 10-K HTML client-side)
item_1a = filing.obj().risk_factors
# OMNI
const item1A = await omni.filings.sections.latest({
ticker: "AAPL", form: "10-K", item: "item_1a"
});
# 4. Balance sheet from XBRL
# edgartools
bs = filing.xbrl().statements.balance_sheet()
# OMNI
const balanceSheet = await omni.statements.balance_sheet({
ticker: "AAPL", period: "annual", limit: 2
});
Reproducibility
All numbers on this page come from public scripts:
scripts/bench/benchmark_search.py (OMNI)scripts/bench/benchmark_edgartools.py (edgartools)scripts/bench/competitive_scorecard.py (4-way aggregator)benchmarks/METHODOLOGY.md (reviewable methodology doc)
Run them yourself. If you find a methodology flaw, file an issue or open a PR.