TradingAgents

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Scan timing: clone 3.11s · analysis 4.08s · 5.2 MB · GitHub API rate-limit (preflight)

https://github.com/TauricResearch/TradingAgents · scanned 2026-06-01 04:00 UTC (4 days, 5 hours ago) · 10 languages

288 findings (151 legacy + 137 scanner) 55th percentile · Python · small (2-20K LoC) Scanner says 91 (lower by 19)

File as issue Image v2 schema

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 4 days, 5 hours ago · v4 · 186 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	75.0	0.15	11.25
`security_score`	49.8	0.25	12.45
`testing_score`	100.0	0.20	20.00
`documentation_score`	91.0	0.15	13.65
`practices_score`	54.0	0.15	8.10
`code_quality`	62.4	0.10	6.24
Overall		1.00	71.7

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 1 High 89 Medium 28 Low 46 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 151 9-layer 35 Crowd 0 Layer: Software 84 Quality 88 Cicd 6 Security 3 Api 1 Hardware 3 Frontend 1

Exclude dismissed / FP Exclude test files

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Fragile runtime 73 findings

What it is: Code that runs but breaks under predictable input — division by zero, missing keys, unbounded loops, off-by-one slicing.

Why it matters: Reaches production undetected because happy-path tests pass. First user with a weird input crashes the request.

How AI causes it: AI loves writing the happy path; doesn't probe edge cases unless explicitly asked.

Fix approach: Add property-based tests. Wrap external inputs with explicit validators. Use the framework's typed deserializer (Pydantic, attrs).

12 matching findings on this repo

medium Bare except continues silently tradingagents/dataflows/alpha_vantage_indicator.py:225
medium Bare except continues silently tradingagents/dataflows/y_finance.py:453
medium Bare except continues silently tradingagents/dataflows/y_finance.py:426
medium Bare except continues silently tradingagents/dataflows/y_finance.py:391
medium Bare except continues silently tradingagents/dataflows/y_finance.py:356
medium Bare except continues silently tradingagents/dataflows/y_finance.py:321
medium Bare except continues silently tradingagents/dataflows/y_finance.py:249
medium Bare except continues silently tradingagents/dataflows/y_finance.py:174
medium Bare except continues silently tradingagents/dataflows/interface.py:167
medium Bare except continues silently tradingagents/dataflows/alpha_vantage_common.py:132
medium Bare except continues silently cli/main.py:1279
medium Bare except continues silently cli/utils.py:185

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Legacy markers 1 finding

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

1 matching finding on this repo

low Legacy-named symbol `test_get_config_returns_deep_copy` in tests/test_dataflows…

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Commented-out code 8 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

8 matching findings on this repo

info Commented-code block (5 lines) in main.py:4
info Commented-code block (7 lines) in tradingagents/__init__.py:3
info Commented-code block (5 lines) in tradingagents/default_config.py:58
info Commented-code block (5 lines) in tradingagents/dataflows/utils.py:10
info Commented-code block (7 lines) in tradingagents/llm_clients/model_catalog.py:33
info Commented-code block (5 lines) in tradingagents/llm_clients/capabilities.py:41
info Commented-code block (5 lines) in tradingagents/llm_clients/openai_client.py:151
info Commented-code block (5 lines) in tradingagents/llm_clients/anthropic_client.py…

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Config drift 5 findings

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

5 matching findings on this repo

medium No CI/CD configuration found
low File has no detected symbols: cli/config.py
low Legacy-named symbol `test_get_config_returns_deep_copy` in tests/test_dataflows…
info Commented-code block (5 lines) in tradingagents/default_config.py:58
low 10 env vars used in code but missing from .env.example

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