Summary

Closed, Completed

Understand
Reviewed assignment and product origin.
Understand
Understand
Assessed file specifications and identified issues.
Understand
Plan
Discussed MVP build ideas and priorities.
Plan
Build
Implemented country-aware search enhancement.
Build
Verify
Validated tests and merged retrieval improvements.
Verify

DealLens — standalone assignment build record · detailed · GPT-5.6 Sol + Fable

Trace guide

DealLens is a standalone Option 1 product conceived for this Tavily assignment: a GP or acquisition analyst enters one company and receives a governed, source-backed risk screen for investment-committee preparation. This detailed record begins with assignment and starter review, preserves the product-selection tradeoffs, and follows the implementation through live Tavily/Nebius runs, deterministic correctness work, analyst UI, evaluations, LangSmith observability, GCP deployment, and submission hardening. Build models are fable and gpt-5.6-sol. Repetitive polling, hidden reasoning, credentials, environment contents, personal paths, unrelated machine output, and unrelated workflow context are omitted. Editorial chapter markers are explicit; retained source events remain in order.

Product origin

The assignment supplied a generic Tavily search agent and asked for one meaningful improvement. Product exploration compared a change monitor with an acquisition diligence screen. DealLens was selected because Tavily could be load-bearing across Research, Search, and Extract, while evidence promotion could be evaluated deterministically within the take-home scope. It is the product built and submitted.

review the files

I’ll inspect the repository state and recent changes first, then review the relevant files for correctness, regressions, and missing tests.

This folder isn’t a Git repository, so there’s no diff or commit context to anchor the review. I’m mapping the files now and will review the source as a standalone codebase.

Found one substantive issue.

::code-comment{title="[P2] Tool-call chunks lose correlation" body="The key changes across streamed chunks when only the first chunk contains an id: later chunks commonly retain only index=0, but chunk.get(\"index\") or \"tool_call\" treats zero as falsy. Arguments can therefore be accumulated under a different buffer, and multiple tool calls may be merged or suppressed. Track calls by index consistently, explicitly accepting zero, and reconcile the ID when it arrives." file="[assignment]/starter_agent.py" start=197 end=203 priority=2}

Additional observations:

The Python file parses and compiles successfully.
There are no tests, so streaming behavior, malformed tool results, and non-string content remain unverified.
The directory is not a Git repository, so I couldn’t review changes relative to a baseline.
The assignment explicitly says the submitted repository should not include starter_agent.py; this copy should be treated only as starter material.

1 · Assignment review and product choice

The work begins by reviewing the starter and assignment, stress-testing two product directions, and narrowing the MVP to DealLens: an evidence-governed acquisition red-flag screen for investment-committee preparation.

what exactly the script does? what assignment is?

The assignment offers two choices and asks you to complete one in roughly 4–6 hours.

Option 1: Improve the application

You take the provided starter_agent.py as inspiration and build a meaningfully better application using Tavily.

Possible improvements include:

Adapting it to a real customer workflow
Improving search and retrieval quality
Producing reliable citations
Adding evaluation or quality checks
Adding tracing and observability
Integrating another useful service

Your submission should include:

A GitHub repository containing your implementation
A short technical statement explaining your approach and its value
A record of your AI-assisted development process
Importantly, the submitted repository should not contain the original starter_agent.py

Option 2: Build an explainer

You create a public-facing technical explainer about an AI-engineering concept related to Tavily, retrieval, agents, or evaluation.

It should be useful to developers or technical customers and preferably include diagrams or interactivity.

You submit:

The explainer
A short technical statement
A record of how you created it

What `starter_agent.py` does

It is a command-line research assistant.

You run it with a question:

Its execution flow is:

Loads environment variables from the shell or .env.
Checks for TAVILY_API_KEY and NEBIUS_API_KEY.
Creates the Nebius-hosted Kimi K2.6 chat model.
gives the model access to Tavily web search.
Sends your question to a LangChain agent.
The model decides whether it needs to search the web.
When it searches, the script displays the search arguments and results.
The model uses those results to generate a streamed answer.
The answer is printed incrementally in the terminal.

Conceptually:

text

User question     ↓Nebius language model     ↓Does it need current information?     ├── No → Generate answer     └── Yes → Tavily search → Search results → Generate answer

User question     ↓Nebius language model     ↓Does it need current information?     ├── No → Generate answer     └── Yes → Tavily search → Search results → Generate answer

The supporting functions mainly format terminal output:

require_env() checks API keys.
message_text() extracts text from streamed messages.
truncate() shortens large results.
format_tool_result() formats Tavily results.
print_tool_call() and print_tool_result() display Rich panels.
main() constructs and runs the agent.

The starter is intentionally basic. The real task is not merely to clean it up—it is to turn the underlying idea into something that creates clear technical or business value for a realistic user.

Files mentioned by the user:

# Watchtower — Specification v1 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

wdyt

I’ll read the Watchtower specification and assess it against the take-home’s scope, differentiation, technical depth, and 4–6 hour constraint.

Files mentioned by the user:

# Watchtower — Specification v2 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

wdyt

I’ll compare v2 against the earlier risks—especially partial retrieval failures, removal confirmation, citation durability, URL deduplication, and scope.

V2 is substantially better. The “missing observations are not observations of absence” principle gives the project a memorable engineering thesis, and the failure case makes the demo unusually credible.

However, there is one important contradiction that should be fixed before implementation.

Critical issue: probe failure does not confirm removal

This rule is still unsafe:

Absent from map → probe /extract fails → page_removed

An extraction failure might mean:

Timeout
Rate limiting
Bot protection
Temporary server error
Unsupported page
Tavily extraction failure
DNS or TLS problem

So probe failure remains a missing observation—not positive evidence of removal. Calling it “positively confirmed” contradicts the core thesis.

Use explicit probe outcomes:

Probe result	Event
Successful extraction	Page still exists
Explicit HTTP 404 or 410	Confirmed removal
Redirect to a clearly different location	`page_moved` or uncertain
401, 403, 429, 5xx, timeout, unknown failure	`observation_gap`
Absent repeatedly across multiple healthy snapshots	Probable removal, with lower confidence

If Tavily does not expose enough failure detail to distinguish 404/410 from operational failures, you cannot confirm removal using Tavily extraction alone. Options:

Perform a small direct HTTP probe for status codes.
Require absence across two healthy runs.
Report possible_removal rather than page_removed.

This is the only conceptual blocker I see.

Joint normalization needs clarification

The stable-boilerplate rule depends on both snapshots:

Strip only if the line appears on >60% of pages in both previous and current snapshots.

That cannot be finalized independently while creating each snapshot. The previous snapshot may have been normalized under a different boilerplate set.

A sound flow is:

text

Snapshot:    retain raw extracted Markdown
Diff:    compute stable boilerplate jointly from old + new raw snapshots    normalize both snapshots using the same rules    hash and compare

Snapshot:    retain raw extracted Markdown
Diff:    compute stable boilerplate jointly from old + new raw snapshots    normalize both snapshots using the same rules    hash and compare

I would therefore avoid treating content_hash as an authoritative snapshot-time value. Either:

Compute comparison hashes during the diff, or
Store both a raw hash and a normalization-policy version.

Otherwise changing the normalization logic can generate misleading diffs.

Define the monitored-page set

There is an edge case around the 15-page curation cap:

An old monitored URL remains in /map.
New higher-priority URLs push it out of the top 15.
It is therefore not extracted, but it also isn’t absent from the map.

What state does it receive?

I suggest separating:

python

mapped_urls        # everything discoveredselected_urls      # pages selected this runmonitored_urls     # stable pages carried between runspages              # actual extraction observations

mapped_urls        # everything discoveredselected_urls      # pages selected this runmonitored_urls     # stable pages carried between runspages              # actual extraction observations

Prefer retaining previously monitored pages unless they disappear, then use remaining capacity for newly discovered pages. Otherwise curation-ranking changes can silently change the monitored surface.

A few smaller refinements

Add not_found to the page/probe states instead of storing every outcome under failed.
Put a per-company cap on removal probes so a broken map response cannot create a cost spike.
Consider possible_removal as an event kind; it makes uncertainty visible without overclaiming.
Version snapshot and normalization schemas so old archives remain readable.
For removed-page citations, make the local archive path clickable in the generated Markdown when possible.
Rephrase “fabricated quotes are structurally impossible” slightly more cautiously: “accepted evidence quotes are guaranteed to occur in the supplied evidence.” It is precise and less absolute.

Scope verdict

It is still ambitious for 4–6 hours, but now it is coherent enough to build. If time becomes tight, I would cut:

Hunk collapsing
Provider abstraction
uncertain verdict
Detailed dollar conversion

I would preserve:

Failure-state semantics
Stable monitoring set
Deterministic diff tests
Grounded evidence
Four-case demo
Evaluation
Cost/usage counts

Overall: this is a compelling submission plan. Fix the “probe failure equals removal” rule and clarify joint normalization and stable page selection. Once those are resolved, I’d stop refining the specification and start building—the remaining value will come from evidence that it actually works.

give me a sample user flow

Build 2: Automated M&A & Vendor Due Diligence MonitorThe Real Problem: Investment analysts and enterprise procurement teams waste dozens of hours manually searching for adverse media, leadership departures, lawsuit filings, and security breaches before signing deals or onboarding high-risk vendors.**How Tavily Solves It:**Leverage Tavily's /research endpoint as a background worker. When given a company name (e.g., "Target Company X"), run a governed search pipeline: apply strict exclude_domains to ignore spammy SEO review sites, and direct search toward news registries, court record aggregators, and regulatory press releases. Combine Tavily's live extraction with an internal vector database (using MongoDB Hybrid Search) to cross-reference fresh web findings against internal compliance policies. Automatically output a structured executive summary memo covering leadership changes, regulatory scrutiny, and financial signals—complete with clickable source links.

how about we do something like this?

This is commercially strong, but I want to separate the compelling customer problem from the parts that may make the take-home feel generic or over-scoped. I’ll quickly verify the current Tavily Research capabilities, then give you a concrete recommendation versus Watchtower.

The customer problem is strong, but as written this is weaker than Watchtower as a take-home submission.

The main reason: it risks becoming a thin wrapper around Tavily Research plus MongoDB. Tavily already performs multi-step research, structured output, and citations, so reviewers may reasonably ask, “What did you engineer beyond calling /research and formatting the response?” Tavily already showcases a similar market-research workflow in its documentation.

There is also an API mismatch: /research currently supports an input prompt, model selection, structured-output schema, streaming, and citation formatting, but its documented request parameters do not expose exclude_domains. Domain inclusion/exclusion belongs to /search. Therefore “strict exclude_domains on /research” is not currently enforceable as described. You could request exclusions in natural language, but that is a preference—not governance. Tavily Research API, Tavily Search API

When this concept works well

It becomes compelling if the product is framed as a governed diligence system, not an automated memo generator:

Given a company and an internal diligence policy, collect evidence for every required risk question, distinguish verified findings from unresolved checks, and produce a reviewable decision packet.

The differentiator should be coverage and provenance:

text

Internal diligence policy          ↓Required checks          ↓Evidence collection          ↓Supported / no evidence found / unresolved          ↓Human-review queue          ↓Executive memo

Internal diligence policy          ↓Required checks          ↓Evidence collection          ↓Supported / no evidence found / unresolved          ↓Human-review queue          ↓Executive memo

For example:

Required check	Status	Evidence
Sanctions or regulatory action	No evidence found	8 sources searched
Material litigation	Review required	Court filing located
Security breach in last 36 months	Supported	Regulator notice
CEO departure	Supported	Company announcement
Financial distress	Unresolved	Conflicting evidence

That is more useful than a confident prose summary because due-diligence teams care about what was checked, what remains unknown, and why a conclusion was reached.

MongoDB concern

MongoDB Hybrid Search is only meaningful if there is a realistic internal corpus to cross-reference:

Procurement policies
Risk tolerances
Previous diligence reports
Restricted-party guidelines
Required contractual clauses
Vendor security standards

Without that, it will look like résumé-driven architecture. For a 4–6 hour assignment, a versioned Markdown or YAML policy pack may demonstrate the same product logic more clearly.

Example:

yaml

checks:  - id: security_incident    question: Has the company disclosed a security incident in the last 36 months?    severity: high    escalation:      - Any incident affecting customer data      - Any unresolved regulator investigation
  - id: leadership_stability    question: Have the CEO, CFO, or CISO departed in the last 24 months?    severity: medium

checks:  - id: security_incident    question: Has the company disclosed a security incident in the last 36 months?    severity: high    escalation:      - Any incident affecting customer data      - Any unresolved regulator investigation
  - id: leadership_stability    question: Have the CEO, CFO, or CISO departed in the last 24 months?    severity: medium

If MongoDB is important to the intended customer, use it narrowly: retrieve the relevant policy clauses for each finding, then cite both the public evidence and the internal policy that triggered escalation.

A better version of the concept

I would call it something like “Diligence Control Room”:

A policy-driven pre-deal diligence pipeline that turns a company name into an evidence-backed risk checklist, explicitly showing supported findings, negative searches, conflicts, and unresolved checks.

A focused implementation could do this:

User supplies company, jurisdiction, and transaction type.
System loads the applicable diligence policy.
Deterministic planner creates 6–8 required risk checks.
Tavily /search performs governed source collection using domain filters.
Tavily /extract captures the primary evidence.
Tavily /research is optionally used for one broader synthesis task.
Every generated finding must cite extracted evidence.
Conflicting or weak findings go to human review.
System renders a structured Markdown memo.

Example command:

bash

uv run diligence investigate \  --company "Acme Industrial GmbH" \  --jurisdiction DE \  --policy policies/ma-target.yaml

uv run diligence investigate \  --company "Acme Industrial GmbH" \  --jurisdiction DE \  --policy policies/ma-target.yaml

Example output:

text

DILIGENCE COMPLETE
2 escalations1 conflicting finding3 checks cleared2 checks unresolved
HIGH — Regulatory actionGerman regulator issued a remediation order in February 2026.Primary evidence captured and archived.
MEDIUM — CFO departureCompany announcement confirms departure, but effective date differsfrom trade-publication reporting. Human review required.
UNRESOLVED — Material litigationNo reliable primary source was accessible. Do not interpret this asconfirmation that no litigation exists.

DILIGENCE COMPLETE
2 escalations1 conflicting finding3 checks cleared2 checks unresolved
HIGH — Regulatory actionGerman regulator issued a remediation order in February 2026.Primary evidence captured and archived.
MEDIUM — CFO departureCompany announcement confirms departure, but effective date differsfrom trade-publication reporting. Human review required.
UNRESOLVED — Material litigationNo reliable primary source was accessible. Do not interpret this asconfirmation that no litigation exists.

Watchtower versus diligence

Dimension	Watchtower	Diligence system
Differentiation	Strong temporal layer	Common research workflow
Customer urgency	Recurring monitoring	High-stakes transaction
Tavily breadth	Map, extract, search	Search, extract, research
Technical originality	High	Medium unless policy-governed
Demo simplicity	Strong fixture demo	Strong one-company demo
Risk of overbuilding	Moderate	High with MongoDB
Evaluation story	Change classification	Coverage and finding accuracy

My recommendation: keep Watchtower if the goal is the strongest take-home. It has a sharper technical thesis and demonstrates something Tavily does not already provide directly.

If you prefer the M&A idea, do not build “Research API → vector database → memo.” Build the narrower policy-driven evidence checklist. Its defining principle should be:

“No evidence found” is not the same as “risk absent.”

That is the due-diligence equivalent of Watchtower’s excellent “missing observations are not observations of absence,” and it gives the project real engineering depth.

i need 1 concrete MVP build idea from this - where we can use tavily at its best

Build this:

DealLens — 10-Minute M&A Red-Flag Screen

A CLI that takes one acquisition target and produces an evidence-backed screening memo across four risk areas:

Leadership and ownership changes
Regulatory action and litigation
Cybersecurity incidents
Financial-distress signals

It is explicitly a first-pass screen, not a legal due-diligence opinion.

User experience

bash

uv run deallens screen \  --company "Acme Industrial Ltd" \  --domain "acme-industrial.com" \  --jurisdiction "UK"

uv run deallens screen \  --company "Acme Industrial Ltd" \  --domain "acme-industrial.com" \  --jurisdiction "UK"

Five minutes later:

text

DEALLENS SCREEN COMPLETE
Target: Acme Industrial LtdRisk level: REVIEW REQUIRED
1 verified red flag2 reported concerns1 unresolved check7 findings rejected as weak or duplicated
Memo: reports/acme-industrial-2026-08-03.mdEvidence: reports/acme-industrial-2026-08-03.jsonTavily usage: 38 credits

DEALLENS SCREEN COMPLETE
Target: Acme Industrial LtdRisk level: REVIEW REQUIRED
1 verified red flag2 reported concerns1 unresolved check7 findings rejected as weak or duplicated
Memo: reports/acme-industrial-2026-08-03.mdEvidence: reports/acme-industrial-2026-08-03.jsonTavily usage: 38 credits

The Tavily pipeline

text

Company name + domain + jurisdiction                  │                  ▼        Tavily /research   broad, multi-angle discovery                  │                  ▼       Candidate risk claims                  │                  ▼         Tavily /search source-controlled verification queries                  │                  ▼         Tavily /extract capture exact supporting evidence                  │                  ▼      Deterministic evidence gate                  │                  ▼        Cited Markdown memo

Company name + domain + jurisdiction                  │                  ▼        Tavily /research   broad, multi-angle discovery                  │                  ▼       Candidate risk claims                  │                  ▼         Tavily /search source-controlled verification queries                  │                  ▼         Tavily /extract capture exact supporting evidence                  │                  ▼      Deterministic evidence gate                  │                  ▼        Cited Markdown memo

Each Tavily primitive has a distinct job:

/research: maximize recall and discover candidate red flags across multiple angles.
/search: verify candidates with controlled domain filters and targeted queries.
/extract: capture the actual source text used as evidence.

That demonstrates Tavily better than using /research as a one-call memo generator.

Step 1: Broad discovery

Call /research with a structured-output schema:

json

{  "company": "Acme Industrial Ltd",  "candidates": [    {      "category": "leadership",      "claim": "The CFO departed in March 2026",      "date": "2026-03",      "source_urls": [        "https://example.com/article"      ],      "verification_query": "\"Acme Industrial\" CFO departure"    }  ]}

{  "company": "Acme Industrial Ltd",  "candidates": [    {      "category": "leadership",      "claim": "The CFO departed in March 2026",      "date": "2026-03",      "source_urls": [        "https://example.com/article"      ],      "verification_query": "\"Acme Industrial\" CFO departure"    }  ]}

Research prompt:

text

Conduct a red-flag screen of Acme Industrial Ltd in the UK.
Look for:- director, founder, CEO, CFO, or ownership changes- regulator investigations, enforcement, and material litigation- cybersecurity incidents and customer-data breaches- insolvency, layoffs, facility closures, covenant problems, or distress
Return candidate findings, not conclusions. Include source URLs and aspecific verification query for every candidate. Do not interpret a lackof findings as proof that no risk exists.

Conduct a red-flag screen of Acme Industrial Ltd in the UK.
Look for:- director, founder, CEO, CFO, or ownership changes- regulator investigations, enforcement, and material litigation- cybersecurity incidents and customer-data breaches- insolvency, layoffs, facility closures, covenant problems, or distress
Return candidate findings, not conclusions. Include source URLs and aspecific verification query for every candidate. Do not interpret a lackof findings as proof that no risk exists.

Cap this at perhaps ten candidate findings.

Step 2: Governed verification

For every candidate, run a targeted /search.

Use category-specific sources:

yaml

UK:  regulatory:    - gov.uk    - fca.org.uk    - ico.org.uk    - cma.gov.uk
  corporate:    - find-and-update.company-information.service.gov.uk
  cyber:    - ncsc.gov.uk    - ico.org.uk
  credible_news:    - reuters.com    - ft.com    - bbc.co.uk

UK:  regulatory:    - gov.uk    - fca.org.uk    - ico.org.uk    - cma.gov.uk
  corporate:    - find-and-update.company-information.service.gov.uk
  cyber:    - ncsc.gov.uk    - ico.org.uk
  credible_news:    - reuters.com    - ft.com    - bbc.co.uk

Exclude low-quality sources:

yaml

exclude_domains:  - crunchbase.com  - zoominfo.com  - signalhire.com  - glassdoor.com  - trustpilot.com  - pitchbook.com

exclude_domains:  - crunchbase.com  - zoominfo.com  - signalhire.com  - glassdoor.com  - trustpilot.com  - pitchbook.com

The lists are customer-editable configuration, not hard-coded logic.

Use two verification searches:

text

"Acme Industrial Ltd" "CFO" departuresite:find-and-update.company-information.service.gov.uk "Acme Industrial Ltd"

"Acme Industrial Ltd" "CFO" departuresite:find-and-update.company-information.service.gov.uk "Acme Industrial Ltd"

This matters because /research itself does not provide strict domain-filter parameters. Governance happens in /search.

Step 3: Capture evidence

Use /extract on the best one to three verification URLs.

Store:

python

class Evidence(BaseModel):    url: str    title: str    publisher: str    published_date: str | None    source_tier: Literal["primary", "credible_secondary", "other"]    quote: str    retrieved_at: datetime

class Evidence(BaseModel):    url: str    title: str    publisher: str    published_date: str | None    source_tier: Literal["primary", "credible_secondary", "other"]    quote: str    retrieved_at: datetime

The final quote must occur verbatim in the extracted content. If extraction fails, the finding cannot be marked verified.

Step 4: Apply deterministic evidence rules

Do not let the model decide whether its own research was successful.

text

VERIFIED    One primary source    OR two independent credible secondary sources
REPORTED    One credible secondary source with extracted evidence
UNRESOLVED    Candidate discovered, but verification is inaccessible or conflicting
REJECTED    Only aggregators, duplicated articles, or unsupported claims found
NO FINDING    Searches completed without a qualifying result    Never rendered as “risk absent”

VERIFIED    One primary source    OR two independent credible secondary sources
REPORTED    One credible secondary source with extracted evidence
UNRESOLVED    Candidate discovered, but verification is inaccessible or conflicting
REJECTED    Only aggregators, duplicated articles, or unsupported claims found
NO FINDING    Searches completed without a qualifying result    Never rendered as “risk absent”

A local policy file determines severity:

yaml

rules:  cybersecurity_incident:    severity: high    escalate_when:      - customer_data_affected      - regulator_involved
  executive_departure:    severity: medium    escalate_when:      - ceo      - cfo      - founder      - multiple_departures_within_12_months

rules:  cybersecurity_incident:    severity: high    escalate_when:      - customer_data_affected      - regulator_involved
  executive_departure:    severity: medium    escalate_when:      - ceo      - cfo      - founder      - multiple_departures_within_12_months

MongoDB is unnecessary for the MVP. The YAML file demonstrates the policy-cross-reference behavior without infrastructure dominating the project.

Sample memo

markdown

# Acquisition Red-Flag Screen
Target: Acme Industrial Ltd  Jurisdiction: United Kingdom  Generated: 3 August 2026
## Executive assessment
Review required. One leadership concern was verified and one regulatorycheck remains unresolved. This screen is an initial evidence review, nota legal or financial diligence opinion.
## Verified findings
### CFO departure — Medium
The company filing records the termination of Jane Smith’s appointmentas a director in March 2026. A company announcement identifies her asthe group CFO.
> “Jane Smith’s appointment was terminated on 14 March 2026.”
Primary source: [Companies House](https://example.com)  Corroboration: [Company announcement](https://example.com)
Policy triggered: Executive departure involving CFO
## Reported concerns
### Facility closure reported by trade press — Medium
One credible trade publication reports that the Leeds facility willclose. No company or regulatory confirmation was located.
> “The company informed employees that its Leeds site will close…”
Source: [Industry publication](https://example.com)
Status: Reported, not independently verified
## Unresolved checks
### Regulatory action
A potential regulator reference was discovered, but the underlyingdocument could not be extracted. Human verification is required.
## Coverage
| Check | Status | Sources reviewed ||---|---|---:|| Leadership and ownership | Verified finding | 6 || Regulatory and litigation | Unresolved | 8 || Cybersecurity | No finding | 7 || Financial distress | Reported concern | 9 |

# Acquisition Red-Flag Screen
Target: Acme Industrial Ltd  Jurisdiction: United Kingdom  Generated: 3 August 2026
## Executive assessment
Review required. One leadership concern was verified and one regulatorycheck remains unresolved. This screen is an initial evidence review, nota legal or financial diligence opinion.
## Verified findings
### CFO departure — Medium
The company filing records the termination of Jane Smith’s appointmentas a director in March 2026. A company announcement identifies her asthe group CFO.
> “Jane Smith’s appointment was terminated on 14 March 2026.”
Primary source: [Companies House](https://example.com)  Corroboration: [Company announcement](https://example.com)
Policy triggered: Executive departure involving CFO
## Reported concerns
### Facility closure reported by trade press — Medium
One credible trade publication reports that the Leeds facility willclose. No company or regulatory confirmation was located.
> “The company informed employees that its Leeds site will close…”
Source: [Industry publication](https://example.com)
Status: Reported, not independently verified
## Unresolved checks
### Regulatory action
A potential regulator reference was discovered, but the underlyingdocument could not be extracted. Human verification is required.
## Coverage
| Check | Status | Sources reviewed ||---|---|---:|| Leadership and ownership | Verified finding | 6 || Regulatory and litigation | Unresolved | 8 || Cybersecurity | No finding | 7 || Financial distress | Reported concern | 9 |

MVP scope

Build only:

One jurisdiction: UK
One-company-at-a-time CLI
Four risk categories
/research candidate discovery
/search verification
/extract evidence capture
YAML source and escalation policies
Markdown and JSON output
Usage/cost footer
Fixture-driven demo
Offline unit tests for evidence classification and quote validation

Do not build:

MongoDB
A UI
Scheduling
Continuous monitoring
Authentication
Multi-tenancy
Email or Slack delivery
A fully autonomous approval decision

Demo command

The fixture should include:

One verified CFO departure
One weak allegation rejected because it only appears on an aggregator
One credible but uncorroborated factory-closure report
One inaccessible regulatory result marked unresolved
One category with “no finding,” clearly not described as “cleared”

Why this is a good submission

The key product principle is:

Discovery can be probabilistic; escalation must be evidence-governed.

That gives you a clear customer problem, strong use of three Tavily primitives, structured citations, failure handling, evaluation opportunities, and a polished demo—all within a realistic take-home scope.

there are something built by claude review it

I’ll inspect what Claude built, run its tests and demo paths where possible, then review it against the DealLens MVP and the assignment criteria.

>uv run pytest -q

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.9 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\n?? .env.example\\n?? .gitignore\\n?? README.md\\n?? fixtures/\\n?? jurisdictions/\\n?? policy.searchfund.yaml\\n?? policy.yaml\\n?? pyproject.toml\\n?? src/\\n?? tests/\\nfatal: your current branch 'main' does not have any commits yet\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n.........................                                                [100%]\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 3 ---\\n# DealLens — 10-minute M&A red-flag screen\\n\\nOne acquisition target in, one evidence-backed screening memo out. DealLens\\nscreens a company across four risk areas — leadership/ownership, regulatory\\nand litigation, cybersecurity incidents, financial distress — and refuses to\\ncall anything *verified* unless deterministic evidence rules say so.\\n\\nIt is explicitly a first-pass screen, not a legal due-diligence opinion.\\n\\n```\\nuv run deallens screen \\\\\\n  --company \\\"Acme Industrial Ltd\\\" \\\\\\n  --domain \\\"acme-industrial.com\\\" \\\\\\n  --jurisdiction UK\\n```\\n\\n```\\n╭────────── DEALLENS SCREEN COMPLETE ──────────╮\\n│ Target: Acme Industrial Ltd                  │\\n│ Risk level: REVIEW REQUIRED                  │\\n│                                              │\\n│ 1 verified red flag(s)                       │\\n│ 1 reported concern(s)                        │\\n│ 1 unresolved check(s)                        │\\n│ 2 finding(s) rejected as weak or unsupported │\\n│                                              │\\n│ Memo: reports/acme-industrial-2026-08-03.md  │\\n│ Evidence: reports/acme-industrial-2026-08-03.json │\\n│ Tavily usage: 41 credits                     │\\n╰──────────────────────────────────────────────╯\\n```\\n\\n## The idea\\n\\n**Discovery can be probabilistic; escalation must be evidence-governed.**\\n\\nResearch agents fail in a specific way: the model that found a claim also\\ngrades it. DealLens splits the two. Each Tavily primitive gets one job, and a\\ndeterministic gate — not the model — decides what status a finding earns.\\n\\n```\\ncompany + domain + jurisdiction\\n        │\\n        ▼\\n  Tavily /research      DISCOVER   recall-first; returns candidates,\\n        │                          never conclusions\\n        ▼\\n  Tavily /search        VERIFY     governed by per-jurisdiction source\\n        │                          tiers (include/exclude domains)\\n        ▼\\n  Tavily /extract       CAPTURE    quotes must occur verbatim in the\\n        │                          extracted text or they are discarded\\n        ▼\\n  Evidence gate         DECIDE     pure functions, unit-tested\\n        │\\n        ▼\\n  memo.md + evidence.json + cost footer\\n```\\n\\nThe gate:\\n\\n| Status | Standard |\\n|---|---|\\n| **VERIFIED** | ≥1 primary source, or ≥2 credible secondary sources on distinct domains |\\n| **REPORTED** | exactly one credible secondary source with captured evidence |\\n| **UNRESOLVED** | candidate discovered, but the backing source could not be extracted — a human must look |\\n| **REJECTED** | only aggregator-tier or unsupported material survived |\\n| **NO FINDING** | no candidates at all — rendered as \\\"no qualifying public findings\\\", never \\\"cleared\\\" |\\n\\nTwo failure modes get first-class treatment:\\n\\n- **A failed extraction can never produce a verified finding.** It surfaces\\n  as UNRESOLVED with the uncaptured URL listed. Missing observations are not\\n  observations of absence.\\n- **A paraphrased quote is discarded, not repaired.** The LLM selects\\n  supporting passages; a validator requires them to occur verbatim in the\\n  extracted content. Fabricated quotes are structurally impossible —\\n  unsupported *interpretations* remain possible, which is what the eval\\n  measures.\\n\\nSeverity is orthogonal to status: status says how well-evidenced a finding\\nis, severity (from `policy.yaml`) says how loud it should be if true. A\\nsingle trade-press report of site closure with redundancies is REPORTED —\\nand High.\\n\\n## Try it in 60 seconds (no API keys)\\n\\n```\\nuv sync\\nuv run deallens demo\\n```\\n\\nThe demo runs quote validation, the gate, and the memo renderer live on five\\nfixture bundles: a Companies-House-verified CFO departure, an\\naggregator-only rumor (rejected), a single-source facility closure\\n(reported, not verified), a blocked regulator document (unresolved), and a\\nquiet cyber category (no finding — with the disclaimer, not a clean bill).\\nOne fixture contains a paraphrased quote so you can watch validation discard\\nit. Zero credits spent.\\n\\n```\\nuv run deallens eval\\n```\\n\\nOffline eval of the validation + gate composition on 12 labelled cases,\\nincluding the traps (syndicated same-domain sources, paraphrases, extraction\\nfailures). Current results: **accuracy 12/12, false-verify rate 0/8** —\\nover-verification is the failure mode this gate exists to prevent.\\n\\n```\\nuv run pytest\\n```\\n\\n25 unit tests cover every gate row, quote validation (including curly-quote\\nand dash normalization), severity escalation, lookalike-domain rejection,\\nand the risk rollup.\\n\\n## Live screen setup\\n\\n```\\ncp .env.example .env   # add TAVILY_API_KEY + ANTHROPIC_API_KEY\\nuv run deallens screen --company \\\"...\\\" --domain \\\"...\\\" --jurisdiction UK\\n```\\n\\n- **Cost:** roughly 20–120 Tavily credits per screen; `/research` (pinned to\\n  its `mini` model) dominates and is dynamically priced. The free tier\\n  (1,000 credits/month) covers ~10–25 screens. Actual usage prints in every\\n  memo footer via `include_usage=True`.\\n- **Model:** `DEALLENS_MODEL` accepts any `init_chat_model` string\\n  (default `anthropic:claude-sonnet-5`; swap the prefix and install the\\n  matching `langchain-*` adapter for another provider). The local model only\\n  selects quotes and writes narratives — it never decides status or severity.\\n- **Tracing (optional):** set `LANGSMITH_TRACING=true` and\\n  `LANGSMITH_API_KEY` to get per-stage traces (`deallens.screen` →\\n  `discover/verify/capture` → Tavily calls) with credit and token metadata.\\n  Silent no-op when unset.\\n\\n## Adapting it to your fund\\n\\nEverything a fund would tune is YAML, not code:\\n\\n- `jurisdictions/uk.yaml` — source tiers. Registries do the heavy lifting\\n  for private targets: Companies House filings (director terminations, new\\n  charges, overdue accounts) and The Gazette (statutory insolvency notices)\\n  are *primary* and can verify a finding alone. Press is tiered secondary;\\n  aggregator/lead-gen sites are excluded outright.\\n- `jurisdictions/nl.yaml` — stub showing jurisdiction = configuration\\n  (KVK, AFM, ACM, rechtspraak.nl). Verify extraction against paywalls\\n  before relying on it.\\n- `policy.yaml` — severity and escalation keywords. `policy.searchfund.yaml`\\n  ships as an example override for owner-operator acquisitions\\n  (`--policy policy.searchfund.yaml`): key-person and distress risk escalate\\n  harder.\\n\\n## Repo map\\n\\n```\\nsrc/deallens/\\n  models.py         typed core (Candidate, Evidence, Finding, ScreenResult)\\n  config.py         jurisdiction + policy loaders, tier matching\\n  tavily_client.py  usage ledger, 429 retry, failure surfacing\\n  discover.py       /research with output_schema → candidates\\n  verify.py         governed /search per candidate (+ registry query)\\n  capture.py        /extract → LLM quote pick → verbatim validation\\n  gate.py           the evidence gate: pure, deterministic, tested\\n  pipeline.py       orchestration; control flow never depends on the model\\n  memo.py           memo.md + evidence.json + console renderers\\n  evalrun.py        offline eval harness\\n  cli.py            screen | demo | eval\\n```\\n\\n## Honest limitations\\n\\n- Syndicated copy on two different domains counts as two independent\\n  sources; near-duplicate text detection is the first v2 item.\\n- Entity ambiguity is handled only by quoting the company name in queries —\\n  a common-named SME will need `verification_query` tuning.\\n- `/research` behaves as a black box for discovery recall; the gate bounds\\n  the damage of both its false positives (rejected) and its confidence\\n  (never trusted), but not its false negatives.\\n- One jurisdiction ships tested. The NL pack is a stub on purpose.\\n[project]\\nname = \\\"deallens\\\"\\nversion = \\\"0.1.0\\\"\\ndescription = \\\"10-minute M&A red-flag screen: Tavily discovery, source-governed verification, deterministic evidence gate.\\\"\\nrequires-python = \\\">=3.11\\\"\\ndependencies = [\\n    \\\"tavily-python>=0.7.12\\\",\\n    \\\"pydantic>=2.8\\\",\\n    \\\"typer>=0.12\\\",\\n    \\\"rich>=13.0\\\",\\n    \\\"pyyaml>=6.0\\\",\\n    \\\"python-dotenv>=1.0\\\",\\n    \\\"langsmith>=0.4\\\",\\n    \\\"langchain>=1.0\\\",\\n    \\\"langchain-anthropic>=1.0\\\",\\n]\\n\\n[project.scripts]\\ndeallens = \\\"deallens.cli:app\\\"\\n\\n[dependency-groups]\\ndev = [\\\"pytest>=8.0\\\"]\\n\\n[build-system]\\nrequires = [\\\"hatchling\\\"]\\nbuild-backend = \\\"hatchling.build\\\"\\n\\n[tool.hatch.build.targets.wheel]\\npackages = [\\\"src/deallens\\\"]\\n\\n[tool.pytest.ini_options]\\ntestpaths = [\\\"tests\\\"]\\naddopts = \\\"-q\\\"\\n\"\n  }\n]"
  }
]

The offline suite is green (25 tests), and the README presents a coherent product. I’m now checking whether the live Tavily path, evidence gate, source-tier matching, usage ledger, and generated memo actually uphold those claims.

>uv run deallens demo && uv run deallens eval

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.5 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\n╭────────────────────────── DEALLENS SCREEN COMPLETE ──────────────────────────╮\\n│ Target: Acme Industrial Ltd                                                  │\\n│ Risk level: REVIEW REQUIRED                                                  │\\n│                                                                              │\\n│ 1 verified red flag(s)                                                       │\\n│ 1 reported concern(s)                                                        │\\n│ 1 unresolved check(s)                                                        │\\n│ 2 finding(s) rejected as weak or unsupported                                 │\\n│                                                                              │\\n│ Memo:                                                                        │\\n│ [repo]/reports/acme-industrial-ltd-2026-08- │\\n│ 03.md                                                                        │\\n│ Evidence:                                                                    │\\n│ [repo]/reports/acme-industrial-ltd-2026-08- │\\n│ 03.json                                                                      │\\n│ Tavily usage: 0 credits                                                      │\\n╰──────────────────────────────────────────────────────────────────────────────╯\\nFixture run: 0 Tavily credits spent. See fixtures/demo_screen.json.\\n                               DealLens gate eval                               \\n┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━┓\\n┃ Case                   ┃ Expected   ┃ Actual     ┃ Discards (exp/act) ┃ Pass ┃\\n┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━┩\\n│ primary_alone_verifies │ verified   │ verified   │ 0/0                │ ✓    │\\n│ two_independent_secon… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ syndication_same_doma… │ reported   │ reported   │ 0/0                │ ✓    │\\n│ single_secondary_repo… │ reported   │ reported   │ 0/0                │ ✓    │\\n│ extraction_failure_un… │ unresolved │ unresolved │ 0/0                │ ✓    │\\n│ aggregator_only_rejec… │ rejected   │ rejected   │ 0/0                │ ✓    │\\n│ nothing_found_rejected │ rejected   │ rejected   │ 0/0                │ ✓    │\\n│ paraphrase_discarded_… │ rejected   │ rejected   │ 1/1                │ ✓    │\\n│ paraphrased_primary_d… │ reported   │ reported   │ 1/1                │ ✓    │\\n│ primary_wins_despite_… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ curly_punctuation_sti… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ empty_quote_discarded  │ rejected   │ rejected   │ 1/1                │ ✓    │\\n└────────────────────────┴────────────┴────────────┴────────────────────┴──────┘\\nAccuracy: 12/12\\nFalse-verify rate: 0/8\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n     1\\t\\\"\\\"\\\"DealLens CLI: screen | demo | eval.\\\"\\\"\\\"\\n     2\\t\\n     3\\tfrom __future__ import annotations\\n     4\\t\\n     5\\timport os\\n     6\\tfrom pathlib import Path\\n     7\\tfrom typing import Annotated\\n     8\\t\\n     9\\timport typer\\n    10\\tfrom dotenv import load_dotenv\\n    11\\tfrom rich.console import Console\\n    12\\t\\n    13\\tfrom .config import PACKAGE_ROOT, load_jurisdiction, load_policy\\n    14\\t\\n    15\\tload_dotenv()\\n    16\\t\\n    17\\tapp = typer.Typer(add_completion=False, help=\\\"10-minute M&A red-flag screen on Tavily.\\\")\\n    18\\tconsole = Console()\\n    19\\t\\n    20\\t\\n    21\\[REDACTED_EMAIL]()\\n    22\\tdef screen(\\n    23\\t    company: Annotated[str, typer.Option(help=\\\"Legal or trading name\\\")],\\n    24\\t    domain: Annotated[str, typer.Option(help=\\\"Company website domain\\\")],\\n    25\\t    jurisdiction: Annotated[str, typer.Option(help=\\\"Jurisdiction pack, e.g. UK\\\")] = \\\"UK\\\",\\n    26\\t    policy: Annotated[str | None, typer.Option(help=\\\"Severity policy YAML\\\")] = None,\\n    27\\t    out: Annotated[Path, typer.Option(help=\\\"Output directory\\\")] = Path(\\\"reports\\\"),\\n    28\\t) -> None:\\n    29\\t    \\\"\\\"\\\"Run a live screen against one acquisition target.\\\"\\\"\\\"\\n    30\\t    if not os.getenv(\\\"TAVILY_API_KEY\\\"):\\n    31\\t        console.print(\\\"[bold red]Missing TAVILY_API_KEY[/bold red] — create one at https://app.tavily.com and add it to .env\\\")\\n    32\\t        raise typer.Exit(1)\\n    33\\t\\n    34\\t    from .llm import LLM\\n    35\\t    from .memo import print_summary, write_outputs\\n    36\\t    from .models import UsageLedger\\n    37\\t    from .pipeline import run_screen\\n    38\\t    from .tavily_client import Tavily\\n    39\\t\\n    40\\t    pack = load_jurisdiction(jurisdiction)\\n    41\\t    severity_policy = load_policy(policy)\\n    42\\t    ledger = UsageLedger()\\n    43\\t\\n    44\\t    with console.status(f\\\"Screening {company} ({jurisdiction.upper()})...\\\"):\\n    45\\t        result = run_screen(\\n    46\\t            company=company,\\n    47\\t            domain=domain,\\n    48\\t            jurisdiction_pack=pack,\\n    49\\t            policy=severity_policy,\\n    50\\t            tavily=Tavily(ledger=ledger),\\n    51\\t            llm=LLM(ledger),\\n    52\\t        )\\n    53\\t\\n    54\\t    memo_path, json_path = write_outputs(result, out)\\n    55\\t    print_summary(result, memo_path, json_path)\\n    56\\t\\n    57\\t\\n    58\\[REDACTED_EMAIL]()\\n    59\\tdef demo() -> None:\\n    60\\t    \\\"\\\"\\\"Run the fixture screen: no API keys, no network, no credits.\\n    61\\t\\n    62\\t    Quote validation and the evidence gate run live on five canonical\\n    63\\t    bundles — including a paraphrased quote that gets discarded and an\\n    64\\t    extraction failure that surfaces as UNRESOLVED rather than verified.\\\"\\\"\\\"\\n    65\\t    from .demo import run_demo\\n    66\\t    from .memo import print_summary, write_outputs\\n    67\\t\\n    68\\t    result = run_demo(PACKAGE_ROOT)\\n    69\\t    memo_path, json_path = write_outputs(result, PACKAGE_ROOT / \\\"reports\\\")\\n    70\\t    print_summary(result, memo_path, json_path)\\n    71\\t    console.print(\\\"[dim]Fixture run: 0 Tavily credits spent. See fixtures/demo_screen.json.[/dim]\\\")\\n    72\\t\\n    73\\t\\n    74\\[REDACTED_EMAIL](\\\"eval\\\")\\n    75\\tdef eval_cmd() -> None:\\n    76\\t    \\\"\\\"\\\"Run the offline gate + quote-validation eval and print metrics.\\\"\\\"\\\"\\n    77\\t    from .evalrun import print_report, run_eval\\n    78\\t\\n    79\\t    pack = load_jurisdiction(\\\"uk\\\")\\n    80\\t    results = run_eval(PACKAGE_ROOT / \\\"fixtures\\\" / \\\"eval_cases.json\\\", pack)\\n    81\\t    ok = print_report(results)\\n    82\\t    raise typer.Exit(0 if ok else 1)\\n    83\\t\\n    84\\t\\n    85\\tif __name__ == \\\"__main__\\\":\\n    86\\t    app()\\n     1\\t\\\"\\\"\\\"Offline eval for the validation + gate composition.\\n     2\\t\\n     3\\tEach labelled case supplies raw picked quotes with their source content; the\\n     4\\trunner applies quote validation, then the gate, and compares against expected\\n     5\\tstatus and expected discard count. The metric that matters most is the\\n     6\\tfalse-verify rate: a screen that over-verifies is worse than one that\\n     7\\tunder-verifies.\\n     8\\t\\\"\\\"\\\"\\n     9\\t\\n    10\\tfrom __future__ import annotations\\n    11\\t\\n    12\\timport json\\n    13\\tfrom dataclasses import dataclass\\n    14\\tfrom pathlib import Path\\n    15\\t\\n    16\\tfrom rich.console import Console\\n    17\\tfrom rich.table import Table\\n    18\\t\\n    19\\tfrom .config import JurisdictionPack\\n    20\\tfrom .gate import classify, quote_in_content\\n    21\\tfrom .models import Candidate, Evidence\\n    22\\t\\n    23\\t\\n    24\\t@dataclass\\n    25\\tclass CaseResult:\\n    26\\t    name: str\\n    27\\t    expected: str\\n    28\\t    actual: str\\n    29\\t    expected_discards: int\\n    30\\t    actual_discards: int\\n    31\\t\\n    32\\t    @property\\n    33\\t    def passed(self) -> bool:\\n    34\\t        return (\\n    35\\t            self.expected == self.actual\\n    36\\t            and self.expected_discards == self.actual_discards\\n    37\\t        )\\n    38\\t\\n    39\\t\\n    40\\tdef run_eval(fixture_path: Path, pack: JurisdictionPack) -> list[CaseResult]:\\n    41\\t    cases = json.loads(fixture_path.read_text())[\\\"cases\\\"]\\n    42\\t    results: list[CaseResult] = []\\n    43\\t\\n    44\\t    for case in cases:\\n    45\\t        candidate = Candidate(**case[\\\"candidate\\\"])\\n    46\\t        evidence: list[Evidence] = []\\n    47\\t        discards = 0\\n    48\\t        for source in case[\\\"sources\\\"]:\\n    49\\t            if quote_in_content(source[\\\"quote\\\"], source[\\\"content\\\"]):\\n    50\\t                evidence.append(\\n    51\\t                    Evidence(\\n    52\\t                        url=source[\\\"url\\\"],\\n    53\\t                        publisher=source[\\\"publisher\\\"],\\n    54\\t                        source_tier=pack.tier_for(source[\\\"url\\\"]),\\n    55\\t                        quote=source[\\\"quote\\\"],\\n    56\\t                    )\\n    57\\t                )\\n    58\\t            else:\\n    59\\t                discards += 1\\n    60\\t\\n    61\\t        finding = classify(candidate, evidence, case[\\\"extraction_failures\\\"])\\n    62\\t        results.append(\\n    63\\t            CaseResult(\\n    64\\t                name=case[\\\"name\\\"],\\n    65\\t                expected=case[\\\"expected_status\\\"],\\n    66\\t                actual=finding.status,\\n    67\\t                expected_discards=case[\\\"expected_discards\\\"],\\n    68\\t                actual_discards=discards,\\n    69\\t            )\\n    70\\t        )\\n    71\\t    return results\\n    72\\t\\n    73\\t\\n    74\\tdef print_report(results: list[CaseResult]) -> bool:\\n    75\\t    table = Table(title=\\\"DealLens gate eval\\\")\\n    76\\t    for column in (\\\"Case\\\", \\\"Expected\\\", \\\"Actual\\\", \\\"Discards (exp/act)\\\", \\\"Pass\\\"):\\n    77\\t        table.add_column(column)\\n    78\\t    for r in results:\\n    79\\t        table.add\n[truncated; use --max-event-chars to read more]"
  }
]

>uv run python - <<'PY'
import inspect
from tavily import TavilyClient
print(inspect.signature(TavilyClient.research))
print(inspect.signature(TavilyClient.search))
print(inspect.signature(TavilyClient.extract))
print(inspect.getsource(TavilyClient.research))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.5 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\n╭────────────────────────── DEALLENS SCREEN COMPLETE ──────────────────────────╮\\n│ Target: Acme Industrial Ltd                                                  │\\n│ Risk level: REVIEW REQUIRED                                                  │\\n│                                                                              │\\n│ 1 verified red flag(s)                                                       │\\n│ 1 reported concern(s)                                                        │\\n│ 1 unresolved check(s)                                                        │\\n│ 2 finding(s) rejected as weak or unsupported                                 │\\n│                                                                              │\\n│ Memo:                                                                        │\\n│ [repo]/reports/acme-industrial-ltd-2026-08- │\\n│ 03.md                                                                        │\\n│ Evidence:                                                                    │\\n│ [repo]/reports/acme-industrial-ltd-2026-08- │\\n│ 03.json                                                                      │\\n│ Tavily usage: 0 credits                                                      │\\n╰──────────────────────────────────────────────────────────────────────────────╯\\nFixture run: 0 Tavily credits spent. See fixtures/demo_screen.json.\\n                               DealLens gate eval                               \\n┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━┓\\n┃ Case                   ┃ Expected   ┃ Actual     ┃ Discards (exp/act) ┃ Pass ┃\\n┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━┩\\n│ primary_alone_verifies │ verified   │ verified   │ 0/0                │ ✓    │\\n│ two_independent_secon… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ syndication_same_doma… │ reported   │ reported   │ 0/0                │ ✓    │\\n│ single_secondary_repo… │ reported   │ reported   │ 0/0                │ ✓    │\\n│ extraction_failure_un… │ unresolved │ unresolved │ 0/0                │ ✓    │\\n│ aggregator_only_rejec… │ rejected   │ rejected   │ 0/0                │ ✓    │\\n│ nothing_found_rejected │ rejected   │ rejected   │ 0/0                │ ✓    │\\n│ paraphrase_discarded_… │ rejected   │ rejected   │ 1/1                │ ✓    │\\n│ paraphrased_primary_d… │ reported   │ reported   │ 1/1                │ ✓    │\\n│ primary_wins_despite_… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ curly_punctuation_sti… │ verified   │ verified   │ 0/0                │ ✓    │\\n│ empty_quote_discarded  │ rejected   │ rejected   │ 1/1                │ ✓    │\\n└────────────────────────┴────────────┴────────────┴────────────────────┴──────┘\\nAccuracy: 12/12\\nFalse-verify rate: 0/8\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n     1\\t\\\"\\\"\\\"DealLens CLI: screen | demo | eval.\\\"\\\"\\\"\\n     2\\t\\n     3\\tfrom __future__ import annotations\\n     4\\t\\n     5\\timport os\\n     6\\tfrom pathlib import Path\\n     7\\tfrom typing import Annotated\\n     8\\t\\n     9\\timport typer\\n    10\\tfrom dotenv import load_dotenv\\n    11\\tfrom rich.console import Console\\n    12\\t\\n    13\\tfrom .config import PACKAGE_ROOT, load_jurisdiction, load_policy\\n    14\\t\\n    15\\tload_dotenv()\\n    16\\t\\n    17\\tapp = typer.Typer(add_completion=False, help=\\\"10-minute M&A red-flag screen on Tavily.\\\")\\n    18\\tconsole = Console()\\n    19\\t\\n    20\\t\\n    21\\[REDACTED_EMAIL]()\\n    22\\tdef screen(\\n    23\\t    company: Annotated[str, typer.Option(help=\\\"Legal or trading name\\\")],\\n    24\\t    domain: Annotated[str, typer.Option(help=\\\"Company website domain\\\")],\\n    25\\t    jurisdiction: Annotated[str, typer.Option(help=\\\"Jurisdiction pack, e.g. UK\\\")] = \\\"UK\\\",\\n    26\\t    policy: Annotated[str | None, typer.Option(help=\\\"Severity policy YAML\\\")] = None,\\n    27\\t    out: Annotated[Path, typer.Option(help=\\\"Output directory\\\")] = Path(\\\"reports\\\"),\\n    28\\t) -> None:\\n    29\\t    \\\"\\\"\\\"Run a live screen against one acquisition target.\\\"\\\"\\\"\\n    30\\t    if not os.getenv(\\\"TAVILY_API_KEY\\\"):\\n    31\\t        console.print(\\\"[bold red]Missing TAVILY_API_KEY[/bold red] — create one at https://app.tavily.com and add it to .env\\\")\\n    32\\t        raise typer.Exit(1)\\n    33\\t\\n    34\\t    from .llm import LLM\\n    35\\t    from .memo import print_summary, write_outputs\\n    36\\t    from .models import UsageLedger\\n    37\\t    from .pipeline import run_screen\\n    38\\t    from .tavily_client import Tavily\\n    39\\t\\n    40\\t    pack = load_jurisdiction(jurisdiction)\\n    41\\t    severity_policy = load_policy(policy)\\n    42\\t    ledger = UsageLedger()\\n    43\\t\\n    44\\t    with console.status(f\\\"Screening {company} ({jurisdiction.upper()})...\\\"):\\n    45\\t        result = run_screen(\\n    46\\t            company=company,\\n    47\\t            domain=domain,\\n    48\\t            jurisdiction_pack=pack,\\n    49\\t            policy=severity_policy,\\n    50\\t            tavily=Tavily(ledger=ledger),\\n    51\\t            llm=LLM(ledger),\\n    52\\t        )\\n    53\\t\\n    54\\t    memo_path, json_path = write_outputs(result, out)\\n    55\\t    print_summary(result, memo_path, json_path)\\n    56\\t\\n    57\\t\\n    58\\[REDACTED_EMAIL]()\\n    59\\tdef demo() -> None:\\n    60\\t    \\\"\\\"\\\"Run the fixture screen: no API keys, no network, no credits.\\n    61\\t\\n    62\\t    Quote validation and the evidence gate run live on five canonical\\n    63\\t    bundles — including a paraphrased quote that gets discarded and an\\n    64\\t    extraction failure that surfaces as UNRESOLVED rather than verified.\\\"\\\"\\\"\\n    65\\t    from .demo import run_demo\\n    66\\t    from .memo import print_summary, write_outputs\\n    67\\t\\n    68\\t    result = run_demo(PACKAGE_ROOT)\\n    69\\t    memo_path, json_path = write_outputs(result, PACKAGE_ROOT / \\\"reports\\\")\\n    70\\t    print_summary(result, memo_path, json_path)\\n    71\\t    console.print(\\\"[dim]Fixture run: 0 Tavily credits spent. See fixtures/demo_screen.json.[/dim]\\\")\\n    72\\t\\n    73\\t\\n    74\\[REDACTED_EMAIL](\\\"eval\\\")\\n    75\\tdef eval_cmd() -> None:\\n    76\\t    \\\"\\\"\\\"Run the offline gate + quote-validation eval and print metrics.\\\"\\\"\\\"\\n    77\\t    from .evalrun import print_report, run_eval\\n    78\\t\\n    79\\t    pack = load_jurisdiction(\\\"uk\\\")\\n    80\\t    results = run_eval(PACKAGE_ROOT / \\\"fixtures\\\" / \\\"eval_cases.json\\\", pack)\\n    81\\t    ok = print_report(results)\\n    82\\t    raise typer.Exit(0 if ok else 1)\\n    83\\t\\n    84\\t\\n    85\\tif __name__ == \\\"__main__\\\":\\n    86\\t    app()\\n     1\\t\\\"\\\"\\\"Offline eval for the validation + gate composition.\\n     2\\t\\n     3\\tEach labelled case supplies raw picked quotes with their source content; the\\n     4\\trunner applies quote validation, then the gate, and compares against expected\\n     5\\tstatus and expected discard count. The metric that matters most is the\\n     6\\tfalse-verify rate: a screen that over-verifies is worse than one that\\n     7\\tunder-verifies.\\n     8\\t\\\"\\\"\\\"\\n     9\\t\\n    10\\tfrom __future__ import annotations\\n    11\\t\\n    12\\timport json\\n    13\\tfrom dataclasses import dataclass\\n    14\\tfrom pathlib import Path\\n    15\\t\\n    16\\tfrom rich.console import Console\\n    17\\tfrom rich.table import Table\\n    18\\t\\n    19\\tfrom .config import JurisdictionPack\\n    20\\tfrom .gate import classify, quote_in_content\\n    21\\tfrom .models import Candidate, Evidence\\n    22\\t\\n    23\\t\\n    24\\t@dataclass\\n    25\\tclass CaseResult:\\n    26\\t    name: str\\n    27\\t    expected: str\\n    28\\t    actual: str\\n    29\\t    expected_discards: int\\n    30\\t    actual_discards: int\\n    31\\t\\n    32\\t    @property\\n    33\\t    def passed(self) -> bool:\\n    34\\t        return (\\n    35\\t            self.expected == self.actual\\n    36\\t            and self.expected_discards == self.actual_discards\\n    37\\t        )\\n    38\\t\\n    39\\t\\n    40\\tdef run_eval(fixture_path: Path, pack: JurisdictionPack) -> list[CaseResult]:\\n    41\\t    cases = json.loads(fixture_path.read_text())[\\\"cases\\\"]\\n    42\\t    results: list[CaseResult] = []\\n    43\\t\\n    44\\t    for case in cases:\\n    45\\t        candidate = Candidate(**case[\\\"candidate\\\"])\\n    46\\t        evidence: list[Evidence] = []\\n    47\\t        discards = 0\\n    48\\t        for source in case[\\\"sources\\\"]:\\n    49\\t            if quote_in_content(source[\\\"quote\\\"], source[\\\"content\\\"]):\\n    50\\t                evidence.append(\\n    51\\t                    Evidence(\\n    52\\t                        url=source[\\\"url\\\"],\\n    53\\t                        publisher=source[\\\"publisher\\\"],\\n    54\\t                        source_tier=pack.tier_for(source[\\\"url\\\"]),\\n    55\\t                        quote=source[\\\"quote\\\"],\\n    56\\t                    )\\n    57\\t                )\\n    58\\t            else:\\n    59\\t                discards += 1\\n    60\\t\\n    61\\t        finding = classify(candidate, evidence, case[\\\"extraction_failures\\\"])\\n    62\\t        results.append(\\n    63\\t            CaseResult(\\n    64\\t                name=case[\\\"name\\\"],\\n    65\\t                expected=case[\\\"expected_status\\\"],\\n    66\\t                actual=finding.status,\\n    67\\t                expected_discards=case[\\\"expected_discards\\\"],\\n    68\\t                actual_discards=discards,\\n    69\\t            )\\n    70\\t        )\\n    71\\t    return results\\n    72\\t\\n    73\\t\\n    74\\tdef print_report(results: list[CaseResult]) -> bool:\\n    75\\t    table = Table(title=\\\"DealLens gate eval\\\")\\n    76\\t    for column in (\\\"Case\\\", \\\"Expected\\\", \\\"Actual\\\", \\\"Discards (exp/act)\\\", \\\"Pass\\\"):\\n    77\\t        table.add_column(column)\\n    78\\t    for r in results:\\n    79\\t        table.add\n[truncated; use --max-event-chars to read more]"
  }
]

>uv run python - <<'PY'
import inspect
from tavily import TavilyClient
print(inspect.getsource(TavilyClient._research))
print('methods containing research:')
print([x for x in dir(TavilyClient) if 'research' in x.lower()])
PY
rg -n "research|include_usage|usage|Candidate|source_urls|searches_run|clean screen|CLEAN" src tests README.md

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.5 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"    def _research(self,\\n                  input: str,\\n                  model: Literal[\\\"mini\\\", \\\"pro\\\", \\\"auto\\\"] = None,\\n                  output_schema: dict = None,\\n                  stream: bool = False,\\n                  citation_format: Literal[\\\"numbered\\\", \\\"mla\\\", \\\"apa\\\", \\\"chicago\\\"] = \\\"numbered\\\",\\n                  timeout: Optional[float] = None,\\n                  **kwargs\\n                  ) -> Union[dict, Generator[bytes, None, None]]:\\n        \\\"\\\"\\\"\\n        Internal research method to send the request to the API.\\n        \\\"\\\"\\\"\\n        data = {\\n            \\\"input\\\": input,\\n            \\\"model\\\": model,\\n            \\\"output_schema\\\": output_schema,\\n            \\\"stream\\\": stream,\\n            \\\"citation_format\\\": citation_format,\\n        }\\n\\n        data = {k: v for k, v in data.items() if v is not None}\\n\\n        override_headers = self._pop_request_headers(kwargs)\\n        if kwargs:\\n            data.update(kwargs)\\n\\n        if stream:\\n            try:\\n                response = self.session.post(\\n                    self.base_url + \\\"/research\\\",\\n                    data=json.dumps(data),\\n                    timeout=timeout,\\n                    stream=True,\\n                    **({\\\"headers\\\": override_headers} if override_headers else {})\\n                )\\n            except requests.exceptions.Timeout:\\n                raise TimeoutError(timeout)\\n\\n            if response.status_code != 200:\\n                self._handle_error_response(response)\\n\\n            def stream_generator() -> Generator[bytes, None, None]:\\n                try:\\n                    for chunk in response.iter_content(chunk_size=None):\\n                        if chunk:\\n                            yield chunk\\n                finally:\\n                    response.close()\\n\\n            return stream_generator()\\n        else:\\n            try:\\n                response = self.session.post(\\n                    self.base_url + \\\"/research\\\",\\n                    data=json.dumps(data),\\n                    timeout=timeout,\\n                    **({\\\"headers\\\": override_headers} if override_headers else {})\\n                )\\n            except requests.exceptions.Timeout:\\n                raise TimeoutError(timeout)\\n\\n            if response.status_code == 200:\\n                return response.json()\\n            else:\\n                self._handle_error_response(response)\\n\\nmethods containing research:\\n['_research', 'get_research', 'research']\\nREADME.md:29:│ Tavily usage: 41 credits                     │\\nREADME.md:45:  Tavily /research      DISCOVER   recall-first; returns candidates,\\nREADME.md:125:- **Cost:** roughly 20–120 Tavily credits per screen; `/research` (pinned to\\nREADME.md:127:  (1,000 credits/month) covers ~10–25 screens. Actual usage prints in every\\nREADME.md:128:  memo footer via `include_usage=True`.\\nREADME.md:159:  models.py         typed core (Candidate, Evidence, Finding, ScreenResult)\\nREADME.md:161:  tavily_client.py  usage ledger, 429 retry, failure surfacing\\nREADME.md:162:  discover.py       /research with output_schema → candidates\\nREADME.md:178:- `/research` behaves as a black box for discovery recall; the gate bounds\\ntests/test_gate.py:12:from deallens.models import Candidate, Evidence\\ntests/test_gate.py:16:    return Candidate(\\ntests/test_gate.py:196:    assert risk_level([classify(candidate(), [], [])]) == \\\"CLEAN SCREEN*\\\"\\nsrc/deallens/demo.py:6:discarded in front of you. Only the network stages (/research, /search,\\nsrc/deallens/demo.py:17:from .models import Candidate, Evidence, Finding, ScreenResult, UsageLedger\\nsrc/deallens/demo.py:26:        candidate = Candidate(**bundle[\\\"candidate\\\"])\\nsrc/deallens/demo.py:47:            candidate, evidence, bundle.get(\\\"extraction_failures\\\", []), searches_run=2\\nsrc/deallens/demo.py:63:        usage=UsageLedger(),  # fixture run: no credits spent — the point\\nsrc/deallens/evalrun.py:21:from .models import Candidate, Evidence\\nsrc/deallens/evalrun.py:45:        candidate = Candidate(**case[\\\"candidate\\\"])\\nsrc/deallens/llm.py:22:from .models import Candidate, UsageLedger\\nsrc/deallens/llm.py:40:class CandidateList(BaseModel):\\nsrc/deallens/llm.py:41:    candidates: list[Candidate]\\nsrc/deallens/llm.py:52:        usage = getattr(message, \\\"usage_metadata\\\", None) or {}\\nsrc/deallens/llm.py:53:        self.ledger.llm_input_tokens += usage.get(\\\"input_tokens\\\", 0)\\nsrc/deallens/llm.py:54:        self.ledger.llm_output_tokens += usage.get(\\\"output_tokens\\\", 0)\\nsrc/deallens/verify.py:17:from .models import Candidate\\nsrc/deallens/verify.py:35:    tavily: Tavily, pack: JurisdictionPack, company: str, candidate: Candidate\\nsrc/deallens/verify.py:69:    pack: JurisdictionPack, candidate: Candidate, results: list[dict]\\nsrc/deallens/verify.py:87:    for url in candidate.source_urls:\\nsrc/deallens/pipeline.py:67:        finding = classify(candidate, evidence, failures, searches_run=2)\\nsrc/deallens/pipeline.py:80:        usage=ledger,\\nsrc/deallens/discover.py:1:\\\"\\\"\\\"Discovery: /research proposes candidate red flags. Recall-first, zero trust.\\nsrc/deallens/discover.py:3:The research model is told to return candidates, not conclusions, and its\\nsrc/deallens/discover.py:4:output is normalized into typed Candidate objects. Nothing here counts as\\nsrc/deallens/discover.py:14:from .llm import LLM, CandidateList\\nsrc/deallens/discover.py:15:from .models import Candidate\\nsrc/deallens/discover.py:50:                    \\\"source_urls\\\": {\\\"type\\\": \\\"array\\\", \\\"items\\\": {\\\"type\\\": \\\"string\\\"}},\\nsrc/deallens/discover.py:60:NORMALIZE_PROMPT = \\\"\\\"\\\"Convert this research output into candidate red-flag findings for {company}.\\nsrc/deallens/discover.py:70:) -> list[Candidate]:\\nsrc/deallens/discover.py:71:    response = tavily.research(\\nsrc/deallens/discover.py:83:def _parse(response: dict, company: str, llm: LLM) -> list[Candidate]:\\nsrc/deallens/discover.py:97:                out.append(Candidate(**raw))\\nsrc/deallens/discover.py:103:        CandidateList,\\nsrc/deallens/capture.py:18:from .models import Candidate, Evidence\\nsrc/deallens/capture.py:42:    candidate: Candidate,\\nsrc/deallens/gate.py:3:Discovery (an LLM inside /research) proposes candidates; this module decides\\nsrc/deallens/gate.py:23:    Candidate,\\nsrc/deallens/gate.py:56:    candidate: Candidate,\\nsrc/deallens/gate.py:59:    searches_run: int = 0,\\nsrc/deallens/gate.py:80:        searches_run=searches_run,\\nsrc/deallens/gate.py:141:    CLEAN SCREEN*      nothing surfaced; the asterisk is rendered with an\\nsrc/deallens/gate.py:153:    return \\\"CLEAN SCREEN*\\\"\\nsrc/deallens/memo.py:73:    usage = result.usage\\nsrc/deallens/memo.py:75:        f\\\"{name} {credits:g}\\\" for name, credits in sorted(usage.credits_by_endpoint.items())\\nsrc/deallens/memo.py:81:        f\\\"- Tavily credits: {usage.tavily_credits:g} ({by_endpoint})\\\",\\nsrc/deallens/memo.py:82:        f\\\"- LLM tokens: {usage.llm_input_tokens:,} in / {usage.llm_output_tokens:,} out\\\",\\nsrc/deallens/memo.py:83:        f\\\"- Wall time: {usage.wall_seconds:.0f}s\\\",\\nsrc/deallens/memo.py:150:        f\\\"Tavily usage: {result.usage.tavily_credits:g} credits\\\",\\nsrc/deallens/tavily_client.py:1:\\\"\\\"\\\"Thin Tavily wrapper: usage accounting, 429 retry, failure surfacing.\\nsrc/deallens/tavily_client.py:3:Every call passes include_usage=True and books credits into a UsageLedger so\\nsrc/deallens/tavily_client.py:45:        usage = response.get(\\\"usage\\\") if isinstance(response, dict) else None\\nsrc/deallens/tavily_client.py:46:        if isinstance(usage, dict):\\nsrc/deallens/tavily_client.py:47:            credits = usage.get(\\\"credits\\\", 0) or 0\\nsrc/deallens/tavily_client.py:52:    @traceable(name=\\\"tavily.research\\\", run_type=\\\"retriever\\\")\\nsrc/deallens/tavily_client.py:53:    def research(self, *, input: str, output_schema: dict | None = None) -> dict:\\nsrc/deallens/tavily_client.py:58:            return self._call(\\\"research\\\", self._client.research, **kwargs)\\nsrc/deallens/tavily_client.py:61:            # plain research; discover.py normalizes free text afterwards.\\nsrc/deallens/tavily_client.py:63:            return self._call(\\\"research\\\", self._client.research, **kwargs)\\nsrc/deallens/tavily_client.py:83:            include_usage=True,\\nsrc/deallens/tavily_client.py:96:            include_usage=True,\\nsrc/deallens/models.py:25:class Candidate(BaseModel):\\nsrc/deallens/models.py:31:    source_urls: list[str] = Field(default_factory=list)\\nsrc/deallens/models.py:49:    candidate: Candidate\\nsrc/deallens/models.py:55:    searches_run: int = 0\\nsrc/deallens/models.py:79:RiskLevel = Literal[\\\"REVIEW REQUIRED\\\", \\\"PROCEED WITH NOTES\\\", \\\"CLEAN SCREEN*\\\"]\\nsrc/deallens/models.py:90:    usage: UsageLedger = Field(default_factory=UsageLedger)\\n\"\n  }\n]"
  }
]

>uv run python - <<'PY'
import inspect
from tavily import TavilyClient
print(inspect.signature(TavilyClient.get_research))
print(inspect.getsource(TavilyClient.get_research))
PY
sed -n '1,260p' reports/acme-industrial-ltd-2026-08-03.md
sed -n '1,280p' fixtures/demo_screen.json

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.2 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"(self, request_id: str) -> dict\\n    def get_research(self,\\n                     request_id: str\\n                     ) -> dict:\\n        \\\"\\\"\\\"\\n        Get research results by request_id.\\n        \\n        Args:\\n            request_id: The research request ID.\\n        \\n        Returns:\\n            dict: Research response containing request_id, created_at, completed_at, status, content, and sources.\\n        \\\"\\\"\\\"\\n        self._check_keyless_supported(\\\"get_research\\\")\\n        try:\\n            response = self.session.get(self.base_url + f\\\"/research/{request_id}\\\")\\n        except Exception as e:\\n            raise Exception(f\\\"Error getting research: {e}\\\")\\n\\n        if response.status_code in (200, 202):\\n            return response.json()\\n        else:\\n            self._handle_error_response(response)\\n\\n# Acquisition Red-Flag Screen\\n\\nTarget: Acme Industrial Ltd\\nDomain: acme-industrial.example\\nJurisdiction: UK\\nGenerated: 03 August 2026\\n\\n## Executive assessment\\n\\n**REVIEW REQUIRED** — 1 verified red flag(s), 1 reported concern(s), 1 unresolved check(s); 2 candidate(s) rejected as weak or unsupported.\\n\\nThis screen is an initial review of public evidence, not a legal or financial diligence opinion. \\\"No qualifying public findings\\\" means the searches completed without a result that met the evidence standard — it is not a statement that no risk exists.\\n\\n## Verified findings\\n\\n### Group CFO Jane Smith departed in March 2026 — High\\n\\nThe Companies House officer register records the termination of Jane Smith's appointment on 14 March 2026, and Insider Media reports she stepped down as chief financial officer after seven years. Both sources agree on the timing and the role.\\n\\n> \\\"SMITH, Jane — Terminated on 14 March 2026\\\"\\n\\nPrimary source: [find-and-update.company-information.service.gov.uk](https://find-and-update.company-information.service.gov.uk/company/00000000/officers)\\n\\n> \\\"chief financial officer Jane Smith has stepped down after seven years\\\"\\n\\nSource: [insidermedia.com](https://www.insidermedia.com/news/acme-cfo-steps-down), 2026-03-16\\n\\nPolicy triggered: cfo\\n\\n\\n## Reported concerns\\n\\n### The Leeds facility will close with redundancies expected — High\\n\\nThe Construction Index reports that employees have been told the Leeds site will close before the end of the year. No company statement or statutory filing corroborating the closure was located.\\n\\n> \\\"The company informed employees that its Leeds site will close before the end of the year\\\"\\n\\nSource: [theconstructionindex.co.uk](https://www.theconstructionindex.co.uk/news/view/acme-to-close-leeds-site), 2026-07-22\\n\\nPolicy triggered: redundancies\\n\\n\\n## Unresolved checks\\n\\n### An HSE enforcement notice may have been issued against the company\\n\\nA potential regulatory reference was discovered, but the underlying source could not be captured for verification. Human review is required.\\n\\nCould not capture: https://resources.hse.gov.uk/notices/notice-4471902\\n\\n\\n## Rejected as weak or unsupported\\n\\n- Acme Industrial is being acquired by a US competitor — no qualifying source survived verification\\n- Acme filed accounts late in 2025 — no qualifying source survived verification\\n\\n## Coverage\\n\\n| Check | Status | Sources reviewed |\\n|---|---|---:|\\n| Leadership and ownership | Verified finding | 13 |\\n| Regulatory and litigation | Unresolved | 8 |\\n| Cybersecurity | No qualifying public findings | 0 |\\n| Financial distress | Reported concern | 13 |\\n\\n## Run footprint\\n\\n- Tavily credits: 0 (n/a)\\n- LLM tokens: 0 in / 0 out\\n- Wall time: 0s\\n{\\n  \\\"target\\\": \\\"Acme Industrial Ltd\\\",\\n  \\\"domain\\\": \\\"acme-industrial.example\\\",\\n  \\\"jurisdiction\\\": \\\"UK\\\",\\n  \\\"comment\\\": \\\"Fictional company. Five canonical outcomes: verified, rejected (aggregator-only plus a paraphrase that fails live quote validation), reported, unresolved (extraction failure), and a category with no candidates at all (cyber -> NO FINDING).\\\",\\n  \\\"bundles\\\": [\\n    {\\n      \\\"candidate\\\": {\\n        \\\"category\\\": \\\"leadership\\\",\\n        \\\"claim\\\": \\\"Group CFO Jane Smith departed in March 2026\\\",\\n        \\\"date\\\": \\\"2026-03\\\",\\n        \\\"source_urls\\\": [\\\"https://insidermedia.example/acme-cfo\\\"],\\n        \\\"verification_query\\\": \\\"\\\\\\\"Acme Industrial\\\\\\\" CFO departure\\\"\\n      },\\n      \\\"sources_reviewed\\\": 6,\\n      \\\"sources\\\": [\\n        {\\n          \\\"url\\\": \\\"https://find-and-update.company-information.service.gov.uk/company/00000000/officers\\\",\\n          \\\"publisher\\\": \\\"find-and-update.company-information.service.gov.uk\\\",\\n          \\\"title\\\": \\\"ACME INDUSTRIAL LTD officers\\\",\\n          \\\"quote\\\": \\\"SMITH, Jane — Terminated on 14 March 2026\\\",\\n          \\\"content\\\": \\\"ACME INDUSTRIAL LTD. Company number 00000000. Officers. SMITH, Jane — Terminated on 14 March 2026. Role: Director. Appointed: 2 May 2019. BROWN, Thomas — Active. Role: Director.\\\"\\n        },\\n        {\\n          \\\"url\\\": \\\"https://www.insidermedia.com/news/acme-cfo-steps-down\\\",\\n          \\\"publisher\\\": \\\"insidermedia.com\\\",\\n          \\\"title\\\": \\\"Acme Industrial CFO steps down\\\",\\n          \\\"published_date\\\": \\\"2026-03-16\\\",\\n          \\\"quote\\\": \\\"chief financial officer Jane Smith has stepped down after seven years\\\",\\n          \\\"content\\\": \\\"Yorkshire manufacturer Acme Industrial has confirmed that chief financial officer Jane Smith has stepped down after seven years with the business. The company said a search for a successor is underway.\\\"\\n        }\\n      ],\\n      \\\"extraction_failures\\\": [],\\n      \\\"narrative\\\": \\\"The Companies House officer register records the termination of Jane Smith's appointment on 14 March 2026, and Insider Media reports she stepped down as chief financial officer after seven years. Both sources agree on the timing and the role.\\\"\\n    },\\n    {\\n      \\\"candidate\\\": {\\n        \\\"category\\\": \\\"leadership\\\",\\n        \\\"claim\\\": \\\"Acme Industrial is being acquired by a US competitor\\\",\\n        \\\"date\\\": null,\\n        \\\"source_urls\\\": [\\\"https://www.crunchbase.com/organization/acme-industrial\\\"],\\n        \\\"verification_query\\\": \\\"\\\\\\\"Acme Industrial\\\\\\\" acquisition\\\"\\n      },\\n      \\\"sources_reviewed\\\": 7,\\n      \\\"sources\\\": [\\n        {\\n          \\\"url\\\": \\\"https://businessgossip.example/acme-buyout-rumour\\\",\\n          \\\"publisher\\\": \\\"businessgossip.example\\\",\\n          \\\"title\\\": \\\"Acme buyout rumour\\\",\\n          \\\"quote\\\": \\\"sources say Acme is definitely being acquired next quarter\\\",\\n          \\\"content\\\": \\\"There is chatter in the sector that Acme could attract interest from overseas buyers at some point, though nothing has been announced and the company declined to comment.\\\"\\n        }\\n      ],\\n      \\\"extraction_failures\\\": [],\\n      \\\"narrative\\\": \\\"\\\"\\n    },\\n    {\\n      \\\"candidate\\\": {\\n        \\\"category\\\": \\\"distress\\\",\\n        \\\"claim\\\": \\\"The Leeds facility will close with redundancies expected\\\",\\n        \\\"date\\\": \\\"2026-07\\\",\\n        \\\"source_urls\\\": [\\\"https://www.theconstructionindex.co.uk/news/acme-leeds\\\"],\\n        \\\"verification_query\\\": \\\"\\\\\\\"Acme Industrial\\\\\\\" Leeds closure\\\"\\n      },\\n      \\\"sources_reviewed\\\": 9,\\n      \\\"sources\\\": [\\n        {\\n          \\\"url\\\": \\\"https://www.theconstructionindex.co.uk/news/view/acme-to-close-leeds-site\\\",\\n          \\\"publisher\\\": \\\"theconstructionindex.co.uk\\\",\\n          \\\"title\\\": \\\"Acme to close Leeds site\\\",\\n          \\\"published_date\\\": \\\"2026-07-22\\\",\\n          \\\"quote\\\": \\\"The company informed employees that its Leeds site will close before the end of the year\\\",\\n          \\\"content\\\": \\\"Acme Industrial is restructuring its northern operations. The company informed employees that its Leeds site will close before the end of the year, according to staff briefed on the plan. Acme did not respond to a request for comment. No insolvency process is involved.\\\"\\n        }\\n      ],\\n      \\\"extraction_failures\\\": [],\\n      \\\"narrative\\\": \\\"The Construction Index reports that employees have been told the Leeds site will close before the end of the year. No company statement or statutory filing corroborating the closure was located.\\\"\\n    },\\n    {\\n      \\\"candidate\\\": {\\n        \\\"category\\\": \\\"regulatory\\\",\\n        \\\"claim\\\": \\\"An HSE enforcement notice may have been issued against the company\\\",\\n        \\\"date\\\": \\\"2026-05\\\",\\n        \\\"source_urls\\\": [\\\"https://resources.hse.gov.uk/notices/acme\\\"],\\n        \\\"verification_query\\\": \\\"\\\\\\\"Acme Industrial\\\\\\\" HSE enforcement notice\\\"\\n      },\\n      \\\"sources_reviewed\\\": 8,\\n      \\\"sources\\\": [],\\n      \\\"extraction_failures\\\": [\\\"https://resources.hse.gov.uk/notices/notice-4471902\\\"],\\n      \\\"narrative\\\": \\\"\\\"\\n    },\\n    {\\n      \\\"candidate\\\": {\\n        \\\"category\\\": \\\"distress\\\",\\n        \\\"claim\\\": \\\"Acme filed accounts late in 2025\\\",\\n        \\\"date\\\": \\\"2025-12\\\",\\n        \\\"source_urls\\\": [],\\n        \\\"verification_query\\\": \\\"\\\\\\\"Acme Industrial\\\\\\\" overdue accounts\\\"\\n      },\\n      \\\"sources_reviewed\\\": 4,\\n      \\\"sources\\\": [\\n        {\\n          \\\"url\\\": \\\"https://find-and-update.company-information.service.gov.uk/company/00000000/filing-history\\\",\\n          \\\"publisher\\\": \\\"find-and-update.company-information.service.gov.uk\\\",\\n          \\\"title\\\": \\\"ACME INDUSTRIAL LTD filing history\\\",\\n          \\\"quote\\\": \\\"accounts were extremely overdue and the company ignored repeated warnings\\\",\\n          \\\"content\\\": \\\"Filing history. Accounts for the period ending 31 March 2025 made up to date, filed 12 January 2026. Confirmation statement filed 3 February 2026. No overdue markers are shown on the current record.\\\"\\n        }\\n      ],\\n      \\\"extraction_failures\\\": [],\\n      \\\"narrative\\\": \\\"\\\"\\n    }\\n  ]\\n}\\n\"\n  }\n]"
  }
]

I found two important correctness problems in the evidence logic and one live-path integration failure. I’m doing a final pass on API response handling and submission completeness before summarizing the findings.

The build has a strong structure and polished offline demo, but it is not submission-ready yet. I found three correctness issues that undermine the central “evidence-governed” claim.

::code-comment{title="[P1] Research results are never collected" body="research() is called non-streaming, which returns a queued research task with a request ID/status—not the completed structured result. The code never calls get_research() or waits for completion, and _parse() does not read the completed response's content. A live screen can therefore normalize an empty/pending response and ultimately report a clean screen. Poll to a terminal state with a timeout, distinguish failed research from zero findings, then parse the completed structured output." file="[repo]/src/deallens/tavily_client.py" start=52 end=63 priority=1}

::code-comment{title="[P1] Contradictory quotes verify the claim" body="The prompt explicitly allows passages that support or contradict the candidate, but QuotePick records no stance. Every validated quote becomes positive Evidence, so one contradicting primary source—or two contradicting secondary sources—marks the candidate verified. Add a structured stance: supports | contradicts | irrelevant; only supporting evidence should count toward verification, while conflicting evidence should produce a conflict/review state." file="[repo]/src/deallens/capture.py" start=24 end=31 priority=1}

::code-comment{title="[P1] Unsearched categories become a clean screen" body="When discovery returns no candidate for a category, no governed /search is executed, yet coverage labels it no_finding; if every category has no candidates, risk_level() returns CLEAN SCREEN*. This also magnifies discovery failures and false negatives. Either run at least one deterministic baseline query per category or represent the state honestly as no_candidate_discovered/not_independently_checked and prohibit an overall clean result without minimum coverage." file="[repo]/src/deallens/gate.py" start=109 end=131 priority=1}

::code-comment{title="[P1] Subdomains count as independent publishers" body="Independence is calculated from Evidence.publisher, but capture stores the hostname rather than the registrable domain. For example, markets.ft.com and ft.com count as two independent sources and incorrectly verify a claim. Normalize using the Public Suffix List or map every URL to its matched jurisdiction-pack publisher identity before applying the two-source rule." file="[repo]/src/deallens/gate.py" start=51 end=52 priority=1}

::code-comment{title="[P2] CLI token usage is written to a discarded ledger" body="The CLI constructs Tavily and LLM with one ledger, but run_screen() immediately creates a different ledger and assigns only Tavily to it. The supplied LLM continues recording tokens in the old object, while the returned result contains the new object, so live reports show zero LLM tokens. Reuse the supplied clients' shared ledger or explicitly assign the same newly created ledger to both." file="[repo]/src/deallens/pipeline.py" start=48 end=52 priority=2}

What I verified:

All 25 unit tests pass.
deallens demo works and generates both outputs.
deallens eval reports 12/12 and zero false verifications.
The source compiles successfully.
I couldn’t run a live screen because no Tavily or Anthropic keys are set.
The repository has no commits yet; every file is currently untracked.

Submission gaps:

The required development record/build log is missing.
There is no explicit technical-statement file, although much of it could be extracted from the README.
The evaluation tests only rules encoded by the same implementation; it does not measure discovery recall, quote-selection accuracy, claim entailment, or entity matching.
Company identity is name-based. Adding a company registration number would materially reduce wrong-entity findings for similarly named businesses.
I would replace CLEAN SCREEN* with NO QUALIFYING FINDINGS; the current label remains too strong for a recall-limited public-web screen.

Overall, Claude built a credible foundation with unusually good documentation and failure-conscious design. Fix the asynchronous research path, evidence stance, and minimum-coverage semantics before polishing anything else.

i think tavily api is added in env and also nebius tokenfactory api

I’ll locate the .env file and verify only the variable names and whether values are non-empty—I won’t print either secret. Then I can run the live path if both providers are configured correctly.

The first redaction command hit a shell-quoting error before reading anything. I’m retrying with a safer parser that reports only key names and set/empty status.

No .env file exists in the workspace, and neither key is exported into the current process. I’m checking whether the variables were placed in shell or Claude settings instead, again without exposing their values.

You’re partly right:

NEBIUS_API_KEY is set in `/Users/[REDACTED]/[local-file]
TAVILY_API_KEY is not set anywhere I could find.
No actual .env exists—only deallens/.env.example.
DealLens currently defaults to Anthropic, so the Nebius key is not being used.
langchain-nebius is also missing from pyproject.toml.

To run with Nebius, create [repo]/.env:

And add this dependency:

There is also an unrelated warning in .zshrc: it tries to source a missing `/Users/[REDACTED]/[local-file]

Once the Tavily key is available and Nebius support is added, the live screen can be tested. The asynchronous /research bug I identified still needs fixing first; otherwise a valid key won’t make the discovery pipeline work correctly.

open env file

There isn’t a real .env yet, so I’ll open the existing .env.example template in your default editor.

macOS has no default app registered for .env.example; I’ll open it explicitly in TextEdit.

Opened deallens/.env.example [blocked] in TextEdit. There is currently no actual .env file.

updated it ensure we use nebius api - not claude - this should be clear in our docs as it is requirement of assignment - we would also have to setup langsmith

I’ll switch the project’s runtime and documentation to Nebius, verify whether the keys were placed in the template or a real .env without exposing them, and configure the LangSmith setup path. I’ll also rerun the tests and CLI checks afterward.

The Tavily credential was entered into .env.example, which is intended to be committed. I’m going to preserve that configured file as the ignored .env, recreate a safe placeholder-only template, and replace the Anthropic runtime dependency/default with Nebius.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

The project is now configured around Nebius, and the committed template is safe again. I’m updating the lockfile and verifying that the Nebius adapter loads, the CLI enforces the correct key, and the offline suite still passes.

>uv lock && uv sync --all-groups && uv run pytest -q && uv run deallens demo >/tmp/deallens-demo-check.txt && tail -4 /tmp/deallens-demo-check.txt

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 2.2 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Resolved 57 packages in 414ms\\nRemoved anthropic v0.120.2\\nRemoved docstring-parser v0.18.0\\nRemoved langchain-anthropic v1.5.3\\nAdded langchain-nebius v0.1.3\\nAdded langchain-openai v1.4.1\\nAdded openai v2.52.1\\nAdded tqdm v4.70.0\\nResolved 57 packages in 3ms\\n   Building deallens @ file://[repo]\\nDownloading openai (1.6MiB)\\n Downloaded openai\\n      Built deallens @ file://[repo]\\nPrepared 5 packages in 423ms\\nUninstalled 4 packages in 109ms\\nInstalled 5 packages in 15ms\\n - anthropic==0.120.2\\n ~ deallens==0.1.0 (from file://[repo])\\n - docstring-parser==0.18.0\\n - langchain-anthropic==1.5.3\\n + langchain-nebius==0.1.3\\n + langchain-openai==1.4.1\\n + openai==2.52.1\\n + tqdm==4.70.0\\n.........................                                                [100%]\\n│ 03.json                                                                      │\\n│ Tavily usage: 0 credits                                                      │\\n╰──────────────────────────────────────────────────────────────────────────────╯\\nFixture run: 0 Tavily credits spent. See fixtures/demo_screen.json.\\n\"\n  }\n]"
  }
]

Dependencies and tests are clean. The adapter check itself hit a quoting-only syntax error, so I’m rerunning that final check with simpler output; no API call or secret display is involved.

That check caught a real integration issue: LangChain’s generic init_chat_model does not recognize nebius: as a provider. I’m switching the implementation to Nebius’s native ChatNebius adapter and using the bare Token Factory model ID, then I’ll validate instantiation again.

>uv run python - <<'PY'
import inspect
from langchain_nebius import ChatNebius
print(inspect.signature(ChatNebius))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.1 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"(*args: Any, name: str | None = None, cache: langchain_core.caches.BaseCache | bool | None = None, verbose: bool = <factory>, callbacks: list[langchain_core.callbacks.base.BaseCallbackHandler] | langchain_core.callbacks.base.BaseCallbackManager | None = None, tags: list[str] | None = None, metadata: dict[str, typing.Any] | None = None, custom_get_token_ids: collections.abc.Callable[[str], list[int]] | None = None, rate_limiter: langchain_core.rate_limiters.BaseRateLimiter | None = None, disable_streaming: Union[bool, Literal['tool_calling']] = False, output_version: str | None = <factory>, profile: langchain_core.language_models.model_profile.ModelProfile | None = None, client: Any = None, async_client: Any = None, root_client: Any = None, root_async_client: Any = None, model: str = 'meta-llama/Llama-3.3-70B-Instruct-fast', temperature: float | None = None, model_kwargs: dict[str, typing.Any] = <factory>, api_key: pydantic.types.SecretStr | None | collections.abc.Callable[[], str] | collections.abc.Callable[[], collections.abc.Awaitable[str]] = None, base_url: str | None = None, organization: str | None = None, openai_proxy: str | None = <factory>, timeout: float | tuple[float, float] | typing.Any | None = None, stream_usage: bool | None = None, max_retries: int | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, seed: int | None = None, logprobs: bool | None = None, top_logprobs: int | None = None, logit_bias: dict[int, int] | None = None, streaming: bool = False, n: int | None = None, top_p: float | None = None, max_tokens: int | None = None, reasoning_effort: str | None = None, reasoning: dict[str, typing.Any] | None = None, verbosity: str | None = None, tiktoken_model_name: str | None = None, default_headers: collections.abc.Mapping[str, str] | None = None, default_query: collections.abc.Mapping[str, object] | None = None, http_client: typing.Any | None = None, http_async_client: typing.Any | None = None, http_socket_options: collections.abc.Sequence[tuple[int, int, int]] | None = None, stream_chunk_timeout: float | None = <factory>, stop_sequences: list[str] | str | None = None, extra_body: collections.abc.Mapping[str, typing.Any] | None = None, include_response_headers: bool = False, disabled_params: dict[str, typing.Any] | None = None, context_management: list[dict[str, typing.Any]] | None = None, include: list[str] | None = None, prompt_cache_options: dict[str, typing.Any] | None = None, service_tier: str | None = None, store: bool | None = None, truncation: str | None = None, use_previous_response_id: bool = False, use_responses_api: bool | None = None) -> None\\n\"\n  }\n]"
  }
]

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

The native Nebius adapter now initializes correctly with moonshotai/Kimi-K2.6, and both Tavily and Nebius credentials are available without being committed. LangSmith is documented and instrumented, but its API key is not configured yet. I’ll make one minimal structured-output call to confirm Nebius works end to end.

The API call reached Nebius, but Kimi returned no parsed structured object through LangChain’s helper. I’m inspecting the non-secret response metadata and parsing error so we can make the structured-output path reliable rather than assuming provider compatibility.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
from pydantic import BaseModel
load_dotenv(".env")
from langchain_nebius import ChatNebius
class Probe(BaseModel):
    status: str
model = ChatNebius(model="moonshotai/Kimi-K2.6", temperature=0)
result = model.with_structured_output(Probe, include_raw=True).invoke("Return status equal to OK. Return no other information.")
print("result keys:", sorted(result.keys()))
print("parsed:", result.get("parsed"))
print("parsing error:", type(result.get("parsing_error")).__name__, str(result.get("parsing_error"))[:500])
raw = result.get("raw")
print("raw content type:", type(raw.content).__name__)
print("raw content:", str(raw.content)[:500])
print("tool calls:", str(getattr(raw, "tool_calls", None))[:500])
print("finish reason:", getattr(raw, "response_metadata", {}).get("finish_reason"))
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.9 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] keys: ['parsed', 'parsing_error', 'raw']\\nparsed: status='OK'\\nparsing error: NoneType None\\nraw content type: str\\nraw content: \\ntool calls: [{'name': 'Probe', 'args': {'status': 'OK'}, 'id': 'chatcmpl-tool-98b378227f9de528', 'type': 'tool_call'}]\\nfinish reason: stop\\n\"\n  }\n]"
  }
]

Nebius is now confirmed end to end: the configured Kimi model returned valid Pydantic structured output and token usage was recorded. I’m doing the final regression check and opening the real ignored .env so you can add the one credential still missing—the LangSmith API key.

DealLens now uses Nebius—not Anthropic.

Changes made:

Replaced langchain-anthropic with langchain-nebius in pyproject.toml [blocked].
Switched to the native ChatNebius adapter in llm.py [blocked].
Default model is moonshotai/Kimi-K2.6.
CLI now requires NEBIUS_API_KEY.
Updated README.md [blocked] with explicit Tavily, Nebius, and LangSmith responsibilities.
Sanitized .env.example [blocked]; the real Tavily credential is preserved in the ignored .env.
Updated and verified the dependency lockfile.

Verification:

Actual Nebius structured-output request succeeded.
Token usage was captured.
25 tests pass.
Eval remains 12/12 with zero false verifications.
No Anthropic references remain.

The real .env is open in TextEdit. LangSmith is the only missing configuration; add:

The tracing decorators are already implemented. After adding the key, we should fix the previously identified asynchronous Tavily Research handling before running and capturing the full live trace.

change model to kimi k3

I’ll verify the exact Kimi K3 model identifier currently exposed by Nebius Token Factory, then update the runtime, template, and documentation and test a structured-output call.

Nebius currently exposes the exact model ID moonshotai/Kimi-K3. I’ll update every runtime and documented default to that identifier and verify structured output against the live model.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

Switched DealLens to moonshotai/Kimi-K3 across:

Runtime default
Real .env
.env.example
README documentation

Verified through Nebius’s live model list and a real structured-output request. Kimi K3 returned valid output, and all 25 tests pass. Nebius documents model discovery through its Models API.

2 · Provider integration and first live run

The implementation is moved from Claude/Anthropic to assignment-aligned Nebius Kimi K3. Tavily Research polling, structured-output compatibility, LangSmith EU tracing, and the first real end-to-end screen are tested and corrected against live APIs.

now - can we actually test?

Yes. Before spending credits on a full screen, I’ll repair the known asynchronous Tavily Research path, add tests for pending/completed/failed task handling, then run one live UK-company screen through Tavily and Kimi K3. LangSmith tracing will remain off until its API key is added.

>uv run python - <<'PY'
from tavily import TavilyClient
print([m for m in dir(TavilyClient) if 'usage' in m.lower()])
PY
nl -ba src/deallens/tavily_client.py | sed -n '1,180p'
nl -ba tests/test_gate.py | sed -n '1,280p'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.4 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"[]\\n     1\\t\\\"\\\"\\\"Thin Tavily wrapper: usage accounting, 429 retry, failure surfacing.\\n     2\\t\\n     3\\tEvery call passes include_usage=True and books credits into a UsageLedger so\\n     4\\teach screen can print what it actually cost.\\n     5\\t\\\"\\\"\\\"\\n     6\\t\\n     7\\tfrom __future__ import annotations\\n     8\\t\\n     9\\timport time\\n    10\\tfrom typing import Any\\n    11\\t\\n    12\\tfrom langsmith import traceable\\n    13\\tfrom tavily import TavilyClient\\n    14\\t\\n    15\\tfrom .models import UsageLedger\\n    16\\t\\n    17\\t_MAX_RETRIES = 2\\n    18\\t\\n    19\\t\\n    20\\tclass Tavily:\\n    21\\t    def __init__(self, api_key: str | None = None, ledger: UsageLedger | None = None):\\n    22\\t        self._client = TavilyClient(api_key=[REDACTED] if api_key else TavilyClient()\\n    23\\t        self.ledger = ledger or UsageLedger()\\n    24\\t\\n    25\\t    # -- internals -----------------------------------------------------------\\n    26\\t\\n    27\\t    def _call(self, endpoint: str, fn, /, **kwargs) -> dict[str, Any]:\\n    28\\t        last_exc: Exception | None = None\\n    29\\t        for attempt in range(_MAX_RETRIES + 1):\\n    30\\t            try:\\n    31\\t                response = fn(**kwargs)\\n    32\\t                self._book(endpoint, response)\\n    33\\t                return response\\n    34\\t            except Exception as exc:  # tavily-python raises per-status exceptions\\n    35\\t                last_exc = exc\\n    36\\t                retry_after = getattr(exc, \\\"retry_after\\\", None)\\n    37\\t                is_rate_limit = \\\"429\\\" in str(exc) or retry_after is not None\\n    38\\t                if attempt < _MAX_RETRIES and is_rate_limit:\\n    39\\t                    time.sleep(float(retry_after or 2 * (attempt + 1)))\\n    40\\t                    continue\\n    41\\t                raise\\n    42\\t        raise last_exc  # pragma: no cover\\n    43\\t\\n    44\\t    def _book(self, endpoint: str, response: dict[str, Any]) -> None:\\n    45\\t        usage = response.get(\\\"usage\\\") if isinstance(response, dict) else None\\n    46\\t        if isinstance(usage, dict):\\n    47\\t            credits = usage.get(\\\"credits\\\", 0) or 0\\n    48\\t            self.ledger.add_credits(endpoint, float(credits))\\n    49\\t\\n    50\\t    # -- endpoints -------------------------------------------------------------\\n    51\\t\\n    52\\t    @traceable(name=\\\"tavily.research\\\", run_type=\\\"retriever\\\")\\n    53\\t    def research(self, *, input: str, output_schema: dict | None = None) -> dict:\\n    54\\t        kwargs: dict[str, Any] = {\\\"input\\\": input, \\\"model\\\": \\\"mini\\\"}\\n    55\\t        if output_schema is not None:\\n    56\\t            kwargs[\\\"output_schema\\\"] = output_schema\\n    57\\t        try:\\n    58\\t            return self._call(\\\"research\\\", self._client.research, **kwargs)\\n    59\\t        except TypeError:\\n    60\\t            # SDK version without output_schema passthrough — fall back to\\n    61\\t            # plain research; discover.py normalizes free text afterwards.\\n    62\\t            kwargs.pop(\\\"output_schema\\\", None)\\n    63\\t            return self._call(\\\"research\\\", self._client.research, **kwargs)\\n    64\\t\\n    65\\t    @traceable(name=\\\"tavily.search\\\", run_type=\\\"retriever\\\")\\n    66\\t    def search(\\n    67\\t        self,\\n    68\\t        *,\\n    69\\t        query: str,\\n    70\\t        include_domains: list[str] | None = None,\\n    71\\t        exclude_domains: list[str] | None = None,\\n    72\\t        topic: str = \\\"general\\\",\\n    73\\t        max_results: int = 8,\\n    74\\t    ) -> dict:\\n    75\\t        return self._call(\\n    76\\t            \\\"search\\\",\\n    77\\t            self._client.search,\\n    78\\t            query=query[:400],  # documented query length limit\\n    79\\t            include_domains=include_domains or [],\\n    80\\t            exclude_domains=exclude_domains or [],\\n    81\\t            topic=topic,\\n    82\\t            max_results=max_results,\\n    83\\t            include_usage=True,\\n    84\\t        )\\n    85\\t\\n    86\\t    @traceable(name=\\\"tavily.extract\\\", run_type=\\\"retriever\\\")\\n    87\\t    def extract(self, *, urls: list[str]) -> dict:\\n    88\\t        \\\"\\\"\\\"Batch extract (<=20 URLs per call, enforced by caller batching).\\n    89\\t        Returns the raw response; failed_results is surfaced, never swallowed.\\\"\\\"\\\"\\n    90\\t        return self._call(\\n    91\\t            \\\"extract\\\",\\n    92\\t            self._client.extract,\\n    93\\t            urls=urls[:20],\\n    94\\t            extract_depth=\\\"basic\\\",\\n    95\\t            format=\\\"markdown\\\",\\n    96\\t            include_usage=True,\\n    97\\t        )\\n     1\\t\\\"\\\"\\\"Offline tests for the evidence gate — every classification row, the\\n     2\\tfailure-mode traps, severity escalation, coverage, and risk rollup.\\\"\\\"\\\"\\n     3\\t\\n     4\\tfrom deallens.config import JurisdictionPack, Policy, PolicyRule\\n     5\\tfrom deallens.gate import (\\n     6\\t    apply_severity,\\n     7\\t    classify,\\n     8\\t    coverage,\\n     9\\t    quote_in_content,\\n    10\\t    risk_level,\\n    11\\t)\\n    12\\tfrom deallens.models import Candidate, Evidence\\n    13\\t\\n    14\\t\\n    15\\tdef candidate(category=\\\"leadership\\\", claim=\\\"The CFO departed in March 2026\\\"):\\n    16\\t    return Candidate(\\n    17\\t        category=category,\\n    18\\t        claim=claim,\\n    19\\t        verification_query=\\\"Acme CFO departure\\\",\\n    20\\t    )\\n    21\\t\\n    22\\t\\n    23\\tdef evidence(tier, publisher, quote=\\\"Jane Smith's appointment was terminated\\\"):\\n    24\\t    return Evidence(\\n    25\\t        url=f\\\"https://{publisher}/x\\\",\\n    26\\t        publisher=publisher,\\n    27\\t        source_tier=tier,\\n    28\\t        quote=quote,\\n    29\\t    )\\n    30\\t\\n    31\\t\\n    32\\t# ---- classification rows ----------------------------------------------------\\n    33\\t\\n    34\\tdef test_one_primary_source_verifies():\\n    35\\t    f = classify(candidate(), [evidence(\\\"primary\\\", \\\"thegazette.co.uk\\\")], [])\\n    36\\t    assert f.status == \\\"verified\\\"\\n    37\\t\\n    38\\t\\n    39\\tdef test_two_independent_secondaries_verify():\\n    40\\t    f = classify(\\n    41\\t        candidate(),\\n    42\\t        [evidence(\\\"credible_secondary\\\", \\\"ft.com\\\"),\\n    43\\t         evidence(\\\"credible_secondary\\\", \\\"reuters.com\\\")],\\n    44\\t        [],\\n    45\\t    )\\n    46\\t    assert f.status == \\\"verified\\\"\\n    47\\t\\n    48\\t\\n    49\\tdef test_two_secondaries_same_domain_only_report():\\n    50\\t    \\\"\\\"\\\"Syndication trap: two articles on one domain are one voice.\\\"\\\"\\\"\\n    51\\t    f = classify(\\n    52\\t        candidate(),\\n    53\\t        [evidence(\\\"credible_secondary\\\", \\\"ft.com\\\"),\\n    54\\t         evidence(\\\"credible_secondary\\\", \\\"ft.com\\\", quote=\\\"second article\\\")],\\n    55\\t        [],\\n    56\\t    )\\n    57\\t    assert f.status == \\\"reported\\\"\\n    58\\t\\n    59\\t\\n    60\\tdef test_single_secondary_reports():\\n    61\\t    f = classify(candidate(), [evidence(\\\"credible_secondary\\\", \\\"bbc.co.uk\\\")], [])\\n    62\\t    assert f.status == \\\"reported\\\"\\n    63\\t\\n    64\\t\\n    65\\tdef test_extraction_failure_is_unresolved_never_verified():\\n    66\\t    \\\"\\\"\\\"The trap from the spec: a candidate whose backing document cannot be\\n    67\\t    extracted must surface as UNRESOLVED, not silently verified or dropped.\\\"\\\"\\\"\\n    68\\t    f = classify(candidate(\\\"regulatory\\\"), [], [\\\"https://fca.org.uk/blocked-doc\\\"])\\n    69\\t    assert f.status == \\\"unresolved\\\"\\n    70\\t\\n    71\\t\\n    72\\tdef test_other_tier_evidence_alone_is_rejected():\\n    73\\t    \\\"\\\"\\\"Aggregator-only trap: sources outside both tiers cannot support a\\n    74\\t    finding, and with no extraction failures the claim is rejected.\\\"\\\"\\\"\\n    75\\t    f = classify(candidate(), [evidence(\\\"other\\\", \\\"randomblog.example\\\")], [])\\n    76\\t    assert f.status == \\\"rejected\\\"\\n    77\\t\\n    78\\t\\n    79\\tdef test_no_evidence_no_failures_is_rejected():\\n    80\\t    f = classify(candidate(), [], [])\\n    81\\t    assert f.status == \\\"rejected\\\"\\n    82\\t\\n    83\\t\\n    84\\tdef test_primary_wins_even_with_failures_present():\\n    85\\t    f = classify(\\n    86\\t        candidate(),\\n    87\\t        [evidence(\\\"primary\\\", \\\"find-and-update.company-information.service.gov.uk\\\")],\\n    88\\t        [\\\"https://ft.com/timeout\\\"],\\n    89\\t    )\\n    90\\t    assert f.status == \\\"verified\\\"\\n    91\\t\\n    92\\t\\n    93\\tdef test_other_tier_does_not_count_toward_verification():\\n    94\\t    f = classify(\\n    95\\t        candidate(),\\n    96\\t        [evidence(\\\"credible_secondary\\\", \\\"ft.com\\\"),\\n    97\\t         evidence(\\\"other\\\", \\\"randomblog.example\\\")],\\n    98\\t        [],\\n    99\\t    )\\n   100\\t    assert f.status == \\\"reported\\\"\\n   101\\t\\n   102\\t\\n   103\\t# ---- quote validation --------------------------------------------------------\\n   104\\t\\n   105\\tdef test_quote_validates_verbatim():\\n   106\\t    content = \\\"Filing history.\\\\n\\\\nJane   Smith's appointment\\\\nwas terminated on 14 March 2026.\\\"\\n   107\\t    assert quote_in_content(\\\"Jane Smith's appointment was terminated\\\", content)\\n   108\\t\\n   109\\t\\n   110\\tdef test_quote_validates_across_curly_punctuation():\\n   111\\t    content = \\\"Jane Smith’s appointment was terminated — effective immediately.\\\"\\n   112\\t    assert quote_in_content(\\\"Jane Smith's appointment was terminated - effective\\\", content)\\n   113\\t\\n   114\\t\\n   115\\tdef test_paraphrase_fails_validation():\\n   116\\t    content = \\\"The CFO left the company in March.\\\"\\n   117\\t    assert not quote_in_content(\\\"The CFO resigned in March\\\", content)\\n   118\\t\\n   119\\t\\n   120\\tdef test_empty_quote_never_validates():\\n   121\\t    assert not quote_in_content(\\\"   \\\", \\\"anything at all\\\")\\n   122\\t\\n   123\\t\\n   124\\t# ---- severity policy ----------------------------------------------------------\\n   125\\t\\n   126\\tPOLICY = Policy(rules={\\n   127\\t    \\\"leadership\\\": PolicyRul\n[truncated; use --max-event-chars to read more]"
  }
]

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && uv run python -m compileall -q src

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.8 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"............................                                             [100%]\\n\"\n  }\n]"
  }
]

The async research fix is covered by three new offline contract tests; the full suite now passes 28 tests. I’m starting a real screen of Darktrace as a UK test target so the run exercises Tavily Research, governed Search, Extract, Kimi K3 structured output, memo rendering, and usage tracking.

>zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/live-test'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.8 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{\\\"chunk_id\\\":\\\"97aa20\\\",\\\"wall_time_seconds\\\":1.60064925,\\\"exit_code\\\":1,\\\"original_token_count\\\":2173,\\\"output\\\":\\\"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] Traceback (most recent call last) ──────────────────────╮\\\\r\\\\n│ [repo]/src/deallens/cli.py:51 in screen     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   48 │   ledger = UsageLedger()                                              │\\\\r\\\\n│   49 │                                                                       │\\\\r\\\\n│   50 │   with console.status(f\\\\\\\"Screening {company}                           │\\\\r\\\\n│      ({jurisdiction.upper()})...\\\\\\\"):                                          │\\\\r\\\\n│ ❱ 51 │   │   result = run_screen(                                            │\\\\r\\\\n│   52 │   │   │   company=company,                                            │\\\\r\\\\n│   53 │   │   │   domain=domain,                                              │\\\\r\\\\n│   54 │   │   │   jurisdiction_pack=pack,                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/pipeline.py:54 in       │\\\\r\\\\n│ run_screen                                                                   │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   51 │   tavily.ledger = ledger                                              │\\\\r\\\\n│   52 │   llm = llm or LLM(ledger)                                            │\\\\r\\\\n│   53 │                                                                       │\\\\r\\\\n│ ❱ 54 │   candidates = discover(tavily, llm, company, domain,                 │\\\\r\\\\n│      jurisdiction_pack.name)                                                 │\\\\r\\\\n│   55 │                                                                       │\\\\r\\\\n│   56 │   findings: list[Finding] = []                                        │\\\\r\\\\n│   57 │   sources_reviewed: dict[str, int] = {}                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/discover.py:71 in       │\\\\r\\\\n│ discover                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    68 def discover(                                                          │\\\\r\\\\n│    69 │   tavily: Tavily, llm: LLM, company: str, domain: str, jurisdiction: │\\\\r\\\\n│       str                                                                    │\\\\r\\\\n│    70 ) -> list[Candidate]:                                                  │\\\\r\\\\n│ ❱  71 │   response = tavily.research(                                        │\\\\r\\\\n│    72 │   │   input=RESEARCH_PROMPT.format(                                  │\\\\r\\\\n│    73 │   │   │   company=company,                                           │\\\\r\\\\n│    74 │   │   │   domain=domain,                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:73 in  │\\\\r\\\\n│ research                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    70 │   │   if output_schema is not None:                                  │\\\\r\\\\n│    71 │   │   │   kwargs[\\\\\\\"output_schema\\\\\\\"] = output_schema                    │\\\\r\\\\n│    72 │   │                                                                  │\\\\r\\\\n│ ❱  73 │   │   response = self._call(\\\\\\\"research\\\\\\\", self._client.research,       │\\\\r\\\\n│       **kwargs)                                                              │\\\\r\\\\n│    74 │   │   if response.get(\\\\\\\"status\\\\\\\") == \\\\\\\"completed\\\\\\\":                      │\\\\r\\\\n│    75 │   │   │   return response                                            │\\\\r\\\\n│    76                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:33 in  │\\\\r\\\\n│ _call                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    30 │   │   last_exc: Exception | None = None                              │\\\\r\\\\n│    31 │   │   for attempt in range(_MAX_RETRIES + 1):                        │\\\\r\\\\n│    32 │   │   │   try:                                                       │\\\\r\\\\n│ ❱  33 │   │   │   │   response = fn(**kwargs)                                │\\\\r\\\\n│    34 │   │   │   │   self._book(endpoint, response)                         │\\\\r\\\\n│    35 │   │   │   │   return response                                        │\\\\r\\\\n│    36 │   │   │   except Exception as exc:  # tavily-python raises           │\\\\r\\\\n│       per-status exceptions                                                  │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:722 in research                                              │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   719 │   │   \\\\\\\"\\\\\\\"\\\\\\\"                                                            │\\\\r\\\\n│   720 │   │   self._check_keyless_supported(\\\\\\\"research\\\\\\\")                      │\\\\r\\\\n│   721 │   │                                                                  │\\\\r\\\\n│ ❱ 722 │   │   return self._research(                                         │\\\\r\\\\n│   723 │   │   │   input=input,                                               │\\\\r\\\\n│   724 │   │   │   model=model,                                               │\\\\r\\\\n│   725 │   │   │   output_schema=output_schema,                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:694 in _research                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   691 │   │   │   if response.status_code == 200:                            │\\\\r\\\\n│   692 │   │   │   │   return response.json()                                 │\\\\r\\\\n│   693 │   │   │   else:                                                      │\\\\r\\\\n│ ❱ 694 │   │   │   │   self._handle_error_response(response)                  │\\\\r\\\\n│   695 │                                                                      │\\\\r\\\\n│   696 │   def research(self,                                                 │\\\\r\\\\n│   697 │   │   │   │    input: str,                                           │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:141 in _handle_error_response                                │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   138 │   │   elif response.status_code == 401:                              │\\\\r\\\\n│   139 │   │   │   raise InvalidAPIKeyError(detail)                           │\\\\r\\\\n│   140 │   │   elif response.status_code == 400:                              │\\\\r\\\\n│ ❱ 141 │   │   │   raise BadRequestError(detail)                              │\\\\r\\\\n│   142 │   │   else:                                                          │\\\\r\\\\n│   143 │   │   │   raise response.raise_for_status()                          │\\\\r\\\\n│   144                                                                        │\\\\r\\\\n╰──────────────────────────────────────────────────────────────────────────────╯\\\\r\\\\nBadRequestError: Output schema contains unexpected keys: type. Only 'properties'\\\\r\\\\nand 'required' are allowed.\\\\r\\\\n\\\"}\"\n  }\n]"
  }
]

The live test correctly reached Tavily but exposed a schema-contract mismatch before any research ran: Tavily accepts only top-level properties and required, while our schema also supplied top-level type. I’m correcting that and adding a regression assertion, then I’ll rerun the same live screen.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/live-test'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 2.3 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{\\\"chunk_id\\\":\\\"e73026\\\",\\\"wall_time_seconds\\\":2.149159667,\\\"exit_code\\\":1,\\\"original_token_count\\\":2185,\\\"output\\\":\\\".............................                                            [100%]\\\\r\\\\n/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] Traceback (most recent call last) ──────────────────────╮\\\\r\\\\n│ [repo]/src/deallens/cli.py:51 in screen     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   48 │   ledger = UsageLedger()                                              │\\\\r\\\\n│   49 │                                                                       │\\\\r\\\\n│   50 │   with console.status(f\\\\\\\"Screening {company}                           │\\\\r\\\\n│      ({jurisdiction.upper()})...\\\\\\\"):                                          │\\\\r\\\\n│ ❱ 51 │   │   result = run_screen(                                            │\\\\r\\\\n│   52 │   │   │   company=company,                                            │\\\\r\\\\n│   53 │   │   │   domain=domain,                                              │\\\\r\\\\n│   54 │   │   │   jurisdiction_pack=pack,                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/pipeline.py:54 in       │\\\\r\\\\n│ run_screen                                                                   │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   51 │   tavily.ledger = ledger                                              │\\\\r\\\\n│   52 │   llm = llm or LLM(ledger)                                            │\\\\r\\\\n│   53 │                                                                       │\\\\r\\\\n│ ❱ 54 │   candidates = discover(tavily, llm, company, domain,                 │\\\\r\\\\n│      jurisdiction_pack.name)                                                 │\\\\r\\\\n│   55 │                                                                       │\\\\r\\\\n│   56 │   findings: list[Finding] = []                                        │\\\\r\\\\n│   57 │   sources_reviewed: dict[str, int] = {}                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/discover.py:70 in       │\\\\r\\\\n│ discover                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    67 def discover(                                                          │\\\\r\\\\n│    68 │   tavily: Tavily, llm: LLM, company: str, domain: str, jurisdiction: │\\\\r\\\\n│       str                                                                    │\\\\r\\\\n│    69 ) -> list[Candidate]:                                                  │\\\\r\\\\n│ ❱  70 │   response = tavily.research(                                        │\\\\r\\\\n│    71 │   │   input=RESEARCH_PROMPT.format(                                  │\\\\r\\\\n│    72 │   │   │   company=company,                                           │\\\\r\\\\n│    73 │   │   │   domain=domain,                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:73 in  │\\\\r\\\\n│ research                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    70 │   │   if output_schema is not None:                                  │\\\\r\\\\n│    71 │   │   │   kwargs[\\\\\\\"output_schema\\\\\\\"] = output_schema                    │\\\\r\\\\n│    72 │   │                                                                  │\\\\r\\\\n│ ❱  73 │   │   response = self._call(\\\\\\\"research\\\\\\\", self._client.research,       │\\\\r\\\\n│       **kwargs)                                                              │\\\\r\\\\n│    74 │   │   if response.get(\\\\\\\"status\\\\\\\") == \\\\\\\"completed\\\\\\\":                      │\\\\r\\\\n│    75 │   │   │   return response                                            │\\\\r\\\\n│    76                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:33 in  │\\\\r\\\\n│ _call                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    30 │   │   last_exc: Exception | None = None                              │\\\\r\\\\n│    31 │   │   for attempt in range(_MAX_RETRIES + 1):                        │\\\\r\\\\n│    32 │   │   │   try:                                                       │\\\\r\\\\n│ ❱  33 │   │   │   │   response = fn(**kwargs)                                │\\\\r\\\\n│    34 │   │   │   │   self._book(endpoint, response)                         │\\\\r\\\\n│    35 │   │   │   │   return response                                        │\\\\r\\\\n│    36 │   │   │   except Exception as exc:  # tavily-python raises           │\\\\r\\\\n│       per-status exceptions                                                  │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:722 in research                                              │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   719 │   │   \\\\\\\"\\\\\\\"\\\\\\\"                                                            │\\\\r\\\\n│   720 │   │   self._check_keyless_supported(\\\\\\\"research\\\\\\\")                      │\\\\r\\\\n│   721 │   │                                                                  │\\\\r\\\\n│ ❱ 722 │   │   return self._research(                                         │\\\\r\\\\n│   723 │   │   │   input=input,                                               │\\\\r\\\\n│   724 │   │   │   model=model,                                               │\\\\r\\\\n│   725 │   │   │   output_schema=output_schema,                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:694 in _research                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   691 │   │   │   if response.status_code == 200:                            │\\\\r\\\\n│   692 │   │   │   │   return response.json()                                 │\\\\r\\\\n│   693 │   │   │   else:                                                      │\\\\r\\\\n│ ❱ 694 │   │   │   │   self._handle_error_response(response)                  │\\\\r\\\\n│   695 │                                                                      │\\\\r\\\\n│   696 │   def research(self,                                                 │\\\\r\\\\n│   697 │   │   │   │    input: str,                                           │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:141 in _handle_error_response                                │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   138 │   │   elif response.status_code == 401:                              │\\\\r\\\\n│   139 │   │   │   raise InvalidAPIKeyError(detail)                           │\\\\r\\\\n│   140 │   │   elif response.status_code == 400:                              │\\\\r\\\\n│ ❱ 141 │   │   │   raise BadRequestError(detail)                              │\\\\r\\\\n│   142 │   │   else:                                                          │\\\\r\\\\n│   143 │   │   │   raise response.raise_for_status()                          │\\\\r\\\\n│   144                                                                        │\\\\r\\\\n╰──────────────────────────────────────────────────────────────────────────────╯\\\\r\\\\nBadRequestError: Property 'candidates' missing required 'description' field\\\\r\\\\n\\\"}\"\n  }\n]"
  }
]

The second live validation exposed another Tavily-specific schema rule: every output property requires a description. The tests passed because this is an API-level constraint, so I’m completing the schema descriptions and strengthening the contract test before retrying.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/live-test'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 2.9 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{\\\"chunk_id\\\":\\\"5bcfd6\\\",\\\"wall_time_seconds\\\":2.700408042,\\\"exit_code\\\":1,\\\"original_token_count\\\":2203,\\\"output\\\":\\\".............................                                            [100%]\\\\r\\\\n/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] Traceback (most recent call last) ──────────────────────╮\\\\r\\\\n│ [repo]/src/deallens/cli.py:51 in screen     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   48 │   ledger = UsageLedger()                                              │\\\\r\\\\n│   49 │                                                                       │\\\\r\\\\n│   50 │   with console.status(f\\\\\\\"Screening {company}                           │\\\\r\\\\n│      ({jurisdiction.upper()})...\\\\\\\"):                                          │\\\\r\\\\n│ ❱ 51 │   │   result = run_screen(                                            │\\\\r\\\\n│   52 │   │   │   company=company,                                            │\\\\r\\\\n│   53 │   │   │   domain=domain,                                              │\\\\r\\\\n│   54 │   │   │   jurisdiction_pack=pack,                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/pipeline.py:54 in       │\\\\r\\\\n│ run_screen                                                                   │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   51 │   tavily.ledger = ledger                                              │\\\\r\\\\n│   52 │   llm = llm or LLM(ledger)                                            │\\\\r\\\\n│   53 │                                                                       │\\\\r\\\\n│ ❱ 54 │   candidates = discover(tavily, llm, company, domain,                 │\\\\r\\\\n│      jurisdiction_pack.name)                                                 │\\\\r\\\\n│   55 │                                                                       │\\\\r\\\\n│   56 │   findings: list[Finding] = []                                        │\\\\r\\\\n│   57 │   sources_reviewed: dict[str, int] = {}                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/discover.py:85 in       │\\\\r\\\\n│ discover                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    82 def discover(                                                          │\\\\r\\\\n│    83 │   tavily: Tavily, llm: LLM, company: str, domain: str, jurisdiction: │\\\\r\\\\n│       str                                                                    │\\\\r\\\\n│    84 ) -> list[Candidate]:                                                  │\\\\r\\\\n│ ❱  85 │   response = tavily.research(                                        │\\\\r\\\\n│    86 │   │   input=RESEARCH_PROMPT.format(                                  │\\\\r\\\\n│    87 │   │   │   company=company,                                           │\\\\r\\\\n│    88 │   │   │   domain=domain,                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:73 in  │\\\\r\\\\n│ research                                                                     │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    70 │   │   if output_schema is not None:                                  │\\\\r\\\\n│    71 │   │   │   kwargs[\\\\\\\"output_schema\\\\\\\"] = output_schema                    │\\\\r\\\\n│    72 │   │                                                                  │\\\\r\\\\n│ ❱  73 │   │   response = self._call(\\\\\\\"research\\\\\\\", self._client.research,       │\\\\r\\\\n│       **kwargs)                                                              │\\\\r\\\\n│    74 │   │   if response.get(\\\\\\\"status\\\\\\\") == \\\\\\\"completed\\\\\\\":                      │\\\\r\\\\n│    75 │   │   │   return response                                            │\\\\r\\\\n│    76                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/src/deallens/tavily_client.py:33 in  │\\\\r\\\\n│ _call                                                                        │\\\\r\\\\n│                                                                              │\\\\r\\\\n│    30 │   │   last_exc: Exception | None = None                              │\\\\r\\\\n│    31 │   │   for attempt in range(_MAX_RETRIES + 1):                        │\\\\r\\\\n│    32 │   │   │   try:                                                       │\\\\r\\\\n│ ❱  33 │   │   │   │   response = fn(**kwargs)                                │\\\\r\\\\n│    34 │   │   │   │   self._book(endpoint, response)                         │\\\\r\\\\n│    35 │   │   │   │   return response                                        │\\\\r\\\\n│    36 │   │   │   except Exception as exc:  # tavily-python raises           │\\\\r\\\\n│       per-status exceptions                                                  │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:722 in research                                              │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   719 │   │   \\\\\\\"\\\\\\\"\\\\\\\"                                                            │\\\\r\\\\n│   720 │   │   self._check_keyless_supported(\\\\\\\"research\\\\\\\")                      │\\\\r\\\\n│   721 │   │                                                                  │\\\\r\\\\n│ ❱ 722 │   │   return self._research(                                         │\\\\r\\\\n│   723 │   │   │   input=input,                                               │\\\\r\\\\n│   724 │   │   │   model=model,                                               │\\\\r\\\\n│   725 │   │   │   output_schema=output_schema,                               │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:694 in _research                                             │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   691 │   │   │   if response.status_code == 200:                            │\\\\r\\\\n│   692 │   │   │   │   return response.json()                                 │\\\\r\\\\n│   693 │   │   │   else:                                                      │\\\\r\\\\n│ ❱ 694 │   │   │   │   self._handle_error_response(response)                  │\\\\r\\\\n│   695 │                                                                      │\\\\r\\\\n│   696 │   def research(self,                                                 │\\\\r\\\\n│   697 │   │   │   │    input: str,                                           │\\\\r\\\\n│                                                                              │\\\\r\\\\n│ [repo]/.venv/lib/python3.12/site-packages/t │\\\\r\\\\n│ avily/tavily.py:141 in _handle_error_response                                │\\\\r\\\\n│                                                                              │\\\\r\\\\n│   138 │   │   elif response.status_code == 401:                              │\\\\r\\\\n│   139 │   │   │   raise InvalidAPIKeyError(detail)                           │\\\\r\\\\n│   140 │   │   elif response.status_code == 400:                              │\\\\r\\\\n│ ❱ 141 │   │   │   raise BadRequestError(detail)                              │\\\\r\\\\n│   142 │   │   else:                                                          │\\\\r\\\\n│   143 │   │   │   raise response.raise_for_status()                          │\\\\r\\\\n│   144                                                                        │\\\\r\\\\n╰──────────────────────────────────────────────────────────────────────────────╯\\\\r\\\\nBadRequestError: Property 'date' 'type' must be a string, got list. Union types \\\\r\\\\n(e.g. ['string', 'null']) are not supported — use a single type.\\\\r\\\\n\\\"}\"\n  }\n]"
  }
]

A third strict-schema rule surfaced: Tavily does not accept JSON Schema union types. Since date is optional already, I’m making it a plain string when present and retrying. These failures are 400-level validation responses, so no research job has been created yet.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/live-test'

[
  {
    "type": "input_text",
    "text": "Script running with cell ID 51\nWall time 11.0 seconds\nOutput:"
  }
]

The request is now accepted and the live research task is running asynchronously. I’m waiting for Tavily to finish, then the pipeline will verify and extract the discovered claims.

Tavily Research is still processing; there’s no error or partial output yet. This is expected for the background Research endpoint, and the run remains active.

The live job has been running for about two minutes and remains within the documented Research timeout. The CLI spinner hides stage details, which is a usability issue we should improve after this validation run.

The full live pipeline completed successfully: Tavily Research → governed Search → Extract → Kimi K3 → memo. It reported 18 metered Search/Extract credits, one verified item, one reported concern, two unresolved checks, and three rejected candidates. I’m auditing the generated memo and evidence JSON now for grounding and accounting accuracy before calling the test successful.

Yes—we ran a real end-to-end test against Darktrace plc.

What worked:

Tavily Research created and completed asynchronously.
Tavily Search and Extract ran successfully.
Kimi K3 produced structured output and narratives.
Markdown and JSON reports were generated.
Runtime: 196 seconds.
Reported usage: 18 Search/Extract credits.
Test suite now passes 29 tests.

The async implementation now follows Tavily’s documented create-and-poll flow: Create Research Task and Get Research Status.

Outputs:

Live memo [blocked]
Evidence JSON [blocked]

However, the test exposed correctness problems:

A finding was marked verified even though its evidence supported only part of the compound claim.
Two unrelated failed URLs produced misleading “unresolved” findings.
LLM token usage incorrectly reported zero because of the split-ledger bug.
The 18-credit total excludes Research credits.
Financial distress was labelled “no findings” despite zero governed sources being reviewed.
LangSmith tracing was inactive because its API key is still missing.

So the infrastructure works end to end, but the output is not yet trustworthy enough for a submission demo. I recommend fixing evidence entailment, coverage semantics, extraction-failure relevance, and accounting before spending credits on another live run.

langsmith api is alr in

I’ll verify the LangSmith variables without printing their values, then confirm whether tracing is actually enabled for the next run.

It isn’t present in the project’s .env. I’m checking the interactive shell environment as well, since your Nebius key was configured there.

Neither the project files nor the interactive shell expose a LangSmith key. It may be entered in an unsaved TextEdit buffer or exist only in your LangSmith account. I’ll reopen the real .env; save it after adding the three variables.

The LangSmith key is not currently visible in:

deallens/.env
.env.example
The parent project
Your interactive shell environment

I reopened the real ignored .env in TextEdit. Add and save:

Once saved, I can verify authentication and run a small traced request.

it is in there

I’ll reread the saved file directly and distinguish between a real configured value and the placeholder, without displaying the key.

The saved .env on disk still contains only TAVILY_API_KEY and DEALLENS_MODEL; TextEdit’s changes appear not to have been saved to that file. I’m checking whether TextEdit created a .txt or alternate copy nearby.

I found the likely source of the mismatch: the LangSmith lines are present only as comments, so python-dotenv ignores them. I’ll check whether the commented API-key line contains a real value or merely the placeholder, without printing it.

The key is indeed there; you were right. All three LangSmith assignments are commented out, which is why the runtime couldn’t see them. I’ll activate those existing lines without displaying or changing their values, then verify LangSmith authentication.

LangSmith variables are now active. I’ll send one minimal traced Kimi K3 call, flush the tracer, and query the deallens project to confirm the trace reached LangSmith.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
load_dotenv(".env", override=True)
from langsmith import Client, traceable
from langchain_core.tracers.context import wait_for_all_tracers
from deallens.llm import LLM
from deallens.models import UsageLedger

@traceable(name="deallens.langsmith_probe")
def probe():
    return LLM(UsageLedger()).text("Reply exactly: TRACE_OK")

result = probe()
wait_for_all_tracers()
client = Client()
runs = list(client.list_runs(project_name="deallens", filter='eq(name, "deallens.langsmith_probe")', limit=1))
print("model response:", result)
print("trace uploaded:", bool(runs))
if runs:
    print("trace status:", runs[0].status)
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.1 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"zsh:1: no matches found: cd [repo] && uv run python - <<\\\"PY\\\"\\\\nfrom dotenv import load_dotenv\\\\nload_dotenv(\\\".env\\\", override=True)\\\\nfrom langsmith import Client, traceable\\\\nfrom langchain_core.tracers.context import wait_for_all_tracers\\\\nfrom deallens.llm import LLM\\\\nfrom deallens.models import UsageLedger\\\\n\\\\n@traceable(name=\\\"deallens.langsmith_probe\\\")\\\\ndef probe():\\\\n    return LLM(UsageLedger()).text(\\\"Reply exactly: TRACE_OK\\\")\\\\n\\\\nresult = probe()\\\\nwait_for_all_tracers()\\\\nclient = Client()\\\\nruns = list(client.list_runs(project_name=\\\"deallens\\\", filter=eq(name, deallens.langsmith_probe), limit=1))\\\\nprint(\\\"model response:\\\", result)\\\\nprint(\\\"trace uploaded:\\\", bool(runs))\\\\nif runs:\\\\n    print(\\\"trace status:\\\", runs[0].status)\\\\nPY\\n\"\n  }\n]"
  }
]

The first trace probe didn’t execute because zsh interpreted the query filter syntax. I’m retrying without a shell-sensitive filter; no credentials were sent on that failed attempt.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
load_dotenv(".env", override=True)
from langsmith import Client, traceable
from langchain_core.tracers.context import wait_for_all_tracers
from deallens.llm import LLM
from deallens.models import UsageLedger

@traceable(name="deallens.langsmith_probe")
def probe():
    return LLM(UsageLedger()).text("Reply exactly: TRACE_OK")

result = probe()
wait_for_all_tracers()
client = Client()
runs = list(client.list_runs(project_name="deallens", limit=10))
matched = [run for run in runs if run.name == "deallens.langsmith_probe"]
print("model response:", result)
print("trace uploaded:", bool(matched))
if matched:
    print("trace status:", matched[0].status)
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.7 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] (most recent call last):\\n  File \\\"<stdin>\\\", line 4, in <module>\\nImportError: cannot import name 'wait_for_all_tracers' from 'langchain_core.tracers.context' ([repo]/.venv/lib/python3.12/site-packages/langchain_core/tracers/context.py)\\n\"\n  }\n]"
  }
]

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
load_dotenv(".env", override=True)
from langsmith import Client, traceable
from deallens.llm import LLM
from deallens.models import UsageLedger

client = Client()
@traceable(name="deallens.langsmith_probe", client=client)
def probe():
    return LLM(UsageLedger()).text("Reply exactly: TRACE_OK")

result = probe()
client.flush()
runs = list(client.list_runs(project_name="deallens", limit=10))
matched = [run for run in runs if run.name == "deallens.langsmith_probe"]
print("model response:", result)
print("trace uploaded:", bool(matched))
if matched:
    print("trace status:", matched[0].status)
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 2.7 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file]<stdin>:14: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nTraceback (most recent call last):\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 181, in raise_for_status_with_text\\n    response.raise_for_status()\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/requests/models.py\\\", line 1167, in raise_for_status\\n    raise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://api.smith.langchain.com/sessions?limit=1&name=deallens&include_stats=False\\n\\nThe above exception was the direct cause of the following exception:\\n\\nTraceback (most recent call last):\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 2029, in request_with_retries\\n    ls_utils.raise_for_status_with_text(response)\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 183, in raise_for_status_with_text\\n    raise requests.HTTPError(str(e), response.text) from e  # type: ignore[call-arg]\\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nrequests.exceptions.HTTPError: [Errno 401 Client Error: Unauthorized for url: https://api.smith.langchain.com/sessions?limit=1&name=deallens&include_stats=False] {\\\"detail\\\":\\\"Invalid token\\\"}\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 14, in <module>\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 4457, in list_runs\\n    [self.read_project(project_name=name).id for name in project_name]\\n     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 169, in wrapper\\n    return func(*args, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 5361, in read_project\\n    response = self.request_with_retries(\\\"GET\\\", path, params=params)\\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 2064, in request_with_retries\\n    raise ls_utils.LangSmithAuthError(\\nlangsmith.utils.LangSmithAuthError: Authentication failed for /sessions. HTTPError('401 Client Error: Unauthorized for url: https://api.smith.langchain.com/sessions?limit=1&name=deallens&include_stats=False', '{\\\"detail\\\":\\\"Invalid token\\\"}')\\n\"\n  }\n]"
  }
]

The lines are active now, but LangSmith rejected the key with 401 Invalid token. The trace call itself ran through Kimi K3, but upload/authentication failed. I’m checking whether this key type requires a workspace ID before concluding that the token itself needs replacement.

The configured value is a correctly formatted LangSmith personal token, so it may belong to the EU LangSmith region rather than the default US endpoint. I’ll test authentication against the official EU endpoint before asking you to replace it.

Found it: the key is valid, but it belongs to LangSmith’s EU region. Authentication succeeds against https://eu.api.smith.langchain.com; the default US endpoint returned the misleading Invalid token. I’ll activate the lines, set the EU endpoint, update the template/docs, and confirm an uploaded trace.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script failed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script error:\\napply_patch verification failed: Failed to find expected lines in [repo]/.env:\\nLANGSMITH_PROJECT=deallens\"\n  }\n]"
  }
]

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script failed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script error:\\napply_patch verification failed: Failed to find context '-11,0 +12 @@' in [repo]/.env\"\n  }\n]"
  }
]

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
load_dotenv(".env", override=True)
from langsmith import Client, traceable
from deallens.llm import LLM
from deallens.models import UsageLedger

client = Client()
@traceable(name="deallens.langsmith_eu_probe", client=client)
def probe():
    return LLM(UsageLedger()).text("Reply exactly: TRACE_OK")

result = probe()
client.flush()
runs = list(client.list_runs(project_name="deallens", limit=10))
matched = [run for run in runs if run.name == "deallens.langsmith_eu_probe"]
print("model response:", result)
print("trace uploaded:", bool(matched))
if matched:
    print("trace status:", matched[0].status)
    print("trace id:", matched[0].id)
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 3.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file]<stdin>:14: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 14, in <module>\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 4457, in list_runs\\n    [self.read_project(project_name=name).id for name in project_name]\\n     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 169, in wrapper\\n    return func(*args, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 5365, in read_project\\n    raise ls_utils.LangSmithNotFoundError(\\nlangsmith.utils.LangSmithNotFoundError: Project deallens not found\\n\"\n  }\n]"
  }
]

EU authentication now works, but the trace project was not created. I’m checking the non-secret tracing flag/project values and the SDK’s current environment-variable expectations; this is configuration now, not credential failure.

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
project='Deal_Lens'
try:
    runs=list(client.list_runs(project_name=project, limit=10))
except Exception as exc:
    print('project query:', type(exc).__name__)
else:
    names=[run.name for run in runs]
    print('project query: success')
    print('probe trace found:', 'deallens.langsmith_eu_probe' in names)
    match=next((run for run in runs if run.name=='deallens.langsmith_eu_probe'), None)
    if match:
        print('trace status:', match.status)
        print('trace id:', match.id)
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.9 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"<stdin>:7: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nproject query: success\\nprobe trace found: True\\ntrace status: success\\ntrace id: 019fc96b-3397-7a31-bca2-1809b2778cbc\\n\"\n  }\n]"
  }
]

Tracing is working. The uploaded probe is visible in the existing Deal_Lens project with status success; the earlier query simply used the wrong project name (deallens). I’ll align the template and README with the actual project name.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

You were right—the LangSmith key was present and valid.

The issues were:

Its lines were commented out.
It is an EU-region token, but the SDK was using the default US endpoint.
The existing project is named Deal_Lens, not deallens.

I activated tracing and configured:

env

LANGSMITH_TRACING="true"LANGSMITH_PROJECT="Deal_Lens"LANGSMITH_ENDPOINT="https://eu.api.smith.langchain.com"

LANGSMITH_TRACING="true"LANGSMITH_PROJECT="Deal_Lens"LANGSMITH_ENDPOINT="https://eu.api.smith.langchain.com"

A real Kimi K3 probe trace uploaded successfully:

Status: success
Trace ID: 019fc96b-3397-7a31-bca2-1809b2778cbc

The template and README now match the working configuration. LangSmith documents the EU endpoint requirement in its API-key setup guide.

let's test our app and make langsmith is correctly config

I’ll run the complete live app with LangSmith enabled and verify the resulting parent/child traces in the EU Deal_Lens project. First I’m fixing the known shared-ledger bug so this run records Kimi token usage correctly, then I’ll execute the same Darktrace screen for a clean comparison.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
project=client.read_project(project_name='Deal_Lens')
print('LangSmith project:', project.name)
print('LangSmith project id:', project.id)
print('LangSmith endpoint: authenticated')
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.9 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \".............................                                            [100%]\\nLangSmith project: Deal_Lens\\nLangSmith project id: 3282786a-5dd9-43e4-aeb2-f8baeb5a0c3e\\nLangSmith endpoint: authenticated\\n\"\n  }\n]"
  }
]

Preflight is clean: all 29 tests pass, the EU endpoint authenticates, and the Deal_Lens project is reachable. I’m starting the full traced run now; it should take roughly three minutes based on the previous live test.

>zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/langsmith-test'

[
  {
    "type": "input_text",
    "text": "Script running with cell ID 90\nWall time 11.0 seconds\nOutput:"
  }
]

The traced run is active and still in Tavily Research; no authentication or upload errors have appeared. LangSmith should already be receiving the parent run and research span while the task is pending.

LangSmith is correctly configured—the full traced run progressed through Research/Search/Extract—but Kimi K3 intermittently returned an unparsed structured response, causing selection=None and a crash during evidence capture. This is exactly the kind of integration failure the live trace was meant to expose. I’ll add a bounded retry with an explicit error, test it offline, confirm the failed trace and its child spans in LangSmith, then rerun.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.4 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\n...............................                                          [100%]\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nscreen trace found: True\\nscreen status: error\\nscreen trace id: 019fc96e-5631-7791-a5a2-82921bf976a3\\nchild spans visible: 33\\nchild span names: ChatNebius, PydanticToolsParser, RunnableAssign<parsed,parsing_error>, RunnableLambda, RunnableParallel<parsed,parsing_error>, RunnableParallel<raw>, RunnableSequence, RunnableWithFallbacks, deallens.capture, deallens.discover, deallens.verify, tavily.extract, tavily.research, tavily.search\\n\"\n  }\n]"
  }
]

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
runs=list(client.list_runs(project_name='Deal_Lens', limit=100))
screens=[run for run in runs if run.name=='deallens.screen']
latest=max(screens, key=lambda run: run.start_time) if screens else None
print('screen trace found:', bool(latest))
if latest:
    children=[run for run in runs if run.trace_id==latest.trace_id and run.id!=latest.id]
    print('screen status:', latest.status)
    print('screen trace id:', latest.trace_id)
    print('child spans visible:', len(children))
    print('child span names:', ', '.join(sorted({run.name for run in children})))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.4 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\n...............................                                          [100%]\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nscreen trace found: True\\nscreen status: error\\nscreen trace id: 019fc96e-5631-7791-a5a2-82921bf976a3\\nchild spans visible: 33\\nchild span names: ChatNebius, PydanticToolsParser, RunnableAssign<parsed,parsing_error>, RunnableLambda, RunnableParallel<parsed,parsing_error>, RunnableParallel<raw>, RunnableSequence, RunnableWithFallbacks, deallens.capture, deallens.discover, deallens.verify, tavily.extract, tavily.research, tavily.search\\n\"\n  }\n]"
  }
]

The failed full run is confirmed in LangSmith with 33 child spans, including Tavily Research/Search/Extract, Kimi, parsing, verification, and capture. The structured-output boundary now retries once and the suite passes 31 tests. I’m rerunning the full app to verify a successful root trace and corrected token accounting.

>zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/langsmith-test-success'

[
  {
    "type": "input_text",
    "text": "Script running with cell ID 95\nWall time 11.0 seconds\nOutput:"
  }
]

The rerun is still processing normally with tracing enabled; no retry or authentication error has surfaced. I’ll continue following it through completion and then query the exact root trace and children.

Kimi K3 returned no structured tool call twice, so the retry correctly failed closed, but the app still cannot complete reliably with tool-based Pydantic output. I’m inspecting the traced response shape—not its source content—to determine whether Kimi returned plain JSON that we can safely validate as a fallback.

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
runs=list(client.list_runs(project_name='Deal_Lens', limit=200))
screens=sorted((r for r in runs if r.name=='deallens.screen'), key=lambda r:r.start_time, reverse=True)
latest=screens[0]
chats=sorted((r for r in runs if r.trace_id==latest.trace_id and r.name=='ChatNebius'), key=lambda r:r.start_time, reverse=True)
print('trace id:', latest.trace_id)
print('chat runs:', len(chats))
for run in chats[:4]:
    outputs=run.outputs or {}
    print('run', run.id, 'status', run.status, 'output keys', sorted(outputs.keys()))
    generations=outputs.get('generations') or []
    print(' generations:', len(generations))
    if generations:
        first=generations[0]
        if isinstance(first, list) and first: first=first[0]
        print(' generation type:', type(first).__name__)
        if isinstance(first, dict):
            print(' generation keys:', sorted(first.keys()))
            message=first.get('message') or {}
            if isinstance(message, dict):
                kwargs=message.get('kwargs') or {}
                content=kwargs.get('content')
                tool_calls=kwargs.get('tool_calls') or []
                print(' content type/len:', type(content).__name__, len(content or ''))
                print(' tool calls:', len(tool_calls))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.8 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nTraceback (most recent call last):\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 181, in raise_for_status_with_text\\n    response.raise_for_status()\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/requests/models.py\\\", line 1167, in raise_for_status\\n    raise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://eu.api.smith.langchain.com/runs/query\\n\\nThe above exception was the direct cause of the following exception:\\n\\nTraceback (most recent call last):\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 2029, in request_with_retries\\n    ls_utils.raise_for_status_with_text(response)\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/utils.py\\\", line 183, in raise_for_status_with_text\\n    raise requests.HTTPError(str(e), response.text) from e  # type: ignore[call-arg]\\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nrequests.exceptions.HTTPError: [Errno 400 Client Error: Bad Request for url: https://eu.api.smith.langchain.com/runs/query] {\\\"detail\\\":\\\"Limit exceeds maximum allowed value of 100\\\"}\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 5, in <module>\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 4518, in list_runs\\n    for i, run in enumerate(\\n                  ^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 2237, in _get_cursor_paginated_list\\n    response = self.request_with_retries(\\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langsmith/client.py\\\", line 2094, in request_with_retries\\n    raise ls_utils.LangSmithError(\\nlangsmith.utils.LangSmithError: Failed to POST /runs/query in LangSmith API. HTTPError('400 Client Error: Bad Request for url: https://eu.api.smith.langchain.com/runs/query', '{\\\"detail\\\":\\\"Limit exceeds maximum allowed value of 100\\\"}')\\n\"\n  }\n]"
  }
]

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
runs=list(client.list_runs(project_name='Deal_Lens', limit=100))
screens=sorted((r for r in runs if r.name=='deallens.screen'), key=lambda r:r.start_time, reverse=True)
latest=screens[0]
chats=sorted((r for r in runs if r.trace_id==latest.trace_id and r.name=='ChatNebius'), key=lambda r:r.start_time, reverse=True)
print('trace id:', latest.trace_id)
print('chat runs:', len(chats))
for run in chats[:4]:
    outputs=run.outputs or {}
    print('run', run.id, 'status', run.status, 'output keys', sorted(outputs.keys()))
    generations=outputs.get('generations') or []
    print(' generations:', len(generations))
    if generations:
        first=generations[0]
        if isinstance(first, list) and first: first=first[0]
        print(' generation type:', type(first).__name__)
        if isinstance(first, dict):
            print(' generation keys:', sorted(first.keys()))
            message=first.get('message') or {}
            if isinstance(message, dict):
                kwargs=message.get('kwargs') or {}
                content=kwargs.get('content')
                tool_calls=kwargs.get('tool_calls') or []
                print(' content type/len:', type(content).__name__, len(content or ''))
                print(' tool calls:', len(tool_calls))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.2 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\ntrace id: 019fc970-278c-7ff3-b5a0-ebb4f2ae1f5b\\nchat runs: 2\\nrun 019fc971-3ed6-7c32-9001-f77109965ea3 status success output keys ['generations', 'llm_output', 'run', 'type']\\n generations: 1\\n generation type: dict\\n generation keys: ['generation_info', 'message', 'text', 'type']\\n content type/len: str 0\\n tool calls: 0\\nrun 019fc971-1c3e-7390-a3c5-9dbf033dba70 status success output keys ['generations', 'llm_output', 'run', 'type']\\n generations: 1\\n generation type: dict\\n generation keys: ['generation_info', 'message', 'text', 'type']\\n content type/len: str 0\\n tool calls: 0\\n\"\n  }\n]"
  }
]

The trace shows Kimi returned an empty message with zero tool calls on both attempts, so tool-based structured output is the wrong mechanism for this model. Nebius recommends its JSON response_format for structured output. I’m checking Kimi K3’s live model capabilities, then I’ll switch the wrapper to schema-constrained JSON if supported.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
from pydantic import BaseModel
load_dotenv(".env", override=True)
from langchain_nebius import ChatNebius
class Probe(BaseModel):
    status: str
schema = Probe.model_json_schema()
model = ChatNebius(model="moonshotai/Kimi-K3", temperature=0)
for label, response_format in [
    ("openai_schema", {"type":"json_schema", "json_schema":{"name":"Probe", "schema":schema, "strict":True}}),
    ("direct_schema", {"type":"json_schema", "json_schema":schema}),
    ("json_object", {"type":"json_object"}),
]:
    try:
        message = model.bind(response_format=response_format).invoke("Return a JSON object with status equal to OK and no other fields.")
        print(label, "content:", repr(message.content)[:300])
    except Exception as exc:
        print(label, "error:", type(exc).__name__, str(exc)[:300])
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.7 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] error: AttributeError 'NoneType' object has no attribute 'chat'\\ndirect_schema error: AttributeError 'NoneType' object has no attribute 'chat'\\njson_object error: AttributeError 'NoneType' object has no attribute 'chat'\\n\"\n  }\n]"
  }
]

Ran

The current langchain-nebius adapter has a provider bug: ordinary calls work, but response_format tries to use an uninitialized OpenAI-compatible root client. I’m testing the supported workaround—supplying Nebius’s OpenAI-compatible root client explicitly—before changing our wrapper.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
import os
from dotenv import load_dotenv
from pydantic import BaseModel
from openai import OpenAI
load_dotenv(".env", override=True)
from langchain_nebius import ChatNebius
class Probe(BaseModel):
    status: str
root=OpenAI(base_url="https://api.tokenfactory.nebius.com/v1/", api_key=[REDACTED]
model=ChatNebius(model="moonshotai/Kimi-K3", temperature=0, root_client=root)
schema=Probe.model_json_schema()
formats=[
 ("openai_schema", {"type":"json_schema","json_schema":{"name":"Probe","schema":schema,"strict":True}}),
 ("json_object", {"type":"json_object"}),
]
for label, fmt in formats:
    try:
        message=model.bind(response_format=fmt).invoke("Return a JSON object with status equal to OK and no other fields.")
        print(label, repr(message.content)[:300])
    except Exception as exc:
        print(label, "error", type(exc).__name__, str(exc)[:500])
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 5.2 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] '{\\\"status\\\":\\\"OK\\\"}'\\njson_object '{\\\"status\\\":\\\"OK\\\"}'\\n\"\n  }\n]"
  }
]

>zsh -ic 'cd [repo] && uv run python - <<"PY"
import os
from dotenv import load_dotenv
from pydantic import BaseModel
from openai import OpenAI
load_dotenv(".env", override=True)
from langchain_nebius import ChatNebius
class Probe(BaseModel): status: str
root=OpenAI(base_url="https://api.tokenfactory.nebius.com/v1/", api_key=[REDACTED]
model=ChatNebius(model="moonshotai/Kimi-K3",temperature=0,root_client=root)
for method in ("json_schema","json_mode"):
 try:
  result=model.with_structured_output(Probe, method=method, include_raw=True).invoke("Return status equal to OK as JSON.")
  print(method, result["parsed"], result["parsing_error"])
 except Exception as exc:
  print(method, type(exc).__name__, str(exc)[:300])
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 3.8 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] status='OK' None\\njson_mode status='OK' None\\n\"\n  }\n]"
  }
]

The workaround succeeds: Kimi K3 returns valid schema-constrained JSON when ChatNebius receives an explicit Nebius OpenAI-compatible root client and method="json_schema". I’ll replace tool-call structured output with that documented JSON-schema path, regression-test it, then make several small live probes before paying for another full Research run.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && zsh -ic 'cd [repo] && uv run python - <<"PY"
from dotenv import load_dotenv
from pydantic import BaseModel
load_dotenv(".env", override=True)
from deallens.llm import LLM
from deallens.models import UsageLedger
class Probe(BaseModel):
    status: str
ledger=UsageLedger()
llm=LLM(ledger)
for index in range(1, 6):
    result=llm.structured(Probe, "Return status equal to OK and no other fields.")
    print("probe", index, result.status)
print("tokens", ledger.llm_input_tokens, ledger.llm_output_tokens)
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 7.4 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"...............................                                          [100%]\\n/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] 1 OK\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 11, in <module>\\n  File \\\"[repo]/src/deallens/llm.py\\\", line 72, in structured\\n    result = model.invoke(prompt)\\n             ^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py\\\", line 3442, in invoke\\n    input_ = context.run(step.invoke, input_, config, **kwargs)\\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py\\\", line 4175, in invoke\\n    key: future.result()\\n         ^^^^^^^^^^^^^^^\\n  File \\\"/Users/[REDACTED]/[local-file]\", line 456, in result\\n    return self.__get_result()\\n           ^^^^^^^^^^^^^^^^^^^\\n  File \\\"/Users/[REDACTED]/[local-file]\", line 401, in __get_result\\n    raise self._exception\\n  File \\\"/Users/[REDACTED]/[local-file]\", line 59, in run\\n    result = self.fn(*self.args, **self.kwargs)\\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py\\\", line 4158, in _invoke_step\\n    return context.run(\\n           ^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py\\\", line 6002, in invoke\\n    return self.bound.invoke(\\n           ^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py\\\", line 476, in invoke\\n    self.generate_prompt(\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py\\\", line 1849, in generate_prompt\\n    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py\\\", line 1656, in generate\\n    self._generate_with_cache(\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py\\\", line 1994, in _generate_with_cache\\n    result = self._generate(\\n             ^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_openai/chat_models/base.py\\\", line 1751, in _generate\\n    raise e\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/langchain_openai/chat_models/base.py\\\", line 1721, in _generate\\n    response = raw_response.parse()\\n               ^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/_legacy_response.py\\\", line 140, in parse\\n    parsed = self._options.post_parser(parsed)\\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/resources/chat/completions/completions.py\\\", line 187, in parser\\n    return _parse_chat_completion(\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/lib/_parsing/_completions.py\\\", line 146, in parse_chat_completion\\n    \\\"parsed\\\": maybe_parse_content(\\n              ^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/lib/_parsing/_completions.py\\\", line 196, in maybe_parse_content\\n    return _parse_content(response_format, message.content)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/lib/_parsing/_completions.py\\\", line 245, in _parse_content\\n    return cast(ResponseFormatT, model_parse_json(response_format, content))\\n                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/openai/_compat.py\\\", line 178, in model_parse_json\\n    return model.model_validate_json(data)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"[repo]/.venv/lib/python3.12/site-packages/pydantic/main.py\\\", line 782, in model_validate_json\\n    return cls.__pydantic_validator__.validate_json(\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\npydantic_core._pydantic_core.ValidationError: 1 validation error for Probe\\n  Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value=' ```json\\\\n{\\\"status\\\":\\\"OK\\\"}\\\\n```', input_type=str]\\n    For further information visit https://errors.pydantic.dev/2.13/v/json_invalid\\n\"\n  }\n]"
  }
]

Kimi K3’s schema mode sometimes wraps valid JSON in Markdown fences, which causes OpenAI’s strict parser to reject it before our retry logic sees the response. I’m testing LangChain’s JSON-mode parser—which is designed to tolerate fenced JSON—across repeated Kimi calls before choosing the final integration.

>zsh -ic 'cd [repo] && uv run python - <<"PY"
import json, os
from dotenv import load_dotenv
from pydantic import BaseModel
from openai import OpenAI
load_dotenv(".env", override=True)
from langchain_nebius import ChatNebius
class Probe(BaseModel): status: str
root=OpenAI(base_url="https://api.tokenfactory.nebius.com/v1/", api_key=[REDACTED]
model=ChatNebius(model="moonshotai/Kimi-K3",temperature=0,root_client=root)
runner=model.with_structured_output(Probe, method="json_mode", include_raw=True)
prompt="Return status equal to OK as JSON matching this schema: "+json.dumps(Probe.model_json_schema())
for i in range(1,6):
 try:
  result=runner.invoke(prompt)
  print(i, result["parsed"], type(result.get("parsing_error")).__name__ if result.get("parsing_error") else None)
 except Exception as exc:
  print(i, "error", type(exc).__name__, str(exc)[:200])
PY'

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 7.6 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"/Users/[REDACTED]/[local-file] no such file or directory: /Users/[REDACTED]/[local-file] status='OK' None\\n2 status='OK' None\\n3 status='OK' None\\n4 status='OK' None\\n5 status='OK' None\\n\"\n  }\n]"
  }
]

JSON mode passed five consecutive live Kimi K3 probes, including LangChain/Pydantic parsing. I’ll switch DealLens to that mode while embedding the exact Pydantic schema in the prompt, then rerun the offline suite and one final full traced screen.

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 0.0 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"{}\"\n  }\n]"
  }
]

>uv run pytest -q && zsh -ic 'cd [repo] && uv run deallens screen --company "Darktrace plc" --domain "darktrace.com" --jurisdiction UK --out reports/langsmith-test-success'

[
  {
    "type": "input_text",
    "text": "Script running with cell ID 110\nWall time 11.0 seconds\nOutput:"
  }
]

The final run is active and has cleared the previous structured-output failure point so far. Tracing remains enabled; I’ll verify both the generated report and LangSmith root status after completion.

Tavily Research is still the dominant latency, but the run remains healthy at roughly two minutes. No Kimi parsing exception or LangSmith authentication error has appeared.

The end-to-end run completed successfully with 21 metered Search/Extract credits and generated both outputs. I’m doing the final audit now: root trace status, child span coverage, Kimi token ledger, and whether the memo’s “verified” claims are actually supported by their quotes.

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
runs=list(client.list_runs(project_name='Deal_Lens', limit=100))
screens=sorted((r for r in runs if r.name=='deallens.screen'), key=lambda r:r.start_time, reverse=True)
latest=screens[0]
children=[r for r in runs if r.trace_id==latest.trace_id and r.id!=latest.id]
print('root status:', latest.status)
print('trace id:', latest.trace_id)
print('root id:', latest.id)
print('child spans:', len(children))
print('errored children:', sum(r.status=='error' for r in children))
print('span names:', ', '.join(sorted({r.name for r in children})))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.8 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 1 ---\\nrisk: REVIEW REQUIRED\\nfindings: 8\\nstatuses: {'verified': 2, 'reported': 0, 'unresolved': 0, 'rejected': 6}\\ntavily credits: 21.0\\nby endpoint: {'search': 16.0, 'extract': 5.0}\\nllm tokens: 42548 in / 24223 out\\nwall seconds: 169.7\\n\\n VERIFIED CEO Jill Popelka stepped down and Chairman Charles Goodman was appointed interim CEO on 27 January 2026.\\n  primary find-and-update.company-information.service.gov.uk '**Termination of appointment** of Jill Jackson Popelka as a director on 30 January 2026'\\n narrative: Find-and-update.company-information.service.gov.uk records the termination of Jill Jackson Popelka's appointment as a director on 30 January 2026. The quoted evidence does not identify her as CEO. It also does not mention Charles Goodman or an interim CEO appointment.\\n\\n VERIFIED Director James Alan Sporle's appointment was terminated on 7 April 2025.\\n  primary find-and-update.company-information.service.gov.uk '**Termination of appointment** of James Alan Sporle as a director on 7 April 2025'\\n narrative: UK Companies House records on find-and-update.company-information.service.gov.uk show the termination of James Alan Sporle's appointment as a director on 7 April 2025. The source lists 7 April 2025 as the date of the termination.\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 2 ---\\n<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 7, in <module>\\nIndexError: list index out of range\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"--- result 3 ---\\n# Acquisition Red-Flag Screen\\n\\nTarget: Darktrace plc\\nDomain: darktrace.com\\nJurisdiction: UK\\nGenerated: 03 August 2026\\n\\n## Executive assessment\\n\\n**REVIEW REQUIRED** — 2 verified red flag(s); 6 candidate(s) rejected as weak or unsupported.\\n\\nThis screen is an initial review of public evidence, not a legal or financial diligence opinion. \\\"No qualifying public findings\\\" means the searches completed without a result that met the evidence standard — it is not a statement that no risk exists.\\n\\n## Verified findings\\n\\n### CEO Jill Popelka stepped down and Chairman Charles Goodman was appointed interim CEO on 27 January 2026. — High\\n\\nFind-and-update.company-information.service.gov.uk records the termination of Jill Jackson Popelka's appointment as a director on 30 January 2026. The quoted evidence does not identify her as CEO. It also does not mention Charles Goodman or an interim CEO appointment.\\n\\n> \\\"**Termination of appointment** of Jill Jackson Popelka as a director on 30 January 2026\\\"\\n\\nPrimary source: [find-and-update.company-information.service.gov.uk](https://find-and-update.company-information.service.gov.uk/company/13264637/filing-history)\\n\\nPolicy triggered: ceo, chair\\n\\n\\n### Director James Alan Sporle's appointment was terminated on 7 April 2025. — Medium\\n\\nUK Companies House records on find-and-update.company-information.service.gov.uk show the termination of James Alan Sporle's appointment as a director on 7 April 2025. The source lists 7 April 2025 as the date of the termination.\\n\\n> \\\"**Termination of appointment** of James Alan Sporle as a director on 7 April 2025\\\"\\n\\nPrimary source: [find-and-update.company-information.service.gov.uk](https://find-and-update.company-information.service.gov.uk/company/08562035/filing-history?page=2)\\n\\n\\n## Rejected as weak or unsupported\\n\\n- CEO Poppy Gustafsson resigned and was succeeded by COO Jill Popelka on 6 September 2024. — no qualifying source survived verification\\n- Suman Raju was appointed Chief Financial Officer and Hein Hellemons as Chief Revenue Officer on 3 November 2025. — no qualifying source survived verification\\n- Thoma Bravo completed its acquisition of Darktrace plc on 1 October 2024. — no qualifying source survived verification\\n- A data breach involving Darktrace was reported on 13 April 2023. — no qualifying source survived verification\\n- LockBit ransomware gang claimed to have compromised Darktrace's internal systems on 13 April 2023, which Darktrace publicly denied. — no qualifying source survived verification\\n- Layoffs were reported among Darktrace staff in early 2026 following the Thoma Bravo acquisition. — no qualifying source survived verification\\n\\n## Coverage\\n\\n| Check | Status | Sources reviewed |\\n|---|---|---:|\\n| Leadership and ownership | Verified finding | 60 |\\n| Regulatory and litigation | No qualifying public findings | 0 |\\n| Cybersecurity | No qualifying public findings | 24 |\\n| Financial distress | No qualifying public findings | 9 |\\n\\n## Run footprint\\n\\n- Tavily credits: 21 (extract 5, search 16)\\n- LLM tokens: 42,548 in / 24,223 out\\n- Wall time: 170s\\n\"\n  }\n]"
  }
]

Token accounting is fixed, and the memo audit confirms one remaining product-correctness issue: one “verified” compound CEO claim is only partially supported, which the narrative itself admits. Separately, the first LangSmith query missed the root because 100 recent child spans filled the page; I’m querying root runs server-side by name and then its trace directly.

>uv run python - <<'PY'
import inspect
from langsmith import Client
print(inspect.signature(Client.list_runs))
PY
uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
screens=list(client.list_runs(project_name='Deal_Lens', filter='eq(name, "deallens.screen")', limit=10))
latest=max(screens, key=lambda r:r.start_time)
children=list(client.list_runs(project_name='Deal_Lens', trace_id=latest.trace_id, limit=100))
children=[r for r in children if r.id!=latest.id]
print('root status:', latest.status)
print('trace id:', latest.trace_id)
print('root id:', latest.id)
print('child spans:', len(children))
print('errored children:', sum(r.status=='error' for r in children))
print('span names:', ', '.join(sorted({r.name for r in children})))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 2.1 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"(self, *, project_id: 'Optional[Union[ID_TYPE, Sequence[ID_TYPE]]]' = None, project_name: 'Optional[Union[str, Sequence[str]]]' = None, run_type: 'Optional[str]' = None, trace_id: 'Optional[ID_TYPE]' = None, reference_example_id: 'Optional[ID_TYPE]' = None, query: 'Optional[str]' = None, filter: 'Optional[str]' = None, trace_filter: 'Optional[str]' = None, tree_filter: 'Optional[str]' = None, is_root: 'Optional[bool]' = None, parent_run_id: 'Optional[ID_TYPE]' = None, start_time: 'Optional[datetime.datetime]' = None, error: 'Optional[bool]' = None, run_ids: 'Optional[Sequence[ID_TYPE]]' = None, select: 'Optional[Sequence[str]]' = None, limit: 'Optional[int]' = None, **kwargs: 'Any') -> 'Iterator[ls_schemas.Run]'\\n<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\n<stdin>:7: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\nroot status: success\\ntrace id: 019fc974-3a6c-79d1-857b-6bf7a0be7b92\\nroot id: 019fc974-3a6c-79d1-857b-6bf7a0be7b92\\nchild spans: 100\\nerrored children: 0\\nspan names: ChatNebius, PydanticOutputParser, RunnableAssign<parsed,parsing_error>, RunnableLambda, RunnableParallel<parsed,parsing_error>, RunnableParallel<raw>, RunnableSequence, RunnableWithFallbacks, deallens.capture, deallens.verify, tavily.extract, tavily.search\\n\"\n  }\n]"
  }
]

>uv run python - <<'PY'
from dotenv import load_dotenv
load_dotenv('.env', override=True)
from langsmith import Client
client=Client()
screens=list(client.list_runs(project_name='Deal_Lens', filter='eq(name, "deallens.screen")', limit=1))
root=screens[0]
runs=list(client.list_runs(project_name='Deal_Lens', trace_id=root.trace_id))
children=[r for r in runs if r.id!=root.id]
print('total spans:', len(runs))
print('child spans:', len(children))
print('errored children:', sum(r.status=='error' for r in children))
print('span names:', ', '.join(sorted({r.name for r in children})))
PY

[
  {
    "type": "input_text",
    "text": "[\n  {\n    \"type\": \"input_text\",\n    \"text\": \"Script completed\\nWall time 1.5 seconds\\nOutput:\\n\"\n  },\n  {\n    \"type\": \"input_text\",\n    \"text\": \"<stdin>:5: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\n<stdin>:7: DeprecationWarning: list_runs() is deprecated and will be removed after Jan 31, 2027. Use client.runs.query() instead. See https://docs.langchain.com/langsmith/smithdb-sdk-migration#runs-query for the migration guide.\\ntotal spans: 125\\nchild spans: 124\\nerrored children: 0\\nspan names: ChatNebius, PydanticOutputParser, RunnableAssign<parsed,parsing_error>, RunnableLambda, RunnableParallel<parsed,parsing_error>, RunnableParallel<raw>, RunnableSequence, RunnableWithFallbacks, deallens.capture, deallens.discover, deallens.verify, tavily.extract, tavily.research, tavily.search\\n\"\n  }\n]"
  }
]

The app now completes end to end with LangSmith correctly configured.

Results:

LangSmith EU authentication: successful
Project: Deal_Lens
Root trace status: success
Trace ID: 019fc974-3a6c-79d1-857b-6bf7a0be7b92
Total spans: 125
Errored child spans: 0
Traced stages include Research, Search, Extract, discovery, verification, capture, Kimi K3, and Pydantic parsing
Runtime: 170 seconds
Kimi usage: 42,548 input / 24,223 output tokens
Reported Tavily Search/Extract usage: 21 credits
Tests: 31 passing

Outputs:

Live memo [blocked]
Evidence JSON [blocked]

I also replaced unreliable tool-call structured output with Nebius JSON mode, following Nebius’s structured-output guidance. LangSmith uses the required EU endpoint configuration.

Two accuracy issues remain:

One compound CEO claim was marked verified even though the primary quote supports only part of it.
The 21-credit total excludes dynamically priced Tavily Research usage.

Infrastructure and observability are working. Evidence entailment and complete cost accounting should be fixed before treating the memo as submission-quality.

3 · Evidence governance and fail-closed correctness

The core pipeline is redesigned around atomic assertions, source independence, entity matching, contradiction handling, four explicit risk probes, local failure isolation, and deterministic promotion gates. Live failures are used to strengthen the contracts.

Completed DealLens assignment with Tavily enhancements

Completed DealLens assignment with Tavily enhancements

Summary

Trace guide

Product origin

1 · Assignment review and product choice

Option 1: Improve the application

Option 2: Build an explainer

What `starter_agent.py` does

Files mentioned by the user:

# Watchtower — Specification v1 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

Files mentioned by the user:

# Watchtower — Specification v2 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

Critical issue: probe failure does not confirm removal

Joint normalization needs clarification

Define the monitored-page set

A few smaller refinements

Scope verdict

When this concept works well

MongoDB concern

A better version of the concept

Watchtower versus diligence

DealLens — 10-Minute M&A Red-Flag Screen

User experience

The Tavily pipeline

Step 1: Broad discovery

Step 2: Governed verification

Step 3: Capture evidence

Step 4: Apply deterministic evidence rules

Sample memo

MVP scope

Demo command

Why this is a good submission

2 · Provider integration and first live run

3 · Evidence governance and fail-closed correctness

Summary

Trace guide

Product origin

1 · Assignment review and product choice

Option 1: Improve the application

Option 2: Build an explainer

What starter_agent.py does

Files mentioned by the user:

# Watchtower — Specification v1 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

Files mentioned by the user:

# Watchtower — Specification v2 One-liner: A change-intelligence pipeline o…: [local-codex-data]

My request for Codex:

Critical issue: probe failure does not confirm removal

Joint normalization needs clarification

Define the monitored-page set

A few smaller refinements

Scope verdict

When this concept works well

MongoDB concern

A better version of the concept

Watchtower versus diligence

DealLens — 10-Minute M&A Red-Flag Screen

User experience

The Tavily pipeline

Step 1: Broad discovery

Step 2: Governed verification

Step 3: Capture evidence

Step 4: Apply deterministic evidence rules

Sample memo

MVP scope

Demo command

Why this is a good submission

2 · Provider integration and first live run

3 · Evidence governance and fail-closed correctness

What `starter_agent.py` does