Fully local web research and report writing assistant
This report summarizes scanner evidence for langchain-ai/local-deep-researcher, a Python project with 9,364 GitHub stars. SynthScan examined 1,693 lines of code across 14 source files, recording 24 pattern matches distributed across 10 syntactic categories. The legacy adjusted pattern signal is 28.8 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 18 finding(s).
Are imports declared and package/API names verified? 1 finding(s).
Are wrappers, guards, and repeated structures necessary? 7 finding(s).
Which changes need focused human review? 24 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 24 distinct pattern matches across 10 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | src/ollama_deep_researcher/utils.py | 18 | Convert configuration values to string format, handling both string and enum types. Args: value (Any): | STRING |
| HIGH | src/ollama_deep_researcher/utils.py | 60 | Format and deduplicate search responses from various search APIs. Takes either a single search response or lis | STRING |
| HIGH | src/ollama_deep_researcher/utils.py | 313 | Search the web using the Perplexity API and return formatted results. Uses the Perplexity API to perform searc | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | .github/workflows/claude-code-review.yml | 58 | # - For React components: Performance, accessibility, and best practices | COMMENT |
| MEDIUM | .github/workflows/claude-code-review.yml | 65 | # 'Please provide a thorough code review focusing on our coding standards and best practices.' }} | COMMENT |
| LOW | src/ollama_deep_researcher/lmstudio.py | 95 | # If any error occurs during cleanup, just use the original response | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/ollama_deep_researcher/lmstudio.py | 93 | except Exception as e: | CODE |
| LOW | src/ollama_deep_researcher/utils.py | 160 | except Exception as e: | CODE |
| LOW⚡ | src/ollama_deep_researcher/utils.py | 216 | except Exception as e: | CODE |
| MEDIUM⚡ | src/ollama_deep_researcher/utils.py | 217 | print(f"Error in DuckDuckGo search: {str(e)}") | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | src/ollama_deep_researcher/lmstudio.py | 45 | # Initialize the base class | COMMENT |
| MEDIUM | src/ollama_deep_researcher/utils.py | 155 | # Create a client with reasonable timeout | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | src/ollama_deep_researcher/utils.py | 206 | # Add result to list | COMMENT |
| LOW | src/ollama_deep_researcher/utils.py | 266 | # Add result to list | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | .github/workflows/claude-code-review.yml | 61 | # Optional: Different prompts for different authors | COMMENT |
| LOW | .github/workflows/claude.yml | 41 | actions: read | COMMENT |
| LOW | .github/workflows/claude.yml | 61 | # Optional: Custom environment variables for Claude | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/ollama_deep_researcher/graph.py | 192 | CODE | |
| LOW | src/ollama_deep_researcher/utils.py | 55 | CODE | |
| LOW | src/ollama_deep_researcher/utils.py | 166 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/ollama_deep_researcher/graph.py | 44 | def generate_search_query_with_structured_output( | CODE |
| LOW | src/ollama_deep_researcher/utils.py | 55 | def deduplicate_and_format_sources( | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/ollama_deep_researcher/lmstudio.py | 16 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | src/ollama_deep_researcher/graph.py | 12 | CODE |