Atlas / Skills / langchain-ai / Web Research

Web ResearchSAFE

skills/langchain-ai/web-research

The batteries-included agent harness.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
30,642
01

Overview

The batteries-included agent harness.

Read from source at commit 5008d6f3b5f7OBSERVED · 2026-10-27
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: web-research
description: Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.
---

# Web Research Skill

## Research Process

### Step 1: Create and Save Research Plan

Before delegating to subagents, you MUST:

1. **Create a research folder** - Organize all research files in a dedicated folder relative to the current working directory:
   ```
   mkdir research_[topic_name]
   ```
   This keeps files organized and prevents clutter in the working directory.

2. **Analyze the research question** - Break it down into distinct, non-overlapping subtopics

3. **Write a research plan file** - Use the `write_file` tool to create `research_[topic_name]/research_plan.md` containing:
   - The main research question
   - 2-5 specific subtopics to investigate
   - Expected information from each subtopic
   - How results will be synthesized

**Planning Guidelines:**
- **Simple fact-finding**: 1-2 subtopics
- **Comparative analysis**: 1 subtopic per comparison element (max 3)
- **Complex investigations**: 3-5 subtopics

### Step 2: Delegate to Research Subagents

For each subtopic in your plan:

1. **Use the `task` tool** to spawn a research subagent with:
   - Clear, specific research question (no acronyms)
   - Instructions to write findings to a file: `research_[topic_name]/findings_[subtopic].md`
   - Budget: 3-5 web searches maximum

2. **Run up to 3 subagents in parallel** for efficient research

**Subagent Instructions Template:**
```
Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.
```

### Step 3: Synthesize Findings

After all subagents complete:

1. **Review the findings files** that were saved locally:
   - First run `list_files research_[topic_name]` to see what files were created
   - Then use `read_file` with the **file paths** (e.g., `research_[topic_name]/findings_*.md`)
   - **Important**: Use `read_file` for LOCAL files only, not URLs

2. **Synthesize the information** - Create a comprehensive response that:
   - Directly answers the original question
   - Integrates insights from all subtopics
   - Cites specific sources with URLs (from the findings files)
   - Identifies any gaps or limitations

3. **Write final report** (optional) - Use `write_file` to create `research_[topic_name]/research_report.md` if requested

**Note**: If you need to fetch additional information from URLs, use the `fetch_url` tool, not `read_file`.

## Best Practices

- **Plan before delegating** - Always write research_plan.md first
- **Clear subtopics** - Ensure each subagent has distinct, non-overlapping scope
- **File-based communication** - Have subagents save findings to files, not return them directly
- **Systematic synthesis** - Read all findings files before creating final response
- **Stop appropriately** - Don't over-research; 3-5 searches per subtopic is usually sufficient
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-27 · audit v0.4.1 · source sha 5008d6f3b5f7full audit observations/trust-audit/skill/langchain-ai__web-research.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-275008d6f3b5f7SAFEB89first audit
05

Questions

What does the web-research — langchain-ai/deepagents skill do?

The batteries-included agent harness.

Is web-research — langchain-ai/deepagents safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can web-research — langchain-ai/deepagents access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

What do I need installed to use web-research — langchain-ai/deepagents?

Its own instructions reference fetch_url. Dependencies are pinned to exact versions.

How current is this page?

The grade is for one exact copy of the source (5008d6f3b5f7), read on 2026-10-27. The repository is watched, and a new audit runs when it changes — this is the first audit.

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