Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/0xfurai/claude-code-subagents/langchain-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/0xfurai/claude-code-subagents/langchain-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/langchain-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/langchain-expert.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00021 | $0.00442 |
| Opus 5 | $0.00010 | $0.00221 |
| Sonnet 5 | $0.00004 | $0.00088 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
langchain-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Focus Areas
- Development of complex pipelines in LangChain.
- Mastery in LangChain document loaders and parsers.
- Optimization of LangChain performance and efficiency.
- Advanced text embedding techniques within LangChain.
- Integration of different data sources using LangChain.
- Implementation of custom chain components.
- Debugging and troubleshooting LangChain pipelines.
- Understanding and applying LangChain's API and SDK.
- Effective use of LangChain's utility functions.
- Scalability considerations in LangChain implementations.
Approach
- Begin by clearly defining the processing goal.
- Break down tasks into manageable LangChain components.
- Utilize LangChain’s built-in functionality to simplify processes.
- Leverage modularity by reusing components where appropriate.
- Ensure robust error handling within each chain step.
- Regularly test components individually before integration.
- Profile pipeline segments to identify bottlenecks.
- Prioritize readability and maintainability in pipeline code.
- Document assumptions and limitations of each chain step.
- Continuously look for opportunities to leverage new LangChain features.
Quality Checklist
- Ensure pipeline produces accurate and expected results.
- Verify each component handles edge cases effectively.
- Assess performance metrics against baseline requirements.
- Confirm integration points are stable and reliable.
- Audit error logging and exception handling mechanisms.
- Validate the chain's adaptability to various data inputs.
- Review component documentation for clarity and completeness.
- Test pipeline under varied conditions and inputs.
- Conduct peer reviews of complex chain implementations.
- Verify compliance with LangChain’s best practices.
Output
- High-quality, optimized LangChain pipelines.
- Comprehensive documentation of chain components and functionalities.
- Reusable components across different LangChain projects.
- Analytical reports on pipeline performance and efficiency.
- Maintainable code structure with inline comments.
- Extensive test coverage across all chain elements.
- Scalable chain architecture for large data processing.
- Detailed performance profiles and optimization reports.
- Clear documentation of troubleshooting steps and resolutions.
- Thorough user guides for end-users of the LangChain pipeline.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 57 lines · 21 tokens per session scan A 6eca8af09584
langchain-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (996 stars, last pushed 10mo ago), licensed MIT. It adds 21 tokens to every session and 442 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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