LangKit
LangKit is an open-source Python toolkit, originally developed by WhyLabs, designed to extract observability signals from LLM prompts and responses.
Publisher review
LangKit is an open-source Python toolkit, originally developed by WhyLabs, designed to extract observability signals from LLM prompts and responses. It targets developers and researchers who need to quantify text quality, safety, and relevance in prototype or Jupyter Notebook workflows, rather than operate a production-grade monitoring service. As of late 2024, WhyLabs has discontinued its commercial platform, but the company has open-sourced the entire WhyLabs stack, including LangKit, under the Apache-2.0 license on GitHub (990 stars, last commit November 22, 2024). This means the toolkit remains freely available for self-hosted use, but it no longer has an active commercial backer or roadmap.
The toolkit provides eight named capabilities: text quality metrics, toxicity scoring, sentiment analysis, prompt injection detection, response relevance scoring, WhyLabs platform integration, batch and streaming analysis, and custom metric definition. LangKit works by ingesting prompt-response pairs and computing scores for each dimension — for example, it can flag a response as toxic or detect a prompt injection attempt using built-in classifiers. It supports both batch processing (e.g., analyzing a CSV of logs) and streaming analysis (e.g., consuming from a message queue), and it allows users to define custom metrics via Python functions. The integration with the now-discontinued WhyLabs platform was its primary path to dashboards and alerting; without that platform, users must build their own visualization layer.
In the open-source LLM monitoring space, LangKit competes with tools like LangFuse, LangChain's built-in callbacks, and broader observability frameworks. Compared to LangFuse, which offers a freemium SaaS model with tracing, cost monitoring, and prompt versioning across Python, JavaScript, and TypeScript, LangKit is purely Python-based and lacks a managed backend. LangChain itself provides out-of-the-box LLM observability callbacks, but LangKit offers more specialized metrics (toxicity, relevance scoring) that LangChain does not natively compute. Other adjacent tools like Private GPT and Anything LLM focus on document interaction and local deployment, not on extracting signal quality metrics.
The honest trade-offs are clear: LangKit is powerful for offline analysis and custom metric development, but it is not a production-ready service. It requires manual setup, has no built-in real-time alerting, and its primary integration path (WhyLabs cloud) is gone. Teams that need dashboards, alerts, or multi-language support should look at LangFuse or commercial alternatives. For researchers and engineers who want fine-grained control over metric definitions and are comfortable building their own monitoring pipeline, LangKit remains a solid, free foundation — but it is now a community-maintained artifact rather than a supported product.
How it works
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Text quality metrics
Computes scores for readability, conciseness, and grammatical correctness of LLM outputs using built-in heuristics.
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Toxicity scoring
Classifies response toxicity on a continuous scale using pre-trained models to flag harmful or offensive language.
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Sentiment analysis
Extracts positive, negative, or neutral sentiment from prompts and responses, enabling trend tracking over time.
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Prompt injection detection
Identifies attempts to override or manipulate the LLM's behavior via crafted input strings.
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Response relevance scoring
Measures how well a response aligns with the original prompt using embedding similarity and keyword overlap.
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WhyLabs platform integration
Originally designed to push metrics to WhyLabs dashboards; this integration is now defunct since WhyLabs discontinued operations.
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Batch and streaming analysis
Supports both offline batch processing of log files and real-time streaming from sources like Kafka or SQS.
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Custom metric definition
Allows users to write arbitrary Python functions that compute new signals from prompt-response pairs.
Strengths and trade-offs
Strengths
- Completely open-source under Apache-2.0 license with 990 GitHub stars and an active community fork potential.
- Provides eight distinct, pre-built metric types (toxicity, relevance, injection detection) that require no external API calls.
- Supports both batch and streaming data ingestion, making it flexible for offline analysis and real-time pipelines.
- Custom metric API lets users define arbitrary Python functions, enabling domain-specific signal extraction beyond the built-in set.
Trade-offs
- WhyLabs, the original developer, has discontinued operations, leaving LangKit without an active commercial roadmap or support.
- Limited to Python and Jupyter Notebook environments; no production-grade service, dashboards, or real-time alerting built in.
- The primary integration path (WhyLabs cloud platform) is defunct, forcing users to build their own visualization and alerting layer.
- No multi-language support (Python only), unlike competitors such as LangFuse which support JavaScript and TypeScript.
Pricing context
Open-source (Apache-2.0 license); no paid tiers or SaaS offering available since WhyLabs discontinued operations.
Getting started with LangKit
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Install LangKit via pip
Open your terminal and run `pip install langkit` to install the package. Ensure you have Python 3.8 or later. This installs all dependencies for text quality, toxicity, and relevance scoring.
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Load prompt-response data
Prepare a CSV file with columns for prompts and responses. Use pandas to read the file: `import pandas as pd; df = pd.read_csv('logs.csv')`. This data will be passed to LangKit for analysis.
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Configure built-in metrics
Import LangKit's schema and enable the metrics you need, such as toxicity and relevance. For example: `from langkit import schema; schema.toxicity.enable()`. This activates the pre-trained classifiers for your data.
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Run batch analysis
Call the `run` function on your DataFrame: `results = schema.run(df)`. This computes scores for each prompt-response pair and returns a new DataFrame with columns like toxicity_score and relevance_score.
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Export results to CSV
Save the scored DataFrame to a new CSV file for further analysis or visualization: `results.to_csv('scored_logs.csv', index=False)`. You can then load this file into any charting tool or dashboard.
Frequently Asked Questions
What is LangKit and what does it do?
LangKit is an open-source Python toolkit by WhyLabs that extracts observability signals from LLM prompts and responses. It computes metrics like text quality, toxicity, sentiment, prompt injection detection, and response relevance, supporting both batch and streaming analysis for developers and researchers.
Is LangKit still maintained after WhyLabs shut down?
LangKit remains available as open-source under Apache-2.0 on GitHub with 990 stars, last committed November 22, 2024. However, WhyLabs discontinued its commercial platform, so LangKit no longer has an active commercial backer or roadmap, making it a community-maintained artifact.
What metrics can LangKit compute for LLM prompts and responses?
LangKit provides eight metrics: text quality, toxicity scoring, sentiment analysis, prompt injection detection, response relevance scoring, WhyLabs integration, batch and streaming analysis, and custom metric definition. These require no external API calls and work on prompt-response pairs.
How does LangKit compare to LangFuse for LLM monitoring?
LangKit is purely Python-based and open-source, while LangFuse offers a freemium SaaS model with tracing, cost monitoring, and multi-language support. LangKit lacks a managed backend and real-time alerting, making it better for offline analysis, whereas LangFuse suits production needs.
Can I use LangKit for real-time streaming analysis?
Yes, LangKit supports streaming analysis from sources like Kafka or SQS, in addition to batch processing of log files. This allows real-time ingestion of prompt-response pairs, but users must build their own visualization and alerting layer since the WhyLabs platform integration is defunct.
How do I create custom metrics in LangKit?
LangKit allows users to define custom metrics by writing arbitrary Python functions that compute new signals from prompt-response pairs. This enables domain-specific signal extraction beyond the built-in set, giving researchers fine-grained control over metric definitions for offline analysis.
Alternatives
How LangKit compares
Direct head-to-head against 3 competitors. Picked by 7wData.
LangKit
- Pricing
- Open-source (Apache-2.0 license); no paid tiers or SaaS offering available since WhyLabs discontinued operations.
- Target
- LangKit is an open-source Python toolkit, originally developed by WhyLabs, designed to extract observability signals from LLM prompts and responses.
- Strength
- Completely open-source under Apache-2.0 license with 990 GitHub stars and an active community fork potential.
- Watch for
- WhyLabs, the original developer, has discontinued operations, leaving LangKit without an active commercial roadmap or support.
Langfuse
- Pricing
- Custom/Contact sales
- Target
- Teams needing open-source LLM observability
- Deployment
- Cloud, Self-hosted
- Strength
- Open-source flexibility and traceability
- Watch for
- Requires manual setup for advanced features
Braintrust
- Pricing
- Custom/Contact sales
- Target
- Enterprises with CI/CD evaluation needs
- Deployment
- Cloud
- Strength
- Integrated CI/CD evaluation gates
- Watch for
- Higher cost for advanced compliance features
Openlayer
- Pricing
- Custom/Contact sales
- Target
- Regulated enterprises needing compliance
- Deployment
- Cloud
- Strength
- 100+ automated tests and real-time guardrails
- Watch for
- Premium pricing for full feature set
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Sources
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