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Description
Unexpected outcomes are a common occurrence in software development. With complete insight into the entire sequence of calls, developers can pinpoint the origins of errors and unexpected results in real time with remarkable accuracy. The discipline of software engineering heavily depends on unit testing to create efficient and production-ready software solutions. LangSmith offers similar capabilities tailored specifically for LLM applications. You can quickly generate test datasets, execute your applications on them, and analyze the results without leaving the LangSmith platform. This tool provides essential observability for mission-critical applications with minimal coding effort. LangSmith is crafted to empower developers in navigating the complexities and leveraging the potential of LLMs. We aim to do more than just create tools; we are dedicated to establishing reliable best practices for developers. You can confidently build and deploy LLM applications, backed by comprehensive application usage statistics. This includes gathering feedback, filtering traces, measuring costs and performance, curating datasets, comparing chain efficiencies, utilizing AI-assisted evaluations, and embracing industry-leading practices to enhance your development process. This holistic approach ensures that developers are well-equipped to handle the challenges of LLM integrations.
Description
Neatlogs serves as a collaborative platform for debugging and enhancing AI reliability, equipping your team with all the necessary tools to effectively identify, comprehend, and resolve issues related to AI agents.
Today, you can accomplish the following tasks:
- Trace and replay: Observe your agent's actions in a detailed, step-by-step manner.
- Detect failures: Automatically identify and flag issues in traces using various conditions, patterns, and classifiers.
- Investigate: Utilize Neat AI to analyze runs and convert its insights into actionable fixes.
- Evals: Direct traces to either human or AI reviewers to assess quality over time.
- Experiment: Create versions of prompts, implement changes, and assess their performance against designated datasets.
- Fix: Examine AI-generated solutions and send them to your coding agent for implementation.
- Connect your tools: Integrate your applications through Tools and MCPs, allowing Copilot and Neat Agent to operate on your behalf.
- Track production: Keep an eye on costs, latency, error rates, tools, and detection trends across all your traces.
In contrast to other tools that cater solely to technical users, Neatlogs is designed to be user-friendly and easily comprehensible, ensuring that team members from various backgrounds can effectively engage with the platform. This approach empowers diverse teams to collaborate seamlessly in optimizing their AI systems.
API Access
Has API
API Access
Has API
Screenshots View All
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Integrations
AgentForge
Azure Marketplace
Disco.dev
LangChain
LangGraph
Noma
TierZero
Voker
ZenML
Integrations
AgentForge
Azure Marketplace
Disco.dev
LangChain
LangGraph
Noma
TierZero
Voker
ZenML
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
LangChain
Country
United States
Website
www.langchain.com/langsmith
Vendor Details
Company Name
neatlogs
Country
India
Website
neatlogs.com
Product Features
Software Testing
Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing