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Average Ratings 0 Ratings
Description
Coral is a developer-focused data access platform that lets teams query different tools and systems with SQL instead of writing custom connectors. It converts APIs, databases, files, and software platforms into readonly schemas that agents and humans can inspect, join, and analyze. Users can connect sources such as GitHub, GitLab, Slack, Linear, Datadog, Sentry, OpenTelemetry, Intercom, Stripe, and incident management tools. Once connected, Coral makes those sources available as tables, allowing cross-system questions to be answered through standard SQL. The platform is designed for AI agent workloads, giving coding agents and operational assistants access to structured context without unsafe write access. Coral works through the command line and over MCP, so multiple agents can share one runtime. It includes query pushdown, caching, pagination handling, schema hints, recommended joins, and relationship learning based on usage patterns. These capabilities help reduce expensive tool loops and improve the quality of agent-generated answers. Coral gives teams a practical way to make scattered operational data accessible, queryable, and useful for engineering, SRE, security, support, and internal operations.
Description
LangMem is a versatile and lightweight Python SDK developed by LangChain that empowers AI agents by providing them with the ability to maintain long-term memory. This enables these agents to capture, store, modify, and access significant information from previous interactions, allowing them to enhance their intelligence and personalization over time. The SDK features three distinct types of memory and includes tools for immediate memory management as well as background processes for efficient updates outside of active user sessions. With its storage-agnostic core API, LangMem can integrate effortlessly with various backends, and it boasts native support for LangGraph’s long-term memory store, facilitating type-safe memory consolidation through Pydantic-defined schemas. Developers can easily implement memory functionalities into their agents using straightforward primitives, which allows for smooth memory creation, retrieval, and prompt optimization during conversational interactions. This flexibility and ease of use make LangMem a valuable tool for enhancing the capability of AI-driven applications.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Claude Code
Yes
Datadog
Yes
GitHub
Yes
LangGraph
No
Linear
Yes
Model Context Protocol (MCP)
Yes
OpenAI Codex
Yes
PagerDuty
Yes
PydanticAI
No
Python
No
Integrations
Claude Code
No
Datadog
No
GitHub
No
LangGraph
Yes
Linear
No
Model Context Protocol (MCP)
No
OpenAI Codex
No
PagerDuty
No
PydanticAI
Yes
Python
Yes
Pricing Details
$249/month
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Coral
Founded
2024
Country
United Kingdom
Website
withcoral.com
Vendor Details
Company Name
LangChain
Founded
2022
Country
United States
Website
langchain-ai.github.io/langmem/