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Context for your AI agents
Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.
Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
...It creates an environment that has its own installation directories, that doesn’t share libraries with other virtualenv environments (and optionally doesn’t access the globally installed libraries either). The basic problem being addressed is one of dependencies and versions, and indirectly permissions. Imagine you have an application that needs version 1 of LibFoo, but another application requires version 2. How can you use both these libraries? If you install everything into your host python (e.g. python3.8) it’s easy to end up in a situation where two packages have conflicting requirements.
Dynamic and static analysis with Sandboxie for Windows, including EDR
Dynamic and static analysis with Sandboxie for Windows, including EDR, ClamAV, YARA-X, custom machine learning AI, behavioral analysis, NLP-based detection, website signatures, Ghidra, Suricata, Sigma, and much more than you can imagine
...In DEAD runs you’ll be able to replay a LIVE run from the database recording, which means that you can start at any point and travel in either direction and the value of the variables will be those that were current at that point of time in the LIVE run.
Imagine your script crashed and you can replay from there backwards, and see how you got there and the values the variables had.
Extra functionality will allow running queries forwards and backwards.