Tools

Build custom tools/functions enabling your AI Agents to hit any 3rd party API service

How does it work

On agent cloud you can build custom python functions able to reliably and repeatably access 1st or 3rd party API endpoints.

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Get started with
Tools
Build your own custom python tools/functions
Access authenticated services
Import an Open API 3.0 compatible spec
Get started

Explore other features

Explore

Security

Ensure LLM chat apps don't share more data than they should

Knowledge Retrieval (RAG)

Enable advanced RAG pipelines, letting you split, chunk, embed and retrieve knowledge from over 300 data sources

Open Source or Cloud LLMs

You can bring your own Open AI key or use one of 10 cloud hosted models (LLAMA2, Mistral 7B, Hugging Face). Agent Cloud Open Source users can also connect to their own Open AI compatible endpoints.
Build a RAG Chat to Bigquery data
Watch showreel

The end to end RAG pipeline

Select your connector

Use our collection of data sources to sync data from other systems like confluence or upload your own pdf, docx, txt or csv file.
When selecting systems like databases (postgres, snowflake, bigquery) you can select tables and even columns to ingest.

Prep your data

For files you can provide instructions on how to split and chunk your data. Leverage Open AI latest text-embedding-3 for embedding or select from open source models like BGE/base.

Vector store your data

Once data has been embedded the platform will store your data within a vector database. We also expose

Keep data fresh

Select what frequency you would like to sync data from the source. This can be manual, scheduled or a cron expression. This means users can query fresh data and know how recent the source was updated.

Start chatting with your data!

Now that data is synced, simply create an agent with your choice of LLM and start a session to talk to your data.

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