No-code multi-agent framework to build LLM Agents, workflows and applications with your data
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Updated
Jun 11, 2024 - Python
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Harness LLMs with Multi-Agent Programming
The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.
On-Call/DevOps Assistant - Get a head start on fixing alerts with AI investigation
Tune LLM in few lines of code
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
AI-to-AI Testing | Simulation framework for LLM-based applications
LLMs prompt augmentation with RAG by integrating external custom data from a variety of sources, allowing chat with such documents
A collection of Spouts that listen to events
A collection of Spouts that query databases
SecGPT: An execution isolation architecture for LLM-based systems
Geniusrise: Framework for building geniuses
Bolts that read data and perform chains of actions with prompts
GoalChain for goal-orientated LLM conversation flows
Experiment on QnA tabular data using LLMs and SQL
Unstract's interface to LLMs, Embeddings and VectorDBs.
Bolts interfacing with the huggingface ecosystem
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