-
autogen
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
AutoGen: Improved and novel types for communication patterns in agent systems are available, termed conversation programming and finite state machines. Also, the AgentEval framework has been integrated into autogen, providing an integrated method for self-improving LLM responses with a given task specification
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
On the other hand, LLMs can be tricked by sophisticated prompts into revealing their training data or generating inappropriate texts. This danger, especially harmful when the access to an LLM is public, emphasizes the importance of careful prompt and LLM answer moderation. Libraries that tackle this challenge are Guardrails and Guidance, and likewise, LLM invocation frameworks add functions to manage prompts more effectively.
-
CrewAi: The new template method provides a simplified and reusable way to configure a project. In essence, it creates a default directory structure with custom YAML files that contain the agents and tasks definition separate from the actual Python code. And this makes the code much more and better readable. The second novel feature is the integration of a new set of built-in tasks from the crewAI-tools project. Finally, crews can now also be trained and a new planner declaration supports task execution by an upfront step.
-
AgentZero: A new framework that strikes a balance between integrated features and open configuration. At the core, a robust agent execution engine with integrated function execution support. Essentially, it routes functions calls directly to Docker containers and returns the result to the LLM. This is a promising feature when custom data sources need to be targeted.
-
LLM answer quality directly relates to its given prompts, and therefore, effective prompt engineering is necessary. The landscape of prompt managing platforms and libraries increased manifold. Some tools now actively incorporate specific tweaks of the most recent commercial models, enabling the formulation of prompts that are injected with model-specific formulations. Example libraries are dspy, LMQL, Outlines, and Prompttools,
-
LLM answer quality directly relates to its given prompts, and therefore, effective prompt engineering is necessary. The landscape of prompt managing platforms and libraries increased manifold. Some tools now actively incorporate specific tweaks of the most recent commercial models, enabling the formulation of prompts that are injected with model-specific formulations. Example libraries are dspy, LMQL, Outlines, and Prompttools,
-
LLM answer quality directly relates to its given prompts, and therefore, effective prompt engineering is necessary. The landscape of prompt managing platforms and libraries increased manifold. Some tools now actively incorporate specific tweaks of the most recent commercial models, enabling the formulation of prompts that are injected with model-specific formulations. Example libraries are dspy, LMQL, Outlines, and Prompttools,
-
prompttools
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
LLM answer quality directly relates to its given prompts, and therefore, effective prompt engineering is necessary. The landscape of prompt managing platforms and libraries increased manifold. Some tools now actively incorporate specific tweaks of the most recent commercial models, enabling the formulation of prompts that are injected with model-specific formulations. Example libraries are dspy, LMQL, Outlines, and Prompttools,
-
guidance
Discontinued A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance] (by microsoft)
On the other hand, LLMs can be tricked by sophisticated prompts into revealing their training data or generating inappropriate texts. This danger, especially harmful when the access to an LLM is public, emphasizes the importance of careful prompt and LLM answer moderation. Libraries that tackle this challenge are Guardrails and Guidance, and likewise, LLM invocation frameworks add functions to manage prompts more effectively.
-
Specialized projects that facilitate automatic document indexing and LLM invocation with the document content are gaining traction, for example PrivateGPT, QAnything, and LazyLLM. Another novelty is the integration of LLMs into applications and tools: The Semantic Kernel project aims to integrate LLM invocation during programming and inside the code itself.
-
Specialized projects that facilitate automatic document indexing and LLM invocation with the document content are gaining traction, for example PrivateGPT, QAnything, and LazyLLM. Another novelty is the integration of LLMs into applications and tools: The Semantic Kernel project aims to integrate LLM invocation during programming and inside the code itself.
-
Specialized projects that facilitate automatic document indexing and LLM invocation with the document content are gaining traction, for example PrivateGPT, QAnything, and LazyLLM. Another novelty is the integration of LLMs into applications and tools: The Semantic Kernel project aims to integrate LLM invocation during programming and inside the code itself.
-
Specialized projects that facilitate automatic document indexing and LLM invocation with the document content are gaining traction, for example PrivateGPT, QAnything, and LazyLLM. Another novelty is the integration of LLMs into applications and tools: The Semantic Kernel project aims to integrate LLM invocation during programming and inside the code itself.
