GitHub - robocorp/llmstatemachine: A Python library for building GPT-powered agents with state machine logic and chat history memory.
github.com
GitHub - robocorp/llmstatemachine: A Python library for building GPT-powered agents with state machine logic and chat history memory.
Darren LI and added
OpenGPTs
This is an open source effort to create a similar experience to OpenAI's GPTs. It builds upon LangChain, LangServe and LangSmith. OpenGPTs gives you more control, allowing you to configure:
This is an open source effort to create a similar experience to OpenAI's GPTs. It builds upon LangChain, LangServe and LangSmith. OpenGPTs gives you more control, allowing you to configure:
- The LLM you use (choose between the 60+ that LangChain offers)
- The prompts you use (use LangSmith to debug those)
- The tools you give it (choose from LangC
langchain-ai • langchain-ai/opengpts
Nicolay Gerold added
memary: Open-Source Longterm Memory for Autonomous Agents
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve relevan... See more
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve relevan... See more
GitHub - kingjulio8238/memary: Longterm Memory for Autonomous Agents.
Nicolay Gerold added
Data
Autonomous Agents & Agent Simulations
blog.langchain.dev
Darren LI added
LangChain excels at chaining LLMs together using agents to delegate actions to models. Its use cases emphasize prompt optimization and context-aware information retrieval/generation; however, with its Pythonic highly modular interface and its huge collection of tools, it is the number-one tool to implement complex business logic.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as... See more
r/singularity - Reddit
Nicolay Gerold added
Chaining LLM Agents instead of LLM calls. Seems like a pretty heavy prompt engineering effort.
They are pushing for agents that are specialized in a certain tasks through RAG / finetuning, where CAMEL and other frameworks failed.
One interesting area for exploration might be finetuning LLMs for collaboration before finetuning them for tasks.
LLM agents rely on their Long-Term Memory (LTM) to store and retrieve crucial memories. Conversations, thoughts, plans, actions, observations, skills, and behaviors are all stored within a vector database. The LTM enables pre-processing and post-processing of memories, ensuring optimal retrieval. By considering factors such as context, recency, imp... See more
Introducing our work on general-purpose LLM Agents | GoodAI
Nicolay Gerold added
The main differences between the AutoGPT project and traditional LangChain agents can be attributed to different objectives. In AutoGPT, the goals are often more open ended and long running. This means that AutoGPT has a different AgentExecutor and different way of doing memory (both of which are more optimized for long running tasks).
Autonomous Agents & Agent Simulations
Darren LI added