GitHub - kingjulio8238/memary: Longterm Memory for Autonomous Agents.
github.com
GitHub - kingjulio8238/memary: Longterm Memory for Autonomous Agents.
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
A Survey on Large Language Model based Autonomous Agents
A comprehensive survey on large language model (LLM)-based autonomous agents, including their architecture design, application domains, and evaluation strategies.
arxiv.org🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
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joaomdmoura • GitHub - joaomdmoura/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
3 days reading through the code of the paper that solved this, here are some notes:
- Long term interactions: the promise of LLM agents is to potentially run over weeks. A lot of the ideas in the paper tackle exactly this problem
- Memory: It’s easy to write down interactions of agents. But how do we track these over time? How do these change with ti... See more
- Long term interactions: the promise of LLM agents is to potentially run over weeks. A lot of the ideas in the paper tackle exactly this problem
- Memory: It’s easy to write down interactions of agents. But how do we track these over time? How do these change with ti... See more