LLM-VM logo
LLM-VM
★ 492
vs
code-act logo
code-act
★ 1.7k

LLM-VM vs code-act

LLM-VM: The Anarchy LLM-VM is an optimized backend designed to run open-source LLMs with modern features like tool usage and persistent memory. It acts as a virtual machine for human language, coordinating models, data, prompts, and tools to optimize batch calls and support various architectures.; code-act: CodeAct unifies LLM agents' actions into an executable code space, enabling dynamic revision and new actions based on execution results. This approach significantly outperforms traditional text and JSON action methods, improving LLM agent success rates on complex tasks.

01

TL;DR

LLM-VM logo
LLM-VMLLM Infra · ★ 492

Accelerate AGI development and prototyping

code-act logo
code-actLLM Infra · ★ 1.7k

Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.

02

Side-by-Side Comparison

Field
LLM-VM logoLLM-VM
code-act logocode-act
Category
LLM Infra
LLM Infra
Stars
★ 492
★ 1.7k
License
MIT
MIT
Install Command
pip install llm-vm
pip install -r requirements.txt
Updated
2y ago
2y ago
Open Source
Yes
Yes
Tags
LLM, Open Source AI, Inference Optimization
LLM Agents, Code Execution, Instruction Tuning
03

Features

LLM-VM logoLLM-VM
01Implicit Agents
02Inference Optimization
03Task Auto-Optimization
04Library Callable
05HTTP Endpoints
code-act logocode-act
01Unified Action Space via Executable Code: Consolidates LLM agents' actions into a unified, executable code space.
02Dynamic Action Revision: Allows LLM agents to dynamically revise prior actions or emit new actions based on real-time observations.
03Integrated Python Interpreter: Seamlessly integrates with a Python interpreter for code execution.
04Superior Performance: Outperforms widely used alternatives like Text and JSON in LLM agent success rates (up to 20% higher).
05Instruction-Tuned Agents (CodeActAgent): Provides pre-trained CodeActAgent models (Mistral, Llama-2) that excel in out-of-domain agent tasks.
04

Use Cases

LLM-VM logoLLM-VM
↳Accelerate AGI development and prototyping
↳Reduce costs for running and testing LLM models locally
↳Flexibly switch and evaluate different open-source LLM models
code-act logocode-act
↳Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.
↳Automated Code Execution and Problem Solving: For scenarios requiring LLMs to execute code, debug, and iterate on solutions based on execution feedback.
↳Complex Task Automation: For automating multi-turn, complex tasks that benefit from dynamic action revision and tool use.
FAQ

FAQ

What is the difference between LLM-VM and code-act?
Both LLM-VM and code-act are in the LLM Infra category. LLM-VM has 492 stars, while code-act has 1.7k stars.
Which is better, LLM-VM or code-act?
The best choice depends on your use case. Choose LLM-VM if Accelerate AGI development and prototyping, and code-act if Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments..
Is LLM-VM free or open source?
Yes, LLM-VM is open source on GitHub (MIT).
Is code-act free or open source?
Yes, code-act is open source on GitHub (MIT).
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