by shizhl · Agent Tool · ★ 221
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CoAgents The code for "cooperative and interactive agents framework" We propose the ConAgents, a Cooperative and interactive agents framework . The grounding agent generates tool-use instructions while execution agent executes selected tools. The observing agent extracts relevant values from execution results. When receiving feedback from the tool environment, the execution agent and observing agent adapt themselves iteratively. Quick start install the main library via register the TMDB platform to get the access token to access the TMDB APIs. Once you get the access token, please make a new file named and add your token into this file. get the OpenAI API to use the (or other models) Once your get the APIs, please add your APIs into the list in . You can add multiple APIs since we will randomly select one APIs from to request the OpenAI backend for each API request. The Random selection strategy is employed to improve the quota limitation for one API (see the OpenAI quota for the details). You can also configure the base URL for your OpenAI APIs python BASEURL = 'customize yo
| Stars | 221 |
| Forks | 2 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 45.2/100 |
| Open Issues | 1 |
| Last Updated | 2024-03-26 |
| Created | 2024-02-13 |
| Platforms | python |
| Est. Tokens | ~31k |
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CoAgents is Official code for paper "Learning to Use Tools via Cooperative and Interactive Agents". It is categorized as a Agent Tool with 221 GitHub stars.
CoAgents is primarily written in Python. It covers topics such as agent, large-language-models, multi-agent.
You can find installation instructions and usage details in the CoAgents GitHub repository at github.com/shizhl/CoAgents. The project has 221 stars and 2 forks, indicating an active community.
The top alternatives to CoAgents on Agent Skills Hub include MassGen, ToolOrchestra, LLM-Tool-Survey. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.