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* add metrics for total_cost * make lint * refact codeact * change metrics into llm * add costs list, add into state * refactor log completion * refactor and test others * make lint * Update opendevin/core/metrics.py Co-authored-by: Boxuan Li <liboxuan@connect.hku.hk> * Update opendevin/llm/llm.py Co-authored-by: Xingyao Wang <xingyao6@illinois.edu> * refactor * add code --------- Co-authored-by: Boxuan Li <liboxuan@connect.hku.hk> Co-authored-by: Xingyao Wang <xingyao6@illinois.edu>
CodeAct Agent Framework
This folder implements the CodeAct idea (paper, tweet) that consolidates LLM agents’ actions into a unified code action space for both simplicity and performance (see paper for more details).
The conceptual idea is illustrated below. At each turn, the agent can:
- Converse: Communicate with humans in natural language to ask for clarification, confirmation, etc.
- CodeAct: Choose to perform the task by executing code
- Execute any valid Linux
bashcommand - Execute any valid
Pythoncode with an interactive Python interpreter. This is simulated throughbashcommand, see plugin system below for more details.
- Execute any valid Linux
Plugin System
To make the CodeAct agent more powerful with only access to bash action space, CodeAct agent leverages OpenDevin's plugin system:
- Jupyter plugin: for IPython execution via bash command
- SWE-agent tool plugin: Powerful bash command line tools for software development tasks introduced by swe-agent.
Demo
https://github.com/OpenDevin/OpenDevin/assets/38853559/f592a192-e86c-4f48-ad31-d69282d5f6ac
Example of CodeActAgent with gpt-4-turbo-2024-04-09 performing a data science task (linear regression)
Work-in-progress & Next step
[] Support web-browsing [] Complete the workflow for CodeAct agent to submit Github PRs