Another take on BabyAGI, focused on workflows that complete. Powered by langchain.
Here's a simple demo: https://twitter.com/pictobit/status/1645504308874563584
- (Optional): Customize the tools provided to the agent
- Install dependencies and run
pip install -r requirements.txt
python -m autolang
Or run with docker:
./run_docker.sh
Runs once at the start, it thinks of a strategy to solve the problem, and produces a task list.
A custom langchain agent, which implements ReAct to solve a single task in the plan. It can be provided any tools in the langchain format.
Here's the interesting part. The system holds an information context string, which starts empty. After each step, the learner merges the result with the current context, as a sort of medium-term memory
Assesses the current task list, based on the current completed tasks and generated info context, and reprioritizes the pending tasks accordingly
Right now, the main limitation is the limited info context. As a next step, I'm planning on adding a "long term memory agent", that extracts information from the context, replacing it with a key. The executor agent will be provided a tool to retrieve these saved snippets if required.