https://github.com/rihp/autogpt-planner-plugin
Simple planner for AutoGPT that can create tasks and checklists to execute commands based on its strategy.
https://github.com/rihp/autogpt-planner-plugin
agents autogpt python
Last synced: 4 days ago
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Simple planner for AutoGPT that can create tasks and checklists to execute commands based on its strategy.
- Host: GitHub
- URL: https://github.com/rihp/autogpt-planner-plugin
- Owner: rihp
- License: mit
- Created: 2023-04-21T13:38:57.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-06-03T07:28:33.000Z (over 2 years ago)
- Last Synced: 2025-04-14T07:12:01.245Z (6 months ago)
- Topics: agents, autogpt, python
- Language: Python
- Homepage:
- Size: 59.6 KB
- Stars: 81
- Watchers: 5
- Forks: 17
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AutoGPT Planner Plugin
Simple planning commands for planning leveraged with chatgpt3.5 and json objects to keep track of its progress on a list of tasks.
### Getting started
After you clone the plugin from the original repo (https://github.com/rihp/autogpt-planner-plugin) Add it to the plugins folder of your AutoGPT repo and then run AutoGPT
Remember to also update your .env to include
```
ALLOWLISTED_PLUGINS=PlannerPlugin
```
# New commands
```python
prompt.add_command(
"check_plan",
"Read the plan.md with the next goals to achieve",
{},
check_plan,
)
prompt.add_command(
"run_planning_cycle",
"Improves the current plan.md and updates it with progress",
{},
update_plan,
)
prompt.add_command(
"create_task",
"creates a task with a task id, description and a completed status of False ",
{
"task_id": "",
"task_description": "",
},
create_task,
)
prompt.add_command(
"load_tasks",
"Checks out the task ids, their descriptionsand a completed status",
{},
load_tasks,
)
prompt.add_command(
"mark_task_completed",
"Updates the status of a task and marks it as completed",
{"task_id": ""},
update_task_status,
)
```
# New config options
By default, the plugin is set ot use what ever your `FAST_LLM_MODEL` environment variable is set to, if none is set it
will fall back to `gpt-3.5-turbo`. If you want to set it individually to a different model you can do that by setting
the environment variable `PLANNER_MODEL` to the model you want to use (example: `gpt-4`).
Similarly, the token limit defaults to the `FAST_TOKEN_LIMIT` environment variable, if none is set it will fall
back to `1500`. If you want to set it individually to a different limit for the plugin you can do that by setting
`PLANNER_TOKEN_LIMIT` to the desired limit (example: `7500`).
And last, but not least, the temperature used defaults to the `TEMPERATURE` environment variable, if none is set it will
fall back to `0.5`. If you want to set it individually to a different temperature for the plugin you can do that by
setting `PLANNER_TEMPERATURE` to the desired temperature (example: `0.3`).
## CODE SAMPLES
Example of generating an improved plan
```python
def generate_improved_plan(prompt: str) -> str:
"""Generate an improved plan using OpenAI's ChatCompletion functionality"""
import openai
tasks = load_tasks()
# Call the OpenAI API for chat completion
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": "You are an assistant that improves and adds crucial points to plans in .md format.",
},
{
"role": "user",
"content": f"Update the following plan given the task status below, keep the .md format:\n{prompt}\nInclude the current tasks in the improved plan, keep mind of their status and track them with a checklist:\n{tasks}\Revised version should comply with the contests of the tasks at hand:",
},
],
max_tokens=1500,
n=1,
temperature=0.5,
)
```
## Testing workflow
Clone the repo and modify the functionality, when you're done you can run
```
zip -ru ../fork/plugins/planner.zip . ; cd ../fork && python3 -m autogpt --debug
```
then you need to cd back to
```
cd ../autogpt-planner-plugin
```