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feat: add ability to set LLM for AgentPLanner on Crew (#1001)

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* feat: add ability to set LLM for AgentPLanner on Crew

* feat: fixes issue on instantiating the ChatOpenAI on the crew

* docs: add docs for the planning_llm new parameter

* docs: change message to ChatOpenAI llm

* feat: add tests
This commit is contained in:
Eduardo Chiarotti
2024-07-26 14:24:29 -03:00
committed by GitHub
parent 2d2154ed65
commit 144e6d203f
5 changed files with 80 additions and 15 deletions
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1
docs/core-concepts/Crews.md
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@@ -33,6 +33,7 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Manager Callbacks** _(optional)_ | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
!!! note "Crew Max RPM"
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.

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docs/core-concepts/Planning.md
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@@ -23,6 +23,25 @@ my_crew = Crew(
From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
#### Planning LLM
Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
```python
from crewai import Crew, Agent, Task, Process
from langchain_openai import ChatOpenAI
# Assemble your crew with planning capabilities and custom LLM
my_crew = Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
planning=True,
planning_llm=ChatOpenAI(model="gpt-4o")
)
```
### Example
When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.