# solo-performance-prompting-agent

This template creates an agent that transforms a single LLM into a cognitive synergist  by engaging in multi-turn self-collaboration with multiple personas.
A cognitive synergist refers to an intelligent agent that collaborates with multiple minds, combining their individual strengths and knowledge, to enhance problem-solving and overall performance in complex tasks. By dynamically identifying and simulating different personas based on task inputs, SPP unleashes the potential of cognitive synergy in LLMs. 

This template will use the `DuckDuckGo` search API. 

## Environment Setup

This template will use `OpenAI` by default. 
Be sure that `OPENAI_API_KEY` is set in your environment.

## Usage

To use this package, you should first have the LangChain CLI installed:

```shell
pip install -U langchain-cli
```

To create a new LangChain project and install this as the only package, you can do:

```shell
langchain app new my-app --package solo-performance-prompting-agent
```

If you want to add this to an existing project, you can just run:

```shell
langchain app add solo-performance-prompting-agent
```

And add the following code to your `server.py` file:
```python
from solo_performance_prompting_agent.agent import agent_executor as solo_performance_prompting_agent_chain

add_routes(app, solo_performance_prompting_agent_chain, path="/solo-performance-prompting-agent")
```

(Optional) Let's now configure LangSmith. 
LangSmith will help us trace, monitor and debug LangChain applications. 
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
If you don't have access, you can skip this section


```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
```

If you are inside this directory, then you can spin up a LangServe instance directly by:

```shell
langchain serve
```

This will start the FastAPI app with a server is running locally at 
[http://localhost:8000](http://localhost:8000)

We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/solo-performance-prompting-agent/playground](http://127.0.0.1:8000/solo-performance-prompting-agent/playground)  

We can access the template from code with:

```python
from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/solo-performance-prompting-agent")
```