This is a simple Langgraph/Langchain based AI agent as a learning experiment of how LLM-based agent will work. Motivation is to automate better seaching in API (in this case Linkedin job), find suitable matching given user resume and write a cover letter for the most matching job. There are different usecases can be extended on top of the current design.
- Langgraph example inspired from Notebook
- Warning: Linkedin Unofficial API. Using it might violate LinkedIn's Terms of Service. Use it at your own risk. Github
pip install -r requirements.txt
These environment variables are required:
OPENAI_API_KEY=<OPENAI_API_KEY>
LINKEDIN_EMAIL=<LINKEDIN_EMAIL>
LINKEDIN_PASS=<LINKEDIN_PASS>
LANGCHAIN_API_KEY=<LANGSMITH_KEY>
LANGCHAIN_TRACING_V2=true
LLM_NAME=<LLM_NAME> groq/openai
Then run on terminal:
streamlit run app.py
Currently works well only with OpenAI GPT-4 / Llama/Groq still unstable.
+-----------+
| __start__ |
+-----------+
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+------------+
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* | supervisor_condition |** * *
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+-----------+ +---------+ +-----------+ +----------+
| Analyzer | | __end__ | | Generator | | Searcher |
+-----------+ +---------+ +-----------+ +----------+
- enrich linkedin search with more params
- bug fixes: Groq Llama, unstable, due to inappropriate routing / token limit
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