Content
# Personal Assistant Agent with LangGraph
A powerful conversational agent with retrieval-augmented generation, MCP server, long- and short-term memory, and tool integrations—powered by LangGraph, LangChain, Redis, PostgreSQL, and Pinecone.
---
## Features
- **Conversational Chat**
- Interactive CLI (`app/run.py`)
- Programmatic access via Python SDK (`langgraph_sdk`)
- **Retrieval-Augmented Generation**
- Ingest & query PDF, Markdown, HTML, CSV & DOCX into Pinecone
- `index_docs(name, path_or_url)` & `query_index(name, question, k)` tools
- **MCP Server**
- 25+ CoinMarketCap endpoints exposed
- **Long-Term Memory**
- Profile, projects & instructions stored in PostgreSQL, namespaced by user
- **Short-Term Memory**
- Rolling summary of recent conversation (pruned after 10+ turns)
- **Tool Integrations**
- **Web & Knowledge**: `web_fetch`, `wiki_search`, `tavily_search`
- **File Handling**: `inspect_file`, `summarise_file`, `extract_tables`, `ocr_image`, `save_uploaded_file`
- **Finance**: `get_stock_quote`, `get_stock_news`
- **Robustness**
- Automatic retries on transient OpenAI errors
- Healthchecks on Redis & Postgres in Docker Compose
## Project Structure
```
personal_assistant/
├── app/
│ ├── config.py
│ ├── run.py # CLI entrypoint
│ ├── graph/
│ │ ├── assistant.py # StateGraph definition
│ │ ├── state.py # ChatState schema
│ │ └── memory/ # summarization, schemas
│ ├── tools/ # external tool fns
│ ├── rag/ # RAG loaders & indexers
│ ├── mcp/ # MCP server logic
│ └── schemas/
├── requirements.txt
├── docker-compose.yml
├── langgraph.json
└── README.md
```
## Prerequisites
- Python 3.11+
- Docker & Docker Compose (for containerized deployment)
- Redis & PostgreSQL (local or via Docker)
- OpenAI, Pinecone, Tavily & CoinMarketCap API keys
## Getting Started
### Local Setup
1. Clone the repo:
```bash
git clone https://github.com/amanzoni1/pa_agent && cd personal_assistant
```
2. Create and activate a virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Create a `.env` file in project root:
```ini
OPENAI_API_KEY=your_openai_key
LANGSMITH_API_KEY=your_langsmith_key
REDIS_URI=redis://localhost:6379
POSTGRES_URI=postgresql://postgres:postgres@localhost:5432/personal_assistant?sslmode=disable
PINECONE_API_KEY=your_pinecone_key
TAVILY_API_KEY=your_tavily_key
COINMARKETCAP_API_KEY=your_cmc_key
```
5. Ensure Redis and Postgres are running locally.
## Command-Line Interface (CLI)
Interact via chat in terminal:
```bash
python -m app.run --thread-id <optional-uuid> --user-id <optional-your_id>
```
Commands:
- `/memory`: Show long-term memory (profile, projects, instructions) stored.
- `/mcp`: get all the tools available from the MCP server.
- `/exit` or Ctrl-D: Quit.
## Docker Compose Deployment
1. Build your LangGraph image:
```bash
langgraph build -t my-assistant
```
2. Launch via Docker Compose:
```bash
docker compose up -d
```
3. Access:
- API: `http://localhost:8123`
- Swagger Docs: `http://localhost:8123/docs`
### Python SDK Usage
Install the SDK:
```bash
pip install langgraph_sdk
```
Example:
```python
import asyncio
from langgraph_sdk import get_client
from langchain_core.messages import HumanMessage
async def main():
client = get_client(url="http://localhost:8123")
thread = await client.threads.create()
run = await client.runs.create(
thread["thread_id"],
"my-assistant",
input={"messages": [HumanMessage(content="Hello!")]},
config={"configurable": {"user_id": "you", "thread_id": thread["thread_id"]}},
)
final = await client.runs.join(thread["thread_id"], run["run_id"])
msgs = final.get("messages", [])
ai = msgs[1]["content"] if len(msgs) > 1 else None
print("AI:", ai)
asyncio.run(main())
```
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