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+# Frequently Asked Questions (FAQ)
+
+## General
+
+### What is Memary?
+
+Memary is **the open source memory layer for autonomous agents**. It emulates human memory to advance AI agents by tracking users' preferences, knowledge breadth, and knowledge depth over time.
+
+### How does Memary work?
+
+Memary integrates three core components:
+
+1. **Knowledge Graphs** — Store entities and relationships from agent interactions
+2. **Memory Stream** — Track all entities with timestamps (knowledge breadth)
+3. **Entity Knowledge Store** — Track frequency and recency of entity references (knowledge depth)
+
+These components work together to provide personalized, context-aware responses.
+
+### How is Memary different from other agent frameworks?
+
+Memary focuses **specifically on memory management**:
+
+- **Memory Layer** — Not a full agent framework, but a memory system you integrate into existing agents
+- **Knowledge Graphs** — FalkorDB/Neo4j graph databases for entity relationships
+- **Human Memory Emulation** — Mimics how human memory evolves and learns over time
+
+Use Memary with LlamaIndex, LangChain, or any agent framework to add persistent memory.
+
+### What is the Memory Stream?
+
+The **Memory Stream** captures all entities inserted into the Knowledge Graph with their timestamps:
+
+- Reflects **knowledge breadth** (concepts users have exposure to)
+- Enables timeline analysis of user interests
+- Tracks evolution of user knowledge over time
+
+### What is the Entity Knowledge Store?
+
+The **Entity Knowledge Store** tracks frequency and recency of entity references:
+
+- Reflects **knowledge depth** (concepts users are familiar with)
+- Ranks entities by relevance (frequency + recency)
+- Helps identify user expertise areas
+
+## Getting Started
+
+### How do I install Memary?
+
+**With pip:**
+
+```bash
+pip install memary
+```
+
+**Locally:**
+
+```bash
+pip install -r requirements.txt
+```
+
+**Note:** Requires Python ≤ 3.11.9
+
+### What models are supported?
+
+Memary supports:
+
+- **Ollama** — Llama 3 8B/40B (suggested defaults), LLaVA (vision)
+- **OpenAI** — gpt-3.5-turbo, gpt-4-vision-preview
+
+Default is locally run models unless explicitly specified.
+
+### How do I run Memary?
+
+```bash
+cd streamlit_app
+streamlit run app.py
+```
+
+### What credentials do I need?
+
+Required credentials (in `.env`):
+
+- `OPENAI_API_KEY` — For GPT models (optional if using Ollama)
+- `PERPLEXITY_API_KEY` — For external queries
+- `GOOGLEMAPS_API_KEY` — For location tool
+- `ALPHA_VANTAGE_API_KEY` — For stocks tool
+- `FALKORDB_URL` or `NEO4J_URL` + `NEO4J_PW` — For graph database
+
+## Features
+
+### What is the Routing Agent?
+
+The **Routing Agent** is a ReAct agent implementation:
+
+- Plans and executes queries using provided tools
+- Uses Knowledge Graph search tool to retrieve information
+- Falls back to external search if no related entities exist
+- Includes vision (LLaVA) and location tools
+
+### What is the Knowledge Graph?
+
+The **Knowledge Graph** stores entities and relationships:
+
+- Graph database (FalkorDB or Neo4j) for structured knowledge
+- Uses Llama Index to add nodes from documents
+- Perplexity for external queries
+- Recursive retrieval + multi-hop reasoning for efficient search
+
+### What is Multi-Graph?
+
+**Multi-Graph** enables multiple agents with separate memory contexts:
+
+- Create multiple graphs with different IDs
+- Switch between agent memory contexts seamlessly
+- Each agent has its own Knowledge Graph and Memory
+
+### How do I add custom tools?
+
+```python
+def multiply(a: int, b: int) -> int:
+    """Multiply two integers and returns the result integer"""
+    return a * b
+
+chat_agent.add_tool({"multiply": multiply})
+```
+
+### How do I remove custom tools?
+
+```python
+chat_agent.remove_tool("multiply")
+```
+
+## Integration
+
+### How do I integrate Memary into my agent?
+
+```python
+from memary.agent.chat_agent import ChatAgent
+
+chat_agent = ChatAgent(
+    "Personal Agent",
+    memory_stream_json,
+    entity_knowledge_store_json,
+    system_persona_txt,
+    user_persona_txt,
+    past_chat_json,
+)
+```
+
+Pass in subset of `['search', 'vision', 'locate', 'stocks']` as `include_from_defaults` for different default tools.
+
+### Can I use Memary with other agent frameworks?
+
+Yes! Memary is designed to integrate with any agent framework:
+
+- **LlamaIndex** — Native integration (ReAct agent)
+- **LangChain** — Use ChatAgent as tool
+- **Custom agents** — Import memory modules separately
+
+### How do I set up user personas?
+
+Edit `streamlit_app/data/user_persona.txt` using the template in `streamlit_app/data/user_persona_template.txt`:
+
+- Replace curly brackets with relevant information
+- User persona shapes agent behavior and responses
+
+## Configuration
+
+### What environment variables are required?
+
+Key environment variables (see `.env.example`):
+
+- `OPENAI_API_KEY` — OpenAI API access
+- `PERPLEXITY_API_KEY` — Perplexity API for external queries
+- `GOOGLEMAPS_API_KEY` — Google Maps geocoding
+- `ALPHA_VANTAGE_API_KEY` — Stock data
+- `FALKORDB_URL` or `NEO4J_URL` + `NEO4J_PW` — Graph database
+
+### How do I configure local models?
+
+1. Install Ollama: `ollama pull llama3`
+2. Run Ollama: `ollama serve`
+3. Memary will auto-detect and use local models
+
+### What databases are supported?
+
+Memary supports:
+
+- **FalkorDB** — Recommended (free tier available)
+- **Neo4j** — Alternative (free tier available)
+
+Both provide graph database functionality for Knowledge Graphs.
+
+## Troubleshooting
+
+### Memary won't install
+
+**Common causes:**
+
+1. **Python version too high**: Requires Python ≤ 3.11.9
+2. **Missing dependencies**: Install requirements.txt
+3. **Pip network issues**: Check internet connection
+
+**Solution:**
+
+```bash
+# Check Python version
+python --version
+
+# Create correct virtual environment
+python3.11 -m venv venv
+source venv/bin/activate
+pip install memary
+```
+
+### Streamlit app won't start
+
+**Common causes:**
+
+1. **Missing credentials**: Check `.env` file
+2. **Database connection failed**: Verify FalkorDB/Neo4j URL
+3. **Port conflicts**: Ensure port 8501 is available
+
+**Solution:**
+
+```bash
+# Check environment
+cat .env
+
+# Test database connection
+python -c "from memary.knowledge_graph import KnowledgeGraph; kg = KnowledgeGraph()"
+
+# Run with verbose
+streamlit run app.py --logger_level debug
+```
+
+### Knowledge Graph search returns empty
+
+**Common causes:**
+
+1. **No entities in graph**: Add documents or run queries first
+2. **Database connection issue**: Check FalkorDB/Neo4j connection
+3. **Query doesn't match entities**: Try different query terms
+
+**Solution:**
+
+```bash
+# Check graph contents
+curl $FALKORDB_URL/graphs
+
+# Add initial documents
+python -c "from memary.knowledge_graph import KnowledgeGraph; kg = KnowledgeGraph(); kg.add_documents(['doc.txt'])"
+```
+
+### Memory Stream not updating
+
+**Common causes:**
+
+1. **Agent not storing responses**: Check `write_back()` is called
+2. **JSON file path incorrect**: Verify memory_stream_json path
+3. **Permission issues**: Check file permissions
+
+**Solution:**
+
+```python
+# Verify write_back is called
+response = chat_agent.query(query)
+chat_agent.write_back()  # Ensures KG writeback
+
+# Check memory file
+cat memory_stream.json
+```
+
+## Help & Resources
+
+- **Documentation**: [memarydocs](https://kingjulio8238.github.io/memarydocs/)
+- **Demo**: [YouTube Demo](https://youtu.be/GnUU3_xK6bg)
+- **LinkedIn**: [Memary Labs](https://www.linkedin.com/company/memary/)
+- **Twitter**: [@memary_labs](https://x.com/memary_labs)
+- **PyPI**: [memary](https://pypi.org/project/memary/)
+- **Issues**: [GitHub Issues](https://github.com/kingjulio8238/Memary/issues)
+
+---
+
+*Last updated: 2026-05-16*
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