Coding Agent Integration

# Install Hebbrix MCP Server
cd mcp
./quick_setup.sh

# Add to your Claude Desktop config
{
  "mcpServers": {
    "hebbrix": {
      "command": "python",
      "args": ["/path/to/hebbrix/mcp/server.py"],
      "env": {
        "HEBBRIX_API_KEY": "your_api_key"
      }
    }
  }
}

from hebbrix import Hebbrix

# Initialize
client = Hebbrix()

# Store conversation context
await client.memories.create(
    content="User asked about API authentication",
    importance=0.7
)

# Retrieve context for next response
results = await client.search(
    query="How do I authenticate?",
    limit=5
)

# Use results to enhance your agent's response
context = "\n".join([r["content"] for r in results["results"]])
response = your_llm.generate(context + user_message)

from langchain.memory import HebbrixMemory
from langchain.chains import ConversationChain
from langchain.llms import OpenAI

# Initialize with Hebbrix memory
memory = HebbrixMemory(
    api_key="your_hebbrix_key",
    collection_id="my_agent"
)

# Create conversation chain
chain = ConversationChain(
    llm=OpenAI(),
    memory=memory,
    verbose=True
)

# Conversations now have persistent memory!
response = chain.predict(input="My name is Alice")
# Later...
response = chain.predict(input="What's my name?")
# Returns: "Your name is Alice"