Add HuggingFace Inference and code optimization

.gitignore

@@ -10,4 +10,6 @@ nanda_agent/__pycache__
 dist/
 *.egg-info/
 
-nanda_adapter/core/__pycache__
+nanda_adapter/core/__pycache__
+
+*/__pycache__/

README.md

@@ -8,6 +8,7 @@ https://docs.google.com/presentation/d/16ehp8yq4-QjEu55unsI9rHJ8BMK9MAi1/edit?us
 ## Features
 
 - **Multiple AI Frameworks**: Support for LangChain, CrewAI, and any custom logic.
+- **Multiple LLM Providers**: Use Anthropic Claude, Hugging Face, or other providers.
 - **Multi-protocol Communication**: Built-in protocol that allows universal communication
 - **Global Index**: Automatic agent discovery via MIT NANDA Index
 - **SSL Support**: Production-ready with Let's Encrypt certificates
@@ -52,6 +53,14 @@ pip install nanda-adapter
 
 > export DOMAIN_NAME="<YOUR_DOMAIN_NAME.COM>
 
+**Alternative: Use Hugging Face instead of Anthropic**
+
+> export HUGGINGFACE_API_KEY="hf_your-api-key-here"
+
+> export HUGGINGFACE_MODEL="meta-llama/Llama-3.3-70B-Instruct"
+
+> export DOMAIN_NAME="<YOUR_DOMAIN_NAME.COM>"
+
 ### 5. Run an example agent (langchain_pirate.py)
 > nohup python3 langchain_pirate.py > out.log 2>&1 &
 
@@ -187,6 +196,50 @@ domain = os.getenv("DOMAIN_NAME")
 nanda.start_server_api(anthropic_key, domain)
 ```
 
+### Deploy with Hugging Face (Alternative to Anthropic)
+
+You can use Hugging Face Inference API instead of Anthropic:
+
+```python
+#!/usr/bin/env python3
+from nanda_adapter import NANDA
+import os
+
+def create_simple_improvement():
+    """Create a simple improvement function"""
+    def simple_improvement(message_text: str) -> str:
+        return f"[Enhanced] {message_text}"
+    return simple_improvement
+
+def main():
+    nanda = NANDA(create_simple_improvement())
+
+    # Use Hugging Face instead of Anthropic
+    nanda.start_server_api(
+        anthropic_key=None,  # Not needed for HuggingFace
+        domain=os.getenv("DOMAIN_NAME"),
+        llm_provider="huggingface",
+        llm_model="meta-llama/Llama-3.3-70B-Instruct",
+        llm_api_key=os.getenv("HUGGINGFACE_API_KEY")
+    )
+
+if __name__ == "__main__":
+    main()
+```
+
+**For local development without SSL:**
+
+```python
+nanda.start_server_api(
+    anthropic_key=None,
+    domain="localhost",
+    llm_provider="huggingface",
+    llm_model="meta-llama/Llama-3.3-70B-Instruct",
+    llm_api_key=os.getenv("HUGGINGFACE_API_KEY"),
+    ssl=False  # Disable SSL for local testing
+)
+```
+
 ## Deploy from Scratch on a barebones machine (Ubuntu on Linode or Amazon Linux on EC2)
 
 ```bash
@@ -242,14 +295,47 @@ The framework will automatically:
 ## Appendix: Configuration Details
 
 ### Environment Variables
-You need the following environment details ()
 
-- `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
-- `DOMAIN_NAME`: Domain name for SSL certificates (required)
+**Core Settings:**
+- `DOMAIN_NAME`: Domain name for SSL certificates (required for production)
 - `AGENT_ID`: Custom agent ID (optional, auto-generated if not provided)
 - `PORT`: Agent bridge port (optional, default: 6000)
 - `IMPROVE_MESSAGES`: Enable/disable message improvement (optional, default: true)
 
+**LLM Provider Settings (choose one):**
+
+*Anthropic (default):*
+- `ANTHROPIC_API_KEY`: Your Anthropic API key
+- `ANTHROPIC_MODEL`: Model to use (optional, default: claude-3-5-sonnet-20241022)
+
+*Hugging Face:*
+- `HUGGINGFACE_API_KEY`: Your Hugging Face API key
+- `HUGGINGFACE_MODEL`: Model to use (optional, default: meta-llama/Llama-3.3-70B-Instruct)
+
+*General:*
+- `LLM_PROVIDER`: Provider to use - "anthropic" or "huggingface" (optional, default: anthropic)
+
+### start_server_api() Parameters
+
+```python
+nanda.start_server_api(
+    anthropic_key,        # Anthropic API key (or None if using other provider)
+    domain,               # Domain name for the server
+    agent_id=None,        # Custom agent ID (auto-generated if not provided)
+    port=6000,            # Agent bridge port
+    api_port=6001,        # Flask API port
+    registry=None,        # Registry URL (optional)
+    public_url=None,      # Public URL for Agent Bridge (optional)
+    api_url=None,         # API URL for User Client (optional)
+    cert=None,            # Path to SSL certificate (optional)
+    key=None,             # Path to SSL key (optional)
+    ssl=True,             # Enable SSL (default: True)
+    llm_provider=None,    # "anthropic" or "huggingface" (default: anthropic)
+    llm_model=None,       # Model name/ID (uses provider default if not set)
+    llm_api_key=None      # API key for the LLM provider
+)
+```
+
 ### Production Deployment
 
 For production deployment with SSL:

nanda_adapter/__init__.py

@@ -4,6 +4,10 @@
 
 This package provides a framework for creating customizable AI agents with pluggable
 message improvement logic, built on top of the python_a2a communication framework.
+
+Supports multiple LLM providers:
+- Anthropic Claude (default)
+- Hugging Face Inference API
 """
 
 from .core.nanda import NANDA
@@ -14,6 +18,15 @@
     get_message_improver, 
     list_message_improvers
 )
+from .core.llm_providers import (
+    LLMProvider,
+    AnthropicProvider,
+    HuggingFaceProvider,
+    get_provider,
+    set_provider,
+    create_provider,
+    init_provider
+)
 
 __version__ = "1.0.0"
 __author__ = "NANDA Team"
@@ -26,5 +39,13 @@
     "message_improver",
     "register_message_improver", 
     "get_message_improver",
-    "list_message_improvers"
+    "list_message_improvers",
+    # LLM Providers
+    "LLMProvider",
+    "AnthropicProvider",
+    "HuggingFaceProvider",
+    "get_provider",
+    "set_provider",
+    "create_provider",
+    "init_provider"
 ]

nanda_adapter/core/__init__.py

@@ -13,12 +13,29 @@
     get_message_improver, 
     list_message_improvers
 )
+from .llm_providers import (
+    LLMProvider,
+    AnthropicProvider,
+    HuggingFaceProvider,
+    get_provider,
+    set_provider,
+    create_provider,
+    init_provider
+)
 
 __all__ = [
     "NANDA",
     "AgentBridge",
     "message_improver",
     "register_message_improver", 
     "get_message_improver",
-    "list_message_improvers"
+    "list_message_improvers",
+    # LLM Providers
+    "LLMProvider",
+    "AnthropicProvider",
+    "HuggingFaceProvider",
+    "get_provider",
+    "set_provider",
+    "create_provider",
+    "init_provider"
 ]

nanda_adapter/core/agent_bridge.py

@@ -7,27 +7,27 @@
 import requests
 from typing import Optional
 from datetime import datetime
-from anthropic import Anthropic, APIStatusError
 from python_a2a import (
     A2AServer, A2AClient, run_server,
     Message, TextContent, MessageRole, ErrorContent, Metadata
 )
 import asyncio
-from mcp_utils import MCPClient
 import base64
 
+# Handle different import contexts
+try:
+    from .llm_providers import get_provider, init_provider
+    from .mcp_utils import MCPClient
+except ImportError:
+    from llm_providers import get_provider, init_provider
+    from mcp_utils import MCPClient
+
 import sys
 sys.stdout.reconfigure(line_buffering=True)
 
-# Set API key through environment variable or directly in the code
-ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY") or "your key"
-
 # Toggle for message improvement feature
 IMPROVE_MESSAGES = os.getenv("IMPROVE_MESSAGES", "true").lower() in ("true", "1", "yes", "y")
 
-# Create Anthropic client with explicit API key
-anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
-
 # Get agent configuration from environment variables
 def get_agent_id():
     """Get AGENT_ID dynamically from environment variables"""
@@ -58,7 +58,9 @@ def get_agent_id():
     "default": "Improve the following message to make it more clear, compelling, and professional without changing the core content or adding fictional information. Keep the same overall meaning but enhance the phrasing and structure. Don't make it too verbose - keep it concise but impactful. Return only the improved message without explanations or introductions."
 }
 
-SMITHERY_API_KEY = os.getenv("SMITHERY_API_KEY") or "bfcb8cec-9d56-4957-8156-bced0bfca532"
+SMITHERY_API_KEY = os.getenv("SMITHERY_API_KEY")
+if not SMITHERY_API_KEY:
+    print("WARNING: SMITHERY_API_KEY not set - Smithery MCP servers will not work")
 
 def get_registry_url():
     """Get the registry URL from file or use default"""
@@ -153,7 +155,7 @@ def log_message(conversation_id, path, source, message_text):
     print(f"Logged message from {source} in conversation {conversation_id}")
 
 def call_claude(prompt: str, additional_context: str, conversation_id: str, current_path: str, system_prompt: str = None) -> Optional[str]:
-    """Wrapper that never raises: returns text or None on failure."""
+    """Wrapper that never raises: returns text or None on failure. Uses configured LLM provider."""
     try:
         # Use the specified system prompt or default to the agent's system prompt
         if system_prompt:
@@ -165,60 +167,41 @@ def call_claude(prompt: str, additional_context: str, conversation_id: str, curr
         # Combine the prompt with additional context if provided
         full_prompt = prompt
         if additional_context and additional_context.strip():
-            full_prompt = f"ADDITIONAL CONTEXT FRseOM USER: {additional_context}\n\nMESSAGE: {prompt}"
+            full_prompt = f"ADDITIONAL CONTEXT FROM USER: {additional_context}\n\nMESSAGE: {prompt}"
 
         agent_id = get_agent_id()
-        print(f"Agent {agent_id}: Calling Claude with prompt: {full_prompt[:50]}...")
-        resp = anthropic.messages.create(
-            model="claude-3-5-sonnet-20241022",
-            max_tokens=512,
-            messages=[{"role":"user","content":full_prompt}],
-            system=system
-        )
-        response_text = resp.content[0].text
+        provider = get_provider()
+        print(f"Agent {agent_id}: Calling {provider.name} with prompt: {full_prompt[:50]}...")
+
+        response_text = provider.complete(full_prompt, system=system, max_tokens=512)
 
-        # Log the Claude response
-        log_message(conversation_id, current_path, f"Claude {agent_id}", response_text)
+        if response_text:
+            # Log the LLM response
+            log_message(conversation_id, current_path, f"{provider.name} {agent_id}", response_text)
 
         return response_text
-    except APIStatusError as e:
-        print(f"Agent {agent_id}: Anthropic API error:", e.status_code, e.message, flush=True)
-        # If we hit a credit limit error, return a fallback message
-        if "credit balance is too low" in str(e):
-            return f"Agent {agent_id} processed (API credit limit reached): {prompt}"
     except Exception as e:
-        print(f"Agent {agent_id}: Anthropic SDK error:", e, flush=True)
+        agent_id = get_agent_id()
+        print(f"Agent {agent_id}: LLM error:", e, flush=True)
         traceback.print_exc()
     return None
 
 def call_claude_direct(message_text: str, system_prompt: str = None) -> Optional[str]:
-    """Wrapper that never raises: returns text or None on failure."""
+    """Wrapper that never raises: returns text or None on failure. Uses configured LLM provider."""
     try:
-        # Use the specified system prompt or default to the agent's system prompt
-
         # Combine the prompt with additional context if provided
         full_prompt = f"MESSAGE: {message_text}"
 
         agent_id = get_agent_id()
-        print(f"Agent {agent_id}: Calling Claude with prompt: {full_prompt[:50]}...")
-        resp = anthropic.messages.create(
-            model="claude-3-5-sonnet-20241022",
-            max_tokens=512,
-            messages=[{"role":"user","content":full_prompt}],
-            system=system_prompt
-        )
-        response_text = resp.content[0].text
+        provider = get_provider()
+        print(f"Agent {agent_id}: Calling {provider.name} with prompt: {full_prompt[:50]}...")
 
-        # Log the Claude response
+        response_text = provider.complete(full_prompt, system=system_prompt, max_tokens=512)
 
         return response_text
-    except APIStatusError as e:
-        print(f"Agent {agent_id}: Anthropic API error:", e.status_code, e.message, flush=True)
-        # If we hit a credit limit error, return a fallback message
-        if "credit balance is too low" in str(e):
-            return f"Agent {agent_id} processed (API credit limit reached): {message_text}"
     except Exception as e:
-        print(f"Agent {agent_id}: Anthropic SDK error:", e, flush=True)
+        agent_id = get_agent_id()
+        print(f"Agent {agent_id}: LLM error:", e, flush=True)
         traceback.print_exc()
     return None