Fixes issues with OpenRouter/Anthropic (disabling streaming until better Pydantic AI implementation)

.env.example

@@ -2,16 +2,18 @@
 # OpenAI: https://api.openai.com/v1
 # Ollama (example): http://localhost:11434/v1
 # OpenRouter: https://openrouter.ai/api/v1
+# Anthropic: https://api.anthropic.com/v1
 BASE_URL=
 
 # For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
+# For Anthropic: https://console.anthropic.com/account/keys
 # For OpenRouter: https://openrouter.ai/keys
 # For Ollama, no need to set this unless you specifically configured an API key
 LLM_API_KEY=
 
 # Get your Open AI API Key by following these instructions -
 # https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
-# Even if using OpenRouter, you still need to set this for the embedding model.
+# Even if using Anthropic or OpenRouter, you still need to set this for the embedding model.
 # No need to set this if using Ollama.
 OPENAI_API_KEY=
 

README.md

@@ -42,7 +42,7 @@ Since V4 is the current version of Archon, all the code for V4 is in both the ma
 - Docker (optional but preferred)
 - Python 3.11+
 - Supabase account (for vector database)
-- OpenAI/OpenRouter API key or Ollama for local LLMs
+- OpenAI/Anthropic/OpenRouter API key or Ollama for local LLMs (note that only OpenAI supports streaming in the Streamlit UI currently)
 
 ### Installation
 

archon/archon_graph.py

@@ -1,3 +1,4 @@
+from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.openai import OpenAIModel
 from pydantic_ai import Agent, RunContext
 from langgraph.graph import StateGraph, START, END
@@ -31,21 +32,29 @@ logfire.configure(send_to_logfire='never')
 
 base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
 api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
+
 is_ollama = "localhost" in base_url.lower()
-reasoner_llm_model = get_env_var('REASONER_MODEL') or 'o3-mini'
+is_anthropic = "anthropic" in base_url.lower()
+is_openai = "openai" in base_url.lower()
+
+reasoner_llm_model_name = get_env_var('REASONER_MODEL') or 'o3-mini'
+reasoner_llm_model = AnthropicModel(reasoner_llm_model_name, api_key=api_key) if is_anthropic else OpenAIModel(reasoner_llm_model_name, base_url=base_url, api_key=api_key)
+
 reasoner = Agent(  
-    OpenAIModel(reasoner_llm_model, base_url=base_url, api_key=api_key),
+    reasoner_llm_model,
     system_prompt='You are an expert at coding AI agents with Pydantic AI and defining the scope for doing so.',  
 )
 
-primary_llm_model = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
+primary_llm_model_name = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
+primary_llm_model = AnthropicModel(primary_llm_model_name, api_key=api_key) if is_anthropic else OpenAIModel(primary_llm_model_name, base_url=base_url, api_key=api_key)
+
 router_agent = Agent(  
-    OpenAIModel(primary_llm_model, base_url=base_url, api_key=api_key),
+    primary_llm_model,
     system_prompt='Your job is to route the user message either to the end of the conversation or to continue coding the AI agent.',  
 )
 
 end_conversation_agent = Agent(  
-    OpenAIModel(primary_llm_model, base_url=base_url, api_key=api_key),
+    primary_llm_model,
     system_prompt='Your job is to end a conversation for creating an AI agent by giving instructions for how to execute the agent and they saying a nice goodbye to the user.',  
 )
 
@@ -124,7 +133,7 @@ async def coder_agent(state: AgentState, writer):
         message_history.extend(ModelMessagesTypeAdapter.validate_json(message_row))
 
     # Run the agent in a stream
-    if is_ollama:
+    if not is_openai:
         writer = get_stream_writer()
         result = await pydantic_ai_coder.run(state['latest_user_message'], deps=deps, message_history= message_history)
         writer(result.data)
@@ -178,7 +187,7 @@ async def finish_conversation(state: AgentState, writer):
         message_history.extend(ModelMessagesTypeAdapter.validate_json(message_row))
 
     # Run the agent in a stream
-    if is_ollama:
+    if not is_openai:
         writer = get_stream_writer()
         result = await end_conversation_agent.run(state['latest_user_message'], message_history= message_history)
         writer(result.data)   

archon/pydantic_ai_coder.py

@@ -11,6 +11,7 @@ import json
 from typing import Dict, Any, List, Optional
 from pydantic import BaseModel
 from pydantic_ai import Agent, ModelRetry, RunContext
+from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.openai import OpenAIModel
 from openai import AsyncOpenAI
 from supabase import Client
@@ -24,13 +25,15 @@ load_dotenv()
 llm = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
 base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
 api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
-model = OpenAIModel(llm, base_url=base_url, api_key=api_key)
+
+is_ollama = "localhost" in base_url.lower()
+is_anthropic = "anthropic" in base_url.lower()
+
+model = AnthropicModel(llm, api_key=api_key) if is_anthropic else OpenAIModel(llm, base_url=base_url, api_key=api_key)
 embedding_model = get_env_var('EMBEDDING_MODEL') or 'text-embedding-3-small'
 
 logfire.configure(send_to_logfire='if-token-present')
 
-is_ollama = "localhost" in base_url.lower()
-
 @dataclass
 class PydanticAIDeps:
     supabase: Client
@@ -38,41 +41,217 @@ class PydanticAIDeps:
     reasoner_output: str
 
 system_prompt = """
-~~ CONTEXT: ~~
+[ROLE AND CONTEXT]
+You are a specialized AI agent engineer focused on building robust Pydantic AI agents. You have comprehensive access to the Pydantic AI documentation, including API references, usage guides, and implementation examples.
 
-You are an expert at Pydantic AI - a Python AI agent framework that you have access to all the documentation to,
+[CORE RESPONSIBILITIES]
-including examples, an API reference, and other resources to help you build Pydantic AI agents.
+1. Agent Development
+   - Create new agents from user requirements
+   - Complete partial agent implementations
+   - Optimize and debug existing agents
+   - Guide users through agent specification if needed
 
-~~ GOAL: ~~
+2. Documentation Integration
+   - Systematically search documentation using RAG before any implementation
+   - Cross-reference multiple documentation pages for comprehensive understanding
+   - Validate all implementations against current best practices
+   - Notify users if documentation is insufficient for any requirement
 
-Your only job is to help the user create an AI agent with Pydantic AI.
+[CODE STRUCTURE AND DELIVERABLES]
-The user will describe the AI agent they want to build, or if they don't, guide them towards doing so.
+All new agents must include these files with complete, production-ready code:
-You will take their requirements, and then search through the Pydantic AI documentation with the tools provided
-to find all the necessary information to create the AI agent with correct code.
 
-It's important for you to search through multiple Pydantic AI documentation pages to get all the information you need.
+1. agent.py
-Almost never stick to just one page - use RAG and the other documentation tools multiple times when you are creating
+   - Primary agent definition and configuration
-an AI agent from scratch for the user.
+   - Core agent logic and behaviors
+   - No tool implementations allowed here
 
-~~ STRUCTURE: ~~
+2. agent_tools.py
+   - All tool function implementations
+   - Tool configurations and setup
+   - External service integrations
 
-When you build an AI agent from scratch, split the agent into this files and give the code for each:
+3. agent_prompts.py
-- `agent.py`: The main agent file, which is where the Pydantic AI agent is defined.
+   - System prompts
-- `agent_tools.py`: A tools file for the agent, which is where all the tool functions are defined. Use this for more complex agents.
+   - Task-specific prompts
-- `agent_prompts.py`: A prompts file for the agent, which includes all system prompts and other prompts used by the agent. Use this when there are many prompts or large ones.
+   - Conversation templates
-- `.env.example`: An example `.env` file - specify each variable that the user will need to fill in and a quick comment above each one for how to do so.
+   - Instruction sets
-- `requirements.txt`: Don't include any versions, just the top level package names needed for the agent.
 
-~~ INSTRUCTIONS: ~~
+4. .env.example
+   - Required environment variables
+   - Clear setup instructions in a comment above the variable for how to do so
+   - API configuration templates
 
-- Don't ask the user before taking an action, just do it. Always make sure you look at the documentation with the provided tools before writing any code.
+5. requirements.txt
-- When you first look at the documentation, always start with RAG.
+   - Core dependencies without versions
-Then also always check the list of available documentation pages and retrieve the content of page(s) if it'll help.
+   - User-specified packages included
-- Always let the user know when you didn't find the answer in the documentation or the right URL - be honest.
+
-- Helpful tip: when starting a new AI agent build, it's a good idea to look at the 'weather agent' in the docs as an example.
+[DOCUMENTATION WORKFLOW]
-- When starting a new AI agent build, always produce the full code for the AI agent - never tell the user to finish a tool/function.
+1. Initial Research
-- When refining an existing AI agent build in a conversation, just share the code changes necessary.
+   - Begin with RAG search for relevant documentation
-- Each time you respond to the user, ask them to let you know either if they need changes or the code looks good.
+   - List all documentation pages using list_documentation_pages
+   - Retrieve specific page content using get_page_content
+   - Cross-reference the weather agent example for best practices
+
+2. Implementation
+   - Provide complete, working code implementations
+   - Never leave placeholder functions
+   - Include all necessary error handling
+   - Implement proper logging and monitoring
+
+3. Quality Assurance
+   - Verify all tool implementations are complete
+   - Ensure proper separation of concerns
+   - Validate environment variable handling
+   - Test critical path functionality
+
+[INTERACTION GUIDELINES]
+- Take immediate action without asking for permission
+- Always verify documentation before implementation
+- Provide honest feedback about documentation gaps
+- Include specific enhancement suggestions
+- Request user feedback on implementations
+- Maintain code consistency across files
+
+[ERROR HANDLING]
+- Implement robust error handling in all tools
+- Provide clear error messages
+- Include recovery mechanisms
+- Log important state changes
+
+[BEST PRACTICES]
+- Follow Pydantic AI naming conventions
+- Implement proper type hints
+- Include comprehensive docstrings, the agent uses this to understand what tools are for.
+- Maintain clean code structure
+- Use consistent formatting
+
+Here is a good example of a Pydantic AI agent:
+
+```python
+from __future__ import annotations as _annotations
+
+import asyncio
+import os
+from dataclasses import dataclass
+from typing import Any
+
+import logfire
+from devtools import debug
+from httpx import AsyncClient
+
+from pydantic_ai import Agent, ModelRetry, RunContext
+
+# 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured
+logfire.configure(send_to_logfire='if-token-present')
+
+
+@dataclass
+class Deps:
+    client: AsyncClient
+    weather_api_key: str | None
+    geo_api_key: str | None
+
+
+weather_agent = Agent(
+    'openai:gpt-4o',
+    # 'Be concise, reply with one sentence.' is enough for some models (like openai) to use
+    # the below tools appropriately, but others like anthropic and gemini require a bit more direction.
+    system_prompt=(
+        'Be concise, reply with one sentence.'
+        'Use the `get_lat_lng` tool to get the latitude and longitude of the locations, '
+        'then use the `get_weather` tool to get the weather.'
+    ),
+    deps_type=Deps,
+    retries=2,
+)
+
+
+@weather_agent.tool
+async def get_lat_lng(
+    ctx: RunContext[Deps], location_description: str
+) -> dict[str, float]:
+    \"\"\"Get the latitude and longitude of a location.
+
+    Args:
+        ctx: The context.
+        location_description: A description of a location.
+    \"\"\"
+    if ctx.deps.geo_api_key is None:
+        # if no API key is provided, return a dummy response (London)
+        return {'lat': 51.1, 'lng': -0.1}
+
+    params = {
+        'q': location_description,
+        'api_key': ctx.deps.geo_api_key,
+    }
+    with logfire.span('calling geocode API', params=params) as span:
+        r = await ctx.deps.client.get('https://geocode.maps.co/search', params=params)
+        r.raise_for_status()
+        data = r.json()
+        span.set_attribute('response', data)
+
+    if data:
+        return {'lat': data[0]['lat'], 'lng': data[0]['lon']}
+    else:
+        raise ModelRetry('Could not find the location')
+
+
+@weather_agent.tool
+async def get_weather(ctx: RunContext[Deps], lat: float, lng: float) -> dict[str, Any]:
+    \"\"\"Get the weather at a location.
+
+    Args:
+        ctx: The context.
+        lat: Latitude of the location.
+        lng: Longitude of the location.
+    \"\"\"
+    if ctx.deps.weather_api_key is None:
+        # if no API key is provided, return a dummy response
+        return {'temperature': '21 °C', 'description': 'Sunny'}
+
+    params = {
+        'apikey': ctx.deps.weather_api_key,
+        'location': f'{lat},{lng}',
+        'units': 'metric',
+    }
+    with logfire.span('calling weather API', params=params) as span:
+        r = await ctx.deps.client.get(
+            'https://api.tomorrow.io/v4/weather/realtime', params=params
+        )
+        r.raise_for_status()
+        data = r.json()
+        span.set_attribute('response', data)
+
+    values = data['data']['values']
+    # https://docs.tomorrow.io/reference/data-layers-weather-codes
+    code_lookup = {
+        ...
+    }
+    return {
+        'temperature': f'{values["temperatureApparent"]:0.0f}°C',
+        'description': code_lookup.get(values['weatherCode'], 'Unknown'),
+    }
+
+
+async def main():
+    async with AsyncClient() as client:
+        # create a free API key at https://www.tomorrow.io/weather-api/
+        weather_api_key = os.getenv('WEATHER_API_KEY')
+        # create a free API key at https://geocode.maps.co/
+        geo_api_key = os.getenv('GEO_API_KEY')
+        deps = Deps(
+            client=client, weather_api_key=weather_api_key, geo_api_key=geo_api_key
+        )
+        result = await weather_agent.run(
+            'What is the weather like in London and in Wiltshire?', deps=deps
+        )
+        debug(result)
+        print('Response:', result.data)
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+```
 """
 
 pydantic_ai_coder = Agent(
@@ -115,7 +294,7 @@ async def retrieve_relevant_documentation(ctx: RunContext[PydanticAIDeps], user_
         user_query: The user's question or query
         
     Returns:
-        A formatted string containing the top 5 most relevant documentation chunks
+        A formatted string containing the top 4 most relevant documentation chunks
     """
     try:
         # Get the embedding for the query
@@ -126,7 +305,7 @@ async def retrieve_relevant_documentation(ctx: RunContext[PydanticAIDeps], user_
             'match_site_pages',
             {
                 'query_embedding': query_embedding,
-                'match_count': 5,
+                'match_count': 4,
                 'filter': {'source': 'pydantic_ai_docs'}
             }
         ).execute()
@@ -220,8 +399,9 @@ async def get_page_content(ctx: RunContext[PydanticAIDeps], url: str) -> str:
         for chunk in result.data:
             formatted_content.append(chunk['content'])
             
-        # Join everything together
+        # Join everything together but limit the characters in case the page is massive (there are a coule big ones)
-        return "\n\n".join(formatted_content)
+        # This will be improved later so if the page is too big RAG will be performed on the page itself
+        return "\n\n".join(formatted_content)[:20000]
         
     except Exception as e:
         print(f"Error retrieving page content: {e}")

iterations/v4-streamlit-ui-overhaul/.env.example

@@ -2,16 +2,18 @@
 # OpenAI: https://api.openai.com/v1
 # Ollama (example): http://localhost:11434/v1
 # OpenRouter: https://openrouter.ai/api/v1
+# Anthropic: https://api.anthropic.com/v1
 BASE_URL=
 
 # For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
+# For Anthropic: https://console.anthropic.com/account/keys
 # For OpenRouter: https://openrouter.ai/keys
 # For Ollama, no need to set this unless you specifically configured an API key
 LLM_API_KEY=
 
 # Get your Open AI API Key by following these instructions -
 # https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
-# Even if using OpenRouter, you still need to set this for the embedding model.
+# Even if using Anthropic or OpenRouter, you still need to set this for the embedding model.
 # No need to set this if using Ollama.
 OPENAI_API_KEY=
 

iterations/v4-streamlit-ui-overhaul/README.md

@@ -25,7 +25,7 @@ This version continues to support both local LLMs with Ollama and cloud-based LL
 - Docker (optional but preferred)
 - Python 3.11+
 - Supabase account (for vector database)
-- OpenAI/OpenRouter API key or Ollama for local LLMs
+- OpenAI/OpenRouter/Anthropic API key or Ollama for local LLMs
 
 ## Installation
 

iterations/v4-streamlit-ui-overhaul/archon/archon_graph.py

@@ -1,3 +1,4 @@
+from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.openai import OpenAIModel
 from pydantic_ai import Agent, RunContext
 from langgraph.graph import StateGraph, START, END
@@ -31,21 +32,29 @@ logfire.configure(send_to_logfire='never')
 
 base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
 api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
+
 is_ollama = "localhost" in base_url.lower()
-reasoner_llm_model = get_env_var('REASONER_MODEL') or 'o3-mini'
+is_anthropic = "anthropic" in base_url.lower()
+is_openai = "openai" in base_url.lower()
+
+reasoner_llm_model_name = get_env_var('REASONER_MODEL') or 'o3-mini'
+reasoner_llm_model = AnthropicModel(reasoner_llm_model_name, api_key=api_key) if is_anthropic else OpenAIModel(reasoner_llm_model_name, base_url=base_url, api_key=api_key)
+
 reasoner = Agent(  
-    OpenAIModel(reasoner_llm_model, base_url=base_url, api_key=api_key),
+    reasoner_llm_model,
     system_prompt='You are an expert at coding AI agents with Pydantic AI and defining the scope for doing so.',  
 )
 
-primary_llm_model = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
+primary_llm_model_name = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
+primary_llm_model = AnthropicModel(primary_llm_model_name, api_key=api_key) if is_anthropic else OpenAIModel(primary_llm_model_name, base_url=base_url, api_key=api_key)
+
 router_agent = Agent(  
-    OpenAIModel(primary_llm_model, base_url=base_url, api_key=api_key),
+    primary_llm_model,
     system_prompt='Your job is to route the user message either to the end of the conversation or to continue coding the AI agent.',  
 )
 
 end_conversation_agent = Agent(  
-    OpenAIModel(primary_llm_model, base_url=base_url, api_key=api_key),
+    primary_llm_model,
     system_prompt='Your job is to end a conversation for creating an AI agent by giving instructions for how to execute the agent and they saying a nice goodbye to the user.',  
 )
 
@@ -124,7 +133,7 @@ async def coder_agent(state: AgentState, writer):
         message_history.extend(ModelMessagesTypeAdapter.validate_json(message_row))
 
     # Run the agent in a stream
-    if is_ollama:
+    if not is_openai:
         writer = get_stream_writer()
         result = await pydantic_ai_coder.run(state['latest_user_message'], deps=deps, message_history= message_history)
         writer(result.data)
@@ -178,7 +187,7 @@ async def finish_conversation(state: AgentState, writer):
         message_history.extend(ModelMessagesTypeAdapter.validate_json(message_row))
 
     # Run the agent in a stream
-    if is_ollama:
+    if not is_openai:
         writer = get_stream_writer()
         result = await end_conversation_agent.run(state['latest_user_message'], message_history= message_history)
         writer(result.data)   

iterations/v4-streamlit-ui-overhaul/archon/pydantic_ai_coder.py

@@ -11,6 +11,7 @@ import json
 from typing import Dict, Any, List, Optional
 from pydantic import BaseModel
 from pydantic_ai import Agent, ModelRetry, RunContext
+from pydantic_ai.models.anthropic import AnthropicModel
 from pydantic_ai.models.openai import OpenAIModel
 from openai import AsyncOpenAI
 from supabase import Client
@@ -24,13 +25,15 @@ load_dotenv()
 llm = get_env_var('PRIMARY_MODEL') or 'gpt-4o-mini'
 base_url = get_env_var('BASE_URL') or 'https://api.openai.com/v1'
 api_key = get_env_var('LLM_API_KEY') or 'no-llm-api-key-provided'
-model = OpenAIModel(llm, base_url=base_url, api_key=api_key)
+
+is_ollama = "localhost" in base_url.lower()
+is_anthropic = "anthropic" in base_url.lower()
+
+model = AnthropicModel(llm, api_key=api_key) if is_anthropic else OpenAIModel(llm, base_url=base_url, api_key=api_key)
 embedding_model = get_env_var('EMBEDDING_MODEL') or 'text-embedding-3-small'
 
 logfire.configure(send_to_logfire='if-token-present')
 
-is_ollama = "localhost" in base_url.lower()
-
 @dataclass
 class PydanticAIDeps:
     supabase: Client
@@ -38,41 +41,217 @@ class PydanticAIDeps:
     reasoner_output: str
 
 system_prompt = """
-~~ CONTEXT: ~~
+[ROLE AND CONTEXT]
+You are a specialized AI agent engineer focused on building robust Pydantic AI agents. You have comprehensive access to the Pydantic AI documentation, including API references, usage guides, and implementation examples.
 
-You are an expert at Pydantic AI - a Python AI agent framework that you have access to all the documentation to,
+[CORE RESPONSIBILITIES]
-including examples, an API reference, and other resources to help you build Pydantic AI agents.
+1. Agent Development
+   - Create new agents from user requirements
+   - Complete partial agent implementations
+   - Optimize and debug existing agents
+   - Guide users through agent specification if needed
 
-~~ GOAL: ~~
+2. Documentation Integration
+   - Systematically search documentation using RAG before any implementation
+   - Cross-reference multiple documentation pages for comprehensive understanding
+   - Validate all implementations against current best practices
+   - Notify users if documentation is insufficient for any requirement
 
-Your only job is to help the user create an AI agent with Pydantic AI.
+[CODE STRUCTURE AND DELIVERABLES]
-The user will describe the AI agent they want to build, or if they don't, guide them towards doing so.
+All new agents must include these files with complete, production-ready code:
-You will take their requirements, and then search through the Pydantic AI documentation with the tools provided
-to find all the necessary information to create the AI agent with correct code.
 
-It's important for you to search through multiple Pydantic AI documentation pages to get all the information you need.
+1. agent.py
-Almost never stick to just one page - use RAG and the other documentation tools multiple times when you are creating
+   - Primary agent definition and configuration
-an AI agent from scratch for the user.
+   - Core agent logic and behaviors
+   - No tool implementations allowed here
 
-~~ STRUCTURE: ~~
+2. agent_tools.py
+   - All tool function implementations
+   - Tool configurations and setup
+   - External service integrations
 
-When you build an AI agent from scratch, split the agent into this files and give the code for each:
+3. agent_prompts.py
-- `agent.py`: The main agent file, which is where the Pydantic AI agent is defined.
+   - System prompts
-- `agent_tools.py`: A tools file for the agent, which is where all the tool functions are defined. Use this for more complex agents.
+   - Task-specific prompts
-- `agent_prompts.py`: A prompts file for the agent, which includes all system prompts and other prompts used by the agent. Use this when there are many prompts or large ones.
+   - Conversation templates
-- `.env.example`: An example `.env` file - specify each variable that the user will need to fill in and a quick comment above each one for how to do so.
+   - Instruction sets
-- `requirements.txt`: Don't include any versions, just the top level package names needed for the agent.
 
-~~ INSTRUCTIONS: ~~
+4. .env.example
+   - Required environment variables
+   - Clear setup instructions in a comment above the variable for how to do so
+   - API configuration templates
 
-- Don't ask the user before taking an action, just do it. Always make sure you look at the documentation with the provided tools before writing any code.
+5. requirements.txt
-- When you first look at the documentation, always start with RAG.
+   - Core dependencies without versions
-Then also always check the list of available documentation pages and retrieve the content of page(s) if it'll help.
+   - User-specified packages included
-- Always let the user know when you didn't find the answer in the documentation or the right URL - be honest.
+
-- Helpful tip: when starting a new AI agent build, it's a good idea to look at the 'weather agent' in the docs as an example.
+[DOCUMENTATION WORKFLOW]
-- When starting a new AI agent build, always produce the full code for the AI agent - never tell the user to finish a tool/function.
+1. Initial Research
-- When refining an existing AI agent build in a conversation, just share the code changes necessary.
+   - Begin with RAG search for relevant documentation
-- Each time you respond to the user, ask them to let you know either if they need changes or the code looks good.
+   - List all documentation pages using list_documentation_pages
+   - Retrieve specific page content using get_page_content
+   - Cross-reference the weather agent example for best practices
+
+2. Implementation
+   - Provide complete, working code implementations
+   - Never leave placeholder functions
+   - Include all necessary error handling
+   - Implement proper logging and monitoring
+
+3. Quality Assurance
+   - Verify all tool implementations are complete
+   - Ensure proper separation of concerns
+   - Validate environment variable handling
+   - Test critical path functionality
+
+[INTERACTION GUIDELINES]
+- Take immediate action without asking for permission
+- Always verify documentation before implementation
+- Provide honest feedback about documentation gaps
+- Include specific enhancement suggestions
+- Request user feedback on implementations
+- Maintain code consistency across files
+
+[ERROR HANDLING]
+- Implement robust error handling in all tools
+- Provide clear error messages
+- Include recovery mechanisms
+- Log important state changes
+
+[BEST PRACTICES]
+- Follow Pydantic AI naming conventions
+- Implement proper type hints
+- Include comprehensive docstrings, the agent uses this to understand what tools are for.
+- Maintain clean code structure
+- Use consistent formatting
+
+Here is a good example of a Pydantic AI agent:
+
+```python
+from __future__ import annotations as _annotations
+
+import asyncio
+import os
+from dataclasses import dataclass
+from typing import Any
+
+import logfire
+from devtools import debug
+from httpx import AsyncClient
+
+from pydantic_ai import Agent, ModelRetry, RunContext
+
+# 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured
+logfire.configure(send_to_logfire='if-token-present')
+
+
+@dataclass
+class Deps:
+    client: AsyncClient
+    weather_api_key: str | None
+    geo_api_key: str | None
+
+
+weather_agent = Agent(
+    'openai:gpt-4o',
+    # 'Be concise, reply with one sentence.' is enough for some models (like openai) to use
+    # the below tools appropriately, but others like anthropic and gemini require a bit more direction.
+    system_prompt=(
+        'Be concise, reply with one sentence.'
+        'Use the `get_lat_lng` tool to get the latitude and longitude of the locations, '
+        'then use the `get_weather` tool to get the weather.'
+    ),
+    deps_type=Deps,
+    retries=2,
+)
+
+
+@weather_agent.tool
+async def get_lat_lng(
+    ctx: RunContext[Deps], location_description: str
+) -> dict[str, float]:
+    \"\"\"Get the latitude and longitude of a location.
+
+    Args:
+        ctx: The context.
+        location_description: A description of a location.
+    \"\"\"
+    if ctx.deps.geo_api_key is None:
+        # if no API key is provided, return a dummy response (London)
+        return {'lat': 51.1, 'lng': -0.1}
+
+    params = {
+        'q': location_description,
+        'api_key': ctx.deps.geo_api_key,
+    }
+    with logfire.span('calling geocode API', params=params) as span:
+        r = await ctx.deps.client.get('https://geocode.maps.co/search', params=params)
+        r.raise_for_status()
+        data = r.json()
+        span.set_attribute('response', data)
+
+    if data:
+        return {'lat': data[0]['lat'], 'lng': data[0]['lon']}
+    else:
+        raise ModelRetry('Could not find the location')
+
+
+@weather_agent.tool
+async def get_weather(ctx: RunContext[Deps], lat: float, lng: float) -> dict[str, Any]:
+    \"\"\"Get the weather at a location.
+
+    Args:
+        ctx: The context.
+        lat: Latitude of the location.
+        lng: Longitude of the location.
+    \"\"\"
+    if ctx.deps.weather_api_key is None:
+        # if no API key is provided, return a dummy response
+        return {'temperature': '21 °C', 'description': 'Sunny'}
+
+    params = {
+        'apikey': ctx.deps.weather_api_key,
+        'location': f'{lat},{lng}',
+        'units': 'metric',
+    }
+    with logfire.span('calling weather API', params=params) as span:
+        r = await ctx.deps.client.get(
+            'https://api.tomorrow.io/v4/weather/realtime', params=params
+        )
+        r.raise_for_status()
+        data = r.json()
+        span.set_attribute('response', data)
+
+    values = data['data']['values']
+    # https://docs.tomorrow.io/reference/data-layers-weather-codes
+    code_lookup = {
+        ...
+    }
+    return {
+        'temperature': f'{values["temperatureApparent"]:0.0f}°C',
+        'description': code_lookup.get(values['weatherCode'], 'Unknown'),
+    }
+
+
+async def main():
+    async with AsyncClient() as client:
+        # create a free API key at https://www.tomorrow.io/weather-api/
+        weather_api_key = os.getenv('WEATHER_API_KEY')
+        # create a free API key at https://geocode.maps.co/
+        geo_api_key = os.getenv('GEO_API_KEY')
+        deps = Deps(
+            client=client, weather_api_key=weather_api_key, geo_api_key=geo_api_key
+        )
+        result = await weather_agent.run(
+            'What is the weather like in London and in Wiltshire?', deps=deps
+        )
+        debug(result)
+        print('Response:', result.data)
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+```
 """
 
 pydantic_ai_coder = Agent(
@@ -115,7 +294,7 @@ async def retrieve_relevant_documentation(ctx: RunContext[PydanticAIDeps], user_
         user_query: The user's question or query
         
     Returns:
-        A formatted string containing the top 5 most relevant documentation chunks
+        A formatted string containing the top 4 most relevant documentation chunks
     """
     try:
         # Get the embedding for the query
@@ -126,7 +305,7 @@ async def retrieve_relevant_documentation(ctx: RunContext[PydanticAIDeps], user_
             'match_site_pages',
             {
                 'query_embedding': query_embedding,
-                'match_count': 5,
+                'match_count': 4,
                 'filter': {'source': 'pydantic_ai_docs'}
             }
         ).execute()
@@ -220,8 +399,9 @@ async def get_page_content(ctx: RunContext[PydanticAIDeps], url: str) -> str:
         for chunk in result.data:
             formatted_content.append(chunk['content'])
             
-        # Join everything together
+        # Join everything together but limit the characters in case the page is massive (there are a coule big ones)
-        return "\n\n".join(formatted_content)
+        # This will be improved later so if the page is too big RAG will be performed on the page itself
+        return "\n\n".join(formatted_content)[:20000]
         
     except Exception as e:
         print(f"Error retrieving page content: {e}")

iterations/v4-streamlit-ui-overhaul/streamlit_ui.py

@@ -21,6 +21,7 @@ from openai import AsyncOpenAI
 from supabase import Client, create_client
 from dotenv import load_dotenv
 from utils.utils import get_env_var, save_env_var, write_to_log
+from future_enhancements import future_enhancements_tab
 
 # Import all the message part classes
 from pydantic_ai.messages import (
@@ -71,13 +72,13 @@ st.set_page_config(
     layout="wide",
 )
 
-# Set custom theme colors to match Archon logo (pink and green)
+# Set custom theme colors to match Archon logo (green and pink)
-# Primary color (pink) and secondary color (green)
+# Primary color (green) and secondary color (pink)
 st.markdown("""
     <style>
     :root {
-        --primary-color: #00CC99;  /* Pink */
+        --primary-color: #00CC99;  /* Green */
-        --secondary-color: #FF69B4; /* Green */
+        --secondary-color: #EB2D8C; /* Pink */
         --text-color: #262730;
     }
     
@@ -487,9 +488,9 @@ def intro_tab():
         create_new_tab_button("Go to the Documentation Section (New Tab)", "Documentation", key="goto_docs", use_container_width=True)
     
     # Step 4: Agent Service
-    with st.expander("Step 4: Agent Service Setup", expanded=False):
+    with st.expander("Step 4: Agent Service Setup (for MCP)", expanded=False):
         st.markdown("""
-        ### Agent Service Setup
+        ### MCP Agent Service Setup
         
         Start the graph service for agent generation:
         
@@ -879,8 +880,8 @@ def show_manual_sql_instructions(sql, recreate=False):
 
 def agent_service_tab():
     """Display the agent service interface for managing the graph service"""
-    st.header("Agent Service")
+    st.header("MCP Agent Service")
-    st.write("Start, restart, and monitor the Archon agent service.")
+    st.write("Start, restart, and monitor the Archon agent service for MCP.")
     
     # Initialize session state variables if they don't exist
     if "service_process" not in st.session_state:
@@ -1102,17 +1103,18 @@ def environment_tab():
     st.write("- Configure your environment variables for Archon. These settings will be saved and used for future sessions.")
     st.write("- NOTE: Press 'enter' to save after inputting a variable, otherwise click the 'save' button at the bottom.")
     st.write("- HELP: Hover over the '?' icon on the right for each environment variable for help/examples.")
+    st.warning("⚠️ If your agent service for MCP is already running, you'll need to restart it after changing environment variables.")
 
     # Define environment variables and their descriptions from .env.example
     env_vars = {
         "BASE_URL": {
             "description": "Base URL for the OpenAI instance (default is https://api.openai.com/v1)",
-            "help": "OpenAI: https://api.openai.com/v1\n\nOllama (example): http://localhost:11434/v1\n\nOpenRouter: https://openrouter.ai/api/v1",
+            "help": "OpenAI: https://api.openai.com/v1\n\n\n\nAnthropic: https://api.anthropic.com/v1\n\nOllama (example): http://localhost:11434/v1\n\nOpenRouter: https://openrouter.ai/api/v1",
             "sensitive": False
         },
         "LLM_API_KEY": {
             "description": "API key for your LLM provider",
-            "help": "For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key\n\nFor OpenRouter: https://openrouter.ai/keys\n\nFor Ollama, no need to set this unless you specifically configured an API key",
+            "help": "For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key\n\nFor Anthropic: https://console.anthropic.com/account/keys\n\nFor OpenRouter: https://openrouter.ai/keys\n\nFor Ollama, no need to set this unless you specifically configured an API key",
             "sensitive": True
         },
         "OPENAI_API_KEY": {
@@ -1207,7 +1209,7 @@ async def main():
     query_params = st.query_params
     if "tab" in query_params:
         tab_name = query_params["tab"]
-        if tab_name in ["Intro", "Chat", "Environment", "Database", "Documentation", "Agent Service", "MCP"]:
+        if tab_name in ["Intro", "Chat", "Environment", "Database", "Documentation", "Agent Service", "MCP", "Future Enhancements"]:
             st.session_state.selected_tab = tab_name
 
     # Add sidebar navigation
@@ -1229,6 +1231,7 @@ async def main():
         docs_button = st.button("Documentation", use_container_width=True, key="docs_button")
         service_button = st.button("Agent Service", use_container_width=True, key="service_button")
         mcp_button = st.button("MCP", use_container_width=True, key="mcp_button")
+        future_enhancements_button = st.button("Future Enhancements", use_container_width=True, key="future_enhancements_button")
         
         # Update selected tab based on button clicks
         if intro_button:
@@ -1245,6 +1248,8 @@ async def main():
             st.session_state.selected_tab = "Database"
         elif docs_button:
             st.session_state.selected_tab = "Documentation"
+        elif future_enhancements_button:
+            st.session_state.selected_tab = "Future Enhancements"
     
     # Display the selected tab
     if st.session_state.selected_tab == "Intro":
@@ -1268,6 +1273,9 @@ async def main():
     elif st.session_state.selected_tab == "Documentation":
         st.title("Archon - Documentation")
         documentation_tab()
+    elif st.session_state.selected_tab == "Future Enhancements":
+        st.title("Archon - Future Enhancements")
+        future_enhancements_tab()
 
 if __name__ == "__main__":
     asyncio.run(main())

streamlit_ui.py

@@ -1103,17 +1103,18 @@ def environment_tab():
     st.write("- Configure your environment variables for Archon. These settings will be saved and used for future sessions.")
     st.write("- NOTE: Press 'enter' to save after inputting a variable, otherwise click the 'save' button at the bottom.")
     st.write("- HELP: Hover over the '?' icon on the right for each environment variable for help/examples.")
+    st.warning("⚠️ If your agent service for MCP is already running, you'll need to restart it after changing environment variables.")
 
     # Define environment variables and their descriptions from .env.example
     env_vars = {
         "BASE_URL": {
             "description": "Base URL for the OpenAI instance (default is https://api.openai.com/v1)",
-            "help": "OpenAI: https://api.openai.com/v1\n\nOllama (example): http://localhost:11434/v1\n\nOpenRouter: https://openrouter.ai/api/v1",
+            "help": "OpenAI: https://api.openai.com/v1\n\n\n\nAnthropic: https://api.anthropic.com/v1\n\nOllama (example): http://localhost:11434/v1\n\nOpenRouter: https://openrouter.ai/api/v1",
             "sensitive": False
         },
         "LLM_API_KEY": {
             "description": "API key for your LLM provider",
-            "help": "For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key\n\nFor OpenRouter: https://openrouter.ai/keys\n\nFor Ollama, no need to set this unless you specifically configured an API key",
+            "help": "For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key\n\nFor Anthropic: https://console.anthropic.com/account/keys\n\nFor OpenRouter: https://openrouter.ai/keys\n\nFor Ollama, no need to set this unless you specifically configured an API key",
             "sensitive": True
         },
         "OPENAI_API_KEY": {