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Troubleshooting

Quick solutions to common issues

Resolve problems quickly with 400+ modules and 237 enterprise features

Enterprise Support

Part of 237 enterprise modules with comprehensive debugging tools. See Enterprise Documentation.

1. Installation Issues

Problem: ModuleNotFoundError: No module named 'agenticaiframework'

Solution:

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# Install the package
pip install agenticaiframework

# Or from source
git clone https://github.com/isathish/agenticaiframework.git
cd agenticaiframework
pip install -e .

Virtual Environment

Always use a virtual environment:

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python -m venv .venv
source .venv/bin/activate # macOS/Linux
.venv\Scripts\activate # Windows
pip install agenticaiframework

2. API Key Errors

Problem: Invalid API key or Authentication failed

Solution:

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# Set environment variables for your LLM provider
export OPENAI_API_KEY="your-key-here"
export ANTHROPIC_API_KEY="your-key-here"
export AZURE_OPENAI_API_KEY="your-key-here"

Or in Python:

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import os
os.environ["OPENAI_API_KEY"] = "your-key-here"

3. Agent Not Found

Problem: ValueError: Agent 'xyz' not found

Solution:

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from agenticaiframework import Agent, AgentManager

# Create and register the agent
agent = Agent(name="xyz", role="assistant", capabilities=["text"])
manager = AgentManager()
manager.register_agent(agent)

# Verify registration
logger.info([a.name for a in manager.agents])

4. Tool Not Found

Problem: ValueError: Tool 'abc' not found

Solution:

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from agenticaiframework.tools import tool_registry

# Check available tools
logger.info(tool_registry.list_tools())

# Register a new tool
from agenticaiframework.tools import register_tool, BaseTool

@register_tool()
class MyTool(BaseTool):
    name = "abc"
    description = "My custom tool"

    def _run(self, input_data):
        return {"result": "success"}

5. LLM Provider Errors

Problem: Provider not supported

Solution:

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from agenticaiframework.llms import LLMManager

llm = LLMManager()

# Check available models
logger.info(llm.list_models())

# Register a custom model
def my_llm_function(prompt, **kwargs):
    return f"Response: {prompt}"

llm.register_model("my-model", my_llm_function)
llm.set_active_model("my-model")

6. Memory Issues

Problem: Data not persisting

Solution:

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from agenticaiframework.memory import MemoryManager

memory = MemoryManager()

# Store in long-term memory for persistence
memory.store("key", "value", memory_type="long_term")

# Or consolidate short-term to long-term
memory.consolidate()

7. Process Execution Errors

Problem: Process 'xyz' not found

Solution:

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from agenticaiframework.processes import ProcessManager

pm = ProcessManager()

# List available processes
logger.info(pm.list_processes())

# Register your process
pm.register_process("xyz", my_process_function)

8. Performance Issues

Problem: Slow agent responses

Solution:

  1. Optimize prompts - Reduce token usage
  2. Enable caching - Cache repeated LLM calls
  3. Use async - Process multiple tasks concurrently
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from agenticaiframework.llms import LLMManager

llm = LLMManager(enable_caching=True)

# Cached responses are returned instantly
response1 = llm.generate("What is AI?") # First call - slower
response2 = llm.generate("What is AI?") # Cached - instant

9. Memory Leaks

Problem: Increasing memory usage over time

Solution:

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from agenticaiframework.memory import MemoryManager

memory = MemoryManager()

# Clear old short-term memory periodically
memory.clear_short_term()

# Or set TTL when storing
memory.store("temp_key", "value", ttl=3600) # Expires in 1 hour

10. Deployment Problems

Problem: Application works locally but fails in production

Checklist:

  • All environment variables are set correctly
  • All dependencies are installed (pip freeze > requirements.txt)
  • Network access for external APIs is available
  • File paths use relative paths or environment variables
  • Memory and CPU resources are sufficient

11. Debugging Tips

Enable Debug Logging

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from agenticaiframework.configurations import ConfigurationManager

config = ConfigurationManager()
config.set_config("Logging", {"log_level": "DEBUG"})

Use Monitoring System

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from agenticaiframework.monitoring import MonitoringSystem

monitor = MonitoringSystem()
monitor.log_event("DebugInfo", {"step": "processing", "data": data})

Run Tests

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pytest tests/ -v --cov=agenticaiframework

12. Getting Help

Community Resources