cogency 0.2.2

Agentic AI out of the box

Cogency (Python)

Multi-step reasoning agents with clean architecture

Installation

pip install cogency

Quick Start

from cogency.agent import Agent
from cogency.llm import GeminiLLM
from cogency.tools import CalculatorTool, WebSearchTool, FileManagerTool

# Create agent with multiple tools
llm = GeminiLLM(api_key="your-key")
agent = Agent(
    name="MyAgent", 
    llm=llm, 
    tools=[
        CalculatorTool(), 
        WebSearchTool(), 
        FileManagerTool()
    ]
)

# Execute with tracing
result = agent.run("What is 15 * 23?", enable_trace=True, print_trace=True)
print(result["response"])

Core Architecture

Cogency uses a clean 5-step reasoning loop:

1. Plan - Decide strategy and if tools are needed
2. Reason - Select tools and prepare inputs
3. Act - Execute tools with validation
4. Reflect - Evaluate results and decide next steps
5. Respond - Format clean answer for user

This separation enables emergent reasoning behavior - agents adapt their tool usage based on results without explicit programming.

Built-in Tools

• CalculatorTool - Basic arithmetic operations
• WebSearchTool - Web search using DuckDuckGo
• FileManagerTool - File system operations

Adding Custom Tools

Create a new tool by extending the BaseTool class:

from cogency.tools.base import BaseTool

class WeatherTool(BaseTool):
    def __init__(self):
        super().__init__(
            name="weather",
            description="Get current weather for a location"
        )
    
    def run(self, location: str) -> dict:
        # Your implementation here
        return {"temperature": 72, "condition": "sunny"}

Tools are automatically discovered and available to agents.

LLM Support

Currently supports Google Gemini:

from cogency.llm import GeminiLLM

# Simple usage
llm = GeminiLLM(api_key="your-key")

# With key rotation
from cogency.llm import KeyRotator
keys = ["key1", "key2", "key3"]
llm = GeminiLLM(key_rotator=KeyRotator(keys))

Execution Tracing

Enable detailed tracing to see your agent's reasoning:

# Simple trace viewing
result = agent.run("Complex task", enable_trace=True, print_trace=True)

# Or capture trace data
result = agent.run("Complex task", enable_trace=True)
trace_data = result["execution_trace"]

Example trace output:

--- Execution Trace (ID: abc123) ---
PLAN     | Need to calculate and then search for information
REASON   | TOOL_CALL: calculator(operation='multiply', num1=15, num2=23)
ACT      | calculator -> {'result': 345}
REFLECT  | Calculation completed, now need to search
REASON   | TOOL_CALL: web_search(query='AI developments 2025')
ACT      | web_search -> {'results': [...]}
REFLECT  | Found relevant search results
RESPOND  | 15 multiplied by 23 equals 345. Recent AI developments include...
--- End Trace ---

Error Handling

All tools include built-in validation and graceful error handling:

# Invalid operations are caught and handled
result = agent.run("Calculate abc + def")
# Agent will respond with helpful error message instead of crashing

CLI Usage

Run examples from the command line:

cd python
python examples/basic_usage.py

Development

Running Tests

pytest

Project Structure

cogency/
├── agent.py          # Core agent implementation
├── llm/              # LLM integrations
├── tools/            # Built-in tools
├── utils/            # Utilities and formatting
└── tests/            # Test suite (115+ tests)

Emergent Behavior

The key insight behind Cogency is that clean architectural separation enables emergent reasoning. When agents fail with one tool, they automatically reflect and try different approaches:

# Agent fails with poor search query, reflects, and tries again
result = agent.run("Tell me about recent AI developments")

# Trace shows:
# 1. Initial search with generic query
# 2. Poor results returned
# 3. Agent reflects on failure
# 4. Adapts query strategy
# 5. Succeeds with better results

This behavior emerges from the Plan → Reason → Act → Reflect → Respond loop, not from explicit programming.

License

MIT License - see LICENSE for details.