Tool Composition¶
Overview¶
Individual tools are powerful. Composed tools are transformative.
Tool composition chains outputs from one tool into inputs of another, creating complex workflows from simple pieces.
The Power of Composition¶
Simple Tool + Simple Tool + Simple Tool = Complex Workflow
search_web() → parse_results() → summarize()
↓ ↓ ↓
"AI trends" → 10 articles → "AI is growing in 3 areas..."
Tool Chains: Sequential Execution¶
class ToolChain:
def __init__(self, *tools):
self.tools = tools
def execute(self, initial_input):
result = initial_input
for tool in self.tools:
result = tool.call(result)
if not result['success']:
return {"error": result['error']}
return result
# Usage
chain = ToolChain(
search_tool,
parse_tool,
summarize_tool
)
result = chain.execute("Latest AI breakthroughs")
# Chains
Conditional Chains¶
class ConditionalChain:
def execute(self, data):
# Step 1: Process
processed = process_tool.call(data)
# Step 2: Check quality
if processed['quality'] > 0.8:
# High quality: summarize directly
return summarize_tool.call(processed['data'])
else:
# Low quality: enhance first
enhanced = enhance_tool.call(processed['data'])
return summarize_tool.call(enhanced['data'])
Parallel Execution¶
class ParallelChain:
async def execute(self, data):
# Run multiple tools in parallel
results = await asyncio.gather(
search_web(data),
search_docs(data),
search_archives(data)
)
# Aggregate results
combined = aggregate(results)
return summarize(combined)
Error Handling in Chains¶
class RobustChain:
def execute(self, data):
for i, tool in enumerate(self.tools):
try:
data = tool.call(data)
except Exception as e:
# Try fallback
fallback = self.fallbacks.get(i)
if fallback:
data = fallback.call(data)
else:
return {"error": f"Step {i} failed: {e}"}
return {"success": True, "data": data}
Best Practices¶
- Use type hints to ensure tool compatibility
- Validate at each step
- Handle errors gracefully
- Monitor performance
- Log execution traces
-
Last Updated: August 9, 2026