Do you have any idea why an agent has less context or computation power than a llm.invoke? Both agent and llm are under the same condition like same prompt without tools. For example, i tell them to add annotations to a go file. The result is that llm can process a much bigger file, as contrast the agent can only process a small one. Anyone has any idea, the source code doesn’t tell why?
thank you
LangGraph agents have additional overhead that reduces effective context compared to direct llm.invoke():
Context consumption:
Agent framework adds system prompts for ReAct reasoning
Tool definitions consume tokens even when not used
Agent maintains conversation history and internal state
Multiple reasoning steps (“I need to analyze this file…”) use tokens
Performance overhead:
Agent processes input through multiple graph nodes
State serialization/deserialization between nodes
Additional parsing and validation steps
Framework abstractions add computational layers
For large file processing: Use llm.invoke() directly when you don’t need tool calling or multi-step reasoning. If you need agent capabilities, try:
Chunking large files
Using streaming with astream_events()
Optimizing your agent prompt to be more concise
The agent framework trades raw performance for flexibility and structured reasoning capabilities.