Context Management
Handling memory and information retrieval effectively.
6 min read
The Context Challenge
AI agents need context to make good decisions. However, language models have limited "context windows" - the amount of text they can process at once. Managing context effectively is crucial for agent performance.
Understanding Context Windows
Context windows are measured in tokens (roughly 4 characters per token):
- GPT-4: 8K-128K tokens
- Claude: 100K-200K tokens
- Gemini: Up to 1M tokens
Larger windows help, but they're not infinite. Effective context management is still essential.
Types of Context
Conversation History
What was said earlier in the current session.
User Profile
Persistent information about the user's preferences and history.
Domain Knowledge
Reference information needed to complete tasks (documents, data, etc.).
System Instructions
The agent's personality, rules, and capabilities.
Retrieval-Augmented Generation (RAG)
RAG is a technique for bringing relevant information into context on-demand:
- Store documents in a vector database
- When a query comes in, find relevant documents
- Include those documents in the agent's context
- Generate a response using the retrieved information
Memory Strategies
Short-Term Memory
Recent conversation turns kept in the context window.
Working Memory
Active information the agent is currently using.
Long-Term Memory
Persistent storage for important information to retrieve later.
Best Practices
- Summarize old conversations instead of keeping full history
- Use embeddings to retrieve only relevant information
- Structure knowledge bases for efficient retrieval
- Regularly prune and update stored information
- Test context quality by asking the agent what it knows