Intermediate
Managing Agent Memory with Sessions
Agents need memory to track context; sessions manage short-term state via trimming/compression.
Core Skills
Fundamental abilities you'll develop
- Implement short-term memory for AI agents.
Practical Skills
Hands-on techniques and methods
- Use trimming and compression techniques for efficiency.
- Maintain session coherence in multi-turn interactions.
- Integrate memory with agent frameworks like LangChain.
- Benchmark memory impact on response quality/latency.
Intermediate Level
Structured Learning Path
🎯 Skill Building
Intermediate Content Notice
This lesson builds upon foundational AI concepts. Basic understanding of AI principles and terminology is recommended for optimal learning.
Managing Agent Memory with Sessions
Introduction
Agents need memory to track context; sessions manage short-term state via trimming/compression.
Key Concepts
- Session Memory: Ephemeral storage for conversation history.
- Trimming: Prune oldest/irrelevant entries.
- Compression: Summarize or embed history.
Implementation Steps
- Basic Session Store:
class AgentSession: def __init__(self): self.history = [] def add(self, message): self.history.append(message) if len(self.history) > 10:
Trim
self.history.pop(0)
2. **Compression**:
- Use LLM to summarize: "Summarize: [history]".
3. **Integration**:
- In agent loop: Retrieve session before responding.
4. **Advanced**: Embed for semantic retrieval.
## Example
Chat agent: Remembers user prefs across turns without full history bloat.
## Evaluation
- Metrics: Coherence scores, token usage.
- Trade-offs: Compression loss vs. speed.
## Conclusion
Effective session memory boosts agent reliability; monitor for hallucinations.
Continue Your AI Journey
Build on your intermediate knowledge with more advanced AI concepts and techniques.