15. Memory Checkpointing for Stateful Agents

Architecture Decision Record: 15. Memory Checkpointing for Stateful Agents

15. Memory Checkpointing for Stateful Agents

Date: 2025-10-13

Status

Accepted

Category

Data & Storage

Context

Conversational agents need:

• Multi-turn conversations
• Context retention
• State persistence across restarts

Stateless agents lose context every request, degrading UX.

Decision

Use LangGraph MemorySaver for conversation state checkpointing.

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory)

Consequences

Positive

• Multi-Turn Conversations: Context preserved
• User Experience: Natural conversation flow
• State Recovery: Resume after crashes

Negative

• Memory Usage: State grows with conversations
• Persistence: MemorySaver not persistent (future: Redis)

Future Enhancements

• Redis checkpointer for distributed deployments
• State compression for long conversations

References

• Implementation: src/mcp_server_langgraph/core/agent.py:206-214