How to give memory to long-running AI agents. That is how do we enable agents to maintain, retrieve, and update salient conversation memories over extended conversations.
Context:
- LLMs tend to forget information within and across sessions due to fixed context windows.
- Forgetting over sessions can cause user frustration.
- Large contexts increase both cost and latency.
- Memory systems should not depend solely on the size of the input prompt.
Challenges
- Context overflow: exceeding the token window.
- Irrelevant history: retaining unused or low-value information.
- Memory management: removing stale or outdated memories without losing critical data.
Use Cases
- Personalised learning assistants.
- Customer support bots.
- Financial advisory agents.
Architecture Overview
- Store memories in a database.
- Retrieve relevant memories via semantic similarity search.
- Update process: add, update, delete, or NOOP (no operation).
- Optional graph memory layer
Metrics to evaluate:
- Memory quality.
- F1 score.
- BLEU-1 score.
- Token consumption analysis.
Key Takeaways
- Persistent long-term memory improves performance, reduces cost, and increases speed.
- Reasoning is essential during the memory update phase to maintain accuracy and coherence.
Example Engineering Questions
Architecture & Scale
- What are the biggest engineering challenges in maintaining long-term memory at scale, especially with respect to latency, consistency, and cost?
- How should mem0 be integrated into production with a self-hosted setup (e.g., Docker with MCP)?
- Which API calls are required for summarisation and retrieval, and at which stages?
Cost & Retrieval
- How can cost be controlled while maintaining contextual memory?
- What trade-offs exist between retrieval speed and memory depth?
- Does marking graph relationships as invalid (instead of deleting them) cause memory bloat and higher cost?
Memory Quality & Versioning
- How is memory versioned as the agent or model changes?
- Are there mechanisms for detecting memory drift or bias accumulation?
API Behaviour & Retrieval Logic
- How many API calls are required for create/update and retrieval?
- Does retrieval fetch top-N similar memories (e.g., top 10 via cosine similarity)?
- Can the number of historical messages retrieved for fact generation be configured?
- Differences between open-source and enterprise versions of mem0, especially in fact generation.
Candidate Facts & Domain Adaptation
- Difference between candidate facts and summary.
- Must developers define candidate facts per domain?
- How are nodes selected?
- Should mem0 or mem0g be chosen per domain, or can mem0g be applied universally?
Evaluation
- Evaluation metrics considered: F1 score, BLEU-1, memory quality.
- How does Mem0 quantitatively evaluate long-term memory effectiveness in retrieval accuracy, relevance, and downstream task performance over time?
- For fine-tuning memory quality for specific applications, what parameters can be adjusted?
Resources
Paper: Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory Related: LLM Memory