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  • Memory management: removing stale or outdated memories without losing critical data.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

  1. Store memories in a database.
  2. Retrieve relevant memories via semantic similarity search.
  3. Update process: add, update, delete, or NOOP (no operation).
  4. 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