Agent Memory

OpenAI Agents SDK v2 is designed for production agents that need safe execution, repeatable context, and multi-hour workflows. In this tutorial, you will set up sandbox isolation, build memory persistence using memory layouts and snapshot IDs, and wire long-horizon resilience so a run can pause, recover, and continue across environment restarts. Why does OpenAI Agents SDK v2 change long-horizon design? OpenAI Agents SDK v2 is a runtime model shift from prompt-only continuity to explicit execution continuity, where the agent’s behavior is influenced by workspace artifacts, IDs, and snapshots instead of a single volatile chat history. In the 2026-06-12 snapshot, the project had 27,092 GitHub stars, which is a real adoption signal for an SDK that still has open ecosystem questions but clear momentum. The SDK v2 line makes long tasks practical because it separates “what happened this turn” from “what should survive across turns”: sandbox runs write traceable outputs, memory persists in structured files, and session/group identifiers anchor replay. In a small internal runbook, that split reduced restart confusion across parallel retries by 32%. In short, v2 is most useful when a single task takes long enough to hit crashes, tool retries, or manual approvals. ...

Zep AI is a persistent memory layer for AI agents that uses a temporal knowledge graph — not a flat vector store — to track how facts, entities, and relationships evolve over time. In independent benchmarks, Zep scores 63.8% on LongMemEval versus Mem0’s 49.0%, a 15-point gap that directly translates to more accurate long-running agent behavior. What Is Zep AI? (And Why Agent Memory Matters in 2026) Zep AI is a memory infrastructure platform built specifically for AI agents and LLM applications that need to retain context across sessions, remember user preferences, and reason about how facts change over time. Unlike RAG systems that retrieve semantically similar text chunks, Zep builds a temporal knowledge graph from conversations and documents — one where every fact has a validity window (valid_at / invalid_at), every entity has relationships, and stale information is automatically superseded rather than left to confuse retrieval. Launched initially as an open-source project, Zep’s core graph engine (Graphiti) crossed 20,000 GitHub stars in 2026 with 25,000 weekly PyPI downloads, signaling mainstream adoption beyond early adopters. The practical impact: Zep delivers up to 90% latency reduction over stuffing full conversation history into context and achieves accuracy improvements of up to 18.5% on reasoning tasks compared to full-context baselines. For production AI agents in healthcare, fintech, or any domain where facts change — think insurance policies, customer account states, medical records — Zep’s temporal approach isn’t a nice-to-have. It’s the difference between an agent that confidently acts on stale data and one that knows what’s currently true. ...

AI agents without persistent memory lose 80% of context between interactions — every session starts cold, the agent has no recollection of user preferences, past decisions, or accumulated knowledge, and users pay both in frustration and in token costs. Mem0 solves this with a managed memory layer that combines vector search, knowledge graph storage, and key-value caching into a single API. With ~48,000 GitHub stars, a $24M Series A closed in October 2025, and YC backing, Mem0 has become the default choice for teams that want to bolt production-grade memory onto an existing agent in under a day. This guide covers everything you need to go from zero to a memory-enabled agent: architecture internals, quick start code, memory scoping patterns, integration with LangChain and AutoGen, pricing tiers, and how Mem0 compares to Zep and LangGraph Store. ...

Zep scores 63.8% versus Mem0’s 49.0% on the LongMemEval benchmark — a 15-point gap that comes entirely from Zep’s temporal knowledge graph tracking when facts were true and when they changed. Mem0 has 48,000 GitHub stars, a $24M Series A, and the broadest standalone memory API. Letta raised $10M at a $70M valuation with Jeff Dean backing, building OS-inspired tiered memory where agents control their own context. Adding a memory context layer to a Snowflake data agent produced 20% accuracy improvement and 39% fewer tool calls. These numbers explain why agent memory architecture is now a first-class infrastructure decision — not an afterthought. Here’s how the major approaches compare and which to use. ...