Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.

Activate this skill when:

Think of memory as a spectrum from volatile context window to persistent storage. Default to the simplest layer that meets retrieval needs, because benchmark evidence shows tool complexity matters less than reliable retrieval — Letta's filesystem agents scored 74% on LoCoMo using basic file operations, beating Mem0's specialized tools at 68.5%. Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.

Select a framework based on the dominant retrieval pattern the agent requires. Use this table to narrow the shortlist, then validate with the benchmark data below.

Choose Zep/Graphiti when the agent needs bi-temporal modeling (tracking both when events occurred and when they were ingested) because its three-tier knowledge graph (episode, semantic entity, community subgraphs) excels at temporal queries. Choose Mem0 when the priority is fast time-to-production with managed infrastructure. Choose Letta when the agent needs deep self-introspection through its Agent Development Environment. Choose Cognee when the agent must build dense multi-layer semantic graphs — it layers text chunks and entity types as nodes with detailed relationship edges, and every core piece (ingestion, entity extraction, post-processing, retrieval) is customizable.

Consult these benchmarks to set expectations, but treat them as signals for specific retrieval dimensions rather than absolute rankings. No single benchmark is definitive.

Key takeaways: Zep achieves up to 18.5% accuracy improvement on LongMemEval while reducing latency by 90%. Cognee outperformed Mem0, Graphiti, and LightRAG on HotPotQA multi-hop reasoning benchmarks across Exact Match, F1, and human-like correctness metrics. Letta's filesystem-based agents achieved 74% on LoCoMo using basic file operations, confirming that reliable retrieval beats tool sophistication.

Pick the shallowest memory layer that satisfies the persistence requirement. Each deeper layer adds infrastructure cost and operational complexity, so only escalate when the shallower layer cannot meet the retrieval or durability need.

Match the retrieval strategy to the query shape. Semantic search handles direct factual lookups well but degrades on multi-hop reasoning; entity-based traversal handles "everything about X" queries but requires graph structure; temporal filtering handles changing facts but requires validity metadata. When accuracy is paramount and infrastructure budget allows, combine strategies into hybrid retrieval.

Zep's hybrid approach achieves 90% latency reduction (2.58s vs 28.9s) by retrieving only relevant subgraphs. Cognee implements hybrid retrieval through its 14 search modes — each mode combines different strategies from its three-store architecture (graph, vector, relational), letting agents select the retrieval strategy that fits the query type rather than using a one-size-fits-all approach.

Run consolidation periodically to prevent unbounded growth, because unchecked memory accumulation degrades retrieval quality over time. Invalidate but do not discard — preserving history matters for temporal queries that need to reconstruct past states. Trigger consolidation on memory count thresholds, degraded retrieval quality, or scheduled intervals. See Implementation Reference for working consolidation code.

Start with the simplest viable layer and add complexity only when retrieval quality degrades. Most agents do not need a temporal knowledge graph on day one. Follow this escalation path:

Load memories just-in-time rather than preloading everything, because large context payloads are expensive and degrade attention quality. Place retrieved memories in attention-favored positions (beginning or end of context) to maximize their influence on generation.

Handle retrieval failures gracefully because memory systems are inherently noisy. Apply these recovery strategies in order:

results = m.search("What theme does the user prefer?", user_id="alice")

**Example 2: Temporal Query**
```python

graph.create_temporal_relationship( source_id=user_node, rel_type="LIVES_AT", target_id=address_node, valid_from=datetime(2024, 1, 15), valid_until=datetime(2024, 9, 1), # moved out )

results = graph.query_at_time( {"type": "LIVES_AT", "source_label": "User"}, query_time=datetime(2024, 3, 1) )

**Example 3: Cognee Memory Ingestion and Search**
```python
import cognee
from cognee.modules.search.types import SearchType

await cognee.add("./docs/") await cognee.add("any data") await cognee.cognify()

await cognee.memify()

results = await cognee.search( query_text="Any query for your memory", query_type=SearchType.GRAPH_COMPLETION, )

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Created: 2025-12-20 Last Updated: 2026-03-17 Author: Agent Skills for Context Engineering Contributors Version: 4.0.0