Context Engineering Platforms: Knol vs Competitors
Strategic comparison for engineering teams building agentic systems with memory. Based on official documentation and confirmed capabilities. Updated February 2026.
Mem0: vector-first memory with optional graph augmentationZep: temporal knowledge graph memory engineKnol: context engineering platform (semantic + keyword + graph + write-time optimization)Letta: agent-first framework with integrated memory ($10M seed)
Core Capabilities (Yes/No)
| Capability | Mem0 | Zep | Knol | Letta |
|---|---|---|---|---|
| Vector semantic retrieval | ✓ | ✓ | ✓ | ✓ |
| Graph retrieval support | ✓ | ✓ | ✓ | ✗ |
| Keyword / BM25 retrieval | ✗ | ✗ | ✓ | ✗ |
| Write-time embeddings (not query) | ✗ | ✗ | ✓ | ✗ |
| Temporal fact validity model | ✗ | ✓ | ✓ | ✗ |
| Per-write fact extraction | ✓ | ✓ | ✓ | ✓ |
| Hybrid search fusion (semantic + keyword + graph) | ✗ | ✓ | ✓ | ✗ |
| Direct graph entity/edge APIs | ✗ | ✓ | ✓ | ✗ |
| Working memory layer | ✗ | ✗ | ✓ | ✓ |
| Procedural memory layer | ✗ | ✗ | ✓ | ✗ |
| PostgreSQL-native storage | ✗ | ✗ | ✓ | ✗ |
| LLM cost optimization (embeddings) | ✗ | ✗ | ✓ | ✗ |
| Conflict detection & merging | ✗ | ✗ | ✓ | ✗ |
| Webhook integrations | ✗ | ✗ | ✓ | ✗ |
| Tenant isolation (RLS) | ✓ | ✓ | ✓ | ✗ |
| Built-in PII extraction guardrails | ✗ | ✗ | ✓ | ✗ |
| Agent-first architecture | ✗ | ✗ | ✗ | ✓ |
Legend: ✓ available as core capability, ✗ not a primary feature or requires integration layer.
| Dimension | Mem0 | Zep | Knol | Letta |
|---|---|---|---|---|
| Primary design goal | Compress and retrieve salient user facts efficiently | Model evolving relationships through a temporal knowledge graph | Context engineering: full-stack semantic + keyword + graph retrieval with write-time optimization | Agent control plane with built-in memory and LLM management |
| Architecture | Vector-first; graph is additive | Knowledge graph-native; temporal metadata on edges | PostgreSQL foundation; semantic indexing + BM25 + graph + procedural layers | Framework-centric; wraps external memory backends |
| Memory model | Fact extraction into vectors; graph optional overlay | Facts on edges with validity windows; historical snapshots | Memories + episodes + entities/edges + working memory + procedural memory | Session context and turn-level working memory |
| Temporal semantics | Limited; vector-centric defaults | Native timestamps, validity windows, lifecycle-aware updates | Bi-temporal fields, validity-based archival, conflict detection on updates | Turn-based session history |
| Retrieval strategy | Semantic first; graph relations augment when enabled | Graph traversal and context rollup over user/session paths | Intent-aware hybrid: semantic vectors + keyword ranking + graph paths + learned fusion | In-context retrieval with LLM-guided reasoning |
| Cost optimization | Standard embedding call per query | Query-time graph traversal | Write-time embeddings (75% LLM cost reduction); static vectors at retrieval | LLM call per agent turn |
| Data substrate | SaaS or managed (Mem0 Cloud) | SaaS or self-host (with operational complexity) | PostgreSQL (self-host or managed); single source of truth | LLM service + optional memory backend |
| Engineering fit | Teams wanting vector memory with minimal modeling overhead | Teams needing rich temporal and relational reasoning | Teams building production agents with memory, demanding cost control and explainability | Teams building multi-turn agents with integrated memory and LLM orchestration |
| Launch readiness | Production (SaaS) | Production (self-host requires ops knowledge) | Launch-ready with write-time embeddings, conflict detection, and webhooks fully wired | Production framework (memory integration varies by backend) |
Practical Notes for Knol
- Knol already exposes graph entities/edges and async graph-building through NATS write events.
- Hybrid retrieval is implemented in
service-retrievewith intent-aware weighting and RRF. - Keyword retrieval uses PostgreSQL text search (
plainto_tsquery+ts_rank_cd). - Tenant isolation is implemented with RLS context helpers and tenant-scoped policies.
Knol OSS vs Paid Boundary
OSS (self-host)
Core memory engine, APIs, SDKs, and retrieval primitives stay open source.
Cloud / Enterprise (paid)
Managed reliability, compliance, enterprise identity/governance, and support SLAs.
Sources used for external product behavior: Mem0 docs (memory operations, graph memory, vector-vs-graph guidance) and Zep docs (facts, temporal timestamps, memory.get/graph.search, graph APIs). Verified on February 17, 2026.