Maximem Synap vs Letta
Letta is the fully open-source agent framework with OS-inspired tiered memory. Maximem Synap is the agentic context management system that scored 92% on LongMemEval and 93.2% on LoCoMo, serving in-conversation reads from a pre-fetched in-process cache at an asserted P75 under 15 ms, designed to plug into any framework.
| Feature | Maximem Synap | Letta |
|---|---|---|
| LongMemEval, reproduced on our open harness | 92% (Best) | Not verified (Not applicable) |
| LongMemEval (self-reported) | 92% (Best) | Not published (Not applicable) |
| Published retrieval latency | Under 15ms P75 in-conversation, from a pre-fetched in-process cache (Best) | Not published (LLM-dependent tool calls) (Not applicable) |
| Open-source eval harness | Full config published (Best) | No (No) |
| Context management paradigm | Active: system captures, compacts, recalls (Best) | Agent-managed: LLM manages its own tiers (Partial) |
| Ingestion approach | Extract-first: multi-stage pipeline (Best) | Agent-directed via tool calls (No) |
| Pipeline customization per agent | Custom architecture per agent via YAML (Best) | Agents self-edit, but pipeline is universal (Partial) |
| Data connectors | Connectors for structured data sources (Best) | Text ingestion only (No) |
| Memory types | 5 structured: facts, preferences, episodes, emotions, temporal (Best) | Generic blocks in 3 tiers (Partial) |
| Context compaction | 4 strategies + quality validation score (Best) | Agent-directed archival (LLM decides) (Partial) |
| Contradiction handling | Explicit detection & resolution (HITL when needed) (Best) | Agent self-edits (Partial) |
| Temporal awareness | Full bitemporal awareness (Best) | Timestamps on operations (Partial) |
| Entity resolution | Automatic, multi-strategy (Best) | No built-in entity resolution (No) |
| Memory scoping mechanism | Intelligent & automated (Best) | Manual (agent tool calls) (No) |
| Scoping levels | Org hierarchy: User → Customer → Client (Best) | Per-agent (core + archival) (Partial) |
| Organizational memory | Customer + Client scopes (Best) | Per-agent only (No) |
| Context sharing in agent-swarm systems | Native: shared context + agent-specific memories (Best) | Native coordinator + sub-agents (Best) |
| SDK languages | Python + JavaScript (Best) | Python + TypeScript (Best) |
| Framework integrations | 23 frameworks (Best) | Own platform (Partial) |
| Observability | Dashboard: pipelines, memories, entity queue (Best) | White-box: full prompt + memory transparency (Best) |
Last updated: April 2026. Benchmarks sourced from Synap's open-source LongMemEval and LoCoMo harness and vendor documentation. Feature availability may change.
01Why Synap
Why Teams Choose Maximem Synap Over Letta
Different design philosophies.
Letta gives the agent control over its own memory. The LLM decides what to store, archive, and retrieve through tool calls. Synap takes the opposite approach: the system manages context so the agent does not have to. The right choice depends on what you are building.
When the agent manages its own memory, the agent needs to be good at it.
Letta's self-editing model means memory quality depends on the LLM's judgment about what to keep and what to discard. In production, this creates a circular dependency: the agent that needs better context is the same agent deciding what context to save. Synap removes that dependency. A multi-stage pipeline extracts structured knowledge (facts, preferences, episodes, temporal events, emotions) before the agent ever sees it.
Entity resolution is a system problem, not an agent problem.
Letta has no built-in entity resolution. If "Sarah," "Sarah Chen," and "SC" appear across conversations, the agent needs to figure that out on its own. Synap resolves entities automatically during ingestion with multiple matching strategies. The agent never needs to handle disambiguation.
Scope isolation for SaaS builders.
Letta's memory is per-agent. There is no built-in organizational memory. If you are building a SaaS product where multiple users interact with agents and you need tenant isolation, that is your responsibility to architect. Synap provides a nested scope chain (User → Customer → Client) with built-in isolation and organizational memory sharing.
Where Letta has the edge.
Letta is fully open-source under Apache 2.0. You can self-host the entire product, inspect every line of code, and modify the agent's memory behavior at any level. Letta's white-box transparency (full visibility into prompt construction and memory state at every step) is the most thorough debugging model in this space. Letta also supports native multi-agent coordination with sub-agents sharing state.
Benchmark note: Letta has not published a LongMemEval score. We will update this page when scores become available for independent comparison.
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Frequently Asked Questions
Synap and Letta solve adjacent problems with opposite philosophies. Letta is an agent framework where the LLM manages its own tiered memory via tool calls. Synap is an agentic context management layer where the system manages context automatically so any agent framework (LangChain, CrewAI, AutoGen, etc.) can consume it.
Synap plugs into any agent framework via its Python and JavaScript SDKs. If you are running Letta agents, you can use Synap as the underlying context layer and let Letta focus on orchestration.
Pick Letta if you need full open-source self-hosting under Apache 2.0, white-box transparency into prompt construction, and native multi-agent coordination as a framework. Pick Synap if you need a context layer that plugs into any framework and delivers 92% LongMemEval and 93.2% LoCoMo accuracy reproduced on our open harness, with an asserted in-conversation P75 under 15 ms.