Comparison8 min read16/04/2026
LangMem vs RetainDB: AI Memory Compared (2026)
LangMem vs RetainDB for AI agent memory: LangChain fit vs RetainDB's 79% LongMemEval and 88% preference recall, plus pricing and self-hosting compared for 2026.
LangMem vs RetainDB for AI agent memory: LangChain fit vs RetainDB's 79% LongMemEval and 88% preference recall, plus pricing and self-hosting compared for 2026.
LangMem and RetainDB come from very different places. LangMem is LangChain's official memory toolkit, backed by $25M+ in ecosystem funding but without published benchmark scores. RetainDB is a small-team effort focused on chronological retrieval and preference recall, claiming 79% on LongMemEval and an 88% state-of-the-art score on preference tasks.
This comparison covers their architectures, benchmark performance, pricing, and ideal use cases to help you decide.
| Factor | LangMem | RetainDB |
|---|---|---|
| Architecture | Modular memory API with LangGraph integration | PostgreSQL + pgvector with chronological retrieval |
| LongMemEval* | Not published | 79% |
| Deployment | Self-hosted with LangGraph | Cloud or self-hosted (PostgreSQL required) |
| Pricing | Open source | Free (10K ops/mo) / $20/mo Pro |
| GitHub Stars | 1.4K | 8 |
| Funding | Part of LangChain ($25M+) | Not disclosed |
LangMem is LangChain's official long-term memory toolkit. It provides a modular memory API, active memory tools for in-conversation operations, automated background handlers for distillation, and native LangGraph storage layer integration.
Backed by LangChain's $25M+ in ecosystem funding, LangMem integrates into the LangGraph workflow but hasn't published benchmark results. The project sits at 1.4K GitHub stars.
Key strengths:
RetainDB provides persistent memory with turn-by-turn extraction and chronological retrieval. Rather than using semantic search to find relevant memories, RetainDB provides the complete memory chronology—ensuring the model sees the full timeline rather than a potentially incomplete semantic selection.
RetainDB claims 88% on preference recall (state-of-the-art for that specific task) and 0% hallucination rate on documentation questions. The benchmark runner ships in the repo for reproducibility. Community is tiny (8 GitHub stars) but the approach is methodologically interesting.
Key strengths:
LangMem provides four core capabilities: a storage-agnostic memory API, active memory tools for real-time operations, automated background handlers for distillation and refresh, and native LangGraph integration.
The architecture is deliberately pluggable—LangMem manages the memory lifecycle while you choose the storage backend. This means retrieval quality depends entirely on your configuration rather than LangMem itself.
RetainDB's pipeline processes conversations turn by turn:
The critical design choice: RetainDB avoids semantic search for retrieval. Instead of embedding queries and finding similar memories, it dumps the complete chronological memory to the model. This eliminates retrieval errors at the cost of larger context windows.
LangMem retrieves selectively; RetainDB dumps chronologically.
Most memory systems—including whatever backend LangMem uses—rely on semantic search to find relevant memories. This is efficient but lossy: if the query doesn't semantically match the stored fact, it won't be retrieved.
RetainDB sidesteps this entirely by providing the complete timeline. The model sees everything and decides what's relevant. This explains the 88% preference recall score—preferences that semantic search might miss are always present in the chronological dump.
The tradeoff is context window consumption. For agents with thousands of memories, dumping everything becomes impractical. RetainDB works best when the memory corpus is small enough to fit in context.
| Benchmark | LangMem | RetainDB |
|---|---|---|
| LongMemEval* | Not published | 79% |
| Preference Recall | Not published | 88% (SOTA) |
| Hallucination Rate | Not published | 0% (claimed) |
LangMem has not published scores on any benchmark. RetainDB scores 79% on LongMemEval overall—lower than many competitors—but excels at preference recall (88%) and claims zero hallucination on documentation queries.
The 79% overall score reflects RetainDB's chronological approach: it works well when the full timeline fits in context but struggles with multi-session aggregation across large histories.
Hypabase achieves 87.4% overall and 100% on personalization (which includes preference recall), demonstrating that structured AMR extraction handles preferences more reliably than either approach.
| Tier | Price | Details |
|---|---|---|
| Open Source | Free | Full toolkit, requires LangGraph |
LangMem is free to use. Hidden costs: storage backend infrastructure, embedding model hosting, and LLM calls for extraction/distillation operations.
| Tier | Price | Limits |
|---|---|---|
| Free | $0 | 10K operations/month |
| Pro | $20/month | 100K queries |
RetainDB is notably affordable. The $20/month Pro tier covers 100K queries—competitive pricing for a managed memory service.
RetainDB wins on cost transparency. LangMem's true cost depends on infrastructure choices; RetainDB's pricing is straightforward. Both are significantly cheaper than Zep ($475/mo) or Mem0 Pro ($249/mo) for similar operation volumes.
Choose LangMem if you:
LangMem fits as a component in LangGraph, but the lack of published benchmarks raises questions about retrieval quality. The engine hasn't been evaluated against standard memory benchmarks.
Choose RetainDB if you:
RetainDB's chronological approach works well for preference-heavy applications, but the 79% overall LongMemEval score and tiny community (8 stars) are considerations. The architecture hasn't scaled to handle the larger memory corpuses that temporal and multi-session scenarios require.
RetainDB's chronological dump works until your memory corpus outgrows the context window—then you're stuck. LangMem's retrieval quality is unknowable without published benchmarks. Hypabase sidesteps both limitations with AMR-based extraction into hyperedges—structured knowledge that scales without context window constraints and retrieves with verified precision.
| Factor | LangMem | RetainDB | Hypabase |
|---|---|---|---|
| Extraction | Depends on backend | Turn-by-turn with 3-turn context | AMR (formal linguistic framework) |
| Representation | Backend-dependent | Chronological flat entries | N-ary hyperedges |
| LongMemEval* | Not published | 79% | 87.4% |
| Personalization | — | 88% preference recall | 100% |
LangMem's extraction depends on your backend—an unknown quantity. RetainDB extracts flat facts turn by turn and retrieves them chronologically, which works for preference recall (88%) but scores only 79% overall because the chronological dump can't handle large multi-session histories. Hypabase uses Abstract Meaning Representation (AMR)—a formal framework from computational linguistics that maps sentences to structured graphs.
AMR extraction produces dense, multi-role facts in PENMAN notation using kāraka semantic roles (from Panini's Sanskrit grammar). Consider a developer preference:
"Peter prefers detailed commit messages and reviews all PRs"
Turn-by-turn extraction (RetainDB):
{eventDate: null, fact: "Peter prefers detailed commit messages and reviews all PRs", confidence: 0.9}
(flat entry, dumped chronologically)
Ad-hoc extraction (LangMem):
(Peter, prefers, detailed commit messages)
(Peter, reviews, all PRs)
AMR extraction (Hypabase):
(prefer :agent Peter :object detailed-commit-messages)
(review :agent Peter :object pull-requests :attribute all)
The difference: Hypabase captures Peter's preferences as structured, individually queryable hyperedges. RetainDB stores the raw fact and relies on dumping the entire timeline—which works with 100 memories but not 10,000. LangMem splits the preferences into triples that lose the "all" qualifier on PR reviews.
| Benefit | How AMR + Hyperedges Deliver It |
|---|---|
| Scales beyond context windows | Structured facts retrieved by role—no need to dump everything |
| Parseable output | PENMAN notation has defined grammar; malformed extractions caught at parse time |
| Preference precision | "Does Peter review PRs?" hits :agent Peter + review + :object pull-requests exactly |
| No chronological bottleneck | Facts indexed by structure, not by time—retrieval doesn't degrade as history grows |
This is why Hypabase achieves 100% on personalization tasks compared to RetainDB's 88%—the structured hyperedge approach captures preferences with role-level precision that chronological dumps and flat triples miss.
Neither publishes comprehensive benchmarks on the same evaluation. LangMem has no published scores; RetainDB scores 79% overall but 88% on preferences. For the highest personalization accuracy with structured extraction, consider Hypabase (87.4%, 100% personalization).
There's no direct migration path—RetainDB stores flat chronological entries while LangMem depends on its backend's format. Migration requires re-ingesting conversation history. If you're evaluating both, Hypabase's single SQLite file makes migration simpler.
LangMem optimizes for LangGraph ecosystem integration. RetainDB optimizes for chronological completeness and preference recall. Hypabase optimizes for extraction quality using AMR and structured hyperedge representation.
LangMem requires LangGraph plus a storage backend. RetainDB requires PostgreSQL. Hypabase runs entirely in a single SQLite file with no external database required—the simplest self-hosting option.
LangMem manages memory within LangGraph but has no published benchmark scores—leaving retrieval quality unknown.
RetainDB scores 79% overall but achieves 88% on preference recall through its chronological approach. Affordable at $20/month but limited by a tiny community (8 stars).
Hypabase achieves 87.4% through AMR-based extraction into hyperedges—structured knowledge representation that preserves relationships ad-hoc extraction fragments. 100% on personalization tasks, surpassing RetainDB's 88% preference recall.
All three are straightforward to integrate:
LongMemEval scores: LangMem has not published scores. RetainDB (79%) self-reported. Hypabase (87.4%) from published benchmark harness.
Related reading: How to read memory benchmark claims
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