An honest, data-driven comparison of memory APIs for LLM applications — using benchmark scores already published on this site. No inflated claims. Full methodology at benchmarks.html.
Comparing BECOMER, mem0, Zep, and Hindsight across accuracy, cost, and developer experience. Benchmark numbers sourced from our published methodology.
Feature
BECOMER
mem0
Zep
Hindsight
LongMemEval (n=500)
94.4%
94.4%
63.8%
91.4%
LOCOMO (n=1,978)
69.5% (retrieval only)
91.6%
—
—
Tokens per recall
0
~6,787
low
unknown
Recall latency (P50)
~150ms
300ms–2s
200–300ms
—
Pro pricing
$12 / month
see their site
see their site
free (self-hosted)
Free tier
✓ 1,000 calls/mo
✓
✓
✓ self-host
MCP server (Claude Desktop)
✓ built-in
~ partial
✗
✗
Cross-LLM shared memory
✓ native
~ per-app
✗
✗
REST API
✓
✓
✓
~
Python SDK
✓ pip install becomer
✓
✓
✓
LangChain integration
✓
✓
✓
~
Encrypted at rest
✓ SQLCipher
✓
✓
depends on host
No third-party LLM in retrieval
✓ always
✗
~
✓
Managed cloud (no self-hosting)
✓
✓
✓
✗ self-host only
Benchmark numbers for BECOMER sourced from becomer.net/benchmarks.html. Competitor numbers from publicly available benchmark publications. Pricing from each provider's public pricing page.
LongMemEval · n=500
Benchmark accuracy comparison
LongMemEval is the industry-standard benchmark for long-term conversational memory. 500 questions across temporal reasoning, knowledge update, and multi-session recall.
LongMemEval — overall accuracy
BECOMER
94.4%
mem0
94.4%
Hindsight
91.4%
Zep
63.8%
LongMemEval — by sub-task
Temporal · BCM
~93%
Temporal · M0
93.2%
Knowledge · BCM
~95%
Knowledge · M0
96.2%
Multi-session · BCM
~87%
Multi-session · M0
86.5%
The LOCOMO difference explained. On LOCOMO (n=1,978), BECOMER scores 69.5% (retrieval only) vs mem0's 91.6%. The gap exists because mem0 runs an LLM reasoning pass over retrieved memories — this helps on multi-hop inference questions. BECOMER retrieves the right context and hands it to your own LLM to reason over. If your use case requires complex multi-hop reasoning inside the memory layer itself, factor this in. For direct recall of stored facts — which covers most production use cases — BECOMER leads on LongMemEval.
Cost at scale
What zero tokens actually means
mem0 runs an LLM during both storage and retrieval — every API call consumes tokens from your LLM provider. BECOMER's retrieval engine uses no LLM, so the token cost is zero regardless of query volume.
Monthly recall volume
BECOMER token cost
mem0 token cost (~6,787/query)
1,000 recalls
0 tokens
~6.8M tokens
10,000 recalls
0 tokens
~67.9M tokens
50,000 recalls (Pro tier)
0 tokens
~339M tokens
Token count for mem0 sourced from BECOMER's published benchmark table. Actual LLM token pricing varies by provider — these are raw token volumes, not dollar costs.
Honest guidance
When to choose BECOMER
No memory API is right for every use case. Here's an honest breakdown.
✓ Choose BECOMER if:
Token cost matters at your usage volume
You use multiple LLMs and want shared memory
You want MCP out of the box (Claude Desktop, Cursor)
You want managed cloud without self-hosting
LongMemEval accuracy is your benchmark
You're building on a budget ($12/mo vs alternatives)
Consider alternatives if:
Multi-hop reasoning inside the memory layer is critical (LOCOMO gap)
You need temporal graph memory (state changes over time)
You require self-hosted deployment
Enterprise SLA with dedicated support is required
Get started
Switch in 3 lines
BECOMER works alongside any existing setup. No migration required — just add recall before and store after your LLM call.
# pip install becomer
from becomer import Client
mem = Client("bk-your-api-key")
# Before your LLM call
context = mem.recall("what does this user prefer?", top_k=5)
# After your LLM call
mem.store("User prefers dark mode and concise answers")
FAQ
Frequently asked questions
Is BECOMER a good mem0 alternative?
BECOMER matches mem0's LongMemEval score (both 94.4%) using zero LLM tokens per query vs mem0's ~6,787 tokens. The Pro plan is $12/month. The key trade-off: BECOMER uses pure retrieval (fast, zero tokens), while mem0 adds an LLM reasoning pass that improves multi-hop tasks. Same accuracy. 6,787 fewer tokens per query.
How does the zero-token claim work?
BECOMER's retrieval engine uses embeddings and semantic search — no language model runs during recall. When you call /v1/recall, the engine searches stored memories using vector similarity and returns the top-k results. No GPT, Claude, or Gemini call is made inside the memory layer. Your own LLM call (outside BECOMER) is unaffected.
Can I use BECOMER with Claude Desktop or Cursor?
Yes. Run python -m becomer with BECOMER_API_KEY set and add it to your mcp.json. Claude Desktop will automatically call store and recall as MCP tools. Full config at docs.html.
What is the LOCOMO benchmark gap about?
LOCOMO tests multi-hop inference — questions that require reasoning across several stored memories simultaneously. BECOMER scores 69.5% (retrieval only) vs mem0's 91.6%. The gap exists because mem0 runs an LLM pass over retrieved results, which helps on these inference tasks. BECOMER retrieves the right context and hands it to your own LLM. For direct fact recall (the majority of real-world use), BECOMER leads on LongMemEval.
Can I share memory across GPT, Claude, and Gemini with one key?
Yes. One BECOMER API key maps to one shared memory store. Any LLM app using that key reads and writes to the same memories. A GPT app can store a fact and a Claude app can recall it instantly — no syncing required. This is a native feature of BECOMER's architecture.
Is BECOMER's data secure?
Memories are stored in an encrypted database (SQLCipher). Isolation is enforced at the database level — one account cannot read another's memories. Your data is never sent to an external LLM, AI service, or analytics provider. DPDP Act 2023 compliant, CCPA-aligned for US users. Full details at the security section.
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1,000 API calls per month, free forever. No credit card required.