Theintelligencelayer
forAIagents.
Compress. Secure. Remember. Every turn, your agents get smarter. One middleware that sits between your agents and LLMs.
EVERY TURN, YOUR CONTEXT GOES THROUGH
CONTEXT INTELLIGENCE ENGINE
Context that gets smarter
every session.
LLM-powered memory extraction. Sub-50ms sync path. Persistent three-tier memory that survives across sessions.
<50ms
Sync latency
3-tier
Memory hierarchy
~$0.001
Per-turn LLM cost
14
API endpoints
MEMORY CASCADE
More than compression.
Six capabilities. One intelligence layer.
Compress
Fast sync pipeline: dedup, classify, pointer replacement. Old tool results compressed to summaries. <50ms per turn.
Secure
Secrets, PII, and prompt injection scanning with flag/redact/block modes. Scanned before memory extraction — redacted content never reaches LLM.
Remember
Three-tier persistent memory: turn-level facts, session-level synthesis, project-level knowledge. Searchable via semantic + text + tag queries.
Extract
GPT-5.4-nano extracts facts and relationships from every tool result. Split-parallel LLM calls for ~1.5s extraction latency.
Relate
Entity relationship graph stored in Neo4j. Traverse connections: service → depends_on → database → has_secret → credential.
Comply
Immutable audit trail. HIPAA, PCI-DSS, SOX, GDPR policy templates. Every memory classified: public, internal, or redacted.
For Enterprise
✓HIPAA / PCI-DSS / SOX compliance templates
✓Customer-managed encryption (BYOK)
✓Private deployment option
✓Dedicated support + SLA
OBSERVABILITY DASHBOARD
MEMORIES
847
+23 todaySESSIONS
24
8 LIVESYNC LATENCY
<42ms
SECURITY
SAFE
MEMORY GROWTH
RECENT SESSIONS
VIEW ALL
SECURITY POSTURE
SAFE
SECURITY POSTURE: OPTIMAL
SECRETS
8
redacted
PII
15
redacted
INJECTIONS
4
flagged
INTEGRITY
OK
verified
FINDINGS TREND
Works with every agent framework.
First-class adapters — not wrappers. Lossless round-trip for every framework.
Install. Import. Intelligent.
One package. Full context intelligence. Works locally with zero config, optionally connects to the cloud.
Coming Soonfrom memosift import MemoSift
sift = MemoSift() # Zero config — works immediately
result = sift.process(session_id="sess-123", messages=current_turn)
# result.compressed → optimized messages for LLM
# result.memories → extracted facts
# result.security → PII/secrets/injection findings
memories = sift.recall(session_id="sess-123", query="what failed?")
# Semantic + keyword + entity graph search
summary = sift.close(session_id="sess-123")
# Session synthesis → project-level knowledgeimport { MemoSift } from 'memosift'
const sift = new MemoSift() // Zero config — works immediately
const result = await sift.process({
sessionId: 'sess-123',
messages: currentTurn,
})
// result.compressed → optimized messages for LLM
// result.memories → extracted facts
// result.security → PII/secrets/injection findings
const memories = await sift.recall({
sessionId: 'sess-123',
query: 'what failed?',
})Grows with your agents.
Start local. Connect to cloud. Scale to production. No code changes.
Local Mode
sift = MemoSift()
SQLite + in-memory vector store. Full compression, security scanning, and memory extraction. Zero external dependencies. Zero config.
Cloud Connected
sift = MemoSift(api_key="ms_live_...")
Same code — now gets observability dashboard, security alerts, session replay, and compliance reports. No migration needed.
Production Scale
sift = MemoSift( api_key="ms_live_...", storage="neon", graph="neo4j", )
Swap to production backends. Neon DB with pgvector, Neo4j relationship graph, Redis caching. Same API, same code.
More than memory. More than compression.
Side-by-side comparison across the capabilities that matter for production AI agents.
| Capability | MemoSift | mem0 | OpenAI Compaction | Anthropic Compaction |
|---|---|---|---|---|
| Memory extraction | LLM + 7 compression engines | LLM fact extraction | ||
| Security scanning | PII, secrets, injection | |||
| Recall method | Semantic + keyword + graph | Vector similarity | ||
| Memory hierarchy | Turn → Session → Project | Flat (user-scoped) | ||
| Context compression | 7 deterministic engines | Lossy compaction | Lossy compaction | |
| Tool call integrity | 100% (SHA-256 verified) | |||
| Observability | Full dashboard | |||
| Compliance | HIPAA, PCI-DSS, SOX, GDPR |
Three surfaces. One layer.
API Server
REST API- ▪14 endpoints: process, recall, store, index, memories, tools
- ▪OpenAI-compatible message format
- ▪Flag/redact/block security modes
- ▪Sync + async processing
Agent Tools
5 tools- ▪Pre-built tool definitions for 5 frameworks
- ▪OpenAI, Anthropic, Google, LangChain, Raw
- ▪recall_memories, store_memory, compress_content
- ▪search_related, get_session_context
Memory System
3-tier- ▪Turn-level: facts extracted every turn
- ▪Session-level: synthesized every 5 turns
- ▪Project-level: synthesized at session close
- ▪Neon DB + Neo4j + Redis
SESSIONS EXPLORER
Sessions Explorer
Memory extraction and context compression analytics
AVG LATENCY
36ms
TOTAL MEMORIES
847
SECURITY HITS
14
Frequently asked questions
What is MemoSift?+
MemoSift is a context intelligence layer for AI agents. It sits between your agent and the LLM, providing deterministic context compression (<50ms), real-time security scanning (PII, secrets, prompt injection), three-tier persistent memory (turn, session, project), and compliance audit trails. It works with any agent framework — OpenAI, Anthropic, LangChain, Google ADK, Vercel AI, CrewAI, and more.
How is MemoSift different from mem0?+
mem0 provides LLM-based memory extraction with flat vector similarity recall. MemoSift provides memory extraction PLUS 7 compression engines, real-time security scanning, three-tier memory hierarchy (turn → session → project), semantic + keyword + entity graph recall, observability dashboards, and compliance audit trails (HIPAA, PCI-DSS, SOX, GDPR). MemoSift also returns compressed, LLM-ready context — not just memories.
Does MemoSift require an LLM?+
No. The core compression pipeline is fully deterministic and runs without any LLM calls in <50ms. Memory extraction uses an LLM (GPT-4.1-nano by default) for fact and relationship extraction, but this runs asynchronously and is optional. You can use MemoSift purely for compression and security scanning with zero LLM cost.
What frameworks does MemoSift support?+
MemoSift has first-class adapters (not wrappers) for 7 frameworks: OpenAI Agents SDK, Anthropic Claude SDK, LangChain, Google ADK, Vercel AI SDK, CrewAI, and Claude Agent SDK. All adapters provide lossless round-trip conversion — framework-specific metadata like thinking blocks, cache control, and tool result nesting are preserved.
Is MemoSift open source?+
Yes. The core compression engine, security scanner, and local persistence are open source under the Apache 2.0 license. The cloud platform (persistent memory, observability dashboard, compliance reports, team management) is available through paid plans starting at $39/month.
How does the pricing work?+
MemoSift has four tiers: Open Source (free forever, Apache 2.0 licensed), Pro ($39/month — persistent memory, Redis, session history, dashboard), Team ($99/month — Neo4j graph, compliance audit trail, RBAC), and Enterprise (custom — HIPAA/PCI-DSS/SOX, BYOK encryption, private deployment, SLA). The open-source package works fully offline with zero cost.
Can I self-host MemoSift?+
Yes. The open-source package runs entirely locally with SQLite and in-memory vector storage. For Enterprise customers, MemoSift offers private deployment options with customer-managed encryption (BYOK), dedicated infrastructure, and SLA guarantees.
What about data privacy?+
MemoSift scans for PII, secrets, and prompt injections BEFORE any data reaches the LLM or persistent storage. Redacted content never leaves your infrastructure. In local mode, all data stays on your machine. The cloud platform supports customer-managed encryption (BYOK) for Enterprise customers, and all compliance events are logged to an immutable audit trail.