Mem0 vs ThreatRecall
Use Mem0 for general-purpose agent memory where the app controls the schema. Use ThreatRecall when memory is evidence and analysts will be deposed.
Mem0 is a solid general-purpose agent memory layer (~48K GitHub stars) — great for apps that need lightweight per-user memory with vector retrieval. ThreatRecall is purpose-built for CTI: every memory record carries evidence provenance, TLP enforcement, a confidence score, and a write-once audit log. The difference isn't a feature gap — it's two different product categories solving two different problems.
| Dimension | ThreatRecall | Mem0 |
|---|---|---|
| Evidence schema source_url, timestamp, file_hash, linked_evidence_ids | ✓ |
~
App-defined metadata field — optional, not required |
| TLP enforcement AMBER/RED blocked from LLM at ingest and query | ✓ | ✗ |
| Audit log export CSV / JSONL, cryptographically signed | ✓ | ✗ |
| Confidence scoring Score + provenance chain + correction UI | ✓ |
~
Vector similarity score only |
| Tenant isolation Row-level RLS + dedicated connection pool per tenant | ✓ |
~
API-key scoped, multi-tenant via user_id partition |
| Correction / merge / reject UX Reject, correct, or merge memory via UI with 24h rollback | ✓ | ✗ |
| CTI data model Actor / TTP / CVE / IOC / ATT&CK / OSINT / Tool / Vulnerability | ✓ | ✗ |
| FedRAMP / compliance Phase 4 in progress, public /security page with CAIQ-Lite v4 | ✓ | ✗ |
| Compliance questionnaire Downloadable CAIQ-Lite + SIG-Lite PDF at /security/questionnaire | ✓ | ✗ |
| STIX 2.1 round-trip Ingest + export — structured CTI bundle standard | ✓ | ✗ |
| Multi-tenant RBAC admin / analyst / readonly / audit roles, TOTP-enforced | ✓ |
~
Basic roles, no memory-level ACLs |
When Mem0 is the better choice
General-purpose AI app memory
If you're building a customer-facing AI app (chatbot, copilot, agent) where memory is app-controlled and the user owns the content, Mem0's per-user model is clean and well-documented.
Apps without evidence requirements
If your product doesn't need to reconstruct why a memory was stored, who ingested it, or what the confidence level was — Mem0's simpler schema is faster to integrate and maintain.
Rapid prototyping with general LLMs
If you're in early-stage product development and need a lightweight memory layer without the CTI schema overhead, Mem0's API-first design gets you running in minutes.
Ingest API comparison
// Every record carries full provenance POST /v1/evidence { "content": "APT29 used FoggyWeb to exfiltrate...", "source_url": "https://microsoft.com/apt29-report", "observed_at": "2025-05-28T12:00:00Z", "file_hash": "sha256:abc123...", "linked_evidence_ids": ["ev_jira_123", "ev_crowd_456"], "tlp_marking": "AMBER", "confidence": 0.87 } // AMBER/RED never reaches the LLM. // Confidence + provenance chain on every recall.
// Metadata field is app-defined, optional POST /v1/memories { "role": "user", "content": "APT29 used FoggyWeb...", "metadata": { "source_url": "https://...", // optional "hash": "sha256:..." // optional } } // No TLP enforcement. // No confidence score. // Provenance is app-responsibility.
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