aivectormemory

aivectormemory is a local, MCP-compatible Python server that provides persistent cross-session “memory” for AI coding IDE workflows. It stores project/user notes as vector embeddings (ONNX) in a local SQLite+sqlite-vec database and exposes MCP tools to remember, recall (semantic search), forget, and manage session/task/issue state. It also runs a local web dashboard for browsing and administration.

Evaluated Mar 30, 2026 (21d ago)
Repo ↗ DevTools mcp memory vector-search sqlite onnx local-first ai-coding-assistant task-tracking semantic-retrieval
⚙ Agent Friendliness
60
/ 100
Can an agent use this?
🔒 Security
48
/ 100
Is it safe for agents?
⚡ Reliability
28
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
78
Documentation
72
Error Messages
0
Auth Simplicity
90
Rate Limits
10

🔒 Security

TLS Enforcement
60
Auth Strength
35
Scope Granularity
25
Dep. Hygiene
55
Secret Handling
70

Security posture is partially documented: it is local-first (no API keys for embeddings/inference per README) and stores data locally. However, the web dashboard explicitly uses default username/password (admin/admin123) and does not clearly specify TLS/network binding behavior in the provided README. MCP tool auth/scopes are not clearly documented; a “token authentication protection” is mentioned for the dashboard but details are missing. Dependencies include onnxruntime, huggingface-hub, jieba, sqlite-vec; no CVE status is provided.

⚡ Reliability

Uptime/SLA
5
Version Stability
40
Breaking Changes
30
Error Recovery
35
AF Security Reliability

Best When

You want a local/on-device MCP memory layer for AI coding assistants that you control end-to-end (IDE + local server + local database).

Avoid When

You need enterprise-grade auth guarantees, externally hosted service guarantees, or you cannot safely expose a local dashboard to untrusted networks/users.

Use Cases

  • Persistent IDE memory for coding assistants (pitfalls, decisions, conventions) across sessions
  • Semantic recall of prior project knowledge even with different wording
  • Workflow automation via task/issue tracking persisted between IDE sessions
  • Managing and curating memories via a local web dashboard
  • Reducing token/context injection by retrieving only relevant memories on-demand

Not For

  • Multi-tenant cloud deployments where you can’t guarantee local data isolation
  • Organizations requiring formally documented security posture, threat model, and compliance evidence
  • Use cases needing strong, standardized REST/GraphQL APIs with published schemas and guarantees

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Web dashboard login (username/password; default admin/admin123 stated)
OAuth: No Scopes: No

README describes web dashboard credentials and “token authentication protection,” but does not document API auth for the MCP tools beyond local operation.

Pricing

Free tier: No
Requires CC: No

Local/self-hosted library + local web server; no pricing model indicated.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • Tool outputs/IDs are not specified in enough detail to design robust retry/idempotency semantics.
  • Task tracking and status/blocking behavior depend on “Steering rules” generated during install; agent behavior may break if hooks/rules are not correctly configured per IDE.
  • Web dashboard default credentials (admin/admin123) may pose security risk if exposed beyond localhost or not changed immediately.

Alternatives

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Scores are editorial opinions as of 2026-03-30.

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