Universal Long-Term Memory & Cross-Agent Handoff Protocol for AI Coding Agents (Antigravity, Cursor, Claude Code, Codex). Combines cross-agent long-term memory with AST symbol intelligence. Index codebases, cut LLM tokens by 97%, and hand off context seamlessly across AI coding agents.
Vibe Memory has 9 tools with inconsistent quality. Most tools have descriptions (7/9), but parameter schemas are incomplete or missing entirely. Tool names follow verb_noun convention (recall, query, create, store, learn, build, init, search, add), which is good. However, input schemas lack proper JSON Schema structure with type definitions for most parameters. Descriptions range from adequate (194 chars avg baseline) to vague. No output schemas documented. Error handling is absent. Security concerns: store_memory and learn tools accept free-form text without validation; no mention of secret injection or permission gates. Composition is weak, multiple tools do similar things (recall_memory vs search vs query_codebase_ast), forcing LLM disambiguation.
Add a new entry to a specific section of AGENT_MEMORY.md (architectural decisions, bug discoveries, or user preferences).
Analyze codebase and build hierarchical community graph using Microsoft GraphRAG methodology. Returns organized module communities: components, hooks/state, API/data, types/schemas, and external dependencies.
Generate a vendor-neutral context handoff snapshot for transferring active task state between AI coding agents. Includes objective, completed steps, next actions, and memory protocol keys.
Initialize a new AGENT_MEMORY.md file in the current workspace with the default template including sections for architectural decisions, bug discoveries, user preferences, and active handoff checkpoints.
Mem0 Adaptive Learning: Autonomously extract and memorize observations from raw conversation snippets, error discoveries, or preferences. Automatically categorizes and tags entries.
Scan and index Abstract Syntax Tree (AST) symbols from the codebase. Returns exported functions, classes, interfaces, types, and their locations with 97% token reduction compared to raw code.
Input schemas lack proper JSON Schema type definitions. Most parameters are documented as strings with descriptions, but no formal type, enum, minLength, maxLength, or pattern constraints. This forces LLMs to guess valid formats and invites hallucinated values.
No output schemas documented. Tools like query_codebase_ast claim to return 'exported functions, classes, interfaces, types, and their locations' but the response structure is not specified. LLMs cannot plan downstream calls or extract fields without knowing the output shape.
Overlapping tool functionality without clear disambiguation. recall_memory, search, and query_codebase_ast all search/retrieve data. The LLM must reason about which to call, recall_memory searches AGENT_MEMORY.md by tag, search searches AGENT_MEMORY.md by query, query_codebase_ast searches AST symbols. Naming does not make this obvious.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 41 | 2026-07-28+ | v2 |
Search and retrieve memory entries from AGENT_MEMORY.md by tag or query string. Returns matching architectural decisions, bug fixes, user preferences, and handoff checkpoints.
Search AGENT_MEMORY.md for entries matching a tag or query string. Returns matching lines grouped by section header.
Persist a new memory entry to AGENT_MEMORY.md. Automatically categorizes as architectural decision, bug discovery, or user preference based on content analysis.
No error handling or recovery guidance. Tools like store_memory and learn accept free-form text without validation. No mention of what happens on failure, whether errors are retryable, or how to self-correct. Error responses would be raw exceptions, not actionable guidance.
Security: No mention of permission gates, audit trails, or secret injection. store_memory and learn write to AGENT_MEMORY.md without documented access control. No indication that credentials or sensitive data are protected from logging.