Platform Overview

memsearch provides plugins for 4 AI coding agent platforms. All plugins share the same core architecture: capture conversations to markdown, index with Milvus, recall via semantic search.

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Comparison Table

Feature	Claude Code	OpenClaw	OpenCode	Codex CLI
Plugin type	Shell hooks	TS registerTool	TS npm plugin	Shell hooks
Capture method	Stop hook (async)	llm_output debounce	SQLite daemon	Stop hook (async)
Summarization	claude -p --model haiku	OpenClaw agent	opencode run	codex exec
Recall mechanism	SKILL.md (context: fork)	memory_search tool	memory_search tool	SKILL.md
L3 transcript format	Claude Code JSONL	OpenClaw JSONL	OpenCode SQLite	Codex rollout JSONL
Isolation	Per-project collection	Per-agent directory	Per-project collection	Per-project collection
Install method	Plugin marketplace	openclaw plugins install	npm + opencode.json	install.sh
Embedding default	ONNX bge-m3 (CPU)	ONNX bge-m3 (CPU)	ONNX bge-m3 (CPU)	ONNX bge-m3 (CPU)
API key required	No (ONNX default)	No (ONNX default)	No (ONNX default)	No (ONNX default)

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Shared Architecture

All plugins follow the same capture-index-recall pattern:

graph TB
    subgraph "Capture"
        CONV[Agent conversation] --> SUM[LLM summarization]
        SUM --> MD["memory/YYYY-MM-DD.md"]
    end

    subgraph "Index"
        MD --> WATCH[memsearch watch/index]
        WATCH --> MIL[(Milvus)]
    end

    subgraph "Recall"
        Q[User question] --> SEARCH[memsearch search]
        SEARCH --> MIL
        MIL --> RES[Relevant memories]
        RES --> EXPAND[memsearch expand]
        EXPAND --> TRANSCRIPT[Transcript drill-down]
    end

    style MD fill:#2a3a5c,stroke:#e0976b,color:#a8b2c1
    style MIL fill:#2a3a5c,stroke:#6ba3d6,color:#a8b2c1

Three-Layer Progressive Disclosure

Every plugin supports the same three-layer recall model:

Layer	Command	What it returns
L1: Search	memsearch search	Top-K relevant chunk snippets
L2: Expand	memsearch expand	Full markdown section around a chunk
L3: Transcript	Platform-specific parser	Original conversation verbatim

Memory File Format

All plugins write to the same markdown format:

# 2026-03-25

## Session 14:30

### 14:30
<!-- session:abc123 turn:def456 transcript:/path/to/session.jsonl -->
- User asked about Redis caching configuration
- Agent implemented cache middleware with 5-minute TTL
- Added Prometheus counters for cache hit/miss metrics

Cross-Platform Memory Sharing

Since all plugins write standard markdown and use the same Milvus index, memories are portable:

• Memories written in Claude Code are searchable from Codex, OpenCode, or OpenClaw
• Point multiple plugins at the same milvus_uri and collection for shared access
• Or use per-project collections for isolation (the default)

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When to Use Which

Scenario	Recommended Platform
Primary Claude Code user	Claude Code plugin -- most mature, marketplace install
OpenClaw agent development	OpenClaw plugin -- native TS integration, multi-agent isolation
OpenCode user	OpenCode plugin -- npm package, SQLite-native capture
Codex CLI user	Codex plugin -- shell hooks, similar to Claude Code
Using multiple platforms	Install plugins on each -- they share the same memory backend

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Prerequisites (all platforms)

• Python 3.10+
• memsearch installed: uv tool install "memsearch[onnx]" (or pip install "memsearch[onnx]")
• First-time ONNX model download: ~558 MB from HuggingFace Hub (cached after first run)