MemSearch Memory Layer

What does this agent do, and when should you use it?

MemSearch is a persistent semantic-memory layer for AI coding agents, with plugins for Claude Code, Codex CLI, OpenClaw, and OpenCode plus a CLI and Python API. It stores captured summaries and other memories as editable, versionable Markdown, then maintains Milvus as a rebuildable shadow index. Retrieval combines dense vectors, BM25 sparse search, and RRF reranking, and can move from a matching chunk to its Markdown section or a raw session transcript. It defaults to local single-file Milvus Lite, while Zilliz Cloud and self-hosted Milvus are configurable alternatives. It fits teams or individual developers who want durable context across coding-agent sessions and tools, provided they are comfortable managing local files, indexing, and embedding runtime choices.

Platform plugins capture conversation turns after a session turn completes, summarize them, and append the result to date-based Markdown files under .memsearch/memory/; the documented Claude Code flow also adds a <!-- session:UUID --> anchor. memsearch index re-chunks Markdown, uses SHA-256 to skip unchanged content, embeds changed chunks, and upserts them into Milvus. memsearch search performs BM25 plus dense-vector hybrid retrieval with RRF reranking, while memsearch expand <chunk_hash> returns the full surrounding Markdown section. For deeper recall, the documented third layer uses parse-transcript <session.jsonl> to access raw dialogue. Developers can also use MemSearch(paths=[...]), await mem.index(), and await mem.search() in a Python agent.

1. A developer alternating between Claude Code and Codex CLI who wants to retrieve an earlier Redis, deployment, or architecture discussion.
2. An engineer maintaining a long-lived codebase who needs to recover why a module, configuration, migration, or API choice was made before changing it.
3. A Python agent developer who wants to index Markdown knowledge and add semantic recall through the MemSearch class.
4. A team keeping project journals in local Markdown and wanting those files to remain editable and version-controllable while indexes stay current.
5. A multi-user team that needs to point the same workflow at Zilliz Cloud or a dedicated self-hosted Milvus instance.

How do you install or deploy this agent?

Python >=3.10 is required. To install the CLI with the default local ONNX embedding option:

uv tool install "memsearch[onnx]"

Then run:

memsearch config init
memsearch index ./memory/

The default ONNX bge-m3 embedder runs locally on CPU and needs no API key, but downloads an approximately 558 MB model from HuggingFace Hub on first launch. To use OpenAI embeddings, run memsearch config set embedding.provider openai and supply OPENAI_API_KEY. For the Codex CLI plugin, the documented commands are:

git clone --depth 1 https://github.com/zilliztech/memsearch.git
bash memsearch/plugins/codex/scripts/install.sh
codex --yolo

How do you use this agent?

Place searchable Markdown in a directory and run memsearch index ./memory/. Query it with memsearch search "Redis caching", or use memsearch search "auth flow" --top-k 10 --json-output for script-friendly results; use memsearch expand <chunk_hash> to inspect the complete section around a result. Run memsearch watch ./memory/ for live re-indexing. In Python, create mem = MemSearch(paths=["./memory"]), call await mem.index(), then call await mem.search("Redis config", top_k=3). After installing the Codex CLI plugin, use $memory-recall what did we discuss about deployment? to recall saved context.

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