yksanjo/gmem
Crypto-aware project memory for AI coding agents. Typed entities for Solana Programs/PDAs and EVM Contracts across Base, Optimism, Polygon, Arbitrum, Ethereum — plus chain-agnostic Decisions, Findings, and Integrations. Anchor + Hardhat auto-ingest, SQLite + FTS5 BM25 ranking, append-only versioning, git-aware diffs.
README
gmem
Persistent project memory for Solana AI agents.
A Model Context Protocol (MCP) server that gives AI coding agents — Claude Code, Cursor, or anything that speaks MCP — durable, Solana-aware memory of a project across sessions: program IDs, IDLs, PDA seeds, deployment state, architectural decisions, audit findings. So agents stop forgetting what they built yesterday.
Status: v1.1 — stable, with EVM support. Seven MCP tools, real implementations (no stubs), backward-compatible wire format:
-
Storage: SQLite via better-sqlite3, one db file per project (auto-resolved from
Anchor.toml/ workspaceCargo.toml, override withGMEM_DB) -
Ranking: SQLite FTS5 BM25 with a recency boost
-
Versioning: append-only — every write inserts a new
(kind, natural_id, version)row; reads return the latest; full history available via the in-processStoreAPI -
Anchor ingest:
gmem.ingest_anchorparsesAnchor.toml, captures IDL sha256s, records git HEAD assourceCommitper Program -
Solana CLI context:
gmem.solana_contextreads~/.config/solana/cli/config.yml, classifies the cluster, derives the active keypair's pubkey (secret never leaks).gmem.writeon a Decision auto-attributesauthor+authorCluster -
Git-aware diff:
gmem.diffaccepts both ISO timestamps and git refs (HEAD, HEAD~3, branch names, full and short SHAs) -
EVM support (v1.1):
gmem.ingest_hardhatparses Hardhat /hardhat-deployworkspaces, classifies networks into canonical chain ids (base-mainnet 8453, optimism-mainnet 10, polygon-mainnet 137, arbitrum-one 42161, ethereum-mainnet 1, plus testnets), captures a reorder-invariant ABI SHA-256. NewContractentity kind for EVM smart contracts. -
License: MIT
-
Spec: see
SPEC.md -
Entity schemas: see
schema/
Why
Solana has moved decisively toward an agent-first developer experience. The Foundation's
awesome-solana-ai repo indexes a strong layer of stateless reference skills —
solana-dev-skill, magicblock-dev-skill, metaplex-skill, helius-phantom-skill,
solana-game-skill, and more — that teach agents how to do things. The Solana
Developer MCP exposes documentation. What's missing is the layer above: persistent
project memory.
Today every Claude Code session on a Solana project starts cold. The agent doesn't remember the program ID it deployed yesterday, the PDA seeds it chose two weeks ago, the audit finding from last sprint, or why a specific Jupiter integration was rejected. The developer compensates by pasting context, hand-maintaining NOTES files, or re-explaining the project every session. That is a tax on every agent-assisted Solana developer.
gmem fixes this by being opinionated about Solana primitives — programs, accounts,
instructions, PDAs, IDLs, cluster state, Anchor configs — rather than being a generic
key-value store. It complements every existing skill in awesome-solana-ai rather than
competing with any of them.
Install
npm install -g @yksanjo/gmem
Or, to run it without a global install:
npx @yksanjo/gmem
The installed binary is still called gmem.
Or, to hack on the source:
git clone https://github.com/yksanjo/gmem.git ~/gmem
cd ~/gmem && npm install && npm run build
Then point your MCP client at it. For Claude Code:
// ~/.claude/mcp_servers.json
{
"mcpServers": {
"gmem": {
"command": "node",
"args": ["/absolute/path/to/gmem/dist/index.js"],
"env": { "GMEM_DB": "~/.gmem/memory.db" }
}
}
}
Tools exposed (v0.1)
| Tool | Purpose |
|---|---|
gmem.recall(query) |
Retrieve memory entries relevant to a natural-language query, ranked by BM25 + recency |
gmem.write(entity) |
Persist a typed memory entry (Program / Account / Instruction / Decision / Finding / Integration); append-only |
gmem.diff(from, to) |
Show how memory state changed between two points in time — accepts ISO timestamps OR git commit refs (HEAD, HEAD~3, branch names, full or short SHAs) |
gmem.list_decisions() |
List all Decision entries for the active project, newest first |
gmem.ingest_anchor() |
Auto-ingest an Anchor workspace: parse Anchor.toml, capture IDL sha256s from target/idl/, record the current git HEAD as source commit, write one Program per (program, cluster) pair |
gmem.solana_context() |
Read the active Solana CLI config (~/.config/solana/cli/config.yml), return the configured cluster + RPC URL + active-keypair pubkey. Used by gmem.write to auto-attribute Decision entries to the developer wallet. Never returns the secret key. |
gmem.ingest_hardhat() |
v1.1 — Auto-ingest a Hardhat / EVM workspace: parses hardhat.config.{ts,js,cjs,mjs}, reads every deployment artifact under deployments/<network>/<Contract>.json (the hardhat-deploy convention), classifies the network into a canonical chain (base-mainnet, optimism-mainnet, etc.), records a reorder-invariant ABI SHA-256 and git HEAD as sourceCommit. Writes one Contract entity per (chain, address) pair. |
Full input/output JSON schemas are in SPEC.md.
Out of scope for v1.0
To keep the v1 scope honest, gmem v1.0 does NOT include: hosted multi-user sync,
cross-project search, agent reputation, on-chain memory anchoring. These are tracked in
ROADMAP.md for v1.x / v2.
Roadmap
- [x] v0.1 — Open spec + JSON schemas + MCP server stub
- [x] v0.2 — SQLite backend, BM25 ranking, append-only versioning
- [x] v0.3 — Anchor workspace auto-ingest
- [x] v0.4 — Solana CLI context capture + Decision auto-attribution
- [x] v0.5 — git ref resolution in
gmem.diff - [x] v1.0 — Stable release with three worked examples (DeFi vault,
cNFT mint, AI agent) — see also
PR #168 in
solana-foundation/awesome-solana-ai - [x] v1.1 — EVM / Hardhat support (Base, Optimism, Polygon, Arbitrum, Ethereum, plus testnets),
new
Contractentity,gmem.ingest_hardhattool, worked example 04-evm-vault
Contributing
This is an early-stage spec. The most useful thing right now is feedback on
SPEC.md — does the entity model cover the Solana primitives that matter
to your project? Open an issue or PR.
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。