Agent skill

Mesh Memory

by sickn33 in sickn33/agentic-awesome-skills

Self-hosted semantic memory for AI agents via MCP. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check passedDatabases

Install Mesh Memory

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mesh-memory .claude/skills/mesh-memory && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .claude/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mesh-memory .agents/skills/mesh-memory && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .agents/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mesh-memory .cursor/skills/mesh-memory && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .cursor/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/mesh-memory

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mesh-memory .gemini/skills/mesh-memory && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .gemini/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mesh-memory .github/skills/mesh-memory && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .github/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install sickn33/agentic-awesome-skills mesh-memory --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mesh-memory .opencode/skills/mesh-memory && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mesh-memory" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/mesh-memory into .opencode/skills/mesh-memory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-memory", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: mesh-memory
GitHub stars: 47k
Used in: 1 other repo
Token cost: ~1.9k tokens
SKILL.md length: 860 words
Files: 1
Skills in repo: 1,394
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Self-hosted semantic memory for AI agents via MCP. An agent skill from sickn33/agentic-awesome-skills.

Tasks that involve MCP servers
SKILL.md covers When to Use This Skill, Prerequisites, Setup and MCP Tools, plus 4 more sections
Calls docker and curl

What it does

Mesh Memory is an agent skill from sickn33/agentic-awesome-skills. Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering MCP servers. It works with Model Context Protocol, pgvector and PostgreSQL. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

Tasks that involve MCP servers

Example prompts

“/mesh-memory”

Requirements

Docker

What it can do on your machine

Read from SKILL.md and the folder at commit 1e53ce2. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- docker
- curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mesh Memory loads about 1.9k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 860 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~49
When it runs · the whole SKILL.md, loaded when a task matches: ~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1e53ce2, republished under its MIT licence (© sickn33). 860 words, ~1,892 tokens.

Download SKILL.mdSave it as .claude/skills/mesh-memory/SKILL.md (or your agent's skills folder).

name: mesh-memory
description: Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.
risk: safe
source: dklymentiev/mesh-memory (MIT)
date_added: 2026-05-23

Mesh Memory

Mesh Memory is a self-hosted semantic memory service with a built-in MCP server. It stores documents (worklogs, decisions, notes, research) in PostgreSQL with pgvector and retrieves them by meaning, so a query like "what database did we pick?" surfaces a saved note that says "chose Redis for caching" even with zero keyword overlap. Embeddings are generated locally with multilingual-e5-base (768 dimensions); the core flow requires no external API keys.

Use this skill when an agent needs persistent memory across sessions: saving its own work, recalling prior decisions, or building a project knowledge base shared between multiple agents.

When to Use This Skill

Saving a session worklog, decision, or research note so a later session can find it.
Recalling past work by topic when you do not remember the exact words you used.
Sharing a long-lived knowledge base across multiple agents, terminals, or teammates.
Organizing context by role or project through workspaces (one workspace per role/project).
Looking up structured tags (e.g. all type:decision entries from one project).

Prerequisites

A running Mesh Memory instance reachable from the MCP server. Local Docker is the common path -- docker compose up -d in the upstream repo brings it up; see https://github.com/dklymentiev/mesh-memory for the full Quick Start.
The MCP server (mcp_server.py) registered with your client (Claude Code, Cursor, Claude Desktop, or any other MCP-aware agent).
MESH_API_URL pointing at the running instance (default: http://localhost:8000).

Setup

json

{
  "mcpServers": {
    "mesh": {
      "command": "python3",
      "args": ["/path/to/mesh-memory/mcp_server.py"],
      "env": {
        "MESH_API_URL": "http://localhost:8000"
      }
    }
  }
}

When the server is reachable, the 13 tools listed below become available.

MCP Tools

Tool	Purpose
`mesh_focus`	Switch the active workspace (optionally prefetch recent docs).
`mesh_add`	Save a document with optional tags. Auto-adds `date:YYYY-MM-DD` and `source:`.
`mesh_update`	Update content, tags, or pinned status of an existing document.
`mesh_delete`	Delete a document by GUID.
`mesh_get`	Fetch a single document by GUID.
`mesh_search`	Semantic search by query, optionally across multiple workspaces with weights.
`mesh_bytag`	List documents that match one or more tags (AND logic).
`mesh_recent`	List most recently created documents, optionally filtered by `type:` tag.
`mesh_projects`	List per-project document counts (uses `guid:` tag as project marker).
`mesh_tags`	List existing tags with counts; optional prefix filter.
`mesh_versions`	Show the version chain of a document (similarity-linked revisions).
`mesh_stats`	Memory statistics for the active workspace.
`mesh_schema`	Show the tag schema (recognized prefixes and types).

Workflows

Save a session worklog

After completing work, persist it for future sessions:

mesh_add(
  content="Investigated 502s on the checkout flow. Root cause: missing CORS header on the cart API. Fix shipped in commit abc123.",
  tags="type:worklog,topic:checkout,date:2026-05-23",
  workspace="developer"
)

date: and source: are added automatically when omitted. Type and topic tags are inferred from nearest neighbors after the embedding completes (5-10 seed documents required before inference kicks in).

Recall past work by meaning

Search across sessions for related context, even with different vocabulary:

mesh_search(query="checkout was failing for some users", limit=5, workspace="developer")

The query shares no keywords with the original note ("502s", "CORS"), but the embedding-based search surfaces it.

Switch role / context

For a multi-role agent, switch the active workspace at the start of a session:

mesh_focus(workspace="sysadmin", prefetch=true, limit=5)

Subsequent calls default to that workspace. Pin a role-prompt document at the top of each workspace so the agent re-orients on every prefetch.

Cross-workspace search with weights

To pull context from related domains without diluting the primary signal:

mesh_search(
  query="nginx rate limit recipe",
  workspaces={"sysadmin": 0.7, "security": 0.2, "developer": 0.1},
  limit=10
)

Results are merged across workspaces and re-scored by workspace weight.

Show full SKILL.md (344 more words)Show less

Structured lookups by tag

When you need an exact filter rather than semantic similarity:

mesh_bytag(tags="type:decision,status:active,guid:my-project", limit=20)

Tag Conventions

Mesh accepts arbitrary tags. The recommended prefixes (used by auto-inference and surfaced by mesh_schema):

Prefix	Meaning
`type:worklog`	Completed work; the most common type.
`type:note`	Quick notes, observations.
`type:decision`	Architecture or product decisions.
`type:research`	Investigation results, findings.
`type:task`	Action items.
`type:rfc`	Proposals for review.
`status:active` / `status:completed` / `status:archived`	Lifecycle.
`date:YYYY-MM-DD`	When the document was created (auto-added).
`source:`	How the document arrived (auto-added: `mcp`, `api`, etc.).
`guid:<project-id>`	Project marker -- use a consistent slug across all docs of a project.

With fewer than ~5-10 documents in a workspace, neighbor inference is skipped; manually tag seed documents until the corpus self-organizes.

Troubleshooting

Tool calls fail with connection errors. The MCP server cannot reach MESH_API_URL. Verify the instance is up (curl $MESH_API_URL/health returns {"status":"healthy"}) and the env var is set in the MCP config.

A saved document does not appear in semantic search yet. Embedding generation runs in the background. After a save, expect a 1-2 second delay before semantic search hits the new document. mesh_get(guid=...) confirms the document exists immediately.

Search returns results from the wrong domain. The active workspace is not what you expected. Call mesh_focus(workspace="<name>") explicitly, or pass workspace= on every call. With no focus and no explicit param, calls land in the default workspace.

Auto-tagging never adds anything. The workspace has too few documents for neighbor inference (~5-10 minimum). Manually tag a handful of seed documents, then auto-inference takes over.

A deleted document still appears in a search result. Embedding indices are eventually consistent; rerun the search after a few seconds, or use mesh_get(guid=...) to confirm deletion.

Limitations

Mesh is a knowledge store, not a chat memory. Long conversation transcripts should be summarized before being saved.
Vector similarity is robust but not perfect; for high-precision structured lookups, prefer mesh_bytag over mesh_search.
Embeddings run on CPU by default; very large corpora (hundreds of thousands of documents) benefit from a dedicated instance and pgvector tuning, not covered here.
The optional AI categorizer requires an OpenAI-compatible LLM endpoint and is disabled by default.

Files

Just SKILL.md in skills/mesh-memory of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 1e53ce2

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

lingxling/awesome-skills-cnantigravity-awesome-skills/skills/mesh-memory antigravity-awesome-skills/plugins/agentic-awesome-skills/skills/mesh-memory+1 more

Compare with similar skills

Mesh Memory next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mesh Memory compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Mesh Memory this skillsickn33/agentic-awesome-skills	47k	1 repos	~1.9k	Automated safety check: Pass	MIT	yesterday
Ogham Recallogham-mcp/ogham-mcp	115	—	~1k	Automated safety check: Pass	MIT	7 days ago
Ogham Researchogham-mcp/ogham-mcp	115	—	~1.4k	Automated safety check: Pass	MIT	7 days ago
Ogham Maintainogham-mcp/ogham-mcp	115	—	~1.1k	Automated safety check: Pass	MIT	7 days ago
Session Ingestdnotitia/akb	161	—	~8.1k	Automated safety check: Pass	Custom licence	today
NubaseOtterMind/Nubase	624	—	~2.2k	Automated safety check: Notes	Apache-2.0	9 days ago

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Questions about Mesh Memory

What does Mesh Memory do?

Self-hosted semantic memory for AI agents via MCP. An agent skill from sickn33/agentic-awesome-skills. Mesh Memory is an agent skill from sickn33/agentic-awesome-skills. Self-hosted semantic memory for AI agents via MCP.

When should I use Mesh Memory?

Mesh Memory fits situations like: tasks that involve MCP servers.

How do I install Mesh Memory in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a claude-code`. Or copy the skill folder (skills/mesh-memory in sickn33/agentic-awesome-skills) into .claude/skills/mesh-memory in your project. Claude Code loads it when a task matches its description.

How do I install Mesh Memory in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a codex`. Or copy the skill folder (skills/mesh-memory in sickn33/agentic-awesome-skills) into .agents/skills/mesh-memory in your project. Codex loads it when a task matches its description.

Can I use Mesh Memory in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill mesh-memory -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mesh-memory, .gemini/skills/mesh-memory, .github/skills/mesh-memory and .opencode/skills/mesh-memory in your project.

What does Mesh Memory need to run?

Going by SKILL.md and its folder, Mesh Memory needs the command-line tools its instructions call (docker and curl). Our summary lists: Docker.

Does Mesh Memory access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Mesh Memory safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mesh Memory use?

Mesh Memory is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mesh Memory use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mesh Memory?

Skills that share tags, products or a category with Mesh Memory: Ogham Recall (ogham-mcp/ogham-mcp, 115 stars), Ogham Research (ogham-mcp/ogham-mcp, 115 stars), Ogham Maintain (ogham-mcp/ogham-mcp, 115 stars) and Session Ingest (dnotitia/akb, 161 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mesh Memory?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,304 GitHub stars. The repository holds 1,394 skills in this directory. The repository was last updated on October 6, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Mesh Memory

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Mesh Memory

When to Use This Skill

Prerequisites

Setup

MCP Tools

Workflows

Save a session worklog

Recall past work by meaning

Switch role / context

Cross-workspace search with weights

Structured lookups by tag

Tag Conventions

Troubleshooting

Limitations

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from sickn33/agentic-awesome-skills

Related

Questions about Mesh Memory