Hive Memory — Local MCP server for persistent, cross-project agent memory

TL;DR in plain English

• Hive Memory is a fully local MCP (Memory & Context Provider) server that provides cross-project memory for AI coding agents: it maintains context, decisions, and knowledge across workspaces. Source: https://github.com/moonx010/hive-memory.
• Run a single instance on a machine you control and point an MCP-capable client at its HTTP endpoint. Agents can then read and write a persistent, inspectable project memory store. See the repo README: https://github.com/moonx010/hive-memory.
• Start with a short pilot to confirm basic reads and writes before trusting the store for important data. Use the repository as your authoritative source: https://github.com/moonx010/hive-memory.

Concrete example (short): two developers share a local Hive Memory instance; Developer A writes a compact decision note, Developer B’s agent later reads it and avoids repeating clarifying prompts.

What you will build and why it helps

You will set up a local Hive Memory server so AI coding agents can persist and share project-specific context across sessions and workspaces. The repository describes itself as “Cross-project memory for AI coding agents. MCP server that maintains context, decisions, and knowledge across workspaces. Fully local.” Source: https://github.com/moonx010/hive-memory.

Why this helps (summary):

• Agents stop re-asking the same clarifying questions when prior context is available.
• A single, local store lets teams inspect and audit past decisions on disk.
• For small teams, a local MCP avoids sending internal notes to third-party services.

Decision frame (quick comparison):

| Option | Where it runs | Auditability | Typical use case | |---|---:|---|---| | Hive Memory (local) | On your host or VM (fully local) | Inspectable local files and logs | Cross-project agent memory for internal use (see repo) | | No dedicated memory | n/a | Harder to centralize decisions | Agents repeat clarifications; knowledge isn't shared |

Source: https://github.com/moonx010/hive-memory

Before you start (time, cost, prerequisites)

Prerequisites (minimal, confirm exact details in the README):

• A machine (laptop, desktop, or VM) that can run a background process and host a local HTTP endpoint reachable from your agent or client. See: https://github.com/moonx010/hive-memory.
• An MCP-capable client or agent that can be configured to call an HTTP endpoint. The repo is intended for clients that support the MCP protocol. See: https://github.com/moonx010/hive-memory.
• Basic terminal skills: clone a repo, edit a small config file, start a process, and read logs.

Quick pre-flight checklist:

• [ ] Confirm you can clone and inspect the repository: https://github.com/moonx010/hive-memory
• [ ] Ensure the host has a writable folder for persisted data
• [ ] Verify your agent/client can target a local HTTP endpoint

Cost note: the project is designed to run fully local; infrastructure cost depends on your host (VM cost, $/month) — check your environment and the repository README for exact runtime requirements: https://github.com/moonx010/hive-memory.

Step-by-step setup and implementation

Methodology note: treat the repository README as authoritative for exact install commands, runtime versions, flags, and storage paths: https://github.com/moonx010/hive-memory.

1. Clone and inspect the repo

git clone https://github.com/moonx010/hive-memory.git
cd hive-memory
less README.md

2. Install and start per README

Follow the exact install and start instructions in the repository README. The project README is the authoritative source for required runtimes, flags, and storage paths: https://github.com/moonx010/hive-memory.

3. Example client config (illustrative pattern — replace with the repo's recommended fields)

{
  "mcp": {
    "host": "http://localhost:YOUR_PORT",
    "client_name": "solo-test-client"
  }
}

(Replace YOUR_PORT with the port the server exposes; confirm in README: https://github.com/moonx010/hive-memory.)

4. Basic verification steps

• Start the server using the README command.
• From the client host, write a short memory entry and read it back.
• Inspect the persisted files where the README indicates they live.

Example verification commands (replace YOUR_PORT per README):

# quick health check (illustrative)
curl -v http://localhost:YOUR_PORT/health || true

# write/read example (illustrative JSON body)
curl -X POST http://localhost:YOUR_PORT/memory -d '{"title":"note","body":"test"}' -H "Content-Type: application/json"

Source: https://github.com/moonx010/hive-memory

Common problems and quick fixes

If things fail, start with these checks. See the repository for authoritative troubleshooting and logs: https://github.com/moonx010/hive-memory.

Common symptoms and quick checks

• Client can’t reach server

  • Check: server not running or wrong host/port
  • Fix: start server, correct client config, test with curl

• No persisted files visible

  • Check: storage path misconfigured or permission denied
  • Fix: verify storage path from README and fix permissions

• Writes succeed but reads fail

  • Check: version or index issues; check logs
  • Fix: review server logs and backup files; follow repo restore guidance

Diagnostic commands (illustrative — confirm exact paths/endpoints in README):

# test reachability (replace port)
curl -v http://localhost:YOUR_PORT/health || true

# tail last 200 lines of an example log file (replace with README path)
tail -n 200 /var/log/hive-memory/server.log || true

Quick remedies

• Restart the service and watch initialization logs for errors.
• Ensure client host and port match the server configuration.
• If storage files appear corrupted, follow your backup/restore plan.

Source: https://github.com/moonx010/hive-memory

First use case for a small team

Target audience: solo founders and very small teams who want a local, inspectable memory store for agents. Repository: https://github.com/moonx010/hive-memory.

Practical starter pattern

1. Run single-host, single-instance
   • Host the Hive Memory server on one machine or VM you control and back up its storage folder.
2. Start with a small scope of memory types (e.g., decisions, debugging notes, heuristics) and agree on simple entry conventions (title, short summary).
3. Keep onboarding simple: one-page note describing how to add/find entries and what not to store.
4. Test a restore from backup once during the first week.

Pilot checklist for a small team:

• [ ] Run a single-host instance and verify read/write
• [ ] Add a short onboarding note inside the project workspace
• [ ] Test a restore from a dated backup (one entry)

Reference: https://github.com/moonx010/hive-memory

Technical notes (optional)

What the repository says (concise): Hive Memory is a local MCP server that maintains cross-workspace context, decisions, and knowledge for AI coding agents; it is described as fully local. See: https://github.com/moonx010/hive-memory.

Example export/backup pattern (illustrative; adapt to README paths):

// Node-style backup snippet — adjust paths per README
import { copyFileSync, readdirSync, mkdirSync } from 'fs';
const src = '/path/to/memory';
const dst = `/path/to/backups/${Date.now()}`;
mkdirSync(dst, { recursive: true });
readdirSync(src).forEach(f => copyFileSync(`${src}/${f}`, `${dst}/${f}`));

Confirm exact storage and any provided export utilities in the project README: https://github.com/moonx010/hive-memory

What to do next (production checklist)

Assumptions / Hypotheses

• The repository provides a local MCP server process and a README with install/start guidance: https://github.com/moonx010/hive-memory.
• Pilot suggestions (hypotheses to validate during testing):
  • Pilot team size: 2–3 people
  • Pilot duration: 7–14 days
  • Initial entry volume target: 10–50 entries during pilot
  • Per-project quota to consider after pilot: 10,000 entries or 100 MB
  • Retention window options to consider: 30–90 days
  • Rate limiting to consider: 100 requests/min per client
  • Performance targets to aim for: p95 metadata request latency <= 300 ms; client timeout example 15,000 ms
  • Restore success target during pilot: >= 90%
  • Operational alerts: downtime if server unreachable > 5 minutes; failed-restore threshold > 10/day

(Validate these numbers against your environment and the README before enforcing.) Source: https://github.com/moonx010/hive-memory

Risks / Mitigations

• Risk: Sensitive data persisted locally.
  • Mitigation: enforce an allow-list, redact or encrypt persisted content, and train users not to store secrets.
• Risk: Uncontrolled storage growth.
  • Mitigation: apply retention rules (30–90 days), per-project quotas (e.g., 10,000 entries or 100 MB), and schedule regular exports.
• Risk: Client misconfiguration causing spikes.
  • Mitigation: implement rate limits (100 req/min), per-client quotas, and circuit breakers.

Next steps

• Read the repository README and follow the exact install/run commands: https://github.com/moonx010/hive-memory
• Run a 7–14 day pilot with 2–3 people and 1–3 projects; aim for >= 90% successful restores during the pilot (hypothesis to validate).
• Implement automated daily backups and retain 14 backups; test restore procedures for single-entry restores.
• Add monitoring and alerts: uptime alert if down > 5 minutes; client error rate alert if > 5% errors; failed-restore alert if > 10/day.

Production checklist (copy and use):

• [ ] Confirm README/commands from repo: https://github.com/moonx010/hive-memory
• [ ] Run pilot (7–14 days)
• [ ] Ensure automated daily backups (retain 14 backups)
• [ ] Set retention policy (30–90 days)
• [ ] Define access control and training doc
• [ ] Define and test rollback/restore procedure

Final repository reference: https://github.com/moonx010/hive-memory

Hive Memory — Local MCP server for persistent, cross-project agent memory

Run Hive Memory locally to give AI coding agents persistent, cross-project context and session history (JSON/Markdown on disk at ~/.cortex). Use MCP clients li…

https://aisignals.dev/posts/2026-03-22-hive-memory-local-mcp-server-for-persistent-cross-project-agent-memory

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