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Nauro

Community
Give your agents the context code leaves out.
How to install ↓ Compare GitHub source repository ↗
Category
Dev Tools #147 of 438
Stars
★ 11 Popular
Transport
stdio (local process)
Runtime
Python 3.10+
Credentials
No credential needed
License
Apache-2.0
Last commit
Tools
1
58FMRS · C

Nauro is a local-first MCP server that provides a shared project memory for AI coding agents, emphasizing human approval and decision rationale. Its install configuration is via uvx/PyPI, and it is suitable for long-lived projects. Official status is not claimed, but the server is maintained by the upstream project's maintainers.

Strongest · Setup experience 12/20 Weakest · Reliability 10/20

Reliability
10/20
Security and permissions
12/20
Maintenance
12/20
Documentation
12/20
Setup experience
12/20
Why each score
Reliability 10/20
The repository includes a server manifest (server.json) and installation instructions, but the static review cannot verify MCP handshake and tool behavior; no CI or test evidence seen, so reliability is capped at 10.
Security and permissions 12/20
No evidence of credential theft or overreach; however, details on permissions and data flow are missing, and no security audit evidence, so score 12.
Maintenance 12/20
Version 1.19.0 suggests active development, Apache-2.0 license, but zero open issues may indicate maintainers have issues disabled; static review cannot confirm commit frequency or dependency updates.
Documentation 12/20
README provides installation and demo instructions, but lacks detailed documentation on tool parameters, limitations, or troubleshooting; documentation exists but lacks depth.
Setup experience 12/20
Installation steps are clear (uv tool install), and a demo is provided, but no config examples for mainstream clients; static review cannot confirm actual setup path, so score set to 12.

Static review · not runListed 2026-09-08

Read the FMRS scoring method →

Fit and risk

What it can accessReads local files

Best for

  • Long-term projects where project context decays across sessions, tools, and handoffs
  • Pairing with AI coding agents like Claude Code, Codex, or Cursor to reduce redundant questions and context loss
  • Teams that value local data ownership and human-approved decision records

Not for

  • Small repositories: if a reliable AGENTS.md or CLAUDE.md keeps agents oriented, Nauro may be overkill
  • CI/CD pipelines requiring strict enforcement on every change—Nauro's retrieval is advisory and never blocks
  • Users wanting fully automatic decision logging without human approval gates

Required permissions

  • The local record is plain Markdown that you own; local use needs no account and sends no analytics
  • Nauro can read project files (e.g., README, code) to assess decision rationale but writes only after your explicit approval
  • Installation may add skill files and workflow agents (e.g., nauro-ship-task) if requested
  • Explicit overwrite via `nauro sync` is the only controlled override path, preserving manually managed files

Risks and side effects

  • Unmanaged AGENTS.md may be overwritten—Nauro preserves it during adoption; `nauro sync` is the sole explicit overwrite path with a `# Manual` section protecting it
  • As an MCP server, it uses stdio; any client with access to the MCP can read the local record content
  • Nauro does not enforce decisions on agents; it is advisory, so agents might ignore stored judgment

Setup

Before you start

Runtime：Python 3.10+

Install Nauro with uv tool install nauro. Ensure uv is installed first (macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh; Windows instructions in docs). Alternatively, with Python 3.10+, run pipx install nauro. After installation, cd into your repository and run nauro adopt --with-skills --with-subagents, restart your agent, then invoke /nauro-adopt in Claude Code or $nauro-adopt in Codex.

Check that it works

Run `nauro init --demo`, then `nauro check-decision "Store dollar amounts as decimal numbers"`; the top result should be D001, "Amounts stored in integer cents, never floating point". You can also run `nauro status` to confirm MCP plus installed skills are active.

Troubleshooting

  1. Run `nauro status` to verify MCP, skills, and workflow agents are properly installed
  2. Run `nauro doctor` to check the health of the project store
  3. If skills are not loading, ensure you adopted with `--with-skills --with-subagents` and restart your agent
  4. For sync issues, check if `nauro sync` is unavailable or if files are in a manual state

Things to try

Once connected, you can ask your AI assistant things like:

  • Look up the decision about storing dollar amounts as decimal numbers
  • check-decision "Store dollar amounts as decimal numbers"
  • Does the project have an approved decision on handling floating point for money?
  • Before making this change, retrieve the relevant rationale from the Nauro record

Tools 1

check-decision read-only
Submit a new decision to the project record for checking and approval.

Use cases

Maintain a shared project memory across sessions, tools, repositories, or machines in longer-lived work
Quickly bring new agents or sessions up to speed on project state and decision rationale
Record and build consensus around key architectural decisions, e.g., storing dollar amounts in decimal

Supported clients

Claude DesktopPartial support

Listed from the project's documentation, not tested by this site.

Overview

Nauro keeps current state, open questions, and human-approved project judgment in one record, ready for every agent you connect. The record combines project scope, state, open questions, and the rationale behind decisions. Nauro surfaces the relevant slice before work, then carries approved judgment and reported progress into later sessions and connected tools.

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Source revision e8b2e4544e7f Data synced 2026-10-19 Read the FMRS scoring method