Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated yesterday
Install
Project Memory
Overview
Description from the maker (original English)
Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"project-memory": {
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Run this in your terminal
claude mcp add project-memory -- uvx project-memory-mcp serve --project '{project_path}'Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"project-memory": {
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"project-memory": {
"type": "stdio",
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Dankaro-projects, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5654 servers in this category
- Published to the registry more recently than 43% of the other 5654 servers in this category
- Basic checks equal to or better than 100% of the other 5654 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Project Memory safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.5.0b9) reached the registry 2 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer★ 48,148MITUpdated 21 days ago
Install
Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated 2 months ago
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Project Memory
Project Memory keeps the plan, the evidence, the decisions, the outcomes and the accepted lessons of one project in a local SQLite database. Your assistant reads and writes those records through three MCP tools. You follow the same records in a local control panel, which is read only, and you decide in the chat what is approved, accepted and merged.
It works for a software product, a consulting engagement and a workflow automation built in n8n. Work items can be code, documents, deliverables or exported workflows.
Public beta. Python 3.11 or newer is required. The runtime uses only the Python standard library. There is no telemetry, no hosted database and no account. Agent checks and delegated work run through the Codex or Claude Code CLI that you already have installed, and they consume that account's usage.
Install
Install uv, then run this inside the project you want to remember:
uvx project-memory-mcp@0.6.0b16 setup --client codex --trustFor Claude Code, replace --client codex with --client claude. Setup preserves existing records and settings, connects the MCP server and the lifecycle hooks, and opens the control panel. --trust approves the project integration in that client; omit it to approve the connection yourself. Start a new assistant session afterwards. Add --no-view for a headless installation.
For a permanent command:
uv tool install project-memory-mcp==0.6.0b16project-memory doctor project-memory view
To install the published wheel directly from GitHub instead of PyPI:
uvx --from https://github.com/Dankaro-projects/project-memory/releases/download/v0.6.0b16/project_memory_mcp-0.6.0b16-py3-none-any.whl project-memory setup --client claude --trustOther local MCP clients use setup --client mcp and the stdio server. Setup and lifecycle covers client configuration, the plugins, upgrades, backups and removal.
Start a project
A new project starts from one of three templates:
project-memory init ~/projects/pricing-tool --template product --client codex| Template | For | Starter documents | Phases |
|---|---|---|---|
product | A software product | docs/brief.md, docs/research.md, docs/architecture.md, docs/stories.md | Goals and brief, Research, Requirements approval, Architecture, User stories and acceptance, Build, Review and release |
engagement | A consulting engagement | engagement/brief.md, engagement/stakeholders.md, engagement/research.md, engagement/findings.md, deliverables/README.md | Scope and brief, Stakeholders and hypotheses, Research and evidence, Analysis and synthesis, Recommendations and deliverables, Client review, Handover |
automation | A workflow automation, with n8n exports in workflows/ | automation/process.md, automation/systems.md, automation/solution-design.md, workflows/README.md | Process discovery, Systems and credentials inventory, Solution design, Workflow stories and test data, Build workflows, Test with sample data, Deployment and handover |
init creates the folder, initialises git unless you pass --no-git, writes any starter document that does not exist yet, connects the clients you name and records each phase as a work item. Running it again creates nothing a second time and never overwrites a document you have edited.
The kickoff conversation
Each template carries seven kickoff questions. Your assistant reads them with memory_get kickoff and asks you, for example what problem the product solves, who the stakeholders are, or which systems the process uses. It records your answers with memory_write answer_kickoff, fills the starter documents from those answers and proposes requirements. Only you approve the requirements baseline. Until that approval exists, the control panel opens on a kickoff checklist that shows the open questions, the research still needed, the starter documents that are still empty and the phases.
The everyday loop
Tell the assistant the result you want, the constraints and what would count as complete. It then records a work item with its objective, its completion criterion, the next action and the files it may change, continues from that record in later sessions, and records each decision with its evidence, alternatives, uncertainty and expected consequence. After acting, it records what actually happened. A failure stays visible after a later success.
You approve requirements, accept or reject proposed lessons, widen the allowed paths, and decide what is merged. Capturing a document does not approve its contents, and a plan does not authorise work.
The control panel
Run project-memory view in the project, or ask the assistant to open Project Memory. The panel reads the same database and refreshes while it is open. It is laid out as a Notion workspace: a sidebar of views, database tables with views, filters, sorting and grouping, and each item opening as a page beside the list, in a light or a dark theme.
| View | Content |
|---|---|
| Now | One sentence on the project position, the kickoff checklist, and the work in progress, paused, ready and done with the next step of each, and recent decisions |
| Plan | The hierarchy of phases, epics, stories, research items, deliverables and workflows as an outline with nested sub-items and progress, and a gallery of the phases |
| Work | A table and a board of work items, and a page for each with the plan, the allowed paths, dependencies, agent runs, lineage and history |
| Architecture | Components from source code, exported n8n workflows and authored items such as systems, stakeholders and deliverables |
| Dependencies | The work dependency graph with blocked chains, and the declared package table |
| Decisions | Decisions with their outcomes, and the lineage from requirement to outcome |
| Learning | Accepted guards and their recurrences, lessons awaiting your acceptance, failures without a lesson and scope widenings |
| Agents | Host availability, agent checks, delegated runs, and the merge or discard decision |
| Records | Search across every record kind, captured documents and host receipts |
| Requirements | The current requirements, their version and their approval evidence |
project-memory view --output review.html --include-bodies --no-open writes an offline snapshot instead. See the control panel guide.
Read the records in Obsidian
project-memory export --obsidian writes the records as a folder of Markdown notes with frontmatter and wikilinks, which Obsidian opens as a vault. A work item is a folder holding its own decisions, outcomes and plans, and sources, lessons and requirement revisions sit in shared folders beside it. The graph, the backlinks and the search of Obsidian then work on the project history, while the panel stays the place where you decide.
The export runs in one direction. The database is the source of truth, the export owns one folder inside the vault and touches nothing outside it, and a note edited inside that folder is lost on the next run. After a session that wrote records, the session stop hook refreshes the vault in a separate process.
Agent checks and delegated work
When Codex or Claude Code is configured, Project Memory can start short, separate host processes. An agent check reads the records and the project and reports on an outcome, on the current intent or on an unconfirmed execution. Delegated work runs a work item in its own git worktree, limited to the paths in its plan, and a second host reviews the resulting diff before you merge it. If one host reports a usage limit or a rate limit, the run reroutes once to the other configured host.
These runs use your installed Codex or Claude Code account and consume that account's usage. A reviewer cannot edit plans, accept lessons or merge anything. See agent checks and delegated work.
Guards and scope
An accepted lesson with a trigger becomes a guard. When a guard matches the paths or the wording of a work item, a decision must list that lesson in lessons_considered with an explicit yes or no and a reason, otherwise the decision is rejected. When a work item has allowed paths, the lifecycle hooks block an edit outside them, record the block and ask you to extend the scope in the chat. The record fields document defines the pattern rules.
Honest boundaries
- A recorded plan is not permission. Autonomy and scope are advisory records; your host's own permissions and your current instructions decide what may run.
- The scope guard reads the file paths of edit tools, patch markers and the targets of MCP write tools. It does not parse shell redirection or other indirect writes inside shell commands.
- Hooks record events mechanically. Only the assistant can record what those events meant, and a connected panel does not prove that the recording is complete.
- An agent verdict is an interpretation. A passing check does not prove that the work is correct, and an empty findings list is not proof either.
- Architecture is read statically from imports, manifests and exported workflow files. Dynamic loading is not detected.
- Bounded retrieval limits the characters a call returns. It cannot control the complete model input of the host.
- Daily productivity, token usage, correction rates and human task time are not measured, and this project makes no claim about them. Testing and limitations states what is verified and what is not.
Documentation
- User guide, from the first conversation to delivery
- Control panel and agent checks and delegated work
- Setup and lifecycle and record fields
- Testing and limitations, contributing, security and releasing
Packages are published on PyPI and in GitHub Releases.