Context7
de upstash
Up-to-date docs for AI agents
Computadora o nube★ 64.224MITActualizado hace 3 días
Instalar
Project Memory
Resumen
Descripción del creador (original en inglés)
Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.
Los tres pilares de PopNx MCP
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
- Opinión de usuariosiAún no hay reseñasLas reseñas se abrirán junto con las cuentas en una etapa posterior
- Nuestras revisiones básicasi1 de 1 superadasRevisado con información pública; el código aún no se ha escaneado
- UsoiAún sin medir★ –
Qué puedes hacer con él
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
- Conexión: Se ejecuta en tu computadora
- Transporte: stdio
- Categoría: Búsqueda y memoria de IA
Instalación
Elige la app de IA que usas y copia la configuración.
Antes de instalar
- Revisiones básicas: 1 de 1 superadas
- Se ejecuta en tu computadora con los permisos de tu usuario (archivos, red, apps).
- El registro no indica ninguna clave que completar.
Necesitas
- PyPIuv de Python instalado (para el comando uvx)
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"project-memory": {
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add project-memory -- uvx project-memory-mcp serve --project '{project_path}'O copia la configuración tú
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"project-memory": {
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
O copia la configuración tú
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"project-memory": {
"type": "stdio",
"command": "uvx",
"args": [
"project-memory-mcp",
"serve",
"--project",
"{project_path}"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Qué puede hacer
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisiones básicasi
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
- ✓ Superada
El creador coincide con el repo
El nombre en el registro se verificó con la cuenta de GitHub Dankaro-projects, dueña del repo
- … Aún sin revisar
Sigue en mantenimiento
Todavía no hemos leído este repo en GitHub
- … Aún sin revisar
Tiene licencia
Todavía no hemos leído este repo en GitHub
- – No aplica
El endpoint responde
Este se ejecuta en tu computadora; no hay endpoint en internet
- – No aplica
Inicio de sesión con OAuth
Se ejecuta en tu computadora y usa ajustes locales en su lugar
- … Aún sin revisar
Sin instrucciones ocultas en las descripciones de herramientas
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con otros en Búsqueda y memoria de IAi
- Más estrellas que el 0% de los otros 7226 servidores de esta categoría
- Publicado en el registro más recientemente que el 42% de los otros 7226 servidores de esta categoría
- Revisiones básicas iguales o mejores que el 100% de los otros 7226 servidores de esta categoría
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Preguntas frecuentes
¿Es seguro Project Memory?
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
¿Qué claves o códigos necesita?
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
¿Se ejecuta en mi computadora o en la nube?
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
¿Sigue en mantenimiento?
El repo de GitHub cambió por última vez –, y la última versión (0.5.0b9) llegó al registro hace 2 meses.
¿Qué apps pueden usarlo?
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
¿Qué puedo usar en su lugar?
En la misma categoría: Context7, Codebase Memory, Exa
Alternativas
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Codebase Memory
de DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Se ejecuta en tu computadora★ 48.148MITActualizado hace 20 días
Instalar
Exa
de exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Se ejecuta en la nube★ 5213MITActualizado hace 44 días
Instalar
README del creador
Abrir el README
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.