Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.
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io.github.Dankaro-projects/project-memory is an MCP server that local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 54/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"io-github-dankaro-projects-project-memory": {
"args": [
"project-memory-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
uvx 'project-memory-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked project-memory-mcp against OSV.dev.
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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 uv, then run this inside the project you want to remember:
uvx project-memory-mcp@0.6.0b16 setup --client codex --trust
For 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==0.6.0b16
project-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 --trust
Other local MCP clients use setup --client mcp and the stdio server. Setup and lifecycle covers client configuration, the plugins, upgrades, backups and removal.
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.
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 ne