pindoc

pindoc

Code-pinned team memory for AI coding agents — typed artifacts (Decision/Analysis/Debug/Task), MCP-native workflow, self-host with Docker Compose.

Category
Visit Server

README

Pindoc

<p> <a href="./README.md"><img alt="English README" src="https://img.shields.io/badge/lang-English-2563eb.svg?style=flat-square"></a> <a href="./README-ko.md"><img alt="Korean README" src="https://img.shields.io/badge/lang-%ED%95%9C%EA%B5%AD%EC%96%B4-6b7280.svg?style=flat-square"></a> </p>

CI License MCP

Code-pinned team memory for AI-assisted development. Agents write the durable record; humans review, discuss, and steer.

Pindoc is a self-hosted project memory system for teams working with AI coding agents. It turns useful agent discoveries into typed artifacts: decisions, debugging paths, task closeouts, verification notes, and code-linked analyses. Every artifact is scoped to a project area and pinned back to commits, files, URLs, resources, or related Pindoc artifacts.

It is still the wiki you never type into, but the point is not automation for its own sake. Pindoc keeps the parts of agent work that teammates and future agents can reuse.

Why It Exists

AI coding sessions are productive, but team context still falls through the cracks:

  • a debugging path dies with the terminal session,
  • the same decision is re-explained to every new agent,
  • useful analysis stays in one operator's chat instead of becoming team knowledge,
  • duplicate documents accumulate across wikis, issue trackers, PRs, and commit messages,
  • in real project environments, the person who finds a problem cannot always change the code immediately; structured evidence helps the team discuss and decide.

Pindoc turns agent work worth keeping into searchable, code-pinned team memory. The next teammate or coding agent can ask Pindoc what matters before it edits.

What Makes Pindoc Different

  • Collaborative memory layer: artifacts are written for teammates and future agents, not as private chat summaries.
  • Agent-only write surface: the Reader UI is for reading and review; durable writes go through agents.
  • MCP-native workflow: tools such as pindoc.context_for_task, pindoc.artifact.propose, and pindoc.task.queue regulate agent behavior instead of acting as a thin CRUD API.
  • Typed artifacts: Decision, Analysis, Debug, Flow, Task, TC, Glossary, and domain-pack types.
  • Code-pinned memory: artifacts can point to commits, files, line ranges, resources, URLs, and related artifacts.
  • Record-worthy by design: Pindoc avoids raw chat archives and keeps only decisions, analyses, debug paths, verification, and task context with future value.
  • Multi-project daemon: one /mcp endpoint can serve multiple projects; each tool call carries project_slug.
  • Self-host first: Docker Compose brings up Postgres, pgvector, the Pindoc daemon, and the Reader SPA.

Public Demo

A read-only public demo is a follow-up track and is not part of this OSS release. Until it ships, the README, docs/, and a self-hosted clone are the primary proof. Operators who want to evaluate Pindoc end-to-end run docker compose up -d --build and inspect their own artifacts.

The follow-up demo plan stays in Public Demo Plan for when a hosted instance is appropriate.

Quick Start

Prerequisites:

  • Docker 27+
  • 2 CPU cores and 4 GB RAM recommended for local dogfood or small-team use
  • 5 GB free disk recommended for Docker images, Postgres data, and the embedding cache; 2 GB is a light fresh-clone minimum
  • outbound HTTPS on first run so the bundled EmbeddingGemma model and runtime can be cached
  • Go 1.25+ only for host-native development
  • Node 20.15+ and pnpm 10+ only for web development outside Docker

The default Docker path includes semantic search through a bundled EmbeddingGemma Q4 ONNX provider, so no embedding sidecar is required. See System Requirements for minimum and optional deployment profiles.

git clone https://github.com/var-gg/pindoc.git
cd pindoc
docker compose up -d --build

Open the Reader:

http://localhost:5830/

On a fresh instance, / first asks for the owner identity (display name and email), then routes to the first-project wizard. To open the project wizard directly after identity setup:

http://localhost:5830/projects/new?welcome=1

Connect an MCP Client

The Docker daemon exposes one account-level MCP endpoint:

{
  "mcpServers": {
    "pindoc": {
      "type": "http",
      "url": "http://127.0.0.1:5830/mcp"
    }
  }
}

Project scope is not encoded in the URL. Agents pass project_slug on project-scoped tool calls. Workspaces generated by pindoc.harness.install store that slug in PINDOC.md frontmatter.

Common Workflows

Ask an agent to start work with project context:

Use Pindoc context before editing. Find the current project, inspect assigned
Tasks, then implement the next acceptance item.

Typical MCP loop:

  1. pindoc.workspace.detect
  2. pindoc.task.queue
  3. pindoc.context_for_task
  4. code or doc work
  5. pindoc.artifact.propose
  6. update Task acceptance and closeout state

Configuration

The default Docker path is single-user and loopback-only:

Variable Default Purpose
PINDOC_DAEMON_PORT 5830 Host port used by Docker Compose.
PINDOC_PROJECT pindoc Default project for unscoped reads/config.
PINDOC_PUBLIC_BASE_URL http://127.0.0.1:${PINDOC_DAEMON_PORT} Public base URL used in generated links and OAuth metadata.
PINDOC_BIND_ADDR 127.0.0.1:5830 Security intent. Non-loopback values require an IdP or explicit public unauthenticated opt-in.
PINDOC_AUTH_PROVIDERS empty Identity providers enabled for external requests. Current provider: github.
PINDOC_ALLOW_PUBLIC_UNAUTHENTICATED false Explicit opt-in for external exposure without an IdP. Use only behind a trusted network/reverse proxy.
PINDOC_FORCE_OAUTH_LOCAL false Development flag that routes loopback /mcp calls through OAuth bearer auth for local QA.
PINDOC_ALLOWED_ORIGINS empty Comma-separated CORS allowlist. Empty means same-origin only; set explicit origins for cross-origin frontends.
PINDOC_DEV_MODE false Development-only flag that permits wildcard CORS for local tooling. Do not enable on public instances.

Do not expose a writable daemon to the public internet without an identity provider. For a public read-only demo, keep /mcp and mutating HTTP routes blocked at the reverse proxy; see SECURITY.md and docs/22-public-demo.md. The daemon also sets baseline security headers itself, including nosniff, clickjacking protection, referrer policy, and hardened asset-blob CSP.

For a writable public or cross-device instance, follow docs/oauth-setup.md. It covers GitHub OAuth App setup, the ${PINDOC_PUBLIC_BASE_URL}/auth/github/callback callback rule, runtime MCP client registration, and local OAuth QA with PINDOC_FORCE_OAUTH_LOCAL.

Development

# Run Go tests. Integration tests that need Postgres are skipped unless
# PINDOC_TEST_DATABASE_URL is set.
go test ./...

# Web checks.
cd web
pnpm install --frozen-lockfile
pnpm typecheck
pnpm test:unit
pnpm build

# Full image build.
docker build -t pindoc-server:local .

To test the OAuth bearer path locally while still connecting through 127.0.0.1, set PINDOC_FORCE_OAUTH_LOCAL=true; the daemon will warn on boot and require Bearer tokens for loopback /mcp calls.

On Windows hosts without a local C toolchain, run Go tests through Docker:

docker run --rm -v "${PWD}:/work" -w /work golang:1.25 go test ./...

Documentation

Status

Pindoc is in active dogfood. The local self-host path, Reader UI, project/area model, artifact proposal flow, task queue, revision history, summaries, and real embedding provider path are implemented. The public OSS launch track is focused on first-run reliability, a read-only dogfood demo, CI, security docs, and clearer collaborative positioning.

License

Apache License 2.0. See LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured