Pindoc

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 bund

... View full README on GitHub