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PlanDB
The issue tracker your AI agents are missing
Claude Code has a to-do list. Codex has a to-do list. Gemini has a to-do list. None of them have a planner. PlanDB replaces flat checklists with a compound dependency graph. Your agents decompose tasks, parallelize independent work, adapt plans mid-flight, and persist knowledge across sessions. The result: 10x more complex projects completed autonomously. One agent built a 3,769-line GPT-2 in Rust - 20 tasks, 7 experiments, zero human intervention. Open source · Rust · SQLite · Zero config
Hey PH! I'm Santosh, building PlanDB at AgentField.
I started where most of you probably are, using GitHub Issues to tell Claude Code what to do. Create issues, organize them, let the agent pick them up. It works... until you're running 4-5 agents on the same codebase. They duplicate work, start coding before dependencies exist, and lose all context between sessions. I was playing air traffic controller via markdown files and issue labels.
That's when I realized: the problem isn't the tool, it's the paradigm. GitHub Issues, Linear, Jira - they were designed for humans planning at human speed. Agents are fundamentally different:
- They decompose mid-flight (a 6-task plan becomes 20 as they learn)
- They need atomic claiming (two agents can't grab the same work)
- They pivot entire subtrees when an approach fails
- They need knowledge to surface automatically, not via search
- They operate at machine speed, seconds, not sprint cycles
None of that maps to a kanban board. Agents need a compound dependency graph, not a task list.
So I built PlanDB: a task graph database designed for how agents actually work. Single Rust binary, SQLite-backed, zero infrastructure. Three interfaces: CLI for shell agents, MCP server for Claude Code/Cursor, HTTP API for custom setups. Install in 10 seconds, agents start using it immediately.
What surprised me: agents don't just get more organized with PlanDB, they get dramatically better at execution. When the graph tells them what's independent, they naturally parallelize. When context auto-surfaces, they stop rediscovering things.
Everything is open source (Apache 2.0).
Would love to hear:
- Are you hitting similar coordination pain with your agents?
- What does your current agent workflow look like?
GitHub: github.com/Agent-Field/plandb
About PlanDB on Product Hunt
“The issue tracker your AI agents are missing”
PlanDB was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #335 on the daily leaderboard. Claude Code has a to-do list. Codex has a to-do list. Gemini has a to-do list. None of them have a planner. PlanDB replaces flat checklists with a compound dependency graph. Your agents decompose tasks, parallelize independent work, adapt plans mid-flight, and persist knowledge across sessions. The result: 10x more complex projects completed autonomously. One agent built a 3,769-line GPT-2 in Rust - 20 tasks, 7 experiments, zero human intervention. Open source · Rust · SQLite · Zero config
On the analytics side, PlanDB competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how PlanDB performed against the three products that launched closest to it on the same day.
Who hunted PlanDB?
PlanDB was hunted by Santosh Radha. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of PlanDB including community comment highlights and product details, visit the product overview.
Santosh Radha