<p align="center">🦀</p>
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<code>cargo install decapod && decapod init</code>
</p>
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<strong>Decapod</strong><br />
A daemonless control plane for AI coding agents.
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Called on demand inside agent loops. No background process, no new workflow, local-first state you can verify.
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</p>
---
## Why Decapod 🧠
AI coding agents are strong at generating code. Most failures happen before and after generation: unclear intent, fuzzy boundaries, and weak completion checks.
Decapod is the missing layer in that loop. Agents call it mid-run to lock intent, enforce boundaries, and prove completion with explicit gates. It shapes inference without doing inference.
Decapod is **daemonless**. There is no long-lived service. The binary starts when an agent calls it and exits immediately after the call.
"Just use Decapod" is literal:
- `cargo install decapod`
- `decapod init`
Then continue with Claude, Codex, Gemini, OpenCode, or any other agent exactly as you already work. Decapod is agent-agnostic and safe for concurrent multi-agent execution.
State is local and durable in `.decapod/`: shared context, decisions, and traces persist across sessions and remain retrievable over time.
Related: [Evaluating AGENTS.md](https://arxiv.org/pdf/2602.11988) (ETH SRI, 2026) on context-file quality and agent cost/performance.
<p align="center">
☕ Like Decapod? <a href="https://ko-fi.com/decapodlabs"><strong>Buy us a coffee on Ko-fi</strong></a> 💙
</p>
## Assurance Model ✅
Decapod centers execution around three outcomes:
- `Advisory`: clear next actions that tighten intent and reduce wasted loops.
- `Interlock`: hard policy boundaries that block unsafe or out-of-contract flow.
- `Attestation`: durable, structured proof that completion criteria actually passed.
## Operating Model ⚙️
```text
Human Intent
|
v
AI Agent(s) <----> Decapod Runtime <----> Repository + Policy
| | |
| | +-- Interlock (enforced boundaries)
| +------- Advisory (guided execution)
+------------ Attestation (verifiable outcomes)
```
## Features ✨
- On-demand CLI/RPC control plane agents call during work, then exit.
- Early intent capture and explicit task boundaries before implementation commits.
- Deterministic policy gates that produce concrete pass/fail completion signals.
- Repo-native durable state in `.decapod/` for historically retrievable traces and decisions.
- Shared cross-agent context that survives sessions and handoffs.
- Multi-agent-safe coordination for concurrent Claude/Codex/Gemini/OpenCode workflows.
## Getting Started 🚀
```
cargo install decapod
decapod init
```
Then keep using your agents normally. Decapod is called from inside those agent runs when control-plane decisions are needed.
Learn more about the embedded constitution via the CLI:
```bash
decapod docs show core/DECAPOD.md
```
Override constitution defaults with plain English in `.decapod/OVERRIDE.md`.
## Contributing 🤝
```bash
git clone https://github.com/DecapodLabs/decapod
cd decapod
cargo build
cargo test
decapod validate
```
## Documentation 📚
- Development guide: [CONTRIBUTING.md](CONTRIBUTING.md)
- Security policy: [SECURITY.md](SECURITY.md)
- Release history: [CHANGELOG.md](CHANGELOG.md)
## Support 💖
- 🐛 [File an issue](https://github.com/DecapodLabs/decapod/issues)
- ☕ [Support on Ko-fi](https://ko-fi.com/decapodlabs)
## License 📄
MIT. See [LICENSE](LICENSE).