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CORAL is a Python framework for orchestrating multiple AI coding agents (Claude Code, Codex, Cursor, etc.) to autonomously solve problems through iterative improvement. Agents work in parallel git worktrees, share findings via a common state directory, and are evaluated by a pluggable grader daemon.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Human-Agent-Society/CORAL |
| Owner | Human-Agent-Society |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 783 |
| Forks | 101 |
| Open issues | 12 |
| Latest release | v0.7.6 (2026-07-05) |
| Last updated | 2026-07-07 |
| Source | https://github.com/Human-Agent-Society/CORAL |
What CORAL is
Multi-agent orchestration platform using isolated git worktrees per agent, symlinked shared state (`.coral/public/`), async grader evaluation, and heartbeat-driven prompts (reflect/consolidate/pivot). Supports LiteLLM gateway for model routing; requires Docker for certain graders (SWE-bench, terminal-bench) but must run on host, not in containers.
Get the CORAL source
Clone the repository and explore it locally.
git clone https://github.com/Human-Agent-Society/CORAL.gitcd CORAL# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Agent runtime (Claude Code, Codex, Cursor, Kiro, OpenCode) must be installed and authenticated separately; each has distinct config and model routing requirements.
- Graders are pluggable Python entrypoints; custom grader requires packaging and wiring via `grader.entrypoint` (legacy auto-discovery removed as of v0.6.0+).
- Docker required for SWE-bench and terminal-bench; CORAL itself must run on host (no DinD support). Multi-island runs (v0.6.0+) partition agents into scoped exploration zones.
- Shared state (`.coral/public/`) is symlinked into all worktrees; agents are unprivileged in Docker (isolate_user mode) and cannot access grader secrets (`.coral/private/`).
- Python 3.11+ required; install via `uv tool install` or manual setup. Development uses pytest, ruff for lint/format.
When to avoid it — and what to weigh
- Deterministic, Audited Workflows Required — Agent behavior is non-deterministic. Regulatory/compliance contexts requiring full reproducibility and decision auditability will struggle with autonomous agent loops.
- Real-Time Production Serving — CORAL is built for batch experimentation and research, not low-latency inference. Multi-agent evaluation loops are inherently slow.
- Limited LLM Budget or Offline Environments — Each agent is an LLM client; parallel runs scale cost linearly. Requires live internet for Claude, Codex, Kiro APIs. No local-only mode documented.
- Windows-First Development — Heavy reliance on bash, git worktrees, Docker, and POSIX primitives. Docker-in-Docker not supported. No explicit Windows compatibility stated.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with liability and trademark disclaimers.
Apache-2.0 is a permissive open-source license. Commercial use, derivative products, and proprietary modifications are explicitly permitted, provided the license and copyright notice are retained. No copyleft requirement. Consult your legal team if you bundle with other licensed code or offer as a service.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Docker isolation (v0.6.0+) runs agents as unprivileged users; agents cannot read `.coral/private/` (grader venv, answer keys) even via bash. Grader and manager remain root in container. On-host isolation is opt-in (`agents.isolate_user`). No explicit mention of supply-chain security, dependency pinning, or vulnerability scanning. Multi-agent evaluation exposes agents to untrusted task specs and grader outputs; sanitization of grader I/O not detailed. Recommend threat modeling before production use.
Alternatives to consider
Anthropic's Claude Coder / Claude Code
Single-agent coding assistant; lacks multi-agent orchestration, persistent shared state, and autonomous loop capabilities. Use if you need a single interactive agent, not research-scale parallelism.
OpenAI Swarm / Bee Agent Framework
Lighter-weight multi-agent frameworks with simpler orchestration. CORAL is heavier and more opinionated (git worktrees, eval loops, grader daemons); Swarm is more modular for custom agent graphs.
LangChain / LlamaIndex Agents
General-purpose LLM agent libraries. CORAL is specialized for autonomous iterative refinement and benchmarking; LangChain is broader but less opinionated on eval-loop and multi-island logic.
Build on CORAL with DEV.co software developers
CORAL is open-source and production-ready. Start with the quick-install, scaffold a task, and launch agents. Review the docs and examples to understand grader authoring and multi-agent orchestration before scaling.
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CORAL FAQ
Can I run CORAL in Docker?
Do I need to pay for agent runtimes?
Can I use local LLMs instead of cloud APIs?
What happens if two agents edit the same file?
Senior engineers for your next build
Our engineers ship ai coding agents software for a living. DEV.co provides software development services, web development services, and ongoing support for teams standardizing on tools like CORAL.
Ready to Automate Agent Research?
CORAL is open-source and production-ready. Start with the quick-install, scaffold a task, and launch agents. Review the docs and examples to understand grader authoring and multi-agent orchestration before scaling.