🔄 AutoLoop

A Rust-native AIOS for governed agent execution

🎯 What Is AutoLoop?

AutoLoop is a Rust-native AIOS for governed agent execution.

It does not just call models and tools.
It turns ambiguous intent into a controlled runtime loop:

clarify → plan → gate → execute → verify → remember → replay → improve

Capability	What It Means
🎯 Structured Sessions	Vague tasks become well-defined, traceable execution flows
🛡️ Policy-Guarded Runtime	All actions pass through configurable policy & safety gates
🔍 Verifiable Outcomes	Results can be audited, replayed, and deterministically validated
🧠 Active Memory	Memory isn't passive storage — it actively feeds future reasoning
📈 Trust-Gated Learning	System upgrades only when verification & trust conditions are met

✨ AutoLoop is for people who want more than "agent demos".
It is for building AI systems that can be governed.

🤔 Why AutoLoop Exists

Traditional Agent Systems	AutoLoop
✅ More tools & integrations	✅ Controlled execution with runtime governance
✅ Longer autonomous chains	✅ Verifiable outcomes with audit trails
✅ Maximum autonomy	✅ Learning with explicit trust boundaries
✅ Polished demo experiences	✅ Operator visibility, replay, and intervention

🔹 AutoLoop is not another free-form agent wrapper.
🔹 It is a governed execution runtime for production-grade AI systems.

🚀 5-Minute Demo

Get started instantly with pre-built demo scripts:

Platform	Script	Description
🪟 Windows	`demo/e2e-5min.ps1`	Full end-to-end workflow on Windows PowerShell
🐧 Linux/macOS	`demo/e2e-5min.sh`	Full end-to-end workflow on Unix-like systems
🎬 Recording Guide	`demo/RECORDING_CHECKLIST.md`	Checklist for capturing demo runs

⚡ Quick Start

Prerequisites

🦀 Rust toolchain (rustup recommended)
🔐 (Optional) SpacetimeDB CLI for persistent state
🐳 (Optional) Docker / Docker Compose for containerized deployment

Run a Swarm Task

cargo run --manifest-path .\Cargo.toml -- \
  --message "Build a swarm that uses graph memory and MCP execution" \
  --swarm

Validate & Test

# Static checks
cargo check --workspace --manifest-path .\Cargo.toml

# Run test suite
cargo test --workspace --manifest-path .\Cargo.toml

Browser Research Runtime

AutoLoop supports multiple real-world research backends:

Backend	Description	Use Case
`browser_fetch`	Browserless-style render endpoint	Lightweight page extraction
`playwright_cli`	Local Node + Playwright	Full browser automation
`firecrawl`	Firecrawl search/scrape APIs	Scalable web crawling

Health Checks:

# System health overview
cargo run --manifest-path .\Cargo.toml -- system health

# Crawl status for specific anchor
cargo run --manifest-path .\Cargo.toml -- crawl status --anchor-id cli:focus

🔄 Governance & Learning Flow

graph TD
    A[User Intent] --> B[Requirement Clarification Agent]
    B --> C{Policy & Rule Engine}
    C -->|reject/revise| B
    C -->|approve| D[Orchestrator: Planner/Critic/Judge]
    D --> E[Capability Catalog Selector]
    E -->|active+verified+trusted| F[Runtime Kernel Guard]
    F -->|identity/budget/timeout/sandbox| G[Execution Pools]
    F -->|block/fail| H[Recovery/Degrade/Retry]
    G --> I[Verifier & Audit Pipeline]
    I -->|pass| J[Learning Proposal Builder]
    I -->|reject| D
    J --> K{Learning Gate}
    K -->|promote| L[Memory + GraphRAG Update]
    K -->|rollback| M[Keep Previous Skill]
    L --> N[Routing/Prompt/Capability Strategy Update]
    N --> O[Observability + Reports + Replay]
    O --> A
    
    style C fill:#2d3748,color:#fff
    style F fill:#2b6cb0,color:#fff
    style I fill:#805ad5,color:#fff
    style K fill:#dd6b20,color:#fff
    style L fill:#38a169,color:#fff

🏆 3 Core Differentiators

1️⃣ Governed Execution, Not Free-Form Calls

// Capabilities are cataloged, verified, and routed through guardrails
let capability = catalog
    .get("web_search")?
    .verify(&policy)?
    .with_guardrails(budget, timeout, sandbox);

✅ Explicit capability registration & versioning
✅ Runtime policy enforcement (domain, action, data egress)
✅ Identity, budget, timeout, and sandbox isolation per execution

2️⃣ Memory That Participates in Decisions

// GraphRAG + learning records actively influence routing & planning
let context = graph_rag
    .query(&intent)
    .merge(learning_records::recent(&skill_id))
    .weight_by_trust_score();

✅ Episodic memory with causal edge tracking
✅ Skill evolution with witness logs & verification proofs
✅ Trust-weighted retrieval for routing & prompt strategy

3️⃣ End-to-End Operability

# One repository, full operational stack
autoloop/
├── runtime (Rust)      # CLI + kernel + agents
├── spacetimedb/        # Persistent state module
├── adapter/            # SpacetimeDB ↔ Rust bridge
├── dashboard-ui/       # React observability frontend
├── deploy/             # Docker, K8s, CI/CD templates
└── tests/              # Unit, integration, E2E suites

✅ CLI-first runtime with structured JSON output
✅ SpacetimeDB for low-latency, replicated state
✅ Dashboard for session replay, audit trails, and metrics
✅ Deployment-ready templates for cloud & edge

✅ What's Implemented in v0.1.0-alpha

Feature	Status	Description
🗣️ Multi-turn Clarification	✅	Scope freeze signals & requirement disambiguation
🎭 CEO + Planner/Critic/Judge	✅	Orchestration artifacts with role separation
🔐 Capability Catalog + Verifier	✅	Gated execution path with trust scoring
🕸️ GraphRAG Pipeline	✅	Snapshot + incremental merge for contextual reasoning
🧠 Learning Persistence	✅	Episodes, skills, causal edges, witness logs
📊 Observability + Dashboard	✅	Structured logs + snapshot serving for replay

⚠️ Current Scope (Honest Boundaries)

This is an engineering alpha, not a fully production-hardened autonomous platform.

Area	Current State	Roadmap
🔌 Provider/Tool Integrations	Functional, limited compatibility	Broaden support + harden error handling
🧠 GraphRAG Depth	Basic snapshot + merge	Advanced retrieval strategies + caching
🛡️ Verifier Policy	Rule-based gating	ML-assisted policy synthesis + adaptation
📈 Learning Strategy	Trust-threshold promotion	Multi-objective optimization + human-in-the-loop
🚀 Deployment	Docker + local SpacetimeDB	K8s operators + managed cloud offerings

🗺️ Project Map

autoloop/
├── src/                          # 🦀 Runtime source (kernel, agents, CLI)
├── spacetimedb/                  # ⚡ Persistent state module (Rust WASM)
├── autoloop-spacetimedb-adapter/ # 🔗 Adapter crate for state sync
├── dashboard-ui/                 # 🎨 React observability frontend
├── deploy/                       # 🐳 Docker, K8s, CI/CD assets
├── tests/                        # 🧪 Unit, integration, E2E test suites
├── docs/                         # 📚 Deep documentation index
├── Cargo.toml                    # 📦 Workspace manifest
├── ARCHITECTURE.md               # 🏗️ System design overview
├── API.md                        # 🔌 API contract summary
├── CONTRIBUTING.md               # 🤝 How to contribute
├── LICENSE                       # 📜 MIT License
└── RELEASE_NOTES_v0.1.0-alpha.md # 🗒️ Current release details

📚 Documentation Index

Document	Purpose
`docs/README.md`	🗂️ Master documentation index
`docs/PROCESS_MODEL.md`	🔄 Neutral naming process model
`docs/P1_P13_UNIFIED_PROTOCOL.md`	📜 AI output contract + layer flows
`docs/RFC_CONTRACTS_V1.md`	🤝 Contracts specification v1
`docs/ROLLOUT_RUNBOOK.md`	🚦 Gray rollout & operational runbook
`ARCHITECTURE.md`	🏗️ High-level architecture deep dive
`API.md`	🔌 API summary & usage examples
`CONTRIBUTING.md`	🛠️ Contribution guidelines
`RELEASE_NOTES_v0.1.0-alpha.md`	🗒️ Detailed v0.1.0-alpha changelog
`docs/ISSUE_BACKLOG_v0.1.0-alpha.md`	📋 Public issue backlog & roadmap

🔒 Security & Responsible Use

🔐 Secrets Management: Never commit API keys; use .env or secret vaults
🌐 Rate Limiting: Built-in throttling for external APIs & LLM providers
🧹 Data Isolation: Execution sandboxes prevent cross-tenant data leakage
📜 Review SECURITY.md before deploying in production or multi-tenant environments

🤝 Contributing

We welcome contributions! Whether you want to:

🐞 Report a bug or propose a feature
🦀 Add a new agent role or runtime capability
🌍 Improve documentation, localization, or examples
🧪 Help expand test coverage or benchmarking

Please start here:

📜 License

Distributed under the MIT License. See LICENSE for details.

💡 Pro Tip: Run with --verbose --trace to observe agent reasoning, policy decisions, and memory updates in real time.
⭐ If AutoLoop helps you build more governable AI systems, please Star the repo to support open-source AI infrastructure! 🚀🦀

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🔄 AutoLoop

🎯 What Is AutoLoop?

🤔 Why AutoLoop Exists

🚀 5-Minute Demo

⚡ Quick Start

Prerequisites

Run a Swarm Task

Validate & Test

Browser Research Runtime

🔄 Governance & Learning Flow

🏆 3 Core Differentiators

1️⃣ Governed Execution, Not Free-Form Calls

2️⃣ Memory That Participates in Decisions

3️⃣ End-to-End Operability

✅ What's Implemented in v0.1.0-alpha

⚠️ Current Scope (Honest Boundaries)

🗺️ Project Map

📚 Documentation Index

🔒 Security & Responsible Use

🤝 Contributing

📜 License

FilesExpand file tree

README.md

Latest commit

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README.md

File metadata and controls

🔄 AutoLoop

🎯 What Is AutoLoop?

🤔 Why AutoLoop Exists

🚀 5-Minute Demo

⚡ Quick Start

Prerequisites

Run a Swarm Task

Validate & Test

Browser Research Runtime

🔄 Governance & Learning Flow

🏆 3 Core Differentiators

1️⃣ Governed Execution, Not Free-Form Calls

2️⃣ Memory That Participates in Decisions

3️⃣ End-to-End Operability

✅ What's Implemented in v0.1.0-alpha

⚠️ Current Scope (Honest Boundaries)

🗺️ Project Map

📚 Documentation Index

🔒 Security & Responsible Use

🤝 Contributing

📜 License