Flow Memory is an open-source autonomous AI agent operating system and local/testnet public-alpha preflight prototype.
git clone https://github.com/FlowmemoryAI/flow-memory.git
cd flow-memory
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,ml]"
python -m flow_memory --neural tiny_torch --json "Explore and report"Neural models advise. Policy and approval gates remain authoritative.
The project now combines:
- FlowLang v0 agent declarations
- FlowIR manifests
- first-class AI agent profiles/state/goals/planning/execution
- layered memory and constitutional memory governance
- safe skill/tool execution seams
- local Economy V3 marketplace, escrow, settlement, disputes, slashing, reputation, receipts
- signed manifest/receipt/provenance prototypes
- SQLite durable storage
- internal API router and optional server seams
- Base Sepolia / ERC-4337 dry-run adapters
- sandbox hardening interfaces
- MCP/A2A/libp2p protocol seams
- dashboard scaffold and CI workflows
Public-alpha RC1 preflight adds clean-clone validation, an agent reliability gauntlet, asymmetric/DID signing seams, scoped API/auth/error contracts, typed dashboard mock API client, Base Sepolia dry-run artifacts, expanded contract security tests, optional Docker sandbox backend seam, storage replay scripts, adversarial economy simulation, and hashed release evidence. Flow Memory is production-shaped, not production-certified. It does not claim audited contracts, hardened sandboxing, production API authentication, mainnet readiness, safe real-funds custody, or trained ML model performance.
PowerShell:
cd E:\FlowMemory\flow-memory
.\.venv\Scripts\python.exe -m pip install -e ".[dev]"If no virtual environment exists:
py -3 -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip
.\.venv\Scripts\python.exe -m pip install -e ".[dev]".\.venv\Scripts\python.exe -m pytest -q
.\.venv\Scripts\python.exe examples\flowlang_compile_demo.py
.\.venv\Scripts\python.exe examples\flowlang_runtime_demo.py
.\.venv\Scripts\python.exe examples\flowlang_economy_demo.py
.\.venv\Scripts\python.exe -m flow_memory --json "Explore and report"
.\.venv\Scripts\python.exe -m flow_memory --flow examples\flowlang_agent.flow --json "Run the declared agent"
bash scripts/verify.sh
.\.venv\Scripts\python.exe scripts\generate_deployment_plan.py
.\.venv\Scripts\python.exe scripts\base_sepolia_dry_run.py
docker compose config
forge build
forge test
git diff --check
.\.venv\Scripts\python.exe scripts\public_alpha_smoke.py --root .
.\.venv\Scripts\python.exe scripts\clean_clone_validation.py --root . --out release_evidence\clean_clone_validation.json
.\.venv\Scripts\python.exe scripts\validate_base_sepolia_artifacts.py --dir deployments\base-sepolia
.\.venv\Scripts\python.exe scripts\export_event_log.py
.\.venv\Scripts\python.exe scripts\replay_event_log.py
.\.venv\Scripts\python.exe scripts\verify_storage_integrity.py
.\.venv\Scripts\python.exe scripts\sandbox_smoke_test.py
.\.venv\Scripts\python.exe scripts\release_decision.py --target public-alphaObserved during the public-alpha RC1 preflight build:
- Python tests:
287 passed, 1 skipped - FlowLang compile demo: passed
- FlowLang runtime demo: passed
- FlowLang economy demo: passed
- CLI smoke: passed
- CLI
--flow: passed - deployment dry-run scripts: passed
- agent reliability gauntlet demo: passed
- adversarial economy simulation demo: passed
- clean clone validation: passed
- public-alpha release decision: passed
.\.venv\Scripts\python.exe -m flow_memory --flow examples\flowlang_agent.flow --json "Run the declared agent".\.venv\Scripts\python.exe examples\agent_profile_demo.py
.\.venv\Scripts\python.exe examples\agent_economy_v3_demo.py
.\.venv\Scripts\python.exe examples\agent_dispute_slashing_demo.py
.\.venv\Scripts\python.exe examples\signed_manifest_demo.py
.\.venv\Scripts\python.exe examples\storage_persistence_demo.pydocs/AI_AGENT_LAYER.mddocs/PUBLIC_ALPHA_QUICKSTART.mddocs/PUBLIC_ALPHA_READINESS.mddocs/CLEAN_CLONE_VALIDATION.mddocs/TESTNET_PREFLIGHT.mddocs/RELEASE_GATES.mddocs/CONTRACT_SECURITY_TESTS.mddocs/DASHBOARD.mddocs/AUDIT_REPLAY.mddocs/ADVERSARIAL_ECONOMY_SIMULATION.mddocs/AGENT_ECONOMY_V3.mddocs/FLOWLANG_RUNTIME_INTEGRATION.mddocs/STORAGE.mddocs/SIGNED_MANIFESTS.mddocs/API_SERVER.mddocs/WEB3_ADAPTERS.mddocs/BASE_SEPOLIA_DEPLOYMENT.mddocs/SANDBOX_HARDENING.mddocs/PROTOCOL_GATEWAYS.mddocs/THREAT_MODEL.mddocs/PRODUCTION_READINESS.mdBUILD_REPORT.mdFLOW_MEMORY_STATUS.md
- FlowLang remains v0/prototype.
- Economy V3 is local/testnet-ready architecture, not a live funds system.
- Contracts are unaudited.
- Signing uses local HMAC by default plus local deterministic asymmetric seams; production key custody is not implemented.
- Base Sepolia scripts produce dry-run payloads and artifacts only.
- Sandbox hardening includes profiles, receipts, policy checks, and an optional Docker backend seam; default local sandboxing is not hardened isolation.
- Protocol gateways are local/offline-safe seams, not production transports.
- Dashboard is a typed mock API scaffold, not a live operator console.
Flow Memory now includes an optional Neural Agent Layer v1. The base install still has no PyTorch requirement. Install flow-memory[ml] to run tiny CPU-safe PyTorch prototypes for dual-stream perception, appearance-suppressed dorsal motion, tiny JEPA-style world modeling, advisory plan scoring, skill routing, risk scoring, and neural memory retrieval. V-JEPA 2 and VideoMAE are adapter seams that require explicit local checkpoints; Flow Memory never downloads checkpoints automatically. Neural scores never override policy or approval gates.
Flow Memory now includes a dependency-free local HTTP API server for public-alpha operator testing. Run it with python scripts/run_local_api_server.py --host 127.0.0.1 --port 8765. Add --api-key dev-local-only --require-scopes to exercise local API-key and scope gates. This is not production internet authentication; it is a local server boundary for smoke tests, demos, and preflight tools.
This repo now includes Flow Arena, a dependency-free local RL environment layer for agent-economy decision training, plus GPU evidence import/release-gate seams. RL policies are advisory only; policy, approval, autonomy, and economy risk controls remain authoritative. Neural GPU validation evidence is stored as text/JSON metadata and hashes; raw checkpoint/model artifacts are not committed.