Implements HybridStateManager providing fast local Redis operations
with background IPNS sync for eventual consistency across L1 nodes.
- hybrid_state.py: Centralized state management (cache, claims, analysis, plans, runs)
- Updated execute_cid.py, analyze_cid.py, orchestrate_cid.py to use state manager
- Background IPNS sync (configurable interval, disabled by default)
- Atomic claiming with Redis SETNX for preventing duplicate work
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Complete pipeline with everything on IPFS:
- register_input_cid / register_recipe_cid
- generate_plan_cid (stores plan on IPFS)
- execute_plan_from_cid (fetches plan from IPFS)
- run_recipe_cid (full pipeline, returns output CID)
- run_from_local (convenience: local files → IPFS → run)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Changed default from /data/cache to ~/.artdag/cache for local runs.
Docker sets CACHE_DIR=/data/cache via environment variable.
Files updated:
- tasks/analyze.py
- tasks/orchestrate.py
- app/config.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Simplified step execution where:
- Steps receive CIDs, produce CIDs
- No local cache management (IPFS handles it)
- Minimal Redis: just claims + cache_id→CID mapping
- Temp workspace for execution, cleaned up after
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
RunStatus now stores:
- plan_id, plan_name for linking to execution plan
- step_results for per-step execution status
- all_outputs for all artifacts from all steps
Plan visualization:
- Shows human-readable step names from recipe structure
- Video/audio artifact preview on node click
- Outputs list with links to cached artifacts
- Stats reflect actual execution status (completed/cached/pending)
Execution:
- Step results include outputs list with cache_ids
- run_plan returns all outputs from all steps
- Support for completed_by_other status
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
New files:
- claiming.py - Redis Lua scripts for atomic task claiming
- tasks/analyze.py - Analysis Celery task
- tasks/execute.py - Step execution with IPFS-backed cache
- tasks/orchestrate.py - Plan orchestration (run_plan, run_recipe)
New API endpoints (/api/v2/):
- POST /api/v2/plan - Generate execution plan
- POST /api/v2/execute - Execute a plan
- POST /api/v2/run-recipe - Full 3-phase pipeline
- GET /api/v2/run/{run_id} - Get run status
Features:
- Hash-based task claiming prevents duplicate work
- Parallel execution within dependency levels
- IPFS-backed cache for durability
- Integration with artdag planning module
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
