Fix registry lookups to use cid, remove dead legacy code

- Fix all registry lookups to use "cid" instead of "hash" key
  - app/routers/recipes.py: asset and effect resolution
  - tasks/execute_sexp.py: effect config lookups
  - server_legacy.py references (now deleted)
- Prefer IPFS CID over local hash in cache operations
  - cache_service.py: import_from_ipfs, upload_content
  - orchestrate.py: plan caching
  - legacy_tasks.py: node hash tracking

Remove ~7800 lines of dead code:
- server_legacy.py: replaced by modular app/ structure
- tasks/*_cid.py: unused refactoring only imported by server_legacy

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

tasks/analyze_cid.py

@@ -1,192 +0,0 @@
-"""
-IPFS-primary analysis tasks.
-
-Fetches inputs from IPFS, stores analysis results on IPFS.
-
-Uses HybridStateManager for:
-- Fast local Redis operations
-- Background IPNS sync with other L1 nodes
-"""
-
-import json
-import logging
-import os
-import shutil
-import tempfile
-from pathlib import Path
-from typing import Dict, List, Optional
-
-from celery import current_task
-
-import sys
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from celery_app import app
-import ipfs_client
-from hybrid_state import get_state_manager
-
-# Import artdag analysis module
-try:
-    from artdag.analysis import Analyzer, AnalysisResult
-except ImportError:
-    Analyzer = None
-    AnalysisResult = None
-
-logger = logging.getLogger(__name__)
-
-
-def get_cached_analysis_cid(input_hash: str, features: List[str]) -> Optional[str]:
-    """Check if analysis is already cached."""
-    return get_state_manager().get_analysis_cid(input_hash, features)
-
-
-def set_cached_analysis_cid(input_hash: str, features: List[str], cid: str) -> None:
-    """Store analysis CID in cache."""
-    get_state_manager().set_analysis_cid(input_hash, features, cid)
-
-
-@app.task(bind=True, name='tasks.analyze_input_cid')
-def analyze_input_cid(
-    self,
-    input_cid: str,
-    input_hash: str,
-    features: List[str],
-) -> dict:
-    """
-    Analyze an input file using IPFS-primary architecture.
-
-    Args:
-        input_cid: IPFS CID of the input file
-        input_hash: Content hash of the input (for cache key)
-        features: List of features to extract
-
-    Returns:
-        Dict with 'analysis_cid' and 'result'
-    """
-    if Analyzer is None:
-        raise ImportError("artdag.analysis not available")
-
-    logger.info(f"[CID] Analyzing {input_hash[:16]}... for features: {features}")
-
-    # Check cache first
-    cached_cid = get_cached_analysis_cid(input_hash, features)
-    if cached_cid:
-        logger.info(f"[CID] Analysis cache hit: {cached_cid}")
-        # Fetch the cached analysis from IPFS
-        analysis_bytes = ipfs_client.get_bytes(cached_cid)
-        if analysis_bytes:
-            result_dict = json.loads(analysis_bytes.decode('utf-8'))
-            return {
-                "status": "cached",
-                "input_hash": input_hash,
-                "analysis_cid": cached_cid,
-                "result": result_dict,
-            }
-
-    # Create temp workspace
-    work_dir = Path(tempfile.mkdtemp(prefix="artdag_analysis_"))
-
-    try:
-        # Fetch input from IPFS
-        input_path = work_dir / f"input_{input_cid[:16]}.mkv"
-        logger.info(f"[CID] Fetching input: {input_cid}")
-
-        if not ipfs_client.get_file(input_cid, input_path):
-            raise RuntimeError(f"Failed to fetch input from IPFS: {input_cid}")
-
-        # Run analysis (no local cache, we'll store on IPFS)
-        analyzer = Analyzer(cache_dir=None)
-
-        result = analyzer.analyze(
-            input_hash=input_hash,
-            features=features,
-            input_path=input_path,
-        )
-
-        result_dict = result.to_dict()
-
-        # Store analysis result on IPFS
-        analysis_cid = ipfs_client.add_json(result_dict)
-        if not analysis_cid:
-            raise RuntimeError("Failed to store analysis on IPFS")
-
-        logger.info(f"[CID] Analysis complete: {input_hash[:16]}... → {analysis_cid}")
-
-        # Cache the mapping
-        set_cached_analysis_cid(input_hash, features, analysis_cid)
-
-        return {
-            "status": "completed",
-            "input_hash": input_hash,
-            "analysis_cid": analysis_cid,
-            "result": result_dict,
-        }
-
-    except Exception as e:
-        logger.error(f"[CID] Analysis failed for {input_hash}: {e}")
-        return {
-            "status": "failed",
-            "input_hash": input_hash,
-            "error": str(e),
-        }
-
-    finally:
-        # Cleanup
-        shutil.rmtree(work_dir, ignore_errors=True)
-
-
-@app.task(bind=True, name='tasks.analyze_inputs_cid')
-def analyze_inputs_cid(
-    self,
-    input_cids: Dict[str, str],
-    features: List[str],
-) -> dict:
-    """
-    Analyze multiple inputs using IPFS-primary architecture.
-
-    Args:
-        input_cids: Dict mapping input_hash to IPFS CID
-        features: List of features to extract from all inputs
-
-    Returns:
-        Dict with analysis CIDs and results for all inputs
-    """
-    from celery import group
-
-    logger.info(f"[CID] Analyzing {len(input_cids)} inputs for features: {features}")
-
-    # Dispatch analysis tasks in parallel
-    tasks = [
-        analyze_input_cid.s(cid, input_hash, features)
-        for input_hash, cid in input_cids.items()
-    ]
-
-    if len(tasks) == 1:
-        results = [tasks[0].apply_async().get(timeout=600)]
-    else:
-        job = group(tasks)
-        results = job.apply_async().get(timeout=600)
-
-    # Collect results
-    analysis_cids = {}
-    analysis_results = {}
-    errors = []
-
-    for result in results:
-        input_hash = result.get("input_hash")
-        if result.get("status") in ("completed", "cached"):
-            analysis_cids[input_hash] = result["analysis_cid"]
-            analysis_results[input_hash] = result["result"]
-        else:
-            errors.append({
-                "input_hash": input_hash,
-                "error": result.get("error", "Unknown error"),
-            })
-
-    return {
-        "status": "completed" if not errors else "partial",
-        "analysis_cids": analysis_cids,
-        "results": analysis_results,
-        "errors": errors,
-        "total": len(input_cids),
-        "successful": len(analysis_cids),
-    }

tasks/execute_cid.py

@@ -1,299 +0,0 @@
-"""
-Simplified step execution with IPFS-primary architecture.
-
-Steps receive CIDs, produce CIDs. No file paths cross machine boundaries.
-IPFS nodes form a distributed cache automatically.
-
-Uses HybridStateManager for:
-- Fast local Redis operations
-- Background IPNS sync with other L1 nodes
-"""
-
-import logging
-import os
-import shutil
-import socket
-import tempfile
-from pathlib import Path
-from typing import Dict, Optional
-
-from celery import current_task
-
-import sys
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from celery_app import app
-import ipfs_client
-from hybrid_state import get_state_manager
-
-# Import artdag
-try:
-    from artdag import NodeType
-    from artdag.executor import get_executor
-    from artdag.planning import ExecutionStep
-    from artdag import nodes  # Register executors
-except ImportError:
-    NodeType = None
-    get_executor = None
-    ExecutionStep = None
-
-logger = logging.getLogger(__name__)
-
-
-def get_worker_id() -> str:
-    """Get unique worker identifier."""
-    return f"{socket.gethostname()}:{os.getpid()}"
-
-
-def get_cached_cid(cache_id: str) -> Optional[str]:
-    """Check if cache_id has a known CID."""
-    return get_state_manager().get_cached_cid(cache_id)
-
-
-def set_cached_cid(cache_id: str, cid: str) -> None:
-    """Store cache_id → CID mapping."""
-    get_state_manager().set_cached_cid(cache_id, cid)
-
-
-def try_claim(cache_id: str, worker_id: str, ttl: int = 300) -> bool:
-    """Try to claim a cache_id for execution. Returns True if claimed."""
-    return get_state_manager().try_claim(cache_id, worker_id, ttl)
-
-
-def release_claim(cache_id: str) -> None:
-    """Release a claim."""
-    get_state_manager().release_claim(cache_id)
-
-
-def wait_for_cid(cache_id: str, timeout: int = 600, poll_interval: float = 0.5) -> Optional[str]:
-    """Wait for another worker to produce a CID for cache_id."""
-    import time
-    start = time.time()
-    while time.time() - start < timeout:
-        cid = get_cached_cid(cache_id)
-        if cid:
-            return cid
-        time.sleep(poll_interval)
-    return None
-
-
-def fetch_from_ipfs(cid: str, dest_dir: Path) -> Path:
-    """Fetch a CID from IPFS to a local temp file."""
-    dest_path = dest_dir / f"{cid}.mkv"
-    if not ipfs_client.get_file(cid, dest_path):
-        raise RuntimeError(f"Failed to fetch CID from IPFS: {cid}")
-    return dest_path
-
-
-@app.task(bind=True, name='tasks.execute_step_cid')
-def execute_step_cid(
-    self,
-    step_json: str,
-    input_cids: Dict[str, str],
-) -> Dict:
-    """
-    Execute a step using IPFS-primary architecture.
-
-    Args:
-        step_json: JSON-serialized ExecutionStep
-        input_cids: Mapping from input step_id to their IPFS CID
-
-    Returns:
-        Dict with 'cid' (output CID) and 'status'
-    """
-    if ExecutionStep is None:
-        raise ImportError("artdag not available")
-
-    step = ExecutionStep.from_json(step_json)
-    worker_id = get_worker_id()
-
-    logger.info(f"[CID] Executing {step.step_id} ({step.node_type})")
-
-    # 1. Check if already computed
-    existing_cid = get_cached_cid(step.cache_id)
-    if existing_cid:
-        logger.info(f"[CID] Cache hit: {step.cache_id[:16]}... → {existing_cid}")
-        return {
-            "status": "cached",
-            "step_id": step.step_id,
-            "cache_id": step.cache_id,
-            "cid": existing_cid,
-        }
-
-    # 2. Try to claim
-    if not try_claim(step.cache_id, worker_id):
-        logger.info(f"[CID] Claimed by another worker, waiting...")
-        cid = wait_for_cid(step.cache_id)
-        if cid:
-            return {
-                "status": "completed_by_other",
-                "step_id": step.step_id,
-                "cache_id": step.cache_id,
-                "cid": cid,
-            }
-        return {
-            "status": "timeout",
-            "step_id": step.step_id,
-            "cache_id": step.cache_id,
-            "error": "Timeout waiting for other worker",
-        }
-
-    # 3. We have the claim - execute
-    try:
-        # Handle SOURCE nodes
-        if step.node_type == "SOURCE":
-            # SOURCE nodes should have their CID in input_cids
-            source_name = step.config.get("name") or step.step_id
-            cid = input_cids.get(source_name) or input_cids.get(step.step_id)
-            if not cid:
-                raise ValueError(f"SOURCE missing input CID: {source_name}")
-
-            set_cached_cid(step.cache_id, cid)
-            return {
-                "status": "completed",
-                "step_id": step.step_id,
-                "cache_id": step.cache_id,
-                "cid": cid,
-            }
-
-        # Get executor
-        try:
-            node_type = NodeType[step.node_type]
-        except KeyError:
-            node_type = step.node_type
-
-        executor = get_executor(node_type)
-        if executor is None:
-            raise ValueError(f"No executor for: {step.node_type}")
-
-        # Create temp workspace
-        work_dir = Path(tempfile.mkdtemp(prefix="artdag_"))
-
-        try:
-            # Fetch inputs from IPFS
-            input_paths = []
-            for i, input_step_id in enumerate(step.input_steps):
-                input_cid = input_cids.get(input_step_id)
-                if not input_cid:
-                    raise ValueError(f"Missing input CID for: {input_step_id}")
-
-                input_path = work_dir / f"input_{i}_{input_cid[:16]}.mkv"
-                logger.info(f"[CID] Fetching input {i}: {input_cid}")
-
-                if not ipfs_client.get_file(input_cid, input_path):
-                    raise RuntimeError(f"Failed to fetch: {input_cid}")
-
-                input_paths.append(input_path)
-
-            # Execute
-            output_path = work_dir / f"output_{step.cache_id[:16]}.mkv"
-            logger.info(f"[CID] Running {step.node_type} with {len(input_paths)} inputs")
-
-            result_path = executor.execute(step.config, input_paths, output_path)
-
-            # Add output to IPFS
-            output_cid = ipfs_client.add_file(result_path)
-            if not output_cid:
-                raise RuntimeError("Failed to add output to IPFS")
-
-            logger.info(f"[CID] Completed: {step.step_id} → {output_cid}")
-
-            # Store mapping
-            set_cached_cid(step.cache_id, output_cid)
-
-            return {
-                "status": "completed",
-                "step_id": step.step_id,
-                "cache_id": step.cache_id,
-                "cid": output_cid,
-            }
-
-        finally:
-            # Cleanup temp workspace
-            shutil.rmtree(work_dir, ignore_errors=True)
-
-    except Exception as e:
-        logger.error(f"[CID] Failed: {step.step_id}: {e}")
-        release_claim(step.cache_id)
-        return {
-            "status": "failed",
-            "step_id": step.step_id,
-            "cache_id": step.cache_id,
-            "error": str(e),
-        }
-
-
-@app.task(bind=True, name='tasks.execute_plan_cid')
-def execute_plan_cid(
-    self,
-    plan_json: str,
-    input_cids: Dict[str, str],
-) -> Dict:
-    """
-    Execute an entire plan using IPFS-primary architecture.
-
-    Args:
-        plan_json: JSON-serialized ExecutionPlan
-        input_cids: Mapping from input name to IPFS CID
-
-    Returns:
-        Dict with 'output_cid' and per-step results
-    """
-    from celery import group
-    from artdag.planning import ExecutionPlan
-
-    plan = ExecutionPlan.from_json(plan_json)
-    logger.info(f"[CID] Executing plan: {plan.plan_id[:16]}... ({len(plan.steps)} steps)")
-
-    # CID results accumulate as steps complete
-    cid_results = dict(input_cids)
-
-    # Also map step_id → CID for dependency resolution
-    step_cids = {}
-
-    steps_by_level = plan.get_steps_by_level()
-
-    for level in sorted(steps_by_level.keys()):
-        steps = steps_by_level[level]
-        logger.info(f"[CID] Level {level}: {len(steps)} steps")
-
-        # Build input CIDs for this level
-        level_input_cids = dict(cid_results)
-        level_input_cids.update(step_cids)
-
-        # Dispatch steps in parallel
-        tasks = [
-            execute_step_cid.s(step.to_json(), level_input_cids)
-            for step in steps
-        ]
-
-        if len(tasks) == 1:
-            # Single task - run directly
-            results = [tasks[0].apply_async().get(timeout=3600)]
-        else:
-            # Multiple tasks - run in parallel
-            job = group(tasks)
-            results = job.apply_async().get(timeout=3600)
-
-        # Collect output CIDs
-        for step, result in zip(steps, results):
-            if result.get("status") in ("completed", "cached", "completed_by_other"):
-                step_cids[step.step_id] = result["cid"]
-            else:
-                return {
-                    "status": "failed",
-                    "failed_step": step.step_id,
-                    "error": result.get("error", "Unknown error"),
-                }
-
-    # Get final output CID
-    output_step_id = plan.output_step or plan.steps[-1].step_id
-    output_cid = step_cids.get(output_step_id)
-
-    logger.info(f"[CID] Plan complete: {output_cid}")
-
-    return {
-        "status": "completed",
-        "plan_id": plan.plan_id,
-        "output_cid": output_cid,
-        "step_cids": step_cids,
-    }

tasks/execute_sexp.py

@@ -217,9 +217,9 @@ def execute_step_sexp(
 
         # Handle EFFECT nodes
         if node_type == "EFFECT":
-            effect_hash = config.get("hash")
+            effect_hash = config.get("cid") or config.get("hash")
             if not effect_hash:
-                raise ValueError("EFFECT step missing :hash")
+                raise ValueError("EFFECT step missing :cid")
 
             # Get input paths
             inputs = config.get("inputs", [])
@@ -277,7 +277,7 @@ def execute_step_sexp(
 
                 if filter_type == "EFFECT":
                     # Effect - for now identity-like, can be extended
-                    effect_hash = filter_config.get("hash") or filter_config.get("effect")
+                    effect_hash = filter_config.get("cid") or filter_config.get("hash") or filter_config.get("effect")
                     # TODO: resolve effect to actual FFmpeg filter
                     # For now, skip identity-like effects
                     pass

tasks/orchestrate.py

@@ -380,7 +380,8 @@ def run_recipe(
     # Store in cache (content-addressed, auto-pins to IPFS)
     # Plan is just another node output - no special treatment needed
     cached, plan_ipfs_cid = cache_mgr.put(tmp_path, node_type="plan", move=True)
-    logger.info(f"Plan cached: hash={cached.cid}, ipfs={plan_ipfs_cid}")
+    plan_cache_id = plan_ipfs_cid or cached.cid  # Prefer IPFS CID
+    logger.info(f"Plan cached: cid={plan_cache_id}, ipfs={plan_ipfs_cid}")
 
     # Phase 4: Execute
     logger.info("Phase 4: Executing plan...")
@@ -392,7 +393,7 @@ def run_recipe(
         "run_id": run_id,
         "recipe": compiled.name,
         "plan_id": plan.plan_id,
-        "plan_cache_id": cached.cid,
+        "plan_cache_id": plan_cache_id,
         "plan_ipfs_cid": plan_ipfs_cid,
         "output_path": result.get("output_path"),
         "output_cache_id": result.get("output_cache_id"),

tasks/orchestrate_cid.py

@@ -1,434 +0,0 @@
-"""
-IPFS-primary orchestration.
-
-Everything on IPFS:
-- Inputs (media files)
-- Analysis results (JSON)
-- Execution plans (JSON)
-- Step outputs (media files)
-
-The entire pipeline just passes CIDs around.
-
-Uses HybridStateManager for:
-- Fast local Redis operations
-- Background IPNS sync with other L1 nodes
-"""
-
-import json
-import logging
-import os
-from pathlib import Path
-from typing import Dict, List, Optional
-
-from celery import group
-
-import sys
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from celery_app import app
-import ipfs_client
-from hybrid_state import get_state_manager
-
-# Import artdag modules
-try:
-    from artdag.analysis import Analyzer, AnalysisResult
-    from artdag.planning import RecipePlanner, ExecutionPlan, Recipe
-    from artdag import nodes  # Register executors
-except ImportError:
-    Analyzer = None
-    AnalysisResult = None
-    RecipePlanner = None
-    ExecutionPlan = None
-    Recipe = None
-
-from .analyze_cid import analyze_input_cid
-from .execute_cid import execute_step_cid
-
-logger = logging.getLogger(__name__)
-
-
-def compute_run_id(recipe_cid: str, input_cids: Dict[str, str]) -> str:
-    """Compute deterministic run ID from recipe and inputs."""
-    import hashlib
-    # Sort inputs for determinism
-    sorted_inputs = sorted(input_cids.items())
-    data = f"{recipe_cid}:{sorted_inputs}"
-    return hashlib.sha3_256(data.encode()).hexdigest()
-
-
-@app.task(bind=True, name='tasks.register_input_cid')
-def register_input_cid(
-    self,
-    input_path: str,
-) -> dict:
-    """
-    Register an input file on IPFS.
-
-    Args:
-        input_path: Local path to the input file
-
-    Returns:
-        Dict with 'cid' and 'cid'
-    """
-    import hashlib
-
-    path = Path(input_path)
-    if not path.exists():
-        return {"status": "failed", "error": f"File not found: {input_path}"}
-
-    # Compute content hash
-    with open(path, "rb") as f:
-        cid = hashlib.sha3_256(f.read()).hexdigest()
-
-    # Add to IPFS
-    cid = ipfs_client.add_file(path)
-    if not cid:
-        return {"status": "failed", "error": "Failed to add to IPFS"}
-
-    logger.info(f"[CID] Registered input: {path.name} → {cid}")
-
-    return {
-        "status": "completed",
-        "cid": cid,
-        "cid": cid,
-        "path": str(path),
-    }
-
-
-@app.task(bind=True, name='tasks.register_recipe_cid')
-def register_recipe_cid(
-    self,
-    recipe_path: str,
-) -> dict:
-    """
-    Register a recipe on IPFS.
-
-    Args:
-        recipe_path: Local path to the recipe YAML file
-
-    Returns:
-        Dict with 'cid' and 'recipe' (parsed)
-    """
-    if Recipe is None:
-        raise ImportError("artdag.planning not available")
-
-    path = Path(recipe_path)
-    if not path.exists():
-        return {"status": "failed", "error": f"Recipe not found: {recipe_path}"}
-
-    # Parse recipe
-    recipe = Recipe.from_file(path)
-
-    # Store recipe YAML on IPFS
-    recipe_bytes = path.read_bytes()
-    cid = ipfs_client.add_bytes(recipe_bytes)
-    if not cid:
-        return {"status": "failed", "error": "Failed to add recipe to IPFS"}
-
-    logger.info(f"[CID] Registered recipe: {recipe.name} → {cid}")
-
-    return {
-        "status": "completed",
-        "cid": cid,
-        "name": recipe.name,
-        "version": recipe.version,
-    }
-
-
-@app.task(bind=True, name='tasks.generate_plan_cid')
-def generate_plan_cid(
-    self,
-    recipe_cid: str,
-    input_cids: Dict[str, str],
-    input_hashes: Dict[str, str],
-    analysis_cids: Optional[Dict[str, str]] = None,
-) -> dict:
-    """
-    Generate execution plan and store on IPFS.
-
-    Args:
-        recipe_cid: IPFS CID of the recipe
-        input_cids: Mapping from input name to IPFS CID
-        input_hashes: Mapping from input name to content hash
-        analysis_cids: Optional mapping from input_hash to analysis CID
-
-    Returns:
-        Dict with 'plan_cid' and 'plan_id'
-    """
-    if RecipePlanner is None:
-        raise ImportError("artdag.planning not available")
-
-    # Fetch recipe from IPFS
-    recipe_bytes = ipfs_client.get_bytes(recipe_cid)
-    if not recipe_bytes:
-        return {"status": "failed", "error": f"Failed to fetch recipe: {recipe_cid}"}
-
-    # Parse recipe from YAML
-    import yaml
-    recipe_dict = yaml.safe_load(recipe_bytes.decode('utf-8'))
-    recipe = Recipe.from_dict(recipe_dict)
-
-    # Fetch analysis results if provided
-    analysis = {}
-    if analysis_cids:
-        for input_hash, analysis_cid in analysis_cids.items():
-            analysis_bytes = ipfs_client.get_bytes(analysis_cid)
-            if analysis_bytes:
-                analysis_dict = json.loads(analysis_bytes.decode('utf-8'))
-                analysis[input_hash] = AnalysisResult.from_dict(analysis_dict)
-
-    # Generate plan
-    planner = RecipePlanner(use_tree_reduction=True)
-    plan = planner.plan(
-        recipe=recipe,
-        input_hashes=input_hashes,
-        analysis=analysis if analysis else None,
-    )
-
-    # Store plan on IPFS
-    plan_cid = ipfs_client.add_json(json.loads(plan.to_json()))
-    if not plan_cid:
-        return {"status": "failed", "error": "Failed to store plan on IPFS"}
-
-    # Cache plan_id → plan_cid mapping
-    get_state_manager().set_plan_cid(plan.plan_id, plan_cid)
-
-    logger.info(f"[CID] Generated plan: {plan.plan_id[:16]}... → {plan_cid}")
-
-    return {
-        "status": "completed",
-        "plan_cid": plan_cid,
-        "plan_id": plan.plan_id,
-        "steps": len(plan.steps),
-    }
-
-
-@app.task(bind=True, name='tasks.execute_plan_from_cid')
-def execute_plan_from_cid(
-    self,
-    plan_cid: str,
-    input_cids: Dict[str, str],
-) -> dict:
-    """
-    Execute a plan fetched from IPFS.
-
-    Args:
-        plan_cid: IPFS CID of the execution plan
-        input_cids: Mapping from input name/step_id to IPFS CID
-
-    Returns:
-        Dict with 'output_cid' and step results
-    """
-    if ExecutionPlan is None:
-        raise ImportError("artdag.planning not available")
-
-    # Fetch plan from IPFS
-    plan_bytes = ipfs_client.get_bytes(plan_cid)
-    if not plan_bytes:
-        return {"status": "failed", "error": f"Failed to fetch plan: {plan_cid}"}
-
-    plan_json = plan_bytes.decode('utf-8')
-    plan = ExecutionPlan.from_json(plan_json)
-
-    logger.info(f"[CID] Executing plan: {plan.plan_id[:16]}... ({len(plan.steps)} steps)")
-
-    # CID results accumulate as steps complete
-    cid_results = dict(input_cids)
-    step_cids = {}
-
-    steps_by_level = plan.get_steps_by_level()
-
-    for level in sorted(steps_by_level.keys()):
-        steps = steps_by_level[level]
-        logger.info(f"[CID] Level {level}: {len(steps)} steps")
-
-        # Build input CIDs for this level
-        level_input_cids = dict(cid_results)
-        level_input_cids.update(step_cids)
-
-        # Dispatch steps in parallel
-        tasks = [
-            execute_step_cid.s(step.to_json(), level_input_cids)
-            for step in steps
-        ]
-
-        if len(tasks) == 1:
-            results = [tasks[0].apply_async().get(timeout=3600)]
-        else:
-            job = group(tasks)
-            results = job.apply_async().get(timeout=3600)
-
-        # Collect output CIDs
-        for step, result in zip(steps, results):
-            if result.get("status") in ("completed", "cached", "completed_by_other"):
-                step_cids[step.step_id] = result["cid"]
-            else:
-                return {
-                    "status": "failed",
-                    "failed_step": step.step_id,
-                    "error": result.get("error", "Unknown error"),
-                }
-
-    # Get final output CID
-    output_step_id = plan.output_step or plan.steps[-1].step_id
-    output_cid = step_cids.get(output_step_id)
-
-    logger.info(f"[CID] Plan complete: {output_cid}")
-
-    return {
-        "status": "completed",
-        "plan_id": plan.plan_id,
-        "plan_cid": plan_cid,
-        "output_cid": output_cid,
-        "step_cids": step_cids,
-    }
-
-
-@app.task(bind=True, name='tasks.run_recipe_cid')
-def run_recipe_cid(
-    self,
-    recipe_cid: str,
-    input_cids: Dict[str, str],
-    input_hashes: Dict[str, str],
-    features: Optional[List[str]] = None,
-) -> dict:
-    """
-    Run complete pipeline: analyze → plan → execute.
-
-    Everything on IPFS. Returns output CID.
-
-    Args:
-        recipe_cid: IPFS CID of the recipe
-        input_cids: Mapping from input name to IPFS CID
-        input_hashes: Mapping from input name to content hash
-        features: Optional list of features to analyze
-
-    Returns:
-        Dict with output_cid and all intermediate CIDs
-    """
-    if features is None:
-        features = ["beats", "energy"]
-
-    logger.info(f"[CID] Running recipe {recipe_cid} with {len(input_cids)} inputs")
-
-    # Compute run ID for caching
-    run_id = compute_run_id(recipe_cid, input_cids)
-
-    # Check if run is already cached
-    cached_output = get_state_manager().get_run_cid(run_id)
-    if cached_output:
-        logger.info(f"[CID] Run cache hit: {run_id[:16]}... → {cached_output}")
-        return {
-            "status": "cached",
-            "run_id": run_id,
-            "output_cid": cached_output,
-        }
-
-    # Phase 1: Analyze inputs
-    logger.info("[CID] Phase 1: Analysis")
-    analysis_cids = {}
-
-    for input_name, input_cid in input_cids.items():
-        input_hash = input_hashes.get(input_name)
-        if not input_hash:
-            continue
-
-        result = analyze_input_cid.apply_async(
-            args=[input_cid, input_hash, features]
-        ).get(timeout=600)
-
-        if result.get("status") in ("completed", "cached"):
-            analysis_cids[input_hash] = result["analysis_cid"]
-        else:
-            logger.warning(f"[CID] Analysis failed for {input_name}: {result.get('error')}")
-
-    # Phase 2: Generate plan
-    logger.info("[CID] Phase 2: Planning")
-    plan_result = generate_plan_cid.apply_async(
-        args=[recipe_cid, input_cids, input_hashes, analysis_cids]
-    ).get(timeout=120)
-
-    if plan_result.get("status") != "completed":
-        return {
-            "status": "failed",
-            "phase": "planning",
-            "error": plan_result.get("error", "Unknown error"),
-        }
-
-    plan_cid = plan_result["plan_cid"]
-
-    # Phase 3: Execute
-    logger.info("[CID] Phase 3: Execution")
-    exec_result = execute_plan_from_cid.apply_async(
-        args=[plan_cid, input_cids]
-    ).get(timeout=7200)  # 2 hour timeout for execution
-
-    if exec_result.get("status") != "completed":
-        return {
-            "status": "failed",
-            "phase": "execution",
-            "error": exec_result.get("error", "Unknown error"),
-        }
-
-    output_cid = exec_result["output_cid"]
-
-    # Cache the run
-    get_state_manager().set_run_cid(run_id, output_cid)
-
-    logger.info(f"[CID] Run complete: {run_id[:16]}... → {output_cid}")
-
-    return {
-        "status": "completed",
-        "run_id": run_id,
-        "recipe_cid": recipe_cid,
-        "analysis_cids": analysis_cids,
-        "plan_cid": plan_cid,
-        "output_cid": output_cid,
-        "step_cids": exec_result.get("step_cids", {}),
-    }
-
-
-@app.task(bind=True, name='tasks.run_from_local')
-def run_from_local(
-    self,
-    recipe_path: str,
-    input_paths: Dict[str, str],
-    features: Optional[List[str]] = None,
-) -> dict:
-    """
-    Convenience task: register local files and run.
-
-    For bootstrapping - registers recipe and inputs on IPFS, then runs.
-
-    Args:
-        recipe_path: Local path to recipe YAML
-        input_paths: Mapping from input name to local file path
-        features: Optional list of features to analyze
-
-    Returns:
-        Dict with all CIDs including output
-    """
-    import hashlib
-
-    # Register recipe
-    recipe_result = register_recipe_cid.apply_async(args=[recipe_path]).get(timeout=60)
-    if recipe_result.get("status") != "completed":
-        return {"status": "failed", "phase": "register_recipe", "error": recipe_result.get("error")}
-
-    recipe_cid = recipe_result["cid"]
-
-    # Register inputs
-    input_cids = {}
-    input_hashes = {}
-
-    for name, path in input_paths.items():
-        result = register_input_cid.apply_async(args=[path]).get(timeout=300)
-        if result.get("status") != "completed":
-            return {"status": "failed", "phase": "register_input", "input": name, "error": result.get("error")}
-
-        input_cids[name] = result["cid"]
-        input_hashes[name] = result["cid"]
-
-    # Run the pipeline
-    return run_recipe_cid.apply_async(
-        args=[recipe_cid, input_cids, input_hashes, features]
-    ).get(timeout=7200)