feat: crewai integration — StorageBackend adapter for unified memory system

- Add synapse_memory/integrations/crewai_memory.py (SynapseCrewStorage)
- Implements CrewAI v1.14+ StorageBackend protocol (save, search, delete,
  update, get_record, list_records, scopes, categories, reset, async)
- Full Cognitive Security pipeline: PII redaction + encryption on every save
- Add examples/crewai_agent.py with standalone demo
- Add tests/integrations/test_crewai_memory.py (37 tests)
- Add crewai optional dependency in pyproject.toml
- Update README with CrewAI integration section
- Lazy imports in integrations __init__ to avoid pulling heavy deps
- 324 tests passing, 93% coverage, zero IP exposure

Author: Ismael Marchi

README.md

@@ -252,6 +252,35 @@ chain_with_history = RunnableWithMessageHistory(
 > strategies, and PRO heuristics are available under separate license.
 > See [synapselayer.org/docs](https://synapselayer.org/docs).
 
+## 🤖 Using Synapse Layer with CrewAI
+
+Synapse Layer plugs into CrewAI as a persistent, encrypted storage backend
+for the unified memory system. Every memory passes through PII redaction,
+intent validation, and AES-256 encryption automatically.
+
+```bash
+pip install synapse-layer[crewai]
+```
+
+```python
+from synapse_memory.integrations.crewai_memory import SynapseCrewStorage
+from crewai.memory.unified_memory import Memory
+from crewai import Crew
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=Memory(storage=SynapseCrewStorage(agent_id="my-crew")),
+)
+```
+
+The adapter implements CrewAI's `StorageBackend` protocol — scope management,
+category filtering, and async variants all work out of the box.
+
+> **Note:** This is the OSS adapter. Advanced scoring, enterprise retrieval
+> strategies, and PRO heuristics are available under separate license.
+> See [synapselayer.org/docs](https://synapselayer.org/docs).
+
 ## Competitive Comparison
 
 | Capability | Synapse Layer | Mem0 | Zep | pgvector (raw) |
@@ -305,7 +334,7 @@ Synapse Layer is not just a library you call — it's **infrastructure your agen
 | Version | Status | Highlights |
 |---|---|---|
 | **v1.0.7** | ✅ **Stable** | Auto-Save Engine, Plugin Architecture, MCP Bridge, Smithery listing |
-| **v1.1.0** | 🚧 In Progress | ✅ LangChain native adapter, CrewAI integration, embedding model selection |
+| **v1.1.0** | 🚧 In Progress | ✅ LangChain native adapter, ✅ CrewAI integration, embedding model selection |
 | **v1.2.0** | 📋 Planned | Synapse Forge visual debugger, real-time memory inspector |
 | **v2.0.0** | 📋 Planned | Multi-tenant vault, team memory spaces, RBAC |
 

examples/crewai_agent.py

@@ -0,0 +1,91 @@
+"""
+Synapse Layer — CrewAI Integration Example
+
+Demonstrates how to use Synapse Layer as the persistent memory
+backend for a CrewAI crew.
+
+Requirements:
+    pip install synapse-layer crewai
+
+Author : Security & Architecture Team @ Synapse Layer
+License: Apache 2.0
+"""
+
+from synapse_memory.integrations.crewai_memory import SynapseCrewStorage
+
+
+def main():
+    # Initialize Synapse Layer as CrewAI's memory storage backend.
+    # Every memory passes through the Cognitive Security pipeline:
+    # PII redaction → Intent validation → AES-256 encryption
+    storage = SynapseCrewStorage(agent_id="research-crew")
+
+    # Use with CrewAI's Memory system:
+    #
+    # from crewai import Agent, Crew, Task
+    # from crewai.memory.unified_memory import Memory
+    #
+    # memory = Memory(storage=storage)
+    #
+    # researcher = Agent(
+    #     role="Senior Researcher",
+    #     goal="Find and summarize key insights",
+    #     backstory="Expert analyst with deep domain knowledge.",
+    # )
+    #
+    # task = Task(
+    #     description="Research the latest trends in AI agent memory.",
+    #     expected_output="A summary of key trends.",
+    #     agent=researcher,
+    # )
+    #
+    # crew = Crew(
+    #     agents=[researcher],
+    #     tasks=[task],
+    #     memory=memory,  # Synapse Layer handles persistence
+    # )
+    #
+    # result = crew.kickoff()
+
+    # --- Standalone demo (no LLM required) ---
+
+    from crewai.memory.types import MemoryRecord
+
+    # Store memories through the Synapse security pipeline
+    records = [
+        MemoryRecord(
+            content="User prefers concise technical reports.",
+            scope="/crew/research",
+            categories=["preference"],
+            importance=0.8,
+        ),
+        MemoryRecord(
+            content="Project deadline is April 15, 2026.",
+            scope="/crew/research",
+            categories=["deadline", "project"],
+            importance=0.9,
+        ),
+    ]
+    storage.save(records)
+    print(f"Stored {len(records)} records through Synapse Layer.")
+    print(f"Total memories in vault: {storage.count()}")
+    print()
+
+    # List records back
+    stored = storage.list_records(scope_prefix="/crew/research")
+    for rec in stored:
+        print(f"  [{rec.importance:.1f}] {rec.content}")
+    print()
+
+    # Scope info
+    info = storage.get_scope_info("/crew/research")
+    print(f"Scope: {info.path}")
+    print(f"  Records: {info.record_count}")
+    print(f"  Categories: {info.categories}")
+    print()
+
+    print("Done. Memory persists across sessions with zero-knowledge security.")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -48,6 +48,9 @@ dependencies = [
 langchain = [
   "langchain-core>=0.3.0",
 ]
+crewai = [
+  "crewai>=1.14.0",
+]
 dev = [
   "pytest>=7.4.0",
   "pytest-cov>=4.1.0",

synapse_memory/integrations/__init__.py

@@ -7,6 +7,19 @@
 License: Apache 2.0
 """
 
-from .langchain_memory import SynapseChatMessageHistory
+# Lazy imports to avoid pulling in optional dependencies at import time.
+# Users import directly from the submodule they need:
+#   from synapse_memory.integrations.langchain_memory import SynapseChatMessageHistory
+#   from synapse_memory.integrations.crewai_memory import SynapseCrewStorage
 
-__all__ = ["SynapseChatMessageHistory"]
+__all__ = ["SynapseChatMessageHistory", "SynapseCrewStorage"]
+
+
+def __getattr__(name: str):
+    if name == "SynapseChatMessageHistory":
+        from .langchain_memory import SynapseChatMessageHistory
+        return SynapseChatMessageHistory
+    if name == "SynapseCrewStorage":
+        from .crewai_memory import SynapseCrewStorage
+        return SynapseCrewStorage
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")