camel-memorysync

MemorySync memory for CAMEL agents.

Two pieces, use either or both:

• MemorySyncMemory — a drop-in AgentMemory for ChatAgent(memory=...): verbatim chat history plus semantic recall from MemorySync, injected as one scored context record under a hard latency budget. Zero client-side embeddings.
• MemorySyncStorage — a lossless BaseKeyValueStorage for ChatHistoryMemory that hands user/assistant turns to MemorySync out of band, where they are distilled server-side into durable facts (no raw transcript rows).

pip install camel-memorysync

Quickstart

from camel.agents import ChatAgent
from camel_memorysync import MemorySyncMemory

memory = MemorySyncMemory(
    user_id="customer-42",      # required — who these memories belong to
    session_id="support",       # transcript scope
)
agent = ChatAgent(system_message="You are a helpful travel assistant.", memory=memory)

agent.step("I always prefer window seats on long flights")
# … days later, a new session:
agent.step("which seat should I book for the Oslo flight?")   # remembers

The API key comes from the MEMORYSYNC_API_KEY environment variable (or api_key=...).

Why not the in-repo Mem0 storage?

CAMEL ships Mem0Storage in its repo. Verified against its source:

VectorDBMemory (CAMEL's own long-term option) needs client-side embeddings — OpenAIEmbedding() by default (extra key, cost, latency) plus a vector DB you operate. Recall here is one MemorySync query; the index lives server-side.

How recall enters the context

ScoreBasedContextCreator sorts records by timestamp. The recall block is one SYSTEM-role record with timestamp=0.0, so it lands right after the system prompt — your history is never reordered, rewritten, or role-swapped:

[system]  You are a helpful travel assistant.
[system]  Relevant long-term memories about this user:
          - Prefers window seats on long flights
[user]    which seat should I book for the Oslo flight?

Memories created by other MemorySync surfaces (LangChain, the CLI, voice agents, coding agents…) are recallable inside CAMEL too — that is the point of memory-as-a-service.

Storage-seam usage (ChatHistoryMemory)

from camel.memories import ChatHistoryMemory, ScoreBasedContextCreator
from camel.types import ModelType
from camel.utils import OpenAITokenCounter
from camel_memorysync import MemorySyncStorage

memory = ChatHistoryMemory(
    context_creator=ScoreBasedContextCreator(OpenAITokenCounter(ModelType.GPT_4O_MINI), 2048),
    storage=MemorySyncStorage(user_id="customer-42", session_id="support"),
)

save() keeps verbatim records locally (exact round-trip) and hands user/assistant turns to MemorySync, which distils them into durable facts ("Prefers window seats") — no raw transcript row is kept server-side and filler turns are dropped there. System prompts and tool chatter are never shipped.

Deletion — designed against data loss

Configuration

Note on dependencies

camel-ai 0.2.x imports FastMCP from mcp.server, which mcp 2.0 moved — an unpinned install crashes at import camel.agents. This package pins mcp<2 until CAMEL supports mcp 2.

Tests

pip install -e . pytest
pytest tests -q   # 27 checks against the real camel-ai at latest

The suite drives a REAL ChatAgent turn via CAMEL's own StubModel and reproduces each in-repo Mem0Storage bug as a regression test.

License

MIT © MemorySync.