coralbricks-langchain 0.2.0

CoralBricks + LangChain integration: retriever, chat history, memory helper, and agent tools.

coralbricks-langchain

Use CoralBricks as the memory backend for your LangChain applications: retriever, chat history, agent memory tools, and RAG middleware — all backed by the CoralBricks Memory API.

• Drop-in CoralBricksRetriever for any LCEL chain or RAG pipeline.
• @dynamic_prompt RAG middleware — automatically injects retrieved context before every LLM call.
• Three agent tools (store, search, delete) for persistent memory across turns.
• CoralBricksChatMessageHistory — persistent chat history backed by the CoralBricks chat API.
• Share memory across agents via project_id / session_id scoping.

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Installation

pip install coralbricks-langchain

Requires Python 3.10+ and LangChain >= 1.0.

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API key and base URL

• API key: Get a CoralBricks API key from the CoralBricks web app.
• Base URL: https://cw.coralbricks.ai

export CORALBRICKS_API_KEY="your_coralbricks_api_key"
export CORAL_MEMORY_BASE_URL="https://cw.coralbricks.ai"

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Quick start

from coralbricks_langchain import CoralBricksClient, CoralBricksMemory

client = CoralBricksClient(
    api_key="your_coralbricks_api_key",
    base_url="https://cw.coralbricks.ai",
)

memory = CoralBricksMemory(
    client=client,
    project_id="langchain:my-app",
    session_id="user-123",
)

# Save a memory (embed + store)
mem_id = memory.save_memory("Pro plan costs $199/month with unlimited ops.")

# Search by meaning
hits = memory.search_memory("What does the Pro plan cost?", top_k=3)
for h in hits:
    print(h.get("score"), h.get("text"))

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RAG with @dynamic_prompt middleware

The modern LangChain >= 1.0 RAG pattern — context is retrieved from CoralBricks and injected into the system prompt automatically before every model call:

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRequest, dynamic_prompt
from langchain_openai import ChatOpenAI

from coralbricks_langchain import CoralBricksClient, CoralBricksMemory, CoralBricksRetriever

client = CoralBricksClient(api_key="your_coralbricks_api_key")
memory = CoralBricksMemory(client=client, project_id="my-kb", session_id="docs-v1")
retriever = CoralBricksRetriever(memory=memory, top_k=5)

@dynamic_prompt
def rag_context(request: ModelRequest) -> str:
    query = request.messages[-1].content if request.messages else ""
    docs = retriever.invoke(query)
    context = "\n\n".join(f"[{i+1}] {d.page_content}" for i, d in enumerate(docs))
    return (
        "Answer the user's question using ONLY the context below.\n\n"
        f"Context:\n{context}"
    )

model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
agent = create_agent(model, middleware=[rag_context])

result = agent.invoke({"messages": [{"role": "user", "content": "What is the Pro plan price?"}]})
print(result["messages"][-1].content)

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Agent memory tools

Give your agent tools to store, search, and delete memories across turns:

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

from coralbricks_langchain import (
    CoralBricksClient,
    CoralBricksMemory,
    get_tools,
    set_global_memory,
)

client = CoralBricksClient(api_key="your_coralbricks_api_key")
memory = CoralBricksMemory(client=client, project_id="my-app", session_id="user-123")

set_global_memory(memory)
tools = get_tools()  # [store_coralbricks_memory, search_coralbricks_memory, delete_coralbricks_memory]

model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
agent = create_agent(
    model,
    tools=tools,
    system_prompt=(
        "You are a helpful assistant with persistent memory (CoralBricks). "
        "Before answering, always search memory for relevant context. "
        "When you learn something important, store it."
    ),
)

result = agent.invoke({"messages": [{"role": "user", "content": "Remember: Alex is on the Enterprise plan."}]})
print(result["messages"][-1].content)

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Retriever (LCEL chains)

CoralBricksRetriever implements BaseRetriever and drops into any LCEL chain:

from coralbricks_langchain import CoralBricksClient, CoralBricksMemory, CoralBricksRetriever

retriever = CoralBricksRetriever(
    memory=CoralBricksMemory(client=client, project_id="my-kb"),
    top_k=5,
)

docs = retriever.invoke("What is the cancellation policy?")
for doc in docs:
    print(doc.page_content)

---

Chat message history

Persistent chat history backed by the CoralBricks chat API:

from coralbricks_langchain import CoralBricksClient, CoralBricksChatMessageHistory

client = CoralBricksClient(api_key="your_coralbricks_api_key")
history = CoralBricksChatMessageHistory(client=client, conversation_id="conv-001")

history.add_user_message("Hello!")
history.add_ai_message("Hi, how can I help?")

for msg in history.messages:
    print(msg.type, msg.content)

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Public API

Symbol	Description
CoralBricksClient	Low-level HTTP client for the CoralBricks Memory API
CoralBricksMemory	High-level helper: save_memory, search_memory, delete_memory
CoralBricksRetriever	LangChain BaseRetriever for LCEL RAG pipelines
CoralBricksChatMessageHistory	LangChain BaseChatMessageHistory backed by CoralBricks chat storage
store_coralbricks_memory	Agent tool — embed and store a memory item
search_coralbricks_memory	Agent tool — semantic search over memories
delete_coralbricks_memory	Agent tool — delete memories by ID
set_global_memory	Configure the global CoralBricksMemory instance used by tools
get_tools	Factory returning all three memory tools

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Conventions

Field	Example	Purpose
project_id	langchain:my-app	App/use-case namespace (shared across agents)
session_id	user-123, conv-001	Conversation or user scope
metadata	{"source": "policy"}	Optional metadata stored with each item

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License

Apache-2.0.