Memory Layer for LangChain Agents

LangChain gives you powerful agent frameworks — but its built-in memory is session-scoped and lossy. 0Latency adds persistent, structured long-term memory that works across sessions, handles contradictions, and respects your context budget. Three lines of Python.

The Problem: Built-in Memory Doesn't Scale

LangChain ships several memory classes. Every one of them has a fundamental limitation:

• ConversationBufferMemory stores everything — until the context window fills up. Then you're truncating from the front, losing your earliest (often most important) context. It's a FIFO queue pretending to be memory.
• ConversationSummaryMemory compresses via summarization. But summarization is lossy. Nuances, specific numbers, negative preferences ("never use yarn") — these get averaged into oblivion. The summary says what happened in broad strokes; it can't tell you what mattered.
• VectorStoreMemory does similarity search, which sounds right but isn't. Similarity isn't relevance. The most semantically similar past conversation might be completely irrelevant to what the agent needs right now. And vector search has no concept of time — a preference from six months ago ranks the same as one from yesterday.

Worse, all three are session-scoped by default. When the Python process exits, the memory is gone. You can persist to a vector store or database, but now you're building your own memory infrastructure — deduplication, contradiction handling, temporal decay, relevance scoring. That's a product, not a weekend project.

The Solution: 0Latency as Your Memory Backend

0Latency replaces LangChain's memory classes with a purpose-built memory layer. The integration is three lines:

pip install zero-latency-sdk

from zero_latency import Memory

mem = Memory("your-api-key")

# Store a memory
mem.add("User prefers Python and deploys to AWS us-east-1")

# Recall — sub-100ms, always
context = mem.recall("Set up the deployment pipeline")

That's it. .add() stores memories (extraction happens asynchronously — your agent never waits). .recall() returns the most relevant memories in under 100ms. Everything else — deduplication, temporal scoring, contradiction detection, knowledge graph construction — happens automatically. No configuration.

Full LangChain Agent Example

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from zero_latency import Memory

# Initialize — that's all the setup you need
mem = Memory("your-api-key")

# Build agent
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful support agent.\n\nRelevant memory:\n{memory_context}"),
    MessagesPlaceholder("chat_history"),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_openai_tools_agent(llm, tools=[], prompt=prompt)
executor = AgentExecutor(agent=agent, tools=[])

# Conversation loop with persistent memory
def chat(user_input, chat_history):
    # Recall relevant memories — sub-100ms
    recalled = mem.recall(user_input)

    result = executor.invoke({
        "input": user_input,
        "chat_history": chat_history,
        "memory_context": recalled.text,
    })

    # Store new memories — returns instantly, processes in background
    mem.add(f"User: {user_input}\nAssistant: {result['output']}")

    return result["output"]

The agent now remembers across sessions. Restart the process, come back tomorrow, deploy to a different server — recall() returns the same memories because they're stored in 0Latency, not in local Python objects.

What You Get vs. Built-in Memory

How 0Latency Memory Actually Works

When you call extract(), the API doesn't just store raw text. It runs a multi-stage pipeline:

1. Fact extraction. The conversation is analyzed for discrete facts, decisions, preferences, and commitments. "The user wants dark mode and deploys to AWS us-east-1" becomes two separate memory records.
2. Deduplication. If the user already said they want dark mode, the existing memory is reinforced (boosting its temporal score) rather than duplicated.
3. Contradiction check. If the user previously said "deploy to GCP" and now says "deploy to AWS," both memories are flagged with the contradiction and the newer one is marked as the current state.
4. Graph linking. Related memories are connected. "Uses Clerk for auth" links to "Clerk webhook at /api/webhook" links to "webhook secret in .env.local." Recalling any one of these can surface the others.

When you call recall(), results are ranked by a composite score that blends semantic relevance, temporal recency, reinforcement count, priority, and graph proximity. You get the most useful memories — not just the most similar ones.

Pricing

Free tier: 1,000 memories, 10,000 recall queries/month. No credit card, no expiration. Pro ($29/month): unlimited. If your LangChain agent handles moderate traffic, free tier is enough. For production deployments with hundreds of users, Pro scales without limits.