velixar 1.0.2

Persistent memory infrastructure for AI applications

Velixar Python SDK

Persistent memory for AI assistants and agents. Give any LLM-powered application long-term recall across sessions.

Velixar is an open memory layer — it works with any AI assistant, agent framework, or LLM pipeline. Store facts, preferences, and context that persist beyond a single conversation.

Installation

pip install velixar

# With LangChain integration
pip install velixar[langchain]

# With LlamaIndex integration
pip install velixar[llamaindex]

# All integrations
pip install velixar[all]

Quick Start

from velixar import Velixar

v = Velixar(api_key="vlx_your_key")  # Or set VELIXAR_API_KEY env var

# Store a memory
memory_id = v.store(
    content="User prefers dark mode and metric units",
    tier=0,  # 0=pinned, 1=session, 2=semantic (default), 3=org
    user_id="user_123",
    tags=["preferences"],
)

# Search memories semantically
results = v.search("user preferences", limit=5)
for memory in results.memories:
    print(f"[{memory.score:.2f}] {memory.content}")

# Get context for LLM prompts
context = v.get_context("What are the user's preferences?", max_tokens=2000)

Async Support

from velixar import AsyncVelixar

async with AsyncVelixar(api_key="vlx_...") as v:
    await v.store("User's favorite color is blue", user_id="user_123")
    results = await v.search("favorite color")

Memory Tiers

Tier	Name	Use Case
0	Pinned	Critical facts, user preferences, never expire
1	Session	Current conversation context
2	Semantic	Long-term memories (default)
3	Organization	Shared team knowledge (Hivemind+)

from velixar import MemoryTier

v.store("User is allergic to peanuts", tier=MemoryTier.PINNED)
v.store("Currently discussing project X", tier=MemoryTier.SESSION)

Cognitive Features by Plan

Feature	Free	Cortex ($29)	Synapse ($75)	Hivemind ($25/seat)
Store & search	✓	✓	✓	✓
Neural ensembles	—	✓	✓	✓
Temporal chains	—	✓	✓	✓
Consolidation	—	✓	✓	✓
Identity modeling	—	—	✓	✓
Org memory (tier 3)	—	—	—	✓

Free tier stores and searches memories. Paid tiers activate cognitive features automatically — no code changes needed. Pricing →

Use With Any AI Assistant

Velixar is assistant-agnostic. Plug it into OpenAI, Anthropic, LangChain, LlamaIndex, custom agents, or any LLM pipeline:

# Inject memories as context before calling your LLM
results = v.search(user_message, limit=5)
context = "\n".join(m.content for m in results.memories)

response = openai.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": f"Relevant memories:\n{context}"},
        {"role": "user", "content": user_message},
    ],
)

# Store important facts after the conversation
v.store("User prefers concise answers", user_id="user_123")

LangChain Integration

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from velixar.integrations.langchain import VelixarChatMessageHistory

def get_session_history(session_id: str):
    return VelixarChatMessageHistory(session_id=session_id, api_key="vlx_...")

chain = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{input}"),
]) | ChatOpenAI()

with_history = RunnableWithMessageHistory(
    chain, get_session_history,
    input_messages_key="input",
    history_messages_key="history",
)

# Memory persists across sessions, restarts, and deployments
config = {"configurable": {"session_id": "user_123"}}
with_history.invoke({"input": "I prefer Python over JavaScript"}, config=config)
with_history.invoke({"input": "What language do I prefer?"}, config=config)

LlamaIndex Integration

from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from velixar.integrations.llamaindex import VelixarMemory

memory = VelixarMemory(api_key="vlx_...", user_id="user_123")
agent = ReActAgent.from_tools(tools=[...], llm=OpenAI(), memory=memory)

Batch Operations

result = v.store_many([
    {"content": "Fact 1", "tier": 0},
    {"content": "Fact 2", "tier": 2, "tags": ["important"]},
    {"content": "Fact 3", "user_id": "user_456"},
])

Error Handling

from velixar import VelixarError, RateLimitError, AuthenticationError

try:
    v.store("test")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except VelixarError as e:
    print(f"Error: {e.message}")

Configuration

v = Velixar(
    api_key="vlx_...",           # Or VELIXAR_API_KEY env var
    base_url="https://...",      # Custom endpoint (optional)
    timeout=30.0,                # Request timeout in seconds
    max_retries=3,               # Retry attempts for failures
)

Get an API Key

Sign up at velixarai.com and generate a key under Settings → API Keys.

Related

• velixar (JavaScript SDK) — TypeScript/JavaScript client
• velixar-mcp-server — MCP server for any MCP-compatible AI assistant

License

MIT