'Alice' — still there. Survives every restart, deploy, and crash.

```

That is the entire setup. Your agent now has persistent memory, loop detection, crash recovery, and an audit trail. No config, no Docker, no Redis, no extra services.

Installation

pip install octopoda              # Core — everything to get started (Python 3.9+)
pip install octopoda[ai]          # + Local embeddings for semantic search
pip install octopoda[server]      # + Local dashboard server (Flask)
pip install octopoda[nlp]         # + spaCy for knowledge graph extraction
pip install octopoda[mcp]         # + MCP server for Claude/Cursor (Python 3.10+)
pip install octopoda[all]         # Everything (Python 3.10+)

Python version note: the core package supports Python 3.9 and up. The [mcp] extra requires Python 3.10+ because the upstream mcp library does. If you're on 3.9 and want everything except MCP, use pip install octopoda[ai,server,nlp].

Local-mode note: running without an API key (pip install octopoda only) gives you a fully working local install with SQLite at ~/.synrix/data/synrix.db. The OCTOPODA_API_KEY environment variable accepts the sentinels local, offline, dev, none, or YOUR_KEY_HERE to explicitly force local mode. Real cloud keys start with sk-octopoda-; anything else is treated as a local sentinel rather than hung on cloud auth.

Updating Claude Code MCP registration: if you change the claude mcp add octopoda ... env vars (for example, swapping from local to cloud), restart the Claude Code window. /mcp reconnect alone won't pick up the new env because the child process inherits Claude Code's cached env at startup.

Quick Start

pip install octopoda

```python from octopoda import AgentRuntime

agent = AgentRuntime("my_chatbot") agent.remember("user_name", "Alice")

Configuration

Framework Integrations

Drop into the framework you already use. One line, your agents get persistent memory.

<details> <summary><b>LangChain — drop-in conversation memory</b></summary>

from octopoda import LangChainMemory
memory = LangChainMemory("my-chain")
memory.save_context({"input": "I prefer dark mode"}, {"output": "Got it!"})
variables = memory.load_memory_variables({})

<details> <summary><b>CrewAI — persistent crew findings and task results</b></summary>

from octopoda import CrewAIMemory
crew = CrewAIMemory("research-crew")
crew.store_finding("researcher", "market_size", {"value": "$4.2B"})
finding = crew.get_finding("market_size")

<details> <summary><b>AutoGen — multi-agent conversation memory</b></summary>

from octopoda import AutoGenMemory
memory = AutoGenMemory("dev-team")
memory.store_message("user_proxy", "assistant", "Research quantum computing")
history = memory.get_conversation_history()

<details> <summary><b>OpenAI Agents SDK — thread and run persistence</b></summary>

from octopoda import OpenAIAgentsMemory
memory = OpenAIAgentsMemory()
memory.store_thread_state("thread_001", {"messages": [...]})
restored = memory.restore_thread("thread_001")

All integrations work locally (no API key) or with cloud sync (set OCTOPODA_API_KEY).

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Local vs Cloud — same code, your choice

Start local. Move to cloud when you need sync, team access, or the managed dashboard. Same Python API both ways.

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