Give your agents persistent memory, loop detection, audit trails, and real-time observability. Everything works automatically once you create an agent.

Track latency, error rates, memory usage, and health scores per agent.

Browse every memory, inspect version history, and see exactly how an agent's knowledge changed over time.

pip install octopoda

from octopoda import AgentRuntime

agent = AgentRuntime("my_agent")

That's it. Your agent now has persistent memory, loop detection, crash recovery, and an audit trail. Everything runs automatically in the background. Memory survives restarts, crashes, and deployments.

Store and retrieve memories when you need to:

agent.remember("key", "value")
agent.recall("key")

Want the cloud dashboard? Just add an API key:

export OCTOPODA_API_KEY=sk-octopoda-...   # Free at octopodas.com

Same code, now with real-time monitoring, semantic search, and multi-agent observability.

When you create an AgentRuntime, all of this is handled for you automatically:

• Persistent memory — everything your agent stores survives restarts and crashes
• Loop detection — catches agents stuck in repetitive patterns before they burn tokens
• Audit trail — every decision, every write, every action is logged
• Crash recovery — automatic heartbeat monitoring with snapshot/restore
• Health scoring — continuous monitoring of memory quality and agent performance
• Heartbeats — background thread tracks agent liveness

You don't need to configure any of this. It just works.

Everything below is optional. Use it when you need it.

Find memories by meaning, not just exact keys.

agent.remember("bio", "Alice is a vegetarian living in London")
results = agent.recall_similar("what does the user eat?")
# Returns the right memory with a similarity score

Agents can talk to each other through shared inboxes.

agent_a.send_message("agent_b", "Found a bug in auth", message_type="alert")
messages = agent_b.read_messages(unread_only=True)

Set goals and track progress. Integrates with drift detection.

agent.set_goal("Migrate to PostgreSQL", milestones=["Backup", "Schema", "Migrate", "Validate"])
agent.update_progress(milestone_index=0, note="Backup done")

agent.forget("outdated_config")       # Delete specific memories
agent.forget_stale(days=30)           # Clean up old memories
agent.consolidate()                   # Merge duplicates
agent.memory_health()                 # Get a health report

agent.snapshot("before_migration")
# ... something goes wrong ...
agent.restore("before_migration")

Multiple agents can share knowledge with conflict detection.

agent_a.share("research_pool", "analysis", {"findings": "..."})
data = agent_b.read_shared("research_pool", "analysis")

bundle = agent.export_memories()
new_agent.import_memories(bundle)

Works with the frameworks you already use. Just swap in Octopoda and your agents get persistent memory.

# LangChain
from synrix_runtime.integrations.langchain_memory import SynrixMemory
memory = SynrixMemory(agent_id="my_chain")

# CrewAI
from synrix_runtime.integrations.crewai_memory import SynrixCrewMemory
crew_memory = SynrixCrewMemory(crew_id="research_crew")

# AutoGen
from synrix_runtime.integrations.autogen_memory import SynrixAutoGenMemory
memory = SynrixAutoGenMemory(group_id="dev_team")

# OpenAI Agents SDK
from synrix.integrations.openai_agents import octopoda_tools
tools = octopoda_tools("my_agent")

Give Claude, Cursor, or any MCP-compatible AI persistent memory with zero code.

pip install octopoda[mcp]

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "octopoda": {
      "command": "octopoda-mcp"
    }
  }
}

25 tools for memory, search, loop detection, goals, messaging, and more.

Sign up free at octopodas.com for the dashboard, managed hosting, and cloud API.

export OCTOPODA_API_KEY=sk-octopoda-...

Or run octopoda-login to sign up from your terminal.

from octopoda import Octopoda

client = Octopoda()
agent = client.agent("my_agent")
agent.write("preference", "dark mode")
results = agent.search("user preferences")

pip install octopoda              # Core — everything you need to get started
pip install octopoda[ai]          # + Local embeddings for semantic search
pip install octopoda[nlp]         # + spaCy for knowledge graph extraction
pip install octopoda[mcp]         # + MCP server for Claude/Cursor
pip install octopoda[all]         # Everything

See CONTRIBUTING.md for setup instructions and guidelines.

See SECURITY.md for reporting vulnerabilities.

MIT — use it however you want. See LICENSE.

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