Octopoda OS

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Why Octopoda

• Agents forget. Every session starts from zero. Octopoda gives them memory that survives restarts, crashes, and deployments — automatically.
• Agents loop. A stuck agent can burn hundreds of dollars in tokens before anyone notices. Octopoda's 5-signal loop detector catches it in seconds.
• Agents are black boxes. Why did it do that? Octopoda logs every decision, every write, every recovery — and visualizes it in a 3D view so you can actually see what's happening.

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Quick Start

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 dashboard? Run the server:

pip install octopoda[server]
octopoda

Open http://localhost:7842 — same dashboard as the cloud version, running against your local data. No account needed.

Want cloud sync across machines? Sign up free at octopodas.com, set your API key, and your agents sync to the cloud automatically:

export OCTOPODA_API_KEY=sk-octopoda-...

Same code, same dashboard — now backed by PostgreSQL with multi-device sync and team access.

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Local vs Cloud

	Local	Cloud
Setup	pip install octopoda	Sign up at octopodas.com
Storage	SQLite on your machine	PostgreSQL + pgvector
Dashboard	http://localhost:7842	octopodas.com/dashboard
Account needed	No	Yes (free)
Data stays on your machine	Yes	Stored on cloud
Multi-device sync	No	Yes
Semantic search	Needs octopoda[ai] extra	Built-in
Upgrade path	Set OCTOPODA_API_KEY	Already there

Start local, upgrade to cloud when you need sync or team access. Both use the same API, same dashboard design, same code.

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What You Get Out of the Box

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.

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Dashboards

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.

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Audit Trail

Every decision, crash, recovery, and anomaly your agents make is logged with full context — including a memory snapshot captured at the moment of decision. Replay any time window and see exactly what each agent knew, decided, and why.

agent.log_decision(
    decision="Keep single VPS instead of Kubernetes",
    reasoning="Current traffic (14k req/day) doesn't justify K8s complexity. VPS handles 100x this load.",
    context={"current_rps": 14000, "threshold_rps": 1000000},
)

Every log_decision call automatically captures a snapshot of the agent's memory at that instant. The audit timeline shows decisions alongside crashes and recoveries, filterable per agent. Built-in loop detection warns you if a decision repeats a recent one.

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Shared Memory

Multiple agents working on the same problem can share knowledge through named memory spaces. Writes are atomic, reads are immediate, and every change is logged with its author — so you always know which agent contributed what.

# Research agent writes a finding into a shared space
research_agent.share("market_size", "$2.1B AI memory market by 2027", space="team-knowledge")

# Any other agent in the same space reads it
result = coding_assistant.read_shared("market_size", space="team-knowledge")
print(result.value)  # "$2.1B AI memory market by 2027"

Each space tracks authorship and timestamps for every write. Use agent.shared_conflicts(space="team-knowledge") to surface disagreements when multiple agents write to the same key.

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When You Need More Control

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

Semantic Search

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

Agent Messaging

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)

Goal Tracking

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")

Memory Management

agent.forget("outdated_config")                    # Delete specific memories
agent.forget_stale(max_age_seconds=30*86400)       # Clean up memories older than 30 days
agent.consolidate(dry_run=False)                   # Merge duplicate memories (omit dry_run to preview)
agent.memory_health()                              # Get a health report

Snapshots

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

Export / Import

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

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Framework Integrations

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

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

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

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

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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MCP Server

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

pip install octopoda[mcp]

Add to Claude Code:

claude mcp add octopoda -s user -e OCTOPODA_API_KEY=sk-octopoda-YOUR_KEY -- python -m synrix_runtime.api.mcp_server

Or add to Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "octopoda": {
      "command": "python",
      "args": ["-m", "synrix_runtime.api.mcp_server"],
      "env": {
        "OCTOPODA_API_KEY": "sk-octopoda-YOUR_KEY"
      }
    }
  }
}

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

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Cloud

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")

	Free	Pro ($19/mo)	Business ($49/mo)	Scale ($99/mo)
Agents	5	25	75	Unlimited
Memories	5,000	250,000	1,000,000	5,000,000
AI extractions	100	10,000	50,000	Unlimited
Rate limit	60 rpm	300 rpm	1,000 rpm	5,000 rpm
Dashboard	Yes	Yes	Yes	Yes

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How It Compares

	Octopoda	Mem0	Zep	LangChain Memory
Open source	MIT	Apache 2.0	Partial (CE)	MIT
Local-first	Yes (SQLite)	Cloud-first	Cloud-first	In-process
Loop detection	5-signal engine	No	No	No
Agent messaging	Built-in	No	No	No
Audit trail	Full history	No	No	No
Crash recovery	Snapshots + restore	N/A	No	No
Shared memory	Built-in	No	No	No
MCP server	28 tools	No	No	No
Semantic search	Local embeddings	Cloud embeddings	Cloud embeddings	Needs vector DB
Integrations	LangChain, CrewAI, AutoGen, OpenAI	LangChain	LangChain	Own only

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Installation

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

Configuration

Variable	Default	Description
OCTOPODA_API_KEY		Cloud API key (free at octopodas.com)
OCTOPODA_LLM_PROVIDER	none	LLM for fact extraction: openai, anthropic, ollama
OCTOPODA_OPENAI_API_KEY		Your OpenAI key for local fact extraction
OCTOPODA_EMBEDDING_MODEL	BAAI/bge-small-en-v1.5	Local embedding model (33MB, CPU)
SYNRIX_DATA_DIR	~/.synrix/data	Local data directory

Contributing

See CONTRIBUTING.md for setup instructions and guidelines.

Security

See SECURITY.md for reporting vulnerabilities.

License

MIT — use it however you want. See LICENSE.

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