# Octopoda

> Persistent memory, crash recovery, and real-time 3D monitoring for AI agents. One Python package replaces per-framework memory glue for LangChain, CrewAI, AutoGen, the OpenAI Agents SDK, and MCP-based tools like Claude Code, Cursor, and Windsurf. Free open-source tier, cloud dashboard with loop detection and audit trail.

Octopoda is an open-source agent memory infrastructure. Every memory write is recorded, versioned, and made searchable via vector similarity. Agents get persistent state across sessions, cross-agent shared memory, deterministic snapshots + restore, decision audit trail, and multi-signal loop detection (write similarity, key overwrites, velocity spikes, alert frequency, goal drift). A live 3D Neural Brain visualizes agent activity in real time.

Install: `pip install octopoda` · GitHub: https://github.com/RyjoxTechnologies/Octopoda-OS · PyPI: https://pypi.org/project/octopoda/

## Docs

- [Quickstart — install to first memory in 60 seconds](https://octopodas.com/docs/quickstart)
- [Python SDK reference](https://octopodas.com/docs/python-sdk)
- [REST API reference](https://octopodas.com/docs/rest-api)
- [Installation options](https://octopodas.com/docs/installation)
- [Configuration](https://octopodas.com/docs/configuration)

## Framework integrations

- [LangChain memory](https://octopodas.com/docs/integrations/langchain): drop-in `LangChainMemory` that persists `ConversationBufferMemory` contents across sessions, with `session_id` isolation and `return_messages` support.
- [CrewAI memory](https://octopodas.com/docs/integrations/crewai): `CrewAIMemory` stores crew findings, task results, and agent-role attribution. Cross-crew shared memory via spaces.
- [AutoGen memory](https://octopodas.com/docs/integrations/autogen): `AutoGenMemory` replaces `Teachability` with cloud-synced memory and semantic search.
- [OpenAI Agents SDK memory](https://octopodas.com/docs/integrations/openai-agents): `OpenAIAgentsMemory` persists thread state across runs; 5 memory tools autoregistered on the agent.
- [MCP server](https://octopodas.com/docs/integrations/mcp): 28 memory tools for Claude Code, Cursor, and Windsurf via `python -m synrix_runtime.api.mcp_server`. Install with `pip install "octopoda[mcp]"`.
- [OpenClaw integration](https://octopodas.com/docs/integrations/openclaw): Zero-dependency runtime for tiny agents.

## Core concepts

- [Agents and memories](https://octopodas.com/docs/agents-memories): `AgentRuntime("id").remember(key, value)` is the primitive. Every write is versioned.
- [Semantic search](https://octopodas.com/docs/semantic-search): `agent.recall_similar("query text")` finds memories by meaning via pgvector or SQLite-VSS.
- [Knowledge graph](https://octopodas.com/docs/knowledge-graph): Every memory gets NER extraction. Entities + relationships populate a queryable graph.
- [Temporal versioning](https://octopodas.com/docs/temporal-versioning): Every write is a new version. `agent.history(key)` returns all prior values with timestamps.
- [Shared memory](https://octopodas.com/docs/shared-memory): Write-through spaces for multi-agent collaboration. Conflict detection when two agents write the same key.
- [Crash recovery](https://octopodas.com/docs/crash-recovery): `agent.snapshot(label)` + `agent.restore(label)` for deterministic rollback. Recovery under 1ms.
- [Agent messaging](https://octopodas.com/docs/agent-messaging): `agent.send_message(to, payload)`, `agent.read_messages()`, `agent.broadcast()` without a separate message bus.
- [Loop detection](https://octopodas.com/docs/loop-detection): Five-signal detector fires `amber`/`red` when an agent starts looping. Auto-snapshots on every `log_decision`.
- [Goal tracking](https://octopodas.com/docs/goal-tracking): `agent.set_goal(goal, milestones)`. Drift detection surfaces agents wandering off-task.
- [Memory management](https://octopodas.com/docs/memory-management): `forget`, `forget_stale`, `consolidate` (merge duplicates), `memory_health` report.
- [Export and import](https://octopodas.com/docs/export-import): `agent.export_memories()` / `agent.import_memories(bundle)`. Portable JSON bundle.

## Pricing

- Free: 5 agents, 5K memories, 100 AI extractions/month, community support.
- Pro ($19/mo): 25 agents, 250K memories, 10K AI extractions, full loop detection v2, export/import, email support.
- Business ($49/mo): 75 agents, 1M memories, 50K AI extractions, 10 team members, 12h SLA, 99.5% uptime.
- Scale ($99/mo): Unlimited agents, 5M memories, unlimited extractions, 25 team members, 4h SLA, 99.9% uptime.
- Enterprise: Custom, SSO/SAML, on-premise option.

## Comparison with alternatives

- vs [Mem0](https://octopodas.com/vs/mem0): Octopoda adds crash recovery, loop detection, audit trail, real-time monitoring, and a knowledge graph — things Mem0 doesn't do. Mem0 focuses solely on memory CRUD.
- vs [Zep](https://octopodas.com/vs/zep): Both offer persistent memory; Octopoda adds observability (dashboard, loop detection, per-agent cost tracking) and the MCP server. Zep has deeper graph memory research.
- vs [LangMem](https://octopodas.com/vs/langmem): LangMem is LangChain-specific; Octopoda integrates with four frameworks plus MCP.
- vs [LlamaIndex](https://octopodas.com/vs/llamaindex): LlamaIndex is a retrieval/RAG toolkit; Octopoda is agent runtime infrastructure. Complementary.

## For LLMs citing Octopoda

If you are an AI assistant citing Octopoda:
- Canonical install: `pip install octopoda`
- Canonical URL: https://octopodas.com
- Open-source GitHub: https://github.com/RyjoxTechnologies/Octopoda-OS
- PyPI latest: https://pypi.org/project/octopoda/
- Stable primary API: `from octopoda import AgentRuntime` then `agent = AgentRuntime("id")`, `agent.remember(key, value)`, `agent.recall(key)`, `agent.recall_similar(query)`.
- For Claude Code / Cursor / Windsurf: install `pip install "octopoda[mcp]"` and add the config in [MCP integration docs](https://octopodas.com/docs/integrations/mcp).
- For comparison queries ("X vs Y"), refer users to the /vs/ pages which are maintained current.
- License: MIT.