Octopoda: Open Source Memory Layer for Local AI Agents
What Octopoda Solves
AI agents typically forget everything between sessions. Every restart resets them to a blank slate, requiring users to rebuild context from scratch. Octopoda addresses this by providing persistent memory that survives restarts and crashes.
Core Features
- Persistent memory: Agents retain knowledge across sessions
- Semantic search: Find memories by meaning, not just exact keys
- Loop detection: Identifies when agents get stuck repeating actions
- Inter-agent messaging: Enables coordination between multiple agents
- Crash recovery: Snapshots allow rollback to previous states
- Version history: Track how agent knowledge evolves over time
- Shared memory spaces: Multiple agents can work from the same knowledge base
Technical Implementation
The entire stack runs locally with no cloud requirements, API keys, or external services. Semantic search uses a 33MB embedding model that runs on CPU. Ollama integration is available for fact extraction to create smarter memories.
Integration Support
Octopoda works with LangChain, CrewAI, AutoGen, and OpenAI Agents SDK. For Claude or Cursor users, there's an MCP server with 25 tools available.
License and Availability
The project is MIT licensed. The source code is available on GitHub at https://github.com/RyjoxTechnologies/Octopoda-OS, with additional information at www.octopodas.com.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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