Octopodas is an AI agent infrastructure platform that provides persistent memory, real-time monitoring, loop detection, and crash recovery for AI agents built on any framework, reducing operational costs and preventing runaway spending.
What is Octopodas?
Octopodas is a runtime that wraps AI agents (LangChain, CrewAI, OpenAI, Anthropic, AutoGen, MCP, or custom frameworks) with memory, audit trails, loop detection, and recovery. It accepts agents built in Python and outputs a unified dashboard showing agent health, memory state, decision logs, and cost metrics. The platform runs locally (SQLite) or syncs to the cloud, and is developed by Octopodas (RyjoxTechnologies).
Key Features
- Persistent Memory – Agents store structured, versioned, searchable memories that survive restarts. Supports semantic search, importance scoring, TTL, and tagging. Up to 1,000,000 memories on the Business plan.
- Loop Detection & Cost Control – A 5-signal detector identifies repeating patterns (e.g., same content stored multiple times, retry loops). Circuit breakers auto-pause agents when spend exceeds configurable per-agent thresholds (e.g., $8.58/min default). Avoids $200+ overnight bills.
- Audit Trail – Every action, decision, input, and failure is recorded in a hash-chained ledger with full replay capability. Supports search by event, agent, key, or payload. Useful for compliance reviews.
- Crash Recovery – Automatic snapshots and heartbeat-based restore. Enables rewinding to a known good state without rebuilding from scratch.
- Multi-Agent Coordination – Shared memory spaces, inter-agent messaging, task handoff, and goal tracking across agents built on different frameworks. Example: research-bot feeds into writing-bot without glue code.
- Framework Agnostic – Works with LangChain, CrewAI, OpenAI SDK, MCP, AutoGen, and custom SDKs. Two-line Python wrapper:
from octopoda import AgentRuntime; agent = AgentRuntime("my_agent").
- Real-Time Dashboard – Live monitoring of agent health scores, latency (avg 40.8ms, p99 32.3ms), operation counts, saved costs ($12.45 shown), and uptime. Anomalies and recoveries are displayed.
Who is it for?
- AI engineers running production agents – Add persistent memory and audit trails to LLM-powered applications without rewriting framework-specific glue.
- Backend leads managing multi-agent systems – Share memory across agents built on LangChain, AutoGen, and MCP simultaneously, with a single dashboard.
- Startup founders deploying AI features – Catch runaway loops and cost overruns early via circuit breakers and per-agent spend caps.
- Compliance-aware teams – Use the hash-chained audit ledger to demonstrate decision logs for SOC 2 or HIPAA reviews.
What can you do with Octopodas?
- Prevent cost explosions – Set per-agent spend limits (e.g., $2.25/min) and auto-pause agents that exceed them. The dashboard shows real-time spend per agent and alerts on anomalies.
- Replay agent decisions – Use the Audit Ledger to trace a failed Gmail call, the retry decision, and the circuit breaker block, with no missing reasoning gaps.
- Share context across agents – A support-bot and billing-bot can both read the same memory store (e.g., customer email thread ID, plan history) without custom integrations.
- Recover from crashes instantly – Restart an agent and restore 47 facts in 84ms, preserving customer context and intermediate results.
How does it work?
- Sign up at app.octopodas.com and receive an API key.
- Run
pip install octopoda and wrap your agent with AgentRuntime("my_agent").
- The runtime automatically enables memory, loop detection, audit logging, and recovery. Monitor via the live dashboard.
- Configure circuit breaker thresholds and dedup guards from the UI – no code changes needed.
