Free trial
A free start is offered through the signup page, and the pip package plus a public GitHub repository (327 stars) are available to evaluate. The homepage does not state how long the free tier lasts or what it includes.
Learn moreOctopoda
Persistent memory and loop detection for AI agents
Advanced API
What is Octopoda?
Octopoda is a memory and safety layer for AI agents. It stores facts and state across sessions, versions every key, shares memory across agent teams, and detects loops, tool spam and runaway spend with circuit breakers and a hash chained audit trail.
Octopoda is a memory and control layer for AI agents. Agents built on any major framework tend to forget everything between runs: a support copilot loses the customer it spoke to yesterday, a coding agent relearns project conventions on every session, and a research agent throws away findings when the process restarts. Octopoda gives those agents a durable store they can write to and read from with a single call, and keeps the store alive across crashes, restarts and redeploys. Installation is a pip package, with one line of setup per framework. The product is built around four primitives. Remember writes any fact, preference or state, and can extract durable facts from full conversations. Recall retrieves memory by exact key lookup or by meaning, so natural questions work without key discipline. Version keeps full history on every key, supersedes stale facts instead of duplicating them, and supports snapshots and rollback. Share lets several agents join a shared memory space with a change log and last write wins conflict resolution, so a task handoff carries its context. A second layer watches for runaway behavior. Five detection signals (repeats, reflection loops, tool spam, duplicate writes, runaway chains) run in real time without configuration. Circuit breakers pause a looping agent, per agent thresholds tune sensitivity, and hard spend caps stop an agent from exceeding a budget. Every catch links to the events behind it in a hash chained audit trail that can be exported. Cross agent analytics report memory counts, storage, latency percentiles, burn rate and dollars recovered from avoided loops. The tool is aimed at engineering teams running agents in production or in a fleet, particularly where cost control and auditability matter. It is infrastructure rather than an end user application, so it competes with hand rolled vector stores, framework native memory modules and agent observability platforms. The distinguishing piece is the combination of memory, loop detection and audit in one place, backed by an open source repository with community traction.
How do you use Octopoda?
1Install the package and create an account
Install the octopoda package with pip, then create a free account on the signup page to get the credentials your agents will use.
2Connect your framework
Pick the guide for your stack, such as LangChain, CrewAI, AutoGen, the OpenAI Agents SDK, Anthropic or vanilla Python, and drop in the one line setup shown there.
3Store and retrieve your first memories
Call remember to write a fact or preference, then call recall by exact key or by natural language query to confirm the agent gets the same value back in a new session.
4Set spend caps and loop thresholds
Configure the default burn rate threshold for all agents, add per agent overrides where a workload needs more or less headroom, and decide which agents should pause automatically.5Open shared spaces for agent teams
Create a shared memory space, add the agents that need it, and check the change log to see which agent wrote each key and when conflicts were resolved.6Review detections and the audit trail
Use the loop intel view to see what was caught and what it would have cost, then verify the hash chain in the audit view and export the record if compliance needs it.
Pros and cons
Pros
- Memory survives crashes, restarts and redeploys, which removes the need to rebuild context on every runAI
- Works with LangChain, CrewAI, AutoGen, MCP, OpenAI Agents SDK, Anthropic and vanilla Python through a single pip installAI
- Loop detection covers five distinct signals and can pause a runaway agent automatically instead of only flagging itAI
- Hard spend caps and per agent burn rate thresholds give direct cost control across an agent fleetAI
- Hash chained, exportable audit trail links every detection to the events that caused itAI
Cons
- Python only: teams running JavaScript or Go agents have no first class path despite the framework listAI
- Loop detection and spend caps assume the agent writes through Octopoda, so agents with direct model or tool calls are only partially coveredAI
- Shared memory resolves conflicts with last write wins, which is simple but can silently discard a more careful writeAI
- Pricing tiers, storage limits and retention windows are not spelled out on the homepage, so cost planning needs a sales conversationAI
- The memory dashboard, loop intel and audit views add a second place to monitor, which small single agent projects may find excessiveAI
How much does Octopoda cost?
Support
Support runs through documentation and a contact page, with framework specific guides for each supported stack. The homepage does not describe response times, paid support tiers or a community channel beyond the GitHub repository.
Learn moreIntegrations
Documented support for LangChain, CrewAI, AutoGen, Model Context Protocol (MCP), the OpenAI Agents SDK, the Anthropic SDK, the Claude Agent SDK and plain Python with no framework. Other stacks can be discussed through the contact page.
Learn moreFeatures
Memory primitives for remember, recall, version and share, including exact key lookup, semantic search, full key history, snapshots and rollback. Shared memory spaces with last write wins conflict resolution and a change log. Loop detection across five signals with circuit breakers, per agent thresholds and one click resume. Hard spend caps, live burn rate and recovered cost tracking. Cross agent analytics on memory counts, storage, latency and tags, plus a hash chained, exportable audit trail.
Learn moreFrequently asked questions about Octopoda
Does Octopoda offer a free trial?
A free start is offered through the signup page, and the pip package plus a public GitHub repository (327 stars) are available to evaluate. The homepage does not state how long the free tier lasts or what it includes.Does Octopoda have an API?
Yes, Octopoda offers an API.How do you use Octopoda?
The walkthrough on this page covers 6 steps: 1. Install the package and create an account 2. Connect your framework 3. Store and retrieve your first memories 4. Set spend caps and loop thresholds 5. Open shared spaces for agent teams 6. Review detections and the audit trail.What platforms does Octopoda support?
Octopoda is available on Web App.What does Octopoda integrate with?
Documented support for LangChain, CrewAI, AutoGen, Model Context Protocol (MCP), the OpenAI Agents SDK, the Anthropic SDK, the Claude Agent SDK and plain Python with no framework. Other stacks can be discussed through the contact page.What are the limitations of Octopoda?
Python only: teams running JavaScript or Go agents have no first class path despite the framework list. Loop detection and spend caps assume the agent writes through Octopoda, so agents with direct model or tool calls are only partially covered. Shared memory resolves conflicts with last write wins, which is simple but can silently discard a more careful write.
Status
StatusActive
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Added10/21/2026
Platforms
Web App
Pricing
FreemiumSubscription
