Acontext

Acontext

MemoDB
Fugu-Ultra v1.1

Fugu-Ultra v1.1

Sakana AI
+
+

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About

Acontext is a context platform for AI agents. It stores multi-modal messages/artifacts, monitors agents' task status, and runs a Store → Observe → Learn → Act loop that identifies successful execution patterns, so autonomous agents can act smarter and succeed more over time. Developer Benefits: Less Tedious Work: Store multi-modal context and artifacts in one place by integrating all context data without configuring Postgres, S3, or Redis, and it only requires a few lines of code. Acontext handles repetitive, time-consuming configuration tasks, so developers don’t have to. Self-Evolving Agents: Similar to Claude Skills, which require predefined rules, Acontext allows agents to automatically learn from past interactions, reducing the need for constant manual updates and tuning. Easy Deployment: Open-source, one-command setup, One-line install. Ultimate Value: Improve agent success rates and reduce running steps, then save costs.

About

Fugu-Ultra v1.1 is Sakana AI’s upgraded multi-agent orchestration model for complex coding, agentic work, and advanced reasoning. Rather than relying on one model, it dynamically coordinates a diverse pool of frontier models, selecting and combining specialized agents for each task while presenting the system through a single model interface. The v1.1 orchestration upgrade incorporates newer frontier models and improves performance across every tracked benchmark, with gains of up to 7.9 points over v1.0 and particularly strong results on ProgramBench and Terminal Bench 2.1. Fugu can now be used directly inside Claude Code through Claude Code-compatible endpoints, bringing a coordinated team of models into familiar terminal workflows for writing, debugging, reviewing, and executing code. A one-command installer configures the integration on Ubuntu and macOS, while manual setup is available for Windows and other environments.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI agent builders, especially for production-grade agents with complicated workflow

Audience

Research engineering teams that need multiple frontier models coordinated inside familiar terminal-based coding workflows

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

$6 per 1M tokens (input)
$6 per 1M tokens (input), $36 per 1M tokens (output)
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

Pros & Cons from Real Users

Pros

  • Fugu-Ultra v1.1 is really interesting from a developer’s point of view because it feels like a different approach from the usual “one giant model does everything” setup. Instead, it acts more like an intelligent orchestration layer that can route work across multiple frontier models and agent patterns. That makes a lot of sense for coding and agentic workflows. Real development tasks are messy, and different parts of the job need different strengths: planning, repo search, debugging, terminal work, reasoning, code generation, and cleanup. I also like that Sakana added a Claude Code-compatible interface. Being able to use Fugu directly inside developer workflows makes it feel much more practical than something you only test in a browser or benchmark page. The benchmark gains are impressive too. If the reported improvements on coding and terminal tasks hold up in real projects, Fugu-Ultra v1.1 could be a strong option for developers building serious coding agents.

Cons

  • The main downside is that orchestration adds complexity. When a system is coordinating multiple models behind the scenes, I want strong transparency, logging, debugging tools, and predictable behavior before I trust it deeply in production. I would also want to test latency and cost carefully. Multi-agent systems can be powerful, but they can also become expensive or slow if they overthink simple tasks.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

MemoDB
Founded: 2025
Singapore
acontext.io

Company Information

Sakana AI
Founded: 2023
Japan
sakana.ai/fugu-1-1-claude-code-interface/

Alternatives

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic
Claude Mythos 5

Claude Mythos 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
CMEM Cloud

CMEM Cloud

cmem.ai
Sakana Fugu

Sakana Fugu

Sakana AI

Categories

Categories

Integrations

Amazon S3
ChatGPT
Claude
Gemini
PostgreSQL
RediSearch
Sakana Fugu
Sakana Fugu Ultra

Integrations

Amazon S3
ChatGPT
Claude
Gemini
PostgreSQL
RediSearch
Sakana Fugu
Sakana Fugu Ultra
Claim Acontext and update features and information
Claim Acontext and update features and information
Claim Fugu-Ultra v1.1 and update features and information
Claim Fugu-Ultra v1.1 and update features and information