❯❯ What’s New
❯ Multi-Agent Trajectory Observability
Every agent, every turn, every step — on one timeline. Raven’s new trajectory view renders a running multi-agent session as a live waterfall, from the whole run down to a single step.
https://github.com/user-attachments/assets/abb0a5e9-1c37-48bd-9be0-1c9267293337
What is Raven
One Surface, All Agents: Raven generates DAGs and orchestrates multiple specialized agents for complex tasks.
Raven is the harness of harnesses, built for recursive self-improvement (RSI). As a Host Agent, it brings built-in and third-party agents together to carry out complex tasks. Its modular architecture supports iterative improvement of Raven’s own harness: proposing changes to how agents plan and act, evaluating those changes, and adopting improvements that pass validation. Powered by EverOS, Raven carries memory and context across sessions to support this process.
Built-in Agents: Raven-Research, Raven-Code, Raven-Design, and Raven-Oncall support research, coding, visual design, and unattended workflow automation.
Raven's Performance on the Multi-Agent Orchestration Benchmark
❯❯ Showcase
These are three complete projects delivered by Raven. In each case, Raven drove a team of specialized agents from the initial brief or objective through execution to a complete set of final deliverables.
❯ THRESHOLD: a complete game development project
The brief came from a person; Raven completed the entire project. Working autonomously for about 4 days, Raven completed 42 rounds of planning, development, and verification to build a playable first-person shooter in Godot 4, centered on an arena boss fight. The full deliverable includes the game, its poster, presentation, and website, all produced by Raven.
❯ Raven RSI: recursive self-improvement in practice
AI that improves AI, with the entire project completed by Raven. Given a task and evaluation criteria it cannot modify, Raven RSI independently plans each round, writes code, runs experiments, and evaluates the results. In nanochat pre-training experiments, it completed 172 training runs across 7 rounds without a single crash, reducing val_bpb by 5.8% within the same 20-minute, single-GPU budget. The same process reduced overshoot in a dam-break simulation by three orders of magnitude and completed an FEA limit-load search in 8 rounds of bisection. The complete deliverable includes the experimental results, visualizations, poster, presentation, and project website, all produced by Raven.
CFD dam-break simulation |
FEA limit-load search |
Website ↗ |
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Poster |
Presentation · PPTX ↓ |
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❯ Raven: a complete product launch project
One raven, a whole flock of specialists. Raven completed the entire project. Its launch kit brings together a browser-based physics mini-game, a 16-slide product overview, posters in English and Chinese, and the README you are reading now, all produced by Raven.
More showcases
❯❯ Built-in Agents
Raven’s modular architecture is designed for harness self-evolution and subagent creation. Its four built-in agents deliver state-of-the-art (SOTA) performance in their respective domains, combining reusable harness components with domain-specific tools, skills, and agent loops. Raven can delegate a focused task to a single agent or orchestrate multiple agents within a shared workflow. The harness they share is refined by the Raven Evolver, a separate tool that consumes Raven as a library and evaluates candidate harness changes against benchmarks; it develops the agents rather than running inside them.
All four agents are built in and ready for orchestration out of the box.
❯ Raven-Research
Raven-Research enables autonomous deep research for complex questions, literature reviews, and technical analysis. It delivers clear, structured reports with traceable sources, helping users understand unfamiliar domains, compare alternatives, and make informed decisions.
Raven-Research's performance on the DeepResearch Mixed benchmark
❯ Raven-Code
Raven-Code enables agentic software development, turning requirements into working, tested code. It supports feature implementation, debugging, refactoring, data processing, and data analysis, helping users build new capabilities, resolve issues, and improve code quality while following their project’s conventions.
Raven-Code's performance on coding benchmarks
Raven-Code tops on DataAgentBench for data analysis (2026-08-24 Live)
❯ Raven-Design
Raven-Design performs visual design, turning ideas and content into polished visual deliverables. It creates PowerPoint slide decks, brand assets, charts, diagrams, and web interfaces, refining layout, typography, and visual consistency to help users communicate clearly and bring their ideas to life.
Raven-Design tops on PresentBench for slide generation
Raven-Design's performance on visual design benchmarks
❯ Raven-Oncall
Raven-Oncall enables unattended workflow automation for experimentation, optimization, and continuous monitoring. It autonomously manages workflows from start to completion, sustaining progress over hours or overnight, delivering results, and involving users only when human judgment is needed.
Raven-Oncall significantly outperforms Claude Code on both quality and cost for AI4AI tasks
Raven-Oncall significantly outperforms Claude Code on both success rate and cost for AI4S tasks
❯❯ Runtime Self-Evolution
Raven is built for this from the ground up. Its agent loop is split into four decoupled strategy modules — Memory for what a turn gets to see, Planning for how it approaches the work, Capability for which tools an iteration exposes, Action for what to do next and for judging it before it runs. A Curator keeps rewriting those four seats: a setting, or a small piece of judgement code written for that agent. What it changes belongs to that agent alone, and once installed the agent runs on it.
A Curator changes more than the prompt — the tools and outside services it reaches for, the skills and procedures it follows, and its own judgement at each point can all be replaced. It keeps going round after round — you put it to work, you say what was wrong, it reworks — until you are satisfied, until it has nothing left worth changing, or until the round budget runs out. Two things hold the quality: nothing is installed before it is verified, and a failed check sends it back; and a round’s signals normally reach it with the reference answer stripped out, which limits how directly the answer is exposed. The Curator is experimental: it ships with the repository rather than the installed package.
A Persona is the first thing it builds. Describe the assistant you want and the Curator assembles one: a lead role that talks to you, and specialists drawn from the agents you already have. What you asked for lands in that assistant’s harness — its division of work, the tools it may reach for, and the checks it must pass before it acts.
Describe what you need once. Raven assembles the assistant, keeps improving it while you work, and afterwards a sentence is enough to put it to work again.
❯❯ Connect Third-Party Agents
Raven can connect to and orchestrate agents via ACP, CLI, or OpenAI-compatible APIs, with presets for 15 third-party agents and nine Mirasim-routed presets to simplify setup, task delegation, and coordination across shared workflows. Try these agents in Raven through a unified interface!
The roster also includes MiniMax Code over its native ACP server (mcode acp), DSH (DeepSeek Harness) over the ACP profile it ships (dsh --profile acp), and nine Mirasim-routed presets such as Claude Code-Mirasim, which run the same agents on Mirasim’s accounts and model routes.
❯❯ News
❯❯ Quick Start
🤖 Install with Your Agent
Let your own agent install Raven for you. Copy this prompt into any agent that
can read a web page and run shell commands, such as Claude Code or Codex:
Read https://evermind-ai.github.io/Raven/quick-start/ and follow it to install Raven, or to update it if it is already installed.
📦 Install
Linux, macOS, or WSL2:
curl -fsSL https://raven.evermind.ai/install.sh | bash
Native Windows PowerShell:
irm https://raven.evermind.ai/install.ps1 | iex
Windows PowerShell 5.1, the version built into Windows, cannot follow that URL’s
redirect and stops with (308) Permanent Redirect. If you see that error, use the
direct installer URL instead:
irm https://raw.githubusercontent.com/EverMind-AI/Raven/refs/heads/main/install.ps1 | iex
Or run it in a container, with nothing on the host but Git and Docker:
git clone https://github.com/EverMind-AI/Raven.git
cd Raven
docker compose -f docker/docker-compose.yml up
Then open http://localhost:18793 in your browser. Prerequisites are
Git and Docker with
Docker Compose. See
docker/README.md for what comes up, how the page signs
itself in, and how to run it behind a remotely exposed port.
Or install from a source checkout, to develop against the code or to run what
has not been released yet:
git clone https://github.com/EverMind-AI/Raven.git
cd Raven
./install.sh
Run as a file, install.sh installs that checkout in editable mode: raven and
its bundled plugins link back to your tree, and the TUI bundle and the served
page are built from it. A piped run installs the published wheel even from
inside a clone, so that a one-line install never picks up whatever a working
tree happens to contain. Set RAVEN_LOCAL_SRC=<dir> to force the editable
install through a pipe.
The agent products ship with raven itself: a wheel carries the agents/
product tree and copies it out to your raven home on first use, and a source
checkout reads the tree in place. Setup asks, for each product, whether it
runs on the model it is tuned for, which needs a key of its own, or on this
raven’s LLM. Nothing is registered: a folder is on the roster because it is
there. See agents/README.md.
Learn more about Raven on the documentation site.
Read the documentation
❯❯ Core Systems
| System |
What it adds |
| Agent Orchestration |
Coordinates agents, manages task dependencies and parallel execution, and turns multi-step collaboration into reusable workflows. |
| Evolver |
Drives harness self-evolution by diagnosing failures, testing candidate improvements, and retaining changes that outperform the baseline in reproducible evaluations. |
| EverOS Memory |
Preserves user context, agent experience, and world knowledge across sessions, recalling relevant memories and reusable skills for future tasks. |
| SkillForge |
Retrieves relevant skills from local libraries, EverOS memory, and SkillHub’s catalog of 114,190 skills, giving agents specialized expertise on demand. |
| Proactivity |
Combines event monitoring and scheduled execution to anticipate user needs, deliver timely reminders, and initiate follow-up work. |
❯❯ Launch WebUI
Raven’s WebUI brings sessions, multi-agent collaboration, and workspace management into your browser. Chat with agents, follow task progress, inspect files and outputs, and browse memory and skills in one place.
raven web
The command opens the WebUI in your browser and keeps Raven running in the background. Use raven web --stop to stop the background service.
New session: one composer, with skills, playbooks, knowledge and memory a click away.
Subagents: every connected agent in one roster, built-in and third-party alike.
❯❯ EverMind Ecosystem
EverMind connects memory research, production-ready products, and practical
integrations into one open-source ecosystem.
| Products |
| EverOS |
A local-first, Markdown-native long-term memory runtime for agents and users. |
| Raven |
A memory-first, self-improving agent harness with proactivity, context control, and reusable skill retrieval. |
| EverMe (CLI) |
A CLI and agent plugin suite for cross-device, cross-agent personal memory. |
| Research & Evaluation |
| SkillCorpus |
Curated, retrieval-ready agent skill corpora with retrieval and evaluation tooling. |
| EverAlgo |
Stateless extraction, ranking, parsing, and memory operators that power EverOS. |
| HyperMem |
Hypergraph-based hierarchical memory for coarse-to-fine long-term conversation retrieval. |
| MSA |
Memory Sparse Attention for scalable latent memory and 100M-token contexts. |
| EverMemBench |
Evaluation of factual recall, applied reasoning, and personalized generalization in memory systems. |
| EvoAgentBench |
Longitudinal evaluation of agent self-evolution, transfer efficiency, error avoidance, and skill use. |
| Integrations |
| OpenClaw |
OpenClaw plugin for automatic recall, capture, and session-memory lifecycle management. |
| Hermes Agent |
Hermes plugin for persistent memory across Hermes sessions. |
| DeepSeek Harness |
DSH plugin for memory-aware DeepSeek Harness agents. |
| Dify |
Self-hosted and cloud tools for explicit memory search and storage in workflows and agents. |
Together, these projects form EverMind’s research-to-runtime stack: methods
and benchmarks become reusable memory infrastructure, products, and agent
integrations.
❯❯ Contributing
Issues and pull requests are welcome. Start with the developer workflow, follow AGENTS.md for repository rules, and use GitHub Discussions for design conversations.
❯❯ License
Apache License 2.0
❯❯ Citation
If you use Raven in your research, please cite the technical report:
@misc{evermind2026raven,
title = {{Raven: The Harness of Harnesses for Composable Agentic Intelligence}},
author = {{EverMind AI}},
year = {2026},
eprint = {2609.33439},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2609.33439}
}
❯❯ Contributors
Thanks to everyone who has helped make Raven better.
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