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Raven

#120 agent harnessunverified rowv0.2.3

Memory-first terminal agent harness with local tracing, skill evolution, proactive scheduling and twelve messaging gateways

Key differences

Memory-first terminal agent harness with local tracing, skill evolution, proactive scheduling and twelve messaging gateways

  • Runs local and sandbox. Free and open source under Apache-2.0; you configure your own provider key, OAuth login or local model during onboarding
  • Includes a Docker sandbox. Listed for 48 of 194 tools in this category.
  • Supports headless CI workflows. Listed for 60 of 194 tools in this category.
  • Keep in mind: Onboarding asks for a sandbox or execution location, documented separately under docs/sandbox/usage.md.

“It has a Sentinel that sends you proactive nudges, so the agent now has opinions about your afternoon.”

Website Docs 5.1k starsCompare vs…Dispute a fact
Appeal a claim or request ownership transfer

What it is

Raven is EverMind's open-source agent harness: a Python runtime with a React/Ink TUI that runs an agent loop over providers, tools and subagents, with a context engine that budgets tokens explicitly and EverOS long-term memory carried across sessions. SkillForge retrieves and evolves skills, a Sentinel handles proactive observations and scheduled nudges, and an Evolver runs reproducible evaluation loops; tracing is on by default and stored locally so reasoning paths stay inspectable without a hosted service. It supports API-key, OAuth, local and OpenAI-compatible providers including Ollama and vLLM, and reaches users through twelve chat gateways. The README marks the project pre-alpha.

Specification

Source verification

Row snapshot checked not yet. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

readme
Needs individual review
install
Needs individual review
models
Needs individual review
license
Needs individual review
capabilities
Needs individual review

Architecture

Type
Agent harness
Runssrc ↗
local, sandbox
Platforms
macos, linux, windows
Context windowsrc ↗
not documented
Languages
any

Models

Backbonesrc ↗
OpenAI, Anthropic, Gemini, OpenRouter, DeepSeek, MiniMax, Moonshot, Groq, Azure OpenAI, GitHub Copilot (OAuth), Codex (OAuth), Ollama, vLLM
Bring your own model
Yes
Local models
Yes

Protocols

MCP clientunsourced
No
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandssrc ↗
Yes
Multi-file edits
Yes
Git operations
No
Browser control
Yes
Deep Research performs web search and page reading via MiroThinker and returns a cited report; there is no general browser-automation tool documented.
Sandboxed execution
Yes
Onboarding asks for a sandbox or execution location, documented separately under docs/sandbox/usage.md.
Multi-agent
Yes
Headless / CI
Yes

Cost

Modelunsourced
byok
Starts at
$0/mo
Free tier
Yes
Bring your own key
Yes

Free and open source under Apache-2.0; you configure your own provider key, OAuth login or local model during onboarding

Openness

Open sourcesrc ↗
Yes
License
Apache-2.0
First release
unknown
open-sourcepreviewterminalmemoryskillstracingmulti-agentmessaging

Los Agentes on Raven

Who are they?
The ruling
El JuezThe judge

El Hacker scores the ownership and El Crítico scores the stability label, and the two numbers describe the same repository at different points in its life.

Trial only
Reasoning and trade-offs · AI analysis

El Hacker rates it high because everything runs on his hardware and nothing phones home. El Crítico rates it low because the project calls itself pre-alpha and warns that interfaces will move, and skills rewrite themselves between sessions. El Profesor sits between them, admiring the measurement apparatus without vouching for the results.

El Crítico wins on timing, not on merit: everything El Hacker likes will still be true in six months, and the interfaces he configured today will not. Trial only, and the exit criterion is two consecutive releases that do not require you to rewrite your configuration.

Agree with El Juez?
El AmigoThe friend

Pick it if you want an assistant that remembers last week; pick a plain terminal agent if you would rather start every session from a clean slate.

6.5
Reasoning and trade-offs · AI analysis

The deciding trait is memory that survives the session. Most agents forget you between windows, so you re-explain the project every morning like a new contractor arrived. This one carries long-term memory across sessions, and after a fortnight the difference is not a feature comparison, it is whether you have to repeat yourself.

The catch is that memory cuts both ways: a wrong belief also persists, and you will spend time correcting one. Pick it if continuity is what you want. Pick something stateless if you prefer a predictable blank page.

reliability
5
usefulness
7
cost
9
longevity
5
Agree with El Amigo?
El CríticoThe critic

The README labels the project pre-alpha and warns that interfaces and configuration may change quickly, while the skill system rewrites its own definitions between runs.

5.8
Reasoning and trade-offs · AI analysis

Two moving surfaces multiply. The vendor's own warning is that configuration will shift under you, and the skill component is designed to evolve the instructions it retrieves, so today's behaviour is produced by material neither you nor the maintainers wrote in full. When something regresses, the question of what changed has two candidate answers and no obvious way to separate them.

What it does right is keeping the reasoning path recorded rather than discarded, which at least makes the postmortem possible.

reliability
4
usefulness
6
cost
8
longevity
5
Agree with El Crítico?
El ProfesorThe professor

The context engine budgets tokens explicitly rather than truncating when it runs out, and a separate component runs evaluation loops the authors describe as reproducible.

6.3
Reasoning and trade-offs · AI analysis
  1. Explicit budgeting is the correct treatment of a finite window. Deciding in advance what share belongs to history, tools and instructions produces failures you can reason about, whereas implicit truncation produces a silent loss whose cause is invisible in the transcript. 2. Shipping an evaluation loop inside the harness is unusual and welcome, since it makes regression a measurable event rather than an impression.

  2. No results from that loop are published, so the apparatus is documented and the outcomes remain unreported.

reliability
6
usefulness
6
cost
7
longevity
6
Agree with El Profesor?
La InversoraThe investor

A named startup with 3,746 stars, no paid tier and a deliberately local product, which is a large audience attached to nothing that bills.

6.0
Reasoning and trade-offs · AI analysis

Attention this size is an asset and it is not revenue. The architecture makes monetisation awkward on purpose, because everything that would normally be the hosted product runs on the user's own machine, and the usual answer to that is a control plane the audience explicitly does not want.

Moat: the memory layer, if the format becomes something users refuse to abandon. Likely path: a hosted team edition, or an acqui-hire by a platform that wants the agent runtime. Position: keep an eye on it, and do not build a company process on a pre-revenue local runtime.

reliability
5
usefulness
6
cost
8
longevity
5
Agree with La Inversora?
La JefaThe CTO

It reaches users through twelve chat gateways, which is twelve places company code can leave through, and there is no console for sixty engineers behind any of them.

5.0
Reasoning and trade-offs · AI analysis

The messaging surface is the whole conversation with my security team. Twelve integrations means twelve destinations where a repository excerpt can be pasted into a consumer platform, with no central policy, no directory and no retention setting to point at during an audit. That is a questionnaire I cannot complete.

It does run headless, which is the one thing that would make it a pipeline component worth measuring. The licence costs nothing across sixty seats and the governance cost is the real number. Not yet.

reliability
4
usefulness
5
cost
7
longevity
4
Agree with La Jefa?
El HackerThe tinkerer

Apache-2.0, Ollama and vLLM among the supported providers, and tracing switched on by default but written to my own disk instead of a vendor's.

7.8
Reasoning and trade-offs · AI analysis

Local tracing by default is the detail that earns my trust. Every other product treats observability as a reason to require an account; here the reasoning path lands on my filesystem and stays there, which means I can inspect a bad run without agreeing to terms of service to do it.

Weights can be mine through two local serving options named in the documentation, the licence keeps a fork viable, and the only thing I miss is MCP, which is absent at both ends and would have saved me writing adapters.

reliability
8
usefulness
8
cost
9
longevity
6
Agree with El Hacker?