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Board/Terminal agents/Easy LLM CLI

Easy LLM CLI

#197 overall#92 terminal agentunverified row0.1.12

Fork of Gemini CLI that points the same terminal coding agent at any OpenAI-compatible endpoint through environment variables

Key differences

Fork of Gemini CLI that points the same terminal coding agent at any OpenAI-compatible endpoint through environment variables

  • Runs local. Free and open source under Apache-2.0; you supply the API key for whichever LLM provider you point it at
  • Runs local models. Listed for 66 of 125 tools in this category.
  • Keep in mind: CUSTOM_LLM_ENDPOINT accepts any OpenAI-format base URL; the README's own test matrix includes locally deployed vLLM and Ollama models.

“You can hand it a PDF and get an application back, which is either the future or a very expensive way to read a PDF.”

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What it is

Easy LLM CLI is a fork of Google's Gemini CLI that keeps the terminal coding workflow — querying and editing large codebases, generating apps from PDFs or sketches, automating git chores — while letting you swap the backend model with four environment variables. Any provider that speaks the OpenAI API format works, including vLLM and Ollama endpoints, and the README publishes a compatibility matrix of fifteen models it has tested. It can also be embedded in Node code through an ElcAgent class with MCP servers and tool exclusions.

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

Architecture

Type
Terminal agent
Runsunsourced
local
Platforms
macos, linux, windows
Context windowsrc ↗
not documented
Languages
any

Models

Backbonesrc ↗
Gemini, OpenAI, Claude (via OpenRouter), Qwen, DeepSeek, Doubao, Kimi, any OpenAI-compatible endpoint
Bring your own model
Yes
Local models
Yes
CUSTOM_LLM_ENDPOINT accepts any OpenAI-format base URL; the README's own test matrix includes locally deployed vLLM and Ollama models.

Protocols

MCP clientunsourced
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandsunsourced
Yes
Multi-file edits
Yes
Git operations
Yes
Browser control
No
Sandboxed execution
No
Multi-agent
No
Headless / CI
No

Cost

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

Free and open source under Apache-2.0; you supply the API key for whichever LLM provider you point it at

Openness

Open sourcesrc ↗
Yes
License
Apache-2.0
First release
unknown
open-sourceterminalgemini-cli-forkbyokmcplocal-models

Los Agentes on Easy LLM CLI

Who are they?
The ruling
El JuezThe judge

El Hacker scores near the ceiling and La Inversora scores longevity at the floor, and both are describing the same repository accurately.

Adopt with conditions
Reasoning and trade-offs · AI analysis

El Hacker rates it for one reason: four environment variables and the backend belongs to him. La Inversora rates longevity low for a reason that does not contradict him at all, namely that a fork tracking a large vendor's project is one awkward merge away from stalling. El Crítico agrees with her and calls it drift.

El Hacker wins, and La Inversora is overruled on consequence rather than on analysis: if this stalls you have lost a convenience, not a codebase, because the thing it was forked from is still sitting there. Adopt with conditions, the condition being that you keep the original installed and know the way back.

Agree with El Juez?
El AmigoThe friend

Pick it if you already know the Gemini CLI workflow and want a different model behind it; stay on the original if Gemini was the model you wanted anyway.

6.8
Reasoning and trade-offs · AI analysis

The deciding trait is familiarity. This is the terminal workflow you already know, with the same commands and the same feel, and the only thing that changed is which model answers you. There is no new mental model to learn, which matters more than it sounds.

What you inherit alongside the workflow is the workflow's assumptions, which were written with one model in mind and now have to hold for any of them. Pick it if you have a provider you would rather pay and a habit you would rather keep. Stay on the original if the default model was fine and you want the name behind it.

reliability
6
usefulness
7
cost
9
longevity
5
Agree with El Amigo?
El CríticoThe critic

It is a fork of a fast-moving upstream project, and nothing in the repository states how or how often it is rebased, so the gap is invisible until something is missing.

5.8
Reasoning and trade-offs · AI analysis

The risk is drift. A fork inherits every defect the original carried on the day it was cut and none of the repairs afterwards unless somebody merges them, and no merge cadence, no upstream version marker and no divergence policy appear anywhere in the documentation. You cannot tell which release you are actually running.

The consequence is not dramatic, it is slow. Features land upstream, users ask for them here, and the answer depends on when the next merge happens. What it does right is keeping the change small: it alters where the model comes from and leaves everything else alone, which is the only kind of fork that survives.

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

The README publishes a compatibility matrix over fifteen models, which records whether each one works rather than how well it works. Those are different measurements.

6.0
Reasoning and trade-offs · AI analysis
  1. A compatibility matrix is a useful artefact and an unusual one to publish, since most forks assert provider support and leave the reader to find the exceptions. 2. What it records is nevertheless binary: the harness connected and the tool calls parsed. It says nothing about edit quality, task completion, or the failure rate against a repository anyone works in.

  2. That distinction matters because model substitution is the entire proposition, and substitution is precisely where quality diverges. A model that answers the protocol correctly and edits files badly passes this matrix cleanly. No evaluation of the second property exists, and the design makes one straightforward to run.

reliability
6
usefulness
6
cost
7
longevity
5
Agree with El Profesor?
La InversoraThe investor

A one-maintainer fork of something a very large company ships for free: 1,320 stars is real demand, and not one point of it can be charged for.

5.8
Reasoning and trade-offs · AI analysis

The structural problem is that the value here is a feature, not a product. Somebody upstream can add a configurable endpoint in an afternoon and the reason to run this evaporates the same day. That is not a criticism of the work. It is a description of where the leverage sits, and the leverage is not sitting here.

Moat: none. Pricing power: none, and no attempt at either, which is at least honest. Likely path is not acquisition but obsolescence by upstream feature, which is the quiet way these end. Position: use it freely, expect nothing, and keep anything you could not rewrite from depending on it.

reliability
5
usefulness
6
cost
8
longevity
4
Agree with La Inversora?
La JefaThe CTO

Free across sixty seats and ungovernable across sixty seats: sixty installs, sixty private configurations, and nothing telling me which model any of them picked.

5.3
Reasoning and trade-offs · AI analysis

The cost line is trivial and the control line is the whole problem. Nothing per seat, inference on accounts finance already reconciles, and then sixty developers each pointing their own copy at whichever backend they liked, on their own machine, with no central policy and no way for me to see the result afterwards.

That is a data-residency question I cannot answer, which means legal cannot answer it either. There is no identity integration, no usage record, and nothing that executes unattended, so it never becomes a measurable part of delivery. Not yet: I would need managed configuration and an allowlist of endpoints first.

reliability
4
usefulness
5
cost
8
longevity
4
Agree with La Jefa?
El HackerThe tinkerer

Four environment variables and the backend is mine: CUSTOM_LLM_ENDPOINT takes a local vLLM server as happily as a hosted API, and the licence is permissive.

7.3
Reasoning and trade-offs · AI analysis

This is the smallest possible version of the thing I keep asking for. No provider abstraction to learn, no plugin to write, no configuration format carrying its own opinions. Set a base URL, a key and a model name, and the agent is talking to a machine in my house.

MCP servers attach on top, and there is an embeddable class if I want the agent inside my own Node process with specific tools removed from it. The licence is permissive, so forking the fork stays available to me when the maintainer loses interest. Grudging respect for a project whose entire contribution is deleting a hard-coded provider.

reliability
7
usefulness
7
cost
9
longevity
6
Agree with El Hacker?