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Sat 07 Nov 10:56 UTC
LLM Toolsevaluationupdated 20 Oct 2026

freellmapi review

FreeLLMAPI is a local gateway that routes requests through your free-provider accounts and custom OpenAI-compatible endpoints. Its README describes quota tracking and fallback routing behind one API. Available capacity still depends on the provider accounts you configure.

+2,708stars / 7d
Verdict

The supplied FreeLLMAPI run passed 3,950 tests, but npm audit reported 29 known vulnerabilities, including 5 critical. Consider it for personal experiments only after reviewing those advisories and your provider terms. The README explicitly excludes production use. The supplied checks do not establish runtime advisory exposure, provider reliability, or the behavior of the later v0.16.5 release.

We ran it

Lab card: what happened when we ran freellmapiScreenshot of freellmapi (freellmapi.co)
Install✓ · 110s823 packages · 549 MB
Build✓ · 29s
Tests✓ · 253s3950 passed · 0 failed · 8 skipped of 3958 (vitest)
Known vulns295 critical · 17 high · 7 moderate · 0 low (npm audit)
Repo1160 files~163,082 lines of source · 18 MB · 4 CI workflows · Dockerfile

Answers from our run

Does freellmapi build from source?

Dependencies installed in 110 seconds (823 packages), and the build succeeded in 29 seconds. We cloned commit 986b211 into a clean Debian container with 3 CPUs and no project-specific setup.

Do freellmapi's tests pass?

Yes. The project's own test command finished in 253 seconds and reported 3950 passed, 0 failed, 8 skipped (vitest).

Does freellmapi have known vulnerabilities in its dependencies?

npm audit flagged 29 known advisories in the dependency tree, including 5 critical at the time of our run.

Who should not use freellmapi?

Customer-facing services: the README limits the project to personal experimentation and learning and recommends a paid API before shipping.

What are the alternatives to freellmapi?

LiteLLM, Portkey AI Gateway. The supplied FreeLLMAPI run passed 3,950 tests, but npm audit reported 29 known vulnerabilities, including 5 critical.

SetupN/AInstallation, build, and tests succeeded, but provider onboarding and a complete inference request were not assessed.
DocsN/AThe supplied README explains setup and limits, but the linked operational guides and example correctness were not assessed sufficiently to support a numerical documentation rating.
CommunityN/AThe October 20 metadata snapshot records a last push at 2026-10-20T05:15:09Z. Release and issue extracts show activity, but support turnaround was not assessed.
MaturityN/AThe passing suite and audit findings are recorded, but runtime advisory exposure, production reliability, and the later v0.16.5 release were not assessed.

Who it’s for

Individual developers who already have provider accounts and want one endpoint for personal experiments.
Coding-agent users prepared to check which model served a request and review its output.
Developers willing to investigate the dependency advisories before storing credentials in the router.

Who it’s NOT for

Customer-facing services: the README limits the project to personal experimentation and learning and recommends a paid API before shipping.
Workflows requiring predictable model quality or response time: the README warns about variable latency, no SLA, and weaker available models as daily quotas run out.
Users who require local inference without configuring a local endpoint: requests go to the upstream providers you enable.
Users who need immediate free-catalog updates: the README says models reach the free snapshot 30 days after joining the live feed.

Setup reality

The supplied FreeLLMAPI run at commit 986b211 installed in 110 seconds, recording 823 dependencies and 549 MB on disk. The build passed in 29 seconds. Vitest passed in 253 seconds: 3,950 passed, 0 failed, and 8 skipped of 3,958. Npm audit reported 29 known vulnerabilities: 5 critical, 17 high, and 7 moderate.

A useful router still needs upstream provider credentials, a local database and encryption key, and a unified key for clients. The README documents Docker, desktop, and source paths. Provider terms still apply, and enabled remote providers receive routed requests.

The README specifies Node 20 or newer. The recorded sandbox configuration had 3 CPUs, 8 GB RAM, and image lab-node:22; that image label does not verify runtime or operating-system versions. Package-level advisory details were not supplied, so exploitability and remediation remain unassessed.

Passing tests leave a dependency review to do

FreeLLMAPI is a local gateway that routes requests through your provider accounts and custom OpenAI-compatible endpoints. The supplied sandbox run passed 3,950 tests, while npm audit reported 29 known vulnerabilities, including 5 critical. Those findings leave a dependency review to complete before entrusting the router with provider credentials.

The project's own README sets a separate usage boundary: personal experimentation and learning, with a paid API before shipping. Consider it for experiments only after reviewing the advisories and your provider terms. The supplied checks do not establish suitability for customer traffic.

The README describes quota tracking and fallback routing across configured accounts. This can reduce client configuration changes when an allocation runs out. It cannot guarantee remaining capacity, consistent response time, or the quality of whichever model handles a fallback.

What happened when we ran it

The supplied run installed FreeLLMAPI commit 986b211 in 110 seconds on October 13, 2026, recording 823 dependencies and 549 MB on disk. The build succeeded in 29 seconds. Vitest finished successfully in 253 seconds with 3,950 passed, 0 failed, and 8 skipped out of 3,958. These results establish a successful repository build and test suite in that sandbox. They do not establish availability or answer quality across real provider accounts.

Npm audit reported 29 known vulnerabilities: 5 critical, 17 high, and 7 moderate. The supplied measurements do not name affected packages, dependency paths, or runtime exposure. They cannot establish whether a particular endpoint is exploitable or whether an update resolves every finding. Before storing provider credentials, inspect the advisories against the exact dependency tree. Passing tests do not resolve those questions, and the audit count alone does not establish an intrusion.

The recorded environment had 3 CPUs and 8 GB RAM and used the image label lab-node:22. That label does not verify the actual Node or operating-system version. The checkout contained 1,160 files, about 163,082 source lines, and 18 MB. The scan found 4 CI workflow files, a Dockerfile, a compose file, and monorepo workspaces. It recorded no tests directory despite the successful Vitest command; the directory scan does not replace the execution result.

Stored keys and routed prompts have different boundaries

The README says provider credentials are encrypted with AES-256-GCM in SQLite and decrypted in memory for requests. Clients use a unified bearer key instead of holding every upstream credential. Protect the database, encryption key, and unified client key. The supplied sandbox checks do not independently verify the encryption implementation.

The README says requests go from your machine to the upstream providers you enabled. A local dashboard therefore does not establish local inference. A custom provider can point to a compatible local endpoint, but you must configure that explicitly. Check the destination and fallback choices before routing sensitive material.

The documented quick start asks users to add provider credentials on the Keys page, arrange a fallback chain, and copy the unified API key into their client. Docker and desktop installation paths are documented, alongside Node 20 or newer. Server installations use an account login; the desktop dashboard signs in locally. The measured source build does not validate every desktop package or container.

A successful fallback can change model quality

The README warns of no frontier models, variable latency, and no SLA. It also says effective intelligence can drop as preferred models exhaust their daily allocations. For coding-agent work, a successful request can therefore come from a different model with different capabilities. Check the served-model information when reviewing output or trying to reproduce a result.

Catalog freshness is another constraint. The README says free installations receive a monthly snapshot, with models arriving 30 days after joining the live feed; premium routers receive them the same day. That describes catalog delivery, not guaranteed upstream capacity. The headline aggregate allowance is a project catalog claim, not capacity measured in this sandbox or guaranteed to your accounts.

Maintenance evidence extends beyond the tested commit

The supplied GitHub metadata snapshot, observed October 20, 2026, records the last push as 2026-10-20T05:15:09Z. The v0.16.5 release, dated October 19, describes a Windows premium-activation connection fix. These records show repository activity after the October 13 sandbox run; that run does not validate the later release.

An October 20 issue reports a Docker startup failure with a UNIQUE constraint error involving models.platform and models.model_id. It was open in the supplied extract. This is a user's report, not a reproduced sandbox failure. The supplied evidence does not establish support turnaround or resolution of that report.

Alternatives depend on the controls you need

LiteLLM documents virtual keys and spend tracking. Consider it when those controls are requirements for a shared gateway. Portkey's gateway documents load balancing and conditional routing. Consider it when configurable routing policy is the main requirement. These are comparisons of documented features, not measured security, speed, or reliability comparisons.

For FreeLLMAPI, the next step is to review the 29 advisories against the tested dependency tree, then check the terms and request destinations of any provider accounts you intend to add. The passing suite supports the build result at commit 986b211; it does not remove the project's personal-use boundary.

Alternatives

ProjectWhat it isPick it when
LiteLLM gh↗An AI gateway and Python SDK whose README documents virtual keys and spend tracking.Consider it when a shared gateway needs virtual keys and spend tracking; those features are documented, but were not tested in this review.
Portkey AI GatewayA model gateway whose README documents load balancing and conditional routing.Consider it when configurable routing policy is your main requirement; its documented routing features were not tested in this review.

What people are saying

  1. [github-trending] tashfeenahmed/freellmapi

Sources

  1. FreeLLMAPI README
  2. FreeLLMAPI v0.16.5 release
  3. FreeLLMAPI Docker startup report, issue 1603
  4. LiteLLM README
  5. Portkey AI Gateway README

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