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BerriAI/litellm

BerriAI/litellm is a developer tool in RepoRadar's AI Gateway section, holding Gold tier and a 'try now' verdict. Its strongest signal is workflow potential, scored 10.0 out of 10.

Open on GitHub →Back to radar
Score9.0
Popularity97.0
Riskconditional
TierGold
Score breakdown
Usefulness9.0
Novelty8.0
Momentum9.0
Maturity9.0
Open-source/build8.4
Evidence7.2
Workflow potential10.0
Setup ease4.2

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Useful if you need a single, auditable routing layer instead of wiring each model provider separately.

Who should use it

builders shipping multi-vendor AI products platform teams managing provider costs and quotas ops teams standardizing LLM observability

Who should skip it

Skip BerriAI/litellm if the source link, documentation, or setup requirements do not align with your current workflow or stack.

About this signal

BerriAI/litellm is tracked by RepoRadar as a developer tool in the AI Gateway section. First seen 2026-06-18; the source record was last checked on 2026-06-18. The current verdict is 'try now' with a Gold tier and advanced setup difficulty. Across RepoRadar's eight signals, BerriAI/litellm is strongest on workflow potential (10.0) and practical usefulness (9.0) and weakest on setup ease (4.2) — a profile worth weighing against your own priorities. This page summarizes the public evidence on the linked source page and states where additional review is still needed.

How this item is evaluated

The BerriAI/litellm record combines a 9.0/10 composite score with separate popularity (97.0), risk (conditional), and setup (advanced) signals. See the scoring methodology for the current weights and evidence definitions.

Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.

Risk explanation

Gateway errors can become system-level failure points; protect provider credentials and test failover and allowlisting before production; The repository includes mixed licensing paths (MIT for core, check enterprise paths before bundling).

Evidence links
Closest alternatives / related signals
llm gateway multi-provider observability cost-control
Verification record

What RepoRadar actually verified

Tested in a bounded workflow

Bounded representative workflow retained by RepoRadar verification harness. Last checked 2026-07-14T00:53:12.671649Z.

failed · cohort-20260712-litellm-import

Tester
RepoRadar automated local verification harness
Started
2026-07-13T03:22:09.504383Z
Completed
2026-07-13T03:22:34.676310Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
0 minute(s)
Evidence scope
Bounded setup or capability check
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Executed bounded check: Install and import LiteLLM in an isolated Python environment.
  • Captured the complete sanitized stdout, stderr, exit status, and 25.17-second wall time.
Observed results
  • Check exited 1 after 25.17 seconds; failure output is retained and the verification stage must not advance.
Observed strengths
  • No product strength was established in this failed attempt; diagnostic evidence is retained.
Friction
  • The command emitted stderr; warnings or errors are preserved in the retained log for review.
Limitations
  • No proxy was started and no provider credentials or model requests were used.
  • This bounded cohort check is not a production benchmark or a claim of real user-workflow adoption.

Pricing assessment: No paid plan or metered provider usage was exercised; package or licensing, hosting, and provider costs remain workflow-dependent.

Privacy assessment: No repository content, user data, or provider prompt was transmitted; broader product data handling was not assessed by this bounded run.

Open retained test log →

passed · cohort-20260713-litellm-mocked-completion-workflow

Tester
RepoRadar automated local verification harness
Started
2026-07-14T00:53:03.005597Z
Completed
2026-07-14T00:53:12.671649Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
1 minute(s)
Evidence scope
Bounded representative workflow
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Created 1 synthetic fixture file(s) inside the disposable work directory; retained hashes prove the exact inputs.
  • Constructed a system/user incident message pair and invoked LiteLLM 1.91.3's completion API with the explicit mock_response argument.
  • Validated LiteLLM's normalized model, assistant content, and finish-reason fields and retained a compact response artifact.
  • Executed bounded check: Send a two-message incident request through LiteLLM's completion API with its explicit local mock_response path and validate the normalized response object.
  • Captured the complete sanitized stdout, stderr, exit status, artifact checks, and 9.67-second wall time.
Observed results
  • Command exited 0 after 9.67 seconds.
  • LiteLLM returned a normalized ModelResponse whose assistant content, model name, stop reason, and two-message input count matched the fixture.
  • Expected marker 'CHECK_OK model=gpt-4o-mini queue=platform-oncall finish=stop messages=2' was observed in retained output.
  • Validated result.json: 4 required marker(s) present and 0 excluded marker(s) absent; size and SHA-256 are retained.
Observed strengths
  • The provider-neutral completion surface and normalized response object can be exercised deterministically without credentials when the explicit mock path is used.
Friction
  • The latest observed 1.92.0 source distribution attempted a Rust build and failed because link.exe was unavailable; this passing run is intentionally pinned to the prior pure-Python 1.91.3 release.
  • Setup or runtime emitted 5 stderr line(s); the complete warnings/errors are preserved in the retained log.
Limitations
  • The pinned 1.91.3 mock-response path does not contact, authenticate to, route among, retry, stream from, or measure any model provider. LiteLLM 1.92.0 was separately probed on this Windows Python 3.11 environment and could not build because the source distribution required an unavailable MSVC linker, so this run does not establish latest-release compatibility.
  • This credential-free disposable workflow does not establish operator use, production scale, model quality, reliability under sustained use, or team adoption.

Pricing assessment: The completion used LiteLLM's explicit mock_response path and no model endpoint, token billing, proxy, or hosted service.

Privacy assessment: The two synthetic messages stayed inside LiteLLM's local mock path; no API key existed and no provider request was made.

Open retained test log →

Verification sources

Longitudinal intelligence

How this decision record is moving

Raw history JSON →

47 dated snapshots retained from 2026-06-18 through 2026-08-13; see the snapshot index for explicit coverage gaps. Stars, version, release, pricing, integration, risk, maintenance, verdict, score, and momentum fields remain explicit even when a source has not reported them. Repository momentum is a normalized 0–10 RepoRadar signal; GitHub stars appear only where the popularity monitor retained exact timestamped observations.

RepoRadar score9.0 current · +0.0 net
Repository momentum9.6 current · +0.6 net
GitHub stars (observed)56,272 current · +2,752 net
GitHub stars56,272 exact observation
Versionv1.98.0-dev.2
Last release2026-08-13T18:00:55Z
Maintenanceactive
Current riskconditional
Current verdicttry now
Pricing baselineNo structured commercial pricing baseline
Pricing checkedNot applicable or not recorded
Pricing freshnessNo dated commercial pricing review
Integrations baselineAnthropic, Azure OpenAI, LangChain, OpenAI

Recent dated points

DateScoreMomentumStarsRiskVerdictMaintenance
2026-08-139.09.656,272conditionaltry nowactive
2026-08-129.09.656,193conditionaltry nowactive
2026-08-119.09.656,095conditionaltry nowactive
2026-08-109.09.656,011conditionaltry nowactive
2026-08-099.09.655,928conditionaltry nowactive
2026-08-089.09.655,856conditionaltry nowactive
2026-08-079.09.655,420conditionaltry nowactive
2026-08-069.09.0Not recordedconditionaltry nownot recorded
2026-08-059.09.0Not recordedconditionaltry nownot recorded
2026-08-049.09.655,420conditionaltry nowactive
2026-08-039.09.655,420conditionaltry nowactive
2026-08-029.09.655,327conditionaltry nowactive

Why the record changed

version changed

Version changed: v1.96.2 → v1.98.0-dev.2.

stars changed

Stars changed: 56193 → 56272.

version changed

Version changed: v1.96.0 → v1.96.2.

stars changed

Stars changed: 56095 → 56193.

version changed

Version changed: v1.97.0-rc.1 → v1.96.0.

stars changed

Stars changed: 56011 → 56095.

stars changed

Stars changed: 55928 → 56011.

stars changed

Stars changed: 55856 → 55928.

version changed

Version changed: v1.94.2 → v1.97.0-rc.1.

version changed

Version changed: v1.95.0-rc.3 → v1.94.2.

stars changed

Stars changed: 55420 → 55856.

stars changed

Stars changed: 55327 → 55420.