Organization

Berri AI

@berriai • The fastest way to take your LLM app to production. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.

Portfolio concentration

63%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

30 repos

Visible snapshot

12 repositories updated in the last 90 days.

Leading language

Python

Portfolio mix

Python (13), Unknown (5), Go (3)

Average size

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

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Updated: 2026-04-28(1d ago)GitHub API fallback30 repositories

Portfolio Shape

63%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

stars per repository in this same snapshot.

Current Mix

Python

is the most common language here, with 12 repositories updated in the last 90 days.

Why this rank

This organization stands out because its public portfolio is relatively balanced across 30 repositories.

Balanced portfolio across 30 reposTop 3 share 63%

Organization pages work best when you separate portfolio breadth from flagship concentration. In Berri AI's case, the visible top three repositories account for about 63% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.

The dominant language mix here is Python (13), Unknown (5), Go (3). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#	Repository	Language	⭐ Stars	🍴 Forks	Updated
1	berriai/litellm-pgvector	Python	66	31	4 months ago
2	berriai/terraform-provider-litellm litellm terraform provider	Go	22	9	2 weeks ago
3	berriai/litellm-skills Agent Skills for managing live LiteLLM proxy deployments — users, teams, keys, orgs, models, MCP servers, agents	Shell	15	4	1 months ago
4	berriai/example_litellm_gcp_cloud_run Example Repo to deploy LiteLLM Proxy (AI Gateway) on GCP Cloud Run	Dockerfile	8	31	1 years ago
5	berriai/litellm-security-wg LiteLLM Ecosystem Security Working Group		7	0	3 weeks ago
6	berriai/litellm-guardrails Registry of public custom code guardrails for the litellm proxy server	Svelte	6	2	2 months ago
7	berriai/litellm-ecs-deployment	HCL	6	2	6 months ago
8	berriai/litellm-backstage	TypeScript	6	4	1 years ago
9	berriai/litellm-observatory End-to-end testing suite for LiteLLM deployments - provider tests, performance metrics, and API validation	Python	5	1	1 months ago
10	berriai/example_anthropic_endpoint An example anthropic API Endpoint	Python	4	1	10 months ago
11	berriai/Automated_Perf_Tests	Python	3	1	4 months ago
12	berriai/provider-litellm-http Crossplane Provider designed to facilitate sending LiteLLM HTTP requests as resources.	Go	3	1	1 years ago
13	berriai/litellm-performance-benchmarks A reproducible benchmarking suite for measuring LiteLLM latency, throughput, and scalability under real-world workloads.	Python	2	0	2 months ago
14	berriai/provider-litellm LiteLLM Gateway (Proxy) crossplane provider	Go	2	0	1 years ago
15	berriai/litellm-docs The official LiteLLM docs repository	JavaScript	1	16	Today
16	berriai/fake_custom_provider mock provider for backend eng tests	Python	1	1	1 months ago
17	berriai/mock-token-exchange-server Mock OAuth 2.0 Token Exchange Server (RFC 8693) for testing LiteLLM OBO flow	Python	1	0	2 months ago
18	berriai/mock-oauth2-mcp-server A mock OAuth2 + MCP (Model Context Protocol) server for testing client_credentials flows. Useful for E2E testing LiteLLM proxy MCP OAuth2 M2M authentication.	Python	1	0	2 months ago
19	berriai/serxng-deployment	Dockerfile	1	2	5 months ago
20	berriai/cloudzero-litellm-etl LiteLLM data analysis, transformation and transmission to CloudZero utility		1	0	9 months ago
21	berriai/locust-load-tester	Python	1	1	8 months ago
22	berriai/prometheus-deploy A Blueprint for deploying Prometheus to render.com	Dockerfile	1	0	2 years ago
23	berriai/proxy_load_tester_2		1	1	2 years ago
24	berriai/oss-pr-review-agent Separate PR Review Agent for OSS users	Python	0	0	Yesterday
25	berriai/pr-review-agent-skills Skills for LiteLLM's pr review agent	Python	0	0	Yesterday
26	berriai/LiteLLM-Performance Performance tracking and benchmarks for LiteLLM.		0	0	6 months ago
27	berriai/litellm-aws-installer	TypeScript	0	0	1 years ago
28	berriai/assing_instance_server	Python	0	0	1 years ago
29	berriai/proxy_load_tester_4	Python	0	0	2 years ago
30	berriai/proxy_load_tester_3		0	0	2 years ago

Next step after the organization read

Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.

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Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.

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How to Read This Snapshot

Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.

For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.