Portkey GatewayNode.js LLM gateway with fallbacks, load balancing and guardrails13.3k starsMITlast commit May 2026
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gptcachePython semantic cache for LLM responses using embeddings and vector search8.2k starsMITlast commit Sep 2026
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evalsPython framework and registry for evaluating LLMs and LLM-based systems19.6k starscustom licenselast commit Apr 2026
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ragasPython library for scoring LLM and RAG application outputs with metrics16.1k starsApache-2.0last commit Feb 2026
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agentopsTracing, session replay and LLM cost tracking for AI agents5.9k starsMITlast commit Jun 2026
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#38
PezzoPrompt management, observability and caching for LLM apps3.3k starsApache-2.0last commit Aug 2026
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#39
CoAIMulti-user chat site plus OpenAI-compatible proxy with billing9.3k starsApache-2.0last commit Mar 2026
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MetaMCPAggregates MCP servers into namespaced endpoints with auth and middleware2.7k starsMITlast commit Jun 2026
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mcpoExposes any MCP server as an OpenAPI HTTP endpoint4.4k starsMITlast commit Feb 2026
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Momentum, Verified build, Docs and Privacy are not measured yet; their weight goes to the signals shown. A dash means the signal is not scored for that kind of project. Hover a number for its rating in words.
LLM gateway routing one OpenAI-compatible endpoint across 372 providers
80.5k starsMITlast commit Oct 2026
OmniRoute is a TypeScript gateway that exposes one endpoint at localhost:20128/v1 and routes requests to 372 providers, including many free tiers, with automatic fallback between them. It accepts OpenAI, Claude, Gemini and Responses API formats, and a dashboard handles keys and free-tier quota tracking. Users bring their own provider accounts; the README's free-token figures are estimates, not a grant.
Strengths
Single /v1 endpoint covers 372 providers with automatic fallback to healthy targets
Dashboard tracks free-tier pools and quota use at /dashboard/free-tiers
Works with coding CLIs such as Claude Code, Codex, Cline and Cursor via one config
Installable via npm, Docker or Electron; MIT licensed; includes MCP and A2A support
Weaknesses
Free-token headline is an estimate; provider limits and terms change and can lower it
Providers flagged tos: avoid are excluded from automatic routing by default
Fresh install may have no eligible route until you connect a provider account
Large surface (372 providers, 19 routing strategies) makes setup and review heavy
no GPU
Docker + Compose
Needs Provider accounts or API keys, Node.js (npm) or Docker
Models: OpenAI API, Claude API, Gemini API, Responses API
Gateway that unifies LLM providers behind OpenAI, Anthropic and Gemini APIs
50.6k starsAGPL-3.0last commit Oct 2026
New API is a Go and Gin gateway that connects upstream providers such as OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock, Vertex AI, DeepSeek and Qwen. It exposes them through OpenAI Chat/Responses, Anthropic Messages and Gemini endpoints. A web console handles channel routing, users, quotas, pricing and usage logs. It runs on SQLite by default, or on MySQL or PostgreSQL with optional Redis.
Strengths
Converts between OpenAI, Anthropic Messages and Gemini request formats via RelayKit
Channel priorities, weights, retries and multiple upstream keys per channel
Per-user quotas, subscriptions, usage logs and expression-based pricing
Starts on SQLite with one container; Compose file adds PostgreSQL and Redis
Weaknesses
AGPL-3.0 with extra terms requiring attribution and a visible project link in modified UIs
Protocol conversion is not exact; tools and fields may not map across protocols
Multi-node setups need shared database, secrets and Redis, or rate limits count per node
Public or resale use brings filing and compliance duties the README leaves to the operator
no GPU
Docker + Compose
Needs SQLite, MySQL >= 5.7.8 or PostgreSQL >= 9.6, Redis (optional), ClickHouse (optional, log database)
Models: OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock
Single mlflow server (port 5000) that records OpenTelemetry traces from 60+ frameworks via one-line autolog, runs evaluations with 50+ metrics and LLM judges, versions and optimizes prompts, and fronts providers through an OpenAI-compatible AI Gateway with rate limits, fallbacks and traffic splitting. Keeps the original experiment tracking, model registry and deployment tooling. For teams wanting one platform for GenAI and ML.
Strengths
One-line autolog for 60+ frameworks in Python, TypeScript and Java; MCP and OTel native
Starts with uvx mlflow server; no separate database needed to begin
AI Gateway adds credential management, guardrails and A/B traffic splitting
Setup wizard lets Claude Code, Codex or OpenCode add tracing to a project
Weaknesses
README covers the quickstart; production backend store and auth setup live in docs
Broad scope (ML tracking plus GenAI) means a large install and UI surface
No Dockerfile or compose file at the repo root
TypeScript and Java coverage is smaller than Python (5 TS and 2 Java frameworks listed)
no GPU
Docker + Compose
Models: any LLM provider via autolog or the AI Gateway
OpenAI-format gateway and Python SDK for calling 100+ LLM providers
61.9k starscustom licenselast commit Oct 2026
LiteLLM translates requests in the OpenAI format to more than 100 LLM providers, and runs either as a Python SDK or as a proxy server. The proxy adds virtual keys, spend tracking, guardrails, load balancing and an admin dashboard. It also fronts A2A agents and MCP servers, and the README cites 8ms P95 latency at 1k RPS.
Strengths
Covers chat, responses, embeddings, images, audio, batches and rerank endpoints across many providers
Works as an in-process Python SDK or a standalone proxy server
Proxy includes virtual keys, spend tracking, guardrails and load balancing
Also fronts MCP servers and A2A agents through the same gateway
Weaknesses
Endpoint support varies per provider; the README table shows many gaps
License is detected as NOASSERTION; check the repo's license files before use
An Enterprise tier exists; the README excerpt does not say which features are paid
Two SDK packages (litellm, litellm-core) overlap and cannot be installed together
OpenAI-compatible gateway pooling free-tier LLM providers with automatic failover
40.5k starsMITlast commit Oct 2026
FreeLLMAPI serves one OpenAI-compatible /v1 endpoint, plus Anthropic Messages, Gemini and opt-in Ollama surfaces, in front of 34 free-tier providers and custom OpenAI-compatible endpoints. A router ranks models, retries on 429/5xx with key rotation, and tracks RPM/RPD/TPM/TPD per key. Provider keys are AES-256-GCM encrypted in SQLite, and a React dashboard manages keys and the fallback chain.
Strengths
Per-key rate counters keep routing under each provider's free-tier caps
Covers chat, embeddings, images, video and speech, plus Anthropic and Gemini wire formats
One-command setup for Claude Code, Codex CLI, Cursor, Aider and other agents
About 40 MB RSS idle on Node 20+, including Raspberry Pi
Weaknesses
Free installs get new models 30 days late; same-day catalog needs $19/yr premium
Catalog updates depend on a signed feed from freellmapi.co
Single-user by design; no multi-user setup described
Quality and limits depend on free tiers that providers change without notice
no GPU
Docker + Compose
Needs Node.js 20+, SQLite, Provider API keys, Docker (optional)
Ingests traces of LLM calls, retrieval and agent steps via Python and JS/TS SDKs or drop-in OpenAI, LangChain, LlamaIndex, LiteLLM and Vercel AI SDK integrations, then adds prompt versioning with caching, LLM-as-a-judge and code evaluators, datasets and a playground. Stores data in ClickHouse; deploys with docker compose, Helm on Kubernetes, or Terraform for AWS, Azure and GCP. For teams debugging and evaluating LLM apps.
Strengths
Public OpenAPI spec, Postman collection and typed Python and JS/TS SDKs
Prompt management with server and client caching adds no request latency
Deployment paths from docker compose to Helm and Terraform templates
Integrations with Dify, Flowise, Langflow, OpenWebUI, LobeChat, CrewAI, smolagents
Weaknesses
MIT except the ee folders; enterprise features need a commercial license
Runs on ClickHouse plus other services; heavier than single-binary tools
Default compose inherits Docker json-file logging with no rotation; disk can fill
No Dockerfile at the repo root; images come from Docker Hub
CLI for evaluating and red-teaming prompts, agents and RAG
26.4k starsMITlast commit Oct 2026
Runs prompt and model evaluations from a YAML config via promptfoo eval, compares providers side by side, and generates red-team vulnerability reports; promptfoo view opens a local web viewer. Installs with npm, Homebrew or pip, runs in CI/CD, and can scan pull requests for LLM security issues, for developers testing prompts and agents before release.
Strengths
Evals run locally; prompts stay on your machine
Red-team scans produce vulnerability reports alongside quality evals
Live reload and caching for fast iteration; npx usage needs no install
MIT licensed and still open source after joining OpenAI
Weaknesses
Primarily a CLI; the web viewer is a local results UI, not a multi-user server
Most providers require an API key; local use needs Ollama or similar
README is short; config syntax, assertions and providers are only in the docs
Dockerfile exists at the root but the README gives no Docker instructions
no GPU
Docker
Needs Node.js (npm) or Python (pip), LLM provider API key or Ollama
Trace, evaluate and monitor LLM apps and agents, Apache-2.0 end to end
22.9k starsApache-2.0last commit Oct 2026
Logs trace trees for LLM calls, tool executions and agent steps via Python and TypeScript SDKs, OpenTelemetry or framework integrations, then runs datasets, experiments and LLM-as-a-judge metrics for hallucination, moderation and RAG quality, with online evaluation rules in production. Self-hosts with ./opik.sh (Docker Compose, UI on port 5173) or a Helm chart. For ML engineers moving agents to production.
Strengths
Full platform (backend, web app, evals, prompt management) under Apache-2.0
Designed for 40M+ traces per day; online evaluation rules on production traffic
PyTest integration gates LLM pipelines in CI
MCP server lets Claude Code, Cursor, Codex or opencode query traces and run evals
Weaknesses
No Dockerfile or compose file at the repo root; install goes through opik.sh
Multi-service stack (databases, caches, backend, frontend); not a single binary
Guardrails and the optimizer are separate profiles and SDKs to enable
README is heavy with Comet Cloud links and UTM tracking
no GPU
Compose
Models: any LLM via SDK, OpenTelemetry or framework integrations (Google ADK, AG2, Autogen, Flowise)
OpenAI-compatible LLM gateway with per-user budgets and traffic-trained routers
11.1k starsApache-2.0last commit Oct 2026
Experiential runs a local gateway that exposes hosted, BYOK and local models through one OpenAI-compatible API, with access rules and spend budgets per user and agent. It can also build a custom router from OpenTelemetry traces and fine-tune an open model on them through Tinker. A hosted platform serves the same API, including Anthropic Messages.
Strengths
Install with pip and start with `exp`; local gateway listens on 127.0.0.1:8000
Per-identity model access and budgets (setup wizard defaults to a $50.00 budget)
Builds a router from OTLP traces and can fine-tune a model from them
OpenAI-compatible gateway routing requests across providers, subscriptions, local models
7.6k starsMITlast commit Oct 2026
Manifest LLM Gateway exposes one OpenAI-compatible endpoint in front of 35 built-in provider connections, 18 subscription flows, local runtimes and custom endpoints. Sending "model": "auto" lets it pick the model. It logs full request bodies for successes and errors, tracks spend with limits and notifications, and falls back to other models when a query fails.
Strengths
Supports 35 provider connections plus any custom /v1/chat/completions or /v1/messages endpoint
Can route through 18 subscription flows (e.g. ChatGPT, Claude, Copilot), not only API keys
Connects local runtimes: Ollama, LM Studio (port 1234), llama.cpp (port 8080)
Full body logs, cost tracking, limits and model fallback on failure
Weaknesses
Self-hosting is Docker-only; the npm-based path is no longer supported
Volume-backed templates are single-instance; scaling out needs S3-compatible storage
Needs a database and recording storage; Postgres appears in the hosted templates
LLM tracing, evaluation and prompt management built on OpenTelemetry
12k starscustom licenselast commit Oct 2026
Phoenix collects OpenTelemetry/OpenInference traces from LLM applications and provides evaluations, versioned datasets, experiments, a prompt playground and prompt version management. It also includes a built-in agent (PXI) and a remote MCP endpoint at /mcp for clients such as Claude Code and Cursor. It installs with pip or uvx, or runs from a Docker image or Helm chart.
Strengths
Auto-instrumentation via OpenInference for OpenAI, Anthropic, LangGraph, LlamaIndex, CrewAI, DSPy and others
Starts with pip install arize-phoenix and phoenix serve, or uvx arize-phoenix serve
Covers tracing, evals, datasets, experiments and prompt versioning in one server
Python and TypeScript packages; Helm chart and cloud deploy templates
Weaknesses
License is reported as NOASSERTION, so terms need checking before commercial use
Managed production workflows are offered through the separate Arize AX product
Standalone stdio MCP server is in maintenance mode, replaced by the built-in remote one
TypeScript evals package is labelled alpha
no GPU
Docker + Compose
Compose runs PostgreSQL
Models: OpenAI, Anthropic, Google GenAI, AWS Bedrock, OpenRouter
Go AI gateway with web UI, fallbacks, budgets and semantic caching
9.1k starsApache-2.0last commit Oct 2026
Bifrost is a Go AI gateway that fronts 23+ providers (OpenAI, Anthropic, Bedrock, Vertex and more) with one OpenAI-compatible API and drop-in paths for the OpenAI, Anthropic and GenAI SDKs. It starts with npx or Docker on port 8080 with a web UI, and adds fallbacks, load balancing, semantic caching, MCP tool access, virtual keys, budgets and Prometheus metrics. Clustering, guardrails and the MCP gateway are enterprise features.
Strengths
Single Go binary via npx or Docker with zero-config web UI on 8080
Drop-in base URLs for OpenAI, Anthropic and Google GenAI SDKs
Virtual keys, team budgets, OIDC provisioning and Prometheus metrics
11 microsecond added latency at 5k RPS in its own benchmark
Weaknesses
Guardrails, clustering, adaptive load balancing and MCP gateway are enterprise-only
Benchmarks are self-reported on t3 instances
23+ providers, fewer than LiteLLM or Portkey
Semantic caching needs a vector store backend
no GPU
Models: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, Groq and more
Local LLM router that picks the cheapest capable model per request
6.6k starsMITlast commit Oct 2026
ClawRouter runs a local proxy on port 8402 with an OpenAI-compatible endpoint. It scores each request across 15 dimensions and picks the cheapest model that can handle it, from about 90 chat models behind BlockRun's gateway. Payment is USDC over x402 on Solana or Base, or a card-funded API key. Seven free models need no signup. It also installs as an OpenClaw plugin.
Strengths
Routing runs locally in under 1 ms, with no external calls for the decision
OpenAI-compatible proxy works with Cursor, continue.dev and the OpenAI SDK
Seven free models usable without signup, key or wallet
Pay by x402 USDC per request or by card-funded API key
Weaknesses
Paid models go through BlockRun's hosted gateway, not directly to each provider
Savings figures come from the vendor's own published workload mix
About 90 chat models, fewer than OpenRouter's listed 200+
Card top-ups cost 5.5% + $0.30; wallet use adds $0.001 per transaction
no GPU
Needs Node.js, BlockRun gateway (blockrun.ai), USDC wallet (Solana or Base) or BlockRun API key
Higress is a cloud-native API gateway built on Istio and Envoy, extended through Wasm plugins written in Go, Rust or JS. For AI use it proxies many LLM providers behind a unified protocol with token rate limiting, caching, load balancing and observability. It can also host remote MCP servers, including ones converted from OpenAPI specs, and doubles as a Kubernetes ingress controller.
Strengths
Wasm plugins in Go/Rust/JS with hot updates, no connection drops on config change
Streams request and response bodies, including SSE, for LLM traffic
Hosts MCP servers with auth, rate limiting and audit logs; OpenAPI-to-MCP conversion
Also a Kubernetes ingress and Gateway API implementation; one-command Docker start
Weaknesses
Heavier to operate than a lightweight LLM proxy; Istio/Envoy concepts apply
Default images are pulled from Alibaba Cloud registries; mirrors needed in some regions
Plugin hub and much of the documentation are hosted on higress.cn
Resource requirements are not stated in the README
no GPU
Docker
Needs Docker or Kubernetes
Models: multiple domestic and international LLM providers via ai-proxy plugin
Pytest-style framework for unit testing LLM apps, RAG pipelines and agents
19.1k starsApache-2.0last commit Oct 2026
DeepEval is a Python framework that runs LLM evaluations as pytest-style tests via `deepeval test run`. It ships metrics such as G-Eval, DAG, task completion, tool correctness, faithfulness and hallucination, scored by an LLM judge of your choice or local NLP models. It supports end-to-end, trace-based and component-level evals, synthetic dataset generation, and standard benchmarks like MMLU and GSM8K.
Strengths
Metrics for RAG, agents, multi-turn chats, MCP and multimodal outputs
Pytest-style tests run in CI with `deepeval test run`
Tracing via @observe supports component-level and agent trajectory evals
Integrations for LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI Agents, Pydantic AI and others
Weaknesses
LLM-judge metrics need a model; the quickstart uses an OpenAI API key
Report sharing and production monitoring sit in the hosted Confident AI platform
README pushes account login; test cases are logged to the cloud once logged in
Judge-based scores depend on the chosen judge model and vary between runs
GPU optional
Needs OpenAI API key or custom LLM judge, Confident AI account (optional)
Models: OpenAI, Anthropic, custom LLMs, local NLP models
LLM tracing, evals, gateway and governance platform for agents and coding assistants
5k starsApache-2.0last commit Oct 2026
LangWatch traces LLM calls and runs agent simulation tests, evaluations and prompt management. It also includes an OpenAI and Anthropic compatible gateway with virtual keys and budgets, plus tracking for coding agents such as Claude Code and Codex. Self-hosting starts with `npx @langwatch/server`, which needs only Node.js.
Strengths
Integrates with LangChain, LangGraph, Vercel AI SDK, CrewAI, DSPy and OpenTelemetry
Tracks coding agents (Claude Code, Codex, Copilot, opencode) with cost per pull request
Gateway offers virtual keys with budgets and one OpenAI and Anthropic compatible endpoint
Self-host quickstart needs only Node.js; SDKs for Python and TypeScript
Weaknesses
Enterprise modules under enterprise/ need a commercial license in production
Broad scope (ops, gateway, governance) makes it heavier than a tracing-only tool
README gives no RAM, database or port requirements for production self-hosting
No Docker or compose file detected in the repository
Agenta is a multi-user workspace where agents are built by chatting, then shared with a team and used in the web app, Slack, Telegram or WhatsApp Business. Agents run on schedules or app events, each session in its own sandbox with a browser and file system, and every run is traced with cost tracking. Integrations go through MCP or Composio (1,000+ apps); models can be hosted APIs or Ollama.
Strengths
Multi-user by design: roles, shared agents and chat sessions, per-agent tool permissions
Each session runs in its own sandbox; model credentials are not exposed to the agent
Every run is traced and costs are tracked per agent, with versions visible
Works with hosted model APIs or self-hosted models via Ollama, plus Claude and ChatGPT subscriptions
Weaknesses
Supports only Claude Code, Pi and Codex harnesses today; more are on the roadmap
Not a visual workflow builder; agents decide steps, so execution is less predictable
Self-hosting requirements (RAM, ports, dependencies) are not given in the README
License reported as NOASSERTION by tooling, though the README states MIT
Needs Composio (optional, for 1,000+ app integrations), Ollama (optional, for self-hosted models)
Models: Hosted model APIs, Ollama, Claude subscription, ChatGPT subscription
Envoy-based data plane that routes, traces and guards agent traffic
7.1k starsApache-2.0last commit Oct 2026
Plano is an Envoy-based proxy for agentic apps: a YAML file declares agents (HTTP servers with an OpenAI chat endpoint), model providers and listeners, and Plano routes each turn to the right agent with its 4B orchestrator model (hosted free or run locally). It also routes LLM calls by model name, alias or preference, captures OpenTelemetry traces with no instrumentation and applies guardrails via filter chains.
Strengths
Declarative multi-agent orchestration; agents are plain OpenAI-compatible HTTP servers
Zero-code OpenTelemetry traces and agentic signals for every request
Filter chains add moderation, jailbreak checks and memory out of process
Model routing by name, alias or preference across providers
Weaknesses
Agent routing depends on Plano's own orchestrator model; hosted by default
Install prerequisites live in external docs; README shows only YAML and curl
Envoy underneath; heavier than a single-binary proxy
No port or resource guidance beyond example listeners
no GPU
Docker
Needs Plano-Orchestrator routing model (hosted or local)
Models: OpenAI, Anthropic and other providers configured as model_providers
Evaluation harness for running agents and models against container-based benchmarks
6.4k starsApache-2.0last commit Oct 2026
Harbor is a Python CLI from the Terminal-Bench team that runs agents such as Claude Code, OpenHands and Codex CLI against benchmark datasets in container environments. It runs locally on Docker or fans out to cloud sandbox providers like Daytona and Modal. It is the official harness for Terminal-Bench 2.0 and can also generate rollouts for RL optimization.
Strengths
Official harness for Terminal-Bench 2.0; SWE-Bench and Aider Polyglot also listed
Same `harbor run` command works on local Docker or cloud providers via `--env`
Parallel runs across sandbox providers including Daytona, Modal, LangSmith and Blaxel
Supports custom benchmarks and environments, plus rollout generation for RL
Weaknesses
Needs model provider API keys and, for scale-out, a sandbox provider account
Local runs depend on Docker; no image or compose file in the repo
README gives no hardware requirements or full list of supported agents
Evaluation only; not an agent builder or observability dashboard
no GPU
Needs Docker, Daytona, Modal
Models: Provider/model strings via --model, e.g. anthropic/...
Python library for evals and red-team scans of LLM agents
5.9k starsApache-2.0last commit Oct 2026
Giskard v3 is a modular Python library for testing agentic systems. giskard-checks provides a scenario API with string, regex, semantic-similarity and LLM-as-judge checks (Groundedness, Conformity, LLMJudge) for multi-turn evals. giskard-scan generates adversarial suites from a plain-language agent description, covering prompt injection, harmful content and misinformation, and adds RAG knowledge-base quality evaluation.
Strengths
Wraps any sync or async callable, so LLMs, black-box agents and pipelines are all testable
Supports multi-turn scenarios and suites, not just single-prompt assertions
Scan generates adversarial tests across OWASP LLM Top-10 categories from a text description
Split into packages, so installs carry only the dependencies needed
Weaknesses
LLM judges and scan generators need a provider extra and API key; default judge is openai/gpt-4o-mini
Requires Python 3.12+
Tabular/ML model scan and Hub exist only in v2, which is no longer actively maintained
Optional telemetry is on unless disabled via environment variable
no GPU
Needs Python 3.12+, LLM provider API key (e.g. OpenAI) for judges and scan generators
Models: OpenAI, Anthropic, other providers via giskard-llm
Registry and proxy federating MCP servers, A2A agents and REST/gRPC APIs
4.7k starsApache-2.0last commit Oct 2026
ContextForge puts MCP servers, A2A agents and REST/gRPC services behind one gateway endpoint with auth, rate limiting, retries and OpenTelemetry tracing. It wraps non-MCP APIs as virtual MCP servers and translates gRPC via reflection. It ships an Admin UI, an optional separate Web UI and 40+ plugins, and runs from PyPI, Docker Compose (PostgreSQL, Redis, Nginx) or a Helm chart.
Strengths
Wraps REST and gRPC (via reflection) services as virtual MCP servers
Exports OpenTelemetry traces to Phoenix, Jaeger, Zipkin and other OTLP backends
Installs from PyPI or GHCR image; Helm chart and Redis-backed federation available
Built-in Admin UI plus 40+ plugins for extra transports and integrations
Weaknesses
Gateway refuses to start without JWT_SECRET_KEY and AUTH_ENCRYPTION_SECRET set
arm64 containers not supported in production; Apple Silicon needs Rosetta or PyPI
Local Docker image build needs a CI-produced wheel closure; must pull from GHCR
Full compose stack runs PostgreSQL, Redis and Nginx, heavy for small setups
OpenTelemetry instrumentations and SDK for tracing LLM calls and vector DBs
7.5k starsApache-2.0last commit Oct 2026
OpenLLMetry is a set of OpenTelemetry extensions that trace calls to LLM providers, vector databases and agent frameworks. The Traceloop SDK is enabled with `Traceloop.init()` after `pip install traceloop-sdk`, and it emits standard OpenTelemetry data that can be exported to backends such as Datadog, Honeycomb, Grafana, SigNoz and Sentry. A separate repo covers JS/TS.
Strengths
Emits standard OpenTelemetry data, so it works with an existing observability stack
Instruments many providers, including OpenAI, Anthropic, Bedrock, Gemini, Ollama and Mistral
Covers vector DBs (Chroma, Pinecone, Qdrant, Weaviate) and frameworks like LangChain and CrewAI
One-line setup with the SDK; instrumentations can also be added individually
Weaknesses
Python only in this repo; JS/TS lives in the separate OpenLLMetry-JS repo
No bundled UI or storage; you need a separate backend to view traces
SDK telemetry was collected in earlier versions; the README says it stops at v0.49.2 and above
No evaluation or prompt management in this repo; the README covers tracing only
no GPU
Needs OpenTelemetry-compatible backend
Models: OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Google Gemini
Agent observability that groups failures and dispatches coding agents to fix them
4.7k starsMITlast commit Oct 2026
Captures traces, sessions and tool calls via a one-line SDK (TypeScript, Python) or OpenTelemetry, groups failing traces into tracked signals, then dispatches Claude Code or Cursor with those traces to open a fix PR and replays fixes against regression datasets. The UI is also reachable from an MCP server and CLI; self-hosts from Docker Hub images via Compose or Helm. For teams operating agents in production.
Strengths
Signals auto-group failing traces with status, size and trend
Agent Dispatch sends sample traces to Claude Code or Cursor via Linear or webhooks
Regression datasets replay fixes against the real failing traces
MIT license; Compose and Helm paths plus Railway one-click
Weaknesses
README quickstart targets the cloud; self-host steps are in external docs
Automatic fixing depends on third-party coding agents and their subscriptions
Claude Code session capture is a separate telemetry package
Storage and service requirements are not stated in the README
no GPU
Docker + Compose
Compose runs PostgreSQL, ClickHouse, Redis
Models: OpenAI, Anthropic, Bedrock, Vercel AI SDK and LangChain apps, any OpenTelemetry source
GoModel is a gateway written in Go that exposes OpenAI-compatible (/v1) and Anthropic-compatible (/v1/messages) endpoints in front of many providers, including OpenAI, Anthropic, Gemini, Bedrock, Ollama and vLLM. It adds exact and semantic caching, cost tracking, budgets, rate limits, failover and an admin dashboard on port 8080. Prompt compression, intelligent routing and OIDC SSO are only in the commercial GoModel Pro build.
Strengths
Accepts OpenAI and Anthropic SDK requests unchanged by only changing the base URL
Covers caching, budgets, rate limits, failover, key rotation and guardrails in one gateway
Exports Prometheus metrics and OpenTelemetry traces, with a built-in dashboard and playground
Public live demo and self-reproducible benchmarks are linked from the README
Weaknesses
Prompt compression, intelligent routing and OIDC SSO require the paid GoModel Pro
Speed and efficiency claims come from the project's own benchmarks
Roadmap points to an upcoming 0.2.0, so the feature set is still changing
Optional Redis, PostgreSQL and MongoDB setups add infrastructure; resource needs are unknown
vLLM Semantic Router sits between an agent harness and several configured model backends (local, private, cloud) and selects or combines models for each call based on a user-defined policy. Clients call one inference endpoint with a public model ID such as `vllm-sr/auto`. Policy can weigh quality, latency and cost, and keep calls within approved locations. Written in Go.
Strengths
One endpoint (vllm-sr/auto) fronts many local, private and cloud backends
Policy can set quality, latency and cost priorities and location limits
Publishes its own routing models on Hugging Face and a hallucination detection endpoint
Hosted playground available; Apache-2.0 with recent commits and releases
Weaknesses
README gives no RAM, GPU or port requirements
Install is a curl-to-bash script; other methods documented only externally
README is mostly news and links; configuration details live on the docs site
Does not run the agent loop or tools; needs a separate agent harness
One proxy for LLM, MCP and A2A traffic with auth and RBAC
5.5k starsApache-2.0last commit Oct 2026
Agentgateway is a Linux Foundation proxy for agent traffic: an LLM gateway (OpenAI-compatible API, budgets, failover), an MCP gateway federating tools over stdio, HTTP, SSE and Streamable HTTP, and an A2A gateway. It adds JWT, API key and OAuth auth, CEL RBAC, rate limits, guardrails and OpenTelemetry, and runs standalone from YAML or as a Kubernetes controller with Gateway API.
Strengths
One proxy for LLM, MCP and A2A traffic with an OpenAI-compatible API
MCP federation over stdio, HTTP, SSE and Streamable HTTP plus OpenAPI tools
JWT, API key and OAuth auth with CEL-based RBAC and rate limits
Standalone YAML mode or Kubernetes controller with Gateway API
Weaknesses
README has no install command, ports or resource needs; quickstart is external
OpenTelemetry-based tracing, evals and guardrails for LLM apps and coding agents
2.9k starsApache-2.0last commit Oct 2026
OpenLIT is a self-hosted dashboard plus Python and TypeScript SDKs that auto-instrument LLM providers, frameworks and vector databases, and export OpenTelemetry GenAI traces and metrics to ClickHouse. It adds cost tracking, LLM-as-a-judge evals, a versioned Prompt Hub, a Rule Engine, Vault for API keys, runtime guardrails, and GPU monitoring. A CLI installs tracing for Claude Code, Cursor, Codex and OpenCode.
Strengths
Follows OpenTelemetry GenAI conventions; OTLP on 4317 (gRPC) and 4318 (HTTP)
One-line openlit.init() instruments 70+ providers, frameworks and vector DBs
Covers evals, prompt versioning, guardrails, secrets and cost in one tool
CLI adds tracing for Claude Code, Cursor, Codex and OpenCode sessions
Weaknesses
Wide scope (Vault, OpenGround, Rule Engine) means more to deploy and learn than a tracer
Storage is ClickHouse, adding a database to operate
Guardrails cover only prompt injection, sensitive topics and topic restriction
Resource requirements are not stated in the README
Observability platform for AI agents with tracing, evals and SQL-queryable traces
3.4k starsApache-2.0last commit Oct 2026
Laminar is a Rust-based observability backend for AI agents. Its OpenTelemetry-native TypeScript and Python SDKs trace calls to OpenAI, Anthropic, Gemini, LangChain, Vercel AI SDK, Browser Use and Stagehand. It adds plain-English signals that alert in Slack, an eval SDK and CLI, dashboards, datasets, and MCP/CLI access for SQL queries over traces.
Strengths
SDKs for TypeScript and Python start tracing with one initialize call
Query traces, spans, metrics and events with SQL, including via MCP and CLI
Signals describe agent behavior in plain English and notify in Slack
Rust backend with 20x trace compression and full-text search over spans
Weaknesses
README recommends the managed platform or the full compose file for production
Chat-with-trace and SQL-with-AI need a separately configured LLM provider
Self-hosted deployments send anonymous telemetry unless disabled by env variable
Self-hosting RAM and disk requirements are not stated
no GPU
Compose
Needs ClickHouse, Postgres, Docker Compose
Models: OpenAI, Anthropic, Gemini, AWS Bedrock, Azure AI Foundry
OptiLLM is a Python proxy that exposes an OpenAI-compatible chat completions endpoint and wraps requests with inference-time techniques such as MoA, MCTS, PlanSearch, CePO and MARS. The technique is chosen by a model-name prefix (e.g. `moa-gpt-4o-mini`), an `extra_body` field or a prompt tag. It forwards to OpenAI, Azure, Cerebras, a local server or LiteLLM providers, and includes plugins for memory, PII anonymization, URL reading, MCP and code execution.
Strengths
Technique chosen per request via model prefix, extra_body or prompt tag; chainable with & and |
Works with any OpenAI-compatible endpoint and LiteLLM providers; clients only change base_url
Plugins for privacy, memory, MCP client, code execution, JSON output and provider failover
Binds to 127.0.0.1 by default; Docker images in full, proxy-only and offline variants
Weaknesses
Techniques like MoA, MCTS and N-sample selection multiply API calls, raising latency and token cost
CoT decoding, entropy decoding and AutoThink are marked not available through the proxy
Accuracy gains are the project's own benchmark claims and vary by model and task
Startup log shows the Flask development server, which the README says not to use in production
GPU optional
Docker + Compose
Needs OpenAI-compatible API or LiteLLM provider, spaCy (privacy plugin), Selenium and Chrome (web_search plugin)
Models: OpenAI, Azure OpenAI, Cerebras, Anthropic, Google Gemini
Future AGI is a Django and Go platform that traces agents over OpenTelemetry, scores outputs with 50+ evaluators, simulates multi-turn conversations, and applies guardrail scanners. It also ships an OpenAI-compatible gateway with routing, caching and virtual keys, plus prompt-optimization algorithms. The default install runs one app container with Postgres and ClickHouse; a distributed Compose setup and a Helm chart cover larger deployments.
Strengths
OTel tracing with instrumentors for 50+ frameworks in Python, TypeScript, Java and C#
Gateway is OpenAI-compatible with 100+ providers, semantic caching and virtual keys
Standalone install is one command and needs 2 vCPUs and 4 GB for Docker
Apache-2.0 core; Compose, production overlay, Helm and air-gapped modes documented
Weaknesses
No supported path to move Standalone data to Distributed or Helm later
Distributed setup needs 4+ vCPUs and 12-16 GB, plus Kafka and PeerDB
Many components (Postgres, ClickHouse, Redis, Temporal) make it heavy to operate
Benchmark figures come from the README; independent verification is unknown
Evidently is a Python library with 100+ built-in metrics for tabular and text data, covering data drift, data quality, classification, regression, ranking, RAG and LLM-as-a-judge evals. Reports can be turned into pass/fail Test Suites for CI checks, and results can be exported as JSON, dict or HTML. A self-hosted monitoring UI (`evidently ui`, localhost:8000) tracks metrics over time.
Strengths
100+ built-in metrics, including 20+ statistical drift tests and LLM-as-a-judge evals
Reports convert to Test Suites with gt/lt conditions, or conditions auto-generated from a reference dataset
Installs with pip or conda; the self-hosted monitoring UI starts with one command
Custom metrics via Python interface; exports to JSON, dict and HTML
Weaknesses
Supports only tabular and text data; no image or audio evals listed
Alerting, dataset and user management, and no-code evals are Cloud-only features
Not a tracing or prompt-management tool; focused on metrics and tests
Self-hosted UI auth and scaling details are not described in the README
Sits as an OpenAI-compatible gateway in front of 100+ models with routing and automatic fallbacks, logging every request with cost, latency and session traces, plus a playground and prompt versioning. Self-hosts via a compose script that runs six services: web app, Jawn log server, Workers proxy, Supabase, ClickHouse and MinIO. For engineers who want observability by swapping an endpoint.
Strengths
One-line integration: point the OpenAI SDK baseURL at the gateway
Async logging path via OpenLLMetry for apps that cannot proxy
Open LLM cost database covering 300+ models; MCP server for data export
Apache-2.0; self-host compose script included
Weaknesses
Six-service stack including Supabase, ClickHouse, MinIO and a Cloudflare Workers proxy
Production Helm chart is enterprise only, by contacting sales
Manual deployment is explicitly not recommended
README quickstart is cloud-first; self-hosting details are in external docs
Node.js LLM gateway with fallbacks, load balancing and guardrails
13.3k starsMITlast commit May 2026
Portkey Gateway is a Node.js proxy that routes requests to 250+ LLM providers through an OpenAI-style API on port 8787, runnable with npx, Docker or Cloudflare Workers. Config objects add retries, fallbacks, load balancing, conditional routing, timeouts and 40+ guardrails; a console at /public shows local logs. Semantic caching, prompt management and RBAC are hosted or enterprise features.
Strengths
Runs with npx in Node.js; 122 KB footprint, sub-millisecond overhead claimed
Fallbacks, retries, load balancing, conditional routing and timeouts via config
40+ built-in guardrails plus bring-your-own
Works with OpenAI, LangChain, LlamaIndex, CrewAI and Autogen SDKs
Weaknesses
Semantic caching, prompt management and provider optimization are hosted or enterprise only
Last commit 2026-05-25; Gateway 2.0 enterprise merge still pre-release
Docs links are portkey.wiki short links
RBAC, PII redaction and compliance features are enterprise
Python semantic cache for LLM responses using embeddings and vector search
8.2k starsMITlast commit Sep 2026
GPTCache is a Python library that caches LLM responses and serves them for exact or semantically similar queries. It embeds each query, looks it up in a vector store, and keeps responses in a SQL cache store. It wraps the OpenAI client and integrates with LangChain and llama_index. The README says no new APIs or models will be added.
Strengths
Modular: swap embedding model, cache store, vector store and similarity evaluator
OpenAI adapter needs a few init lines, no changes to existing calls
Storage options include SQLite, PostgreSQL, MySQL, DynamoDB; vectors in FAISS or Milvus
Temperature setting can skip the cache; sample benchmark reports hit ratio, latency, recall
Weaknesses
README says no support will be added for new LLM APIs or models
Last release 2024-08-01; examples use the older openai.ChatCompletion style
Semantic matching can return false-positive hits; threshold tuning is left to the user
README warns the API may change at any time
no GPU
Needs Python 3.8.1+, SQLite or another SQL database, FAISS or Milvus vector store, OpenAI API key (for OpenAI adapter)
Python framework and registry for evaluating LLMs and LLM-based systems
19.6k starscustom licenselast commit Apr 2026
Runs evals against OpenAI models and LLM-built systems, using a registry of existing evals plus custom ones defined as JSON data and YAML config. Model-graded evals need no evaluation code, and a Completion Function Protocol covers prompt chains and tool-using agents. Results can optionally be logged to Snowflake.
Strengths
Basic and model-graded evals need only JSON data and YAML, no code
Completion Function Protocol supports prompt chains and tool-using agents
Registry of existing evals, with private evals possible on your own data
Optional result logging to Snowflake
Weaknesses
Setup is documented around an OpenAI API key; other providers are not described
Registry data is stored in Git-LFS and must be pulled separately
Not accepting contributed evals with custom code
Known issue: runs can hang after the final report until interrupted
no GPU
Needs OpenAI API key, Git-LFS, Snowflake (optional)
Python library for scoring LLM and RAG application outputs with metrics
16.1k starsApache-2.0last commit Feb 2026
Ragas is a Python package for evaluating LLM applications. It ships pre-built and custom metrics, including LLM-judged ones such as DiscreteMetric, and can generate test datasets when none exist. It integrates with LangChain and observability tools, and a `ragas quickstart rag_eval` command scaffolds an example RAG evaluation project.
Strengths
Installs with pip; quickstart command scaffolds a RAG evaluation project
Custom metrics defined by a prompt and allowed values, scored asynchronously
Generates test datasets when none are available
Apache-2.0 licensed; telemetry can be disabled with RAGAS_DO_NOT_TRACK
Weaknesses
LLM-based metrics need an external model, such as an OpenAI key in the example
Only the rag_eval quickstart template exists; other templates are listed as coming soon
Collects anonymized usage analytics by default; opt-out required
Tracing, session replay and LLM cost tracking for AI agents
5.9k starsMITlast commit Jun 2026
AgentOps is a Python SDK plus a dashboard and API backend that records agent runs as spans, with session replay, execution graphs and per-provider LLM spend. Decorators (@session, @agent, @operation, @task, @workflow) and integrations for OpenAI Agents SDK, CrewAI, AG2, LangChain, Anthropic, Mistral, Cohere and LiteLLM feed the traces. The app is MIT-licensed and can be self-hosted using app/README.md.
Strengths
Two-line init plus span decorators; supports async functions and generators
Prompt management, observability and caching for LLM apps
3.3k starsApache-2.0last commit Aug 2026
Stores and versions prompts, logs requests with cost and latency, and caches LLM responses, exposed through Node.js and Python clients and a LangChain integration. Runs on PostgreSQL, ClickHouse, Redis and SuperTokens via Docker Compose, with a GraphQL API server and a console UI. For small teams that want prompt delivery without code changes.
Strengths
Prompts delivered from the console without redeploying application code
Built-in response caching to cut repeated-call cost and latency
Node.js and Python clients plus LangChain support
Apache-2.0; infra is all open source (PostgreSQL, ClickHouse, Redis, SuperTokens)
Weaknesses
Last commit August 2026 with no release notes in the README
Four backing services for a modest feature set
README is thin; features are shown as screenshots, details only in docs
Multi-user chat site plus OpenAI-compatible proxy with billing
9.3k starsApache-2.0last commit Mar 2026
CoAI pairs a multi-user chat frontend with an OpenAI-compatible API proxy and billing for operators of commercial AI sites. A Go backend on MySQL and Redis routes across channels with priority, weight, retries and model redirection for OpenAI, Anthropic, Gemini, Midjourney, Ollama and more; the React UI adds file parsing, SearXNG search and image generation. Docker Compose serves it on port 8000.
Strengths
Chat UI and OpenAI-compatible proxy in one deployment
Channel priorities, weights, retries and model redirection for routing
Subscription and per-token billing with gift and redemption codes
Midjourney, DALL-E and Stable Diffusion image generation in chat
Weaknesses
Default admin login root / chatnio123456 must be changed after deploy
RAG, TTS/STT, OAuth login and rate limiting are in the paid Pro version
Aggregates MCP servers into namespaced endpoints with auth and middleware
2.7k starsMITlast commit Jun 2026
MetaMCP groups MCP servers into namespaces and publishes each as one MCP endpoint over SSE, Streamable HTTP or OpenAPI, with API-key or OAuth auth, per-tool toggles, name overrides and middleware. It runs with Docker Compose beside PostgreSQL on port 12008, adds OIDC SSO, multi-tenancy and rate limits, and includes an inspector with saved configs. The author reports maintenance delays.
Strengths
Namespaces group servers, toggle tools and override names and annotations
Endpoints over SSE, Streamable HTTP and OpenAPI with API key or MCP OAuth
OIDC SSO, multi-tenancy and registration controls for organizations
Built-in inspector with saved server configs
Weaknesses
Author notes maintenance delays; a community fork exists
Endpoints are remote-only; stdio clients like Claude Desktop need mcp-proxy
Rate-limit counters are in-memory per instance, not cluster-wide
MCP servers needing more than uvx or npx require a custom Dockerfile
Exposes any MCP server as an OpenAPI HTTP endpoint
4.4k starsMITlast commit Feb 2026
mcpo wraps an MCP server command, SSE or Streamable HTTP endpoint and exposes its tools as an OpenAPI REST server on port 8000 with generated docs, so HTTP clients such as Open WebUI can call MCP tools. A Claude Desktop-style config serves several servers under separate routes with hot reload; OAuth 2.1 dynamic client registration handles protected upstreams. Runs via uvx, pip or Docker.
Strengths
One command turns any MCP server into an OpenAPI server with /docs
stdio, SSE and Streamable HTTP upstreams; OAuth 2.1 with dynamic registration
Config file in Claude Desktop format with hot reload
Docker image and --root-path for reverse proxies
Weaknesses
Last commit 2026-02-27
Single shared API key; no users or RBAC
Converts to OpenAPI only; does not aggregate servers into one MCP endpoint