AgentToolDrop

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Inspect

Defines and runs model and agent evaluations in Python from datasets, solvers and scorers

Install with pip

pip install inspect-ai

Copied from the project's docs at inspect.aisi.org.uk

Why you'd want it

Inspect, from the UK AI Security Institute and Meridian Labs, builds evaluations from composable datasets, solvers (from a single generate call to multi-turn agents with tools) and scorers, and runs them with inspect eval. It supports custom and MCP tools plus built-in bash, python, web browsing and computer tools, can evaluate external agents like Claude Code, Codex CLI and Gemini CLI, and sandboxes untrusted model code in Docker, Kubernetes, Modal and other systems. It ships over 200 pre-built benchmark evals, support for over 20 model providers, the Inspect View log viewer and a VS Code extension.

Best for

  • Benchmarking models and agents
  • Agentic evals that run code in sandboxes
  • Research and safety evaluation teams

Use it when

You want to measure how a model or agent performs on a task suite, including agentic tasks with tools and sandboxed code, and compare results across providers.

Skip it if

You need tracing of a live production app; Inspect is an evaluation framework, and evals are written in Python.

Related tools

Same category or shared tags, by drop score.

  1. Rank 62

    DeepEval

    Evals

    Unit tests LLM apps and agents in a Pytest-style workflow with LLM-as-a-judge and other metrics

    • score 36
    • 18.9k stars
    • pushed
    • python-v4.2.4 on
    • Apache-2.0

    Install with pip

    pip install -U deepeval
    Use it when, skip it if, stats for DeepEval

    Why you'd want it

    DeepEval works like Pytest for LLM apps: you write test cases and assert them against metrics such as G-Eval, task completion, tool correctness, answer relevancy, faithfulness and hallucination, then run them with deepeval test run. Metrics cover agents, RAG, multi-turn chat, MCP use and multimodal output, can be powered by any LLM, and support both end-to-end and component-level evaluation. It also generates synthetic datasets, runs benchmarks like MMLU and GSM8K, and integrates with LangChain, LangGraph, CrewAI, OpenAI Agents, Pydantic AI and others.

    Best for

    • Python teams that want evals in their test suite
    • RAG and agent metrics
    • CI checks on LLM output quality

    Use it when

    You want LLM quality checks written as tests that run locally or in any CI environment.

    Skip it if

    You are not working in Python (it requires Python 3.9+). Shared reports, iteration comparisons and production monitoring live in the separate Confident AI platform, which needs an account.

    Stats

    Stars
    18,867
    Last push
    Release
    python-v4.2.4,
    License
    Apache-2.0
    Pricing
    Open source
    Drop score
    36 of 100

    Full stat block for DeepEval

  2. Rank 5

    Langfuse

    Evals

    Traces LLM calls and agent steps, then runs evals, datasets and prompt versioning on top of them

    • score 85
    • 35.8k stars
    • pushed
    • v4.59.0 on
    • MIT

    Install the Python SDK

    pip install langfuse
    Use it when, skip it if, stats for Langfuse

    Why you'd want it

    Langfuse instruments an app through Python and JS/TS SDKs or integrations such as the OpenAI SDK, LangChain, LlamaIndex, Vercel AI SDK and Mastra, and records LLM calls, retrieval and agent actions as traces you can inspect. The same platform covers prompt management with server and client caching, datasets for pre-deployment tests, and evaluations via LLM-as-a-judge, code evaluators, user feedback and manual labeling. You can use Langfuse Cloud or self-host it with Docker Compose, on a VM or on Kubernetes via Helm.

    Best for

    • Teams that want to self-host LLM observability
    • Tracing, prompts and evals in one place
    • Python and JS/TS stacks

    Use it when

    You need to see what your LLM app or agent did on each request and want to evaluate and iterate on prompts against the same traces.

    Skip it if

    You do not want to run a server stack yourself and cannot use a hosted account; self-hosting means running the Docker Compose or Kubernetes deployment. Code under the ee/ directories is not MIT licensed.

    Stats

    Stars
    35,832
    Last push
    Release
    v4.59.0,
    License
    MIT
    Pricing
    Open source
    Works with
    Docker
    Drop score
    85 of 100

    Full stat block for Langfuse

  3. Rank 24

    Arize Phoenix

    Evals

    Traces LLM and agent apps over OpenTelemetry and runs evals, datasets and experiments in a local UI

    • score 63
    • 11.8k stars
    • pushed
    • arize-phoenix-v20.21.4 on
    • Elastic License 2.0

    Install with pip

    pip install arize-phoenix
phoenix serve
    Use it when, skip it if, stats for Arize Phoenix

    Why you'd want it

    Phoenix collects traces through OpenTelemetry-based OpenInference instrumentation, with integrations for frameworks such as the OpenAI Agents SDK, Claude Agent SDK, LangGraph and Vercel AI SDK across Python, JavaScript, Java and Go. On top of traces it offers LLM-based evals, versioned datasets, experiments, a prompt playground and prompt management. It runs locally with phoenix serve, in a container or on Kubernetes, and exposes a remote MCP endpoint so Claude Code, Cursor and other MCP clients can query traces, datasets and experiments.

    Best for

    • Local trace debugging during development
    • OpenTelemetry-based stacks
    • Self-hosted eval and experiment tracking

    Use it when

    You want an observability and eval UI on your own machine or infrastructure, instrumented with OpenTelemetry rather than a vendor-specific SDK.

    Skip it if

    You need an OSI open source license or want to offer it as a hosted service; the Elastic License 2.0 forbids that. For a managed platform Arize points to its separate Arize AX product.

    Stats

    Stars
    11,839
    Last push
    Release
    arize-phoenix-v20.21.4,
    License
    Elastic License 2.0
    Pricing
    Free
    Works with
    Docker
    Drop score
    63 of 100

    Full stat block for Arize Phoenix

For agents

The same entry, as files an agent can read without scraping this page. 

# read one entry
curl -s https://agenttooldrop.com/tools/inspect-ai.md

# list the MCP servers in the catalog
curl -s https://agenttooldrop.com/tools.json \
  | jq -r '.tools[] | select(.category=="mcp-server") | .slug'