AgentToolDrop

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LangGraph

Orchestrates long-running, stateful agents as graphs with durable execution and human-in-the-loop

Install with pip

pip install -U langgraph

Copied from the project's docs at github.com/langchain-ai/langgraph

Why you'd want it

LangGraph is a low-level orchestration layer for agents and workflows that need to persist through failures and resume exactly where they left off. It provides short-term working memory and long-term memory across sessions, and lets a human inspect and modify agent state at any point during execution. It is built by LangChain Inc. but can be used without LangChain, and an equivalent JS/TS library, LangGraph.js, is available.

Best for

  • Long-running stateful agents
  • Workflows with human review steps
  • Teams already using LangChain or LangSmith

Use it when

You want explicit control over an agent's control flow and state, with checkpoints it can resume from after a failure or a human review.

Skip it if

You want a quick agent with planning, subagents and file system use out of the box; the README points to Deep Agents, a higher-level package built on LangGraph, for that.

Related tools

Same category or shared tags, by drop score.

  1. Rank 9

    LangChain

    Framework

    Provides one interface and a large integration library for models, embeddings, vector stores and tools

    • score 81
    • 148k stars
    • pushed
    • langchain-core==1.6.10 on
    • MIT

    Install with pip

    pip install -U langchain
    Use it when, skip it if, stats for LangChain

    Why you'd want it

    LangChain is a framework for building agents and LLM apps from interoperable components, with a large library of integrations for model providers, tools, vector stores and retrievers. Its standard model interface lets you swap models as you experiment without rebuilding the app. An equivalent JS/TS library, LangChain.js, is available, and it pairs with LangGraph for lower-level orchestration and LangSmith for evals and tracing.

    Best for

    • Apps that need many model or data integrations
    • Switching between model providers
    • Prototyping LLM apps in Python or JS

    Use it when

    You want ready-made integrations for models, vector stores and retrievers, and a single interface for switching providers.

    Skip it if

    You need fine-grained control over agent flow and state; the README points to LangGraph for advanced customization and agent orchestration.

    Stats

    Stars
    147,836
    Last push
    Release
    langchain-core==1.6.10,
    License
    MIT
    Pricing
    Open source
    Drop score
    81 of 100

    Full stat block for LangChain

  2. Rank 21

    Pydantic AI

    Framework

    Runs a typed Python agent loop with validated tools and outputs, and models swapped by a string

    • score 65
    • 20.6k stars
    • pushed
    • v2.56.0 on
    • MIT

    Install with uv

    uv add pydantic-ai
    Use it when, skip it if, stats for Pydantic AI

    Why you'd want it

    Pydantic AI gives you an agent whose tools and outputs are typed: tool arguments are validated before your code runs, and a run is guaranteed to return the output type you declared. The same agent can run behind a web frontend, in the terminal, on a voice call, or under durable execution with Temporal, DBOS or Prefect. Pydantic AI Harness adds capabilities such as memory, guardrails, sub-agents, planning and context management, and its instrumentation is plain OpenTelemetry.

    Best for

    • Typed data extraction
    • Python teams already using Pydantic
    • Durable background agents on Temporal or DBOS

    Use it when

    You want type checking and validation across tools and outputs, plus the option to switch model providers without rewriting the agent.

    Skip it if

    You are not working in Python.

    Stats

    Stars
    20,619
    Last push
    Release
    v2.56.0,
    License
    MIT
    Pricing
    Open source
    Drop score
    65 of 100

    Full stat block for Pydantic AI

  3. Rank 25

    CrewAI

    Framework

    Orchestrates teams of role-based agents (Crews) and event-driven workflows (Flows) in Python

    • score 63
    • 59.7k stars
    • pushed
    • 1.15.28 on
    • MIT

    Install the CLI with uv

    uv tool install crewai
    Use it when, skip it if, stats for CrewAI

    Why you'd want it

    CrewAI is a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration. Crews let role-based agents collaborate and delegate tasks autonomously, while Flows give event-driven control with state management and conditional branching, and the two can be combined. The crewai CLI scaffolds a project where agents and tasks are defined in JSONC files and run with crewai run.

    Best for

    • Multi-agent teams with defined roles
    • Business process automation
    • Mixing autonomous agents with deterministic steps

    Use it when

    You want several specialized agents with clear roles and tasks, wrapped in deterministic, event-driven workflows in regular Python.

    Skip it if

    You are not on Python (it requires Python 3.10 to 3.13). It collects anonymous telemetry by default (disable with OTEL_SDK_DISABLED=true), and the managed control plane, CrewAI AMP, is a separate commercial product.

    Stats

    Stars
    59,696
    Last push
    Release
    1.15.28,
    License
    MIT
    Pricing
    Open source
    Drop score
    63 of 100

    Full stat block for CrewAI

For agents

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

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

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