AgentToolDrop

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LangChain

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

Install with pip

pip install -U langchain

Copied from the project's docs at docs.langchain.com/oss/python/langchain/install

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.

Related tools

Same category or shared tags, by drop score.

  1. Rank 50

    LlamaIndex

    Framework

    Connects LLM agents to your data with connectors, indexes, query engines and event-driven workflows

    • score 49
    • 52.5k stars
    • pushed
    • v0.14.25 on
    • MIT

    Install with pip

    pip install llama-index
    Use it when, skip it if, stats for LlamaIndex

    Why you'd want it

    The LlamaIndex framework is a Python toolkit for context augmentation: data connectors ingest APIs, PDFs, docs and SQL, indexes structure the data, and query and chat engines retrieve context for the model. Agents can call a RAG pipeline as one tool among many, and Workflows add event-driven, multi-step processes with branching, retries and human-in-the-loop review. You can start with the llama-index starter package or install llama-index-core plus only the integrations you need from over 300 packages.

    Best for

    • RAG over private documents
    • Agents that query your own data
    • Pairing with LlamaParse for hard documents

    Use it when

    Your agent's main job is answering questions or taking actions over your own documents and data sources.

    Skip it if

    You want a framework that is its maintainer's main focus: the README says the company's primary focus has shifted to LlamaParse, while the OSS framework stays available.

    Stats

    Stars
    52,500
    Last push
    Release
    v0.14.25,
    License
    MIT
    Pricing
    Open source
    Drop score
    49 of 100

    Full stat block for LlamaIndex

  2. Rank 4

    LangGraph

    Framework

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

    • score 88
    • 43.3k stars
    • pushed
    • cli==0.4.35 on
    • MIT

    Install with pip

    pip install -U langgraph
    Use it when, skip it if, stats for 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.

    Stats

    Stars
    43,281
    Last push
    Release
    cli==0.4.35,
    License
    MIT
    Pricing
    Open source
    Drop score
    88 of 100

    Full stat block for LangGraph

  3. 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

For agents

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

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

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