Comparisons / AutoGen vs LangChain
AutoGen vs LangChain: Which Agent Framework to Use?
AutoGen autogen by microsoft models agents as conversableagents that chat with each other. LangChain langchain is the most popular agent framework. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
LangChain
132.3k
21.8k
Python
MIT
2022-10-17
Harrison Chase
Sequoia Capital, Benchmark
$25M Series A (2023), $25M Series B (2024)
3.5M
LangSmith (observability), LangServe (deployment)
Yes
Used by: Notion, Elastic, Instacart
github.com/langchain-ai/langchain →GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | LangChain | Plain Python |
|---|---|---|---|
| Agent | ConversableAgent with system_message, llm_config | AgentExecutor with LLMChain, PromptTemplate, OutputParser | A function with a system prompt that POSTs to the LLM API |
| Tools | register_for_llm() and register_for_execution() | @tool decorator, StructuredTool, BaseTool class hierarchy | A dict of callables + JSON schema descriptions |
| Conversation | Two-agent chat with initiate_chat(), message history | ConversationBufferMemory, ConversationSummaryMemory | A messages array that grows with each turn |
| Multi-Agent | GroupChat with GroupChatManager, speaker selection | — | Multiple agent functions called in sequence on shared messages |
| Nested Chats | register_nested_chats() for sub-task handling | — | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | is_termination_msg callback, max_consecutive_auto_reply | — | The while loop exits when no tool_calls or max_turns reached |
| Agent Loop | — | AgentExecutor.invoke() with internal iteration | A while loop: call LLM, check for tool_calls, execute, repeat |
| State | — | LangGraph state channels with typed reducers | A dict updated inside the loop: state["turns"] += 1 |
| Memory | — | VectorStoreRetrieverMemory, ConversationEntityMemory | A dict injected into the system prompt, saved via a remember() tool |
| Guardrails | — | OutputParser, PydanticOutputParser, custom validators | Two lists of lambda rules checked before and after the LLM call |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both AutoGen and LangChain wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use AutoGen
AutoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler.
What AutoGen does
AutoGen's core abstraction is the ConversableAgent — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. GroupChat extends this to N agents, with a GroupChatManager that selects the next speaker (round-robin, random, or LLM-based selection). Nested chats allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of conversations, not chains or graphs. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A ConversableAgent is a function that takes a messages array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a while loop where you alternate between calling agent_a(messages) and agent_b(messages), appending each response. GroupChat is the same loop but with a speaker selection step — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a tools dict with their JSON schemas. The conversation-as-primitive model is just messages arrays passed between functions.
When to use LangChain
LangChain adds value when you need production integrations (vector stores, specific LLM providers, deployment tooling). But if you want to understand what's happening — or your use case is straightforward — the plain Python version is easier to debug, modify, and reason about.
What LangChain does
LangChain provides a unifying interface across LLM providers, a class hierarchy for tools and memory, and orchestration via AgentExecutor and LangGraph. The core value proposition is interchangeable components: swap OpenAI for Anthropic by changing one class, plug in a vector store for retrieval, add memory without rewriting your loop. It also ships with dozens of integrations — document loaders, text splitters, embedding models, vector stores — that save you from writing boilerplate HTTP calls. For teams that need to compose many integrations quickly, this catalog is genuinely useful. The tradeoff is that you inherit a large dependency tree and a set of abstractions that sit between you and the actual API calls.
The plain Python equivalent
Every LangChain abstraction maps to a small piece of plain Python. AgentExecutor is a while loop that calls the LLM, checks for tool_calls in the response, executes the matching function from a tools dict, appends the result to a messages array, and repeats. Memory is a dict you inject into the system prompt. Output parsing is a function that validates the LLM's response before returning it. The entire agent — tool dispatch, conversation history, state tracking, guardrails — fits in about 60 lines of Python. No base classes, no decorators, no chain composition. Just a function, a dict, a list, and a loop. When something breaks, you read your 60 lines instead of navigating a class hierarchy.
Or build your own in 60 lines
Both AutoGen and LangChain implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
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