From 4c1786146193decd450165b8f54ddcbb39014611 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 13:41:06 +0100 Subject: [PATCH 01/12] Smolagents announcement post --- _blog.yml | 12 +++ smolagents.md | 198 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 210 insertions(+) create mode 100644 smolagents.md diff --git a/_blog.yml b/_blog.yml index 04bcb58b6d..5949100345 100644 --- a/_blog.yml +++ b/_blog.yml @@ -5260,3 +5260,15 @@ - train - memory - gpu + +- local: smolagents + title: "Introducing smolagents: simple agents that write actions in code." + author: m-ric + thumbnail: /blog/assets/smolagents/thumbnail.png + date: December 31, 2024 + tags: + - agents + - open-source + - nlp + - tools + - community \ No newline at end of file diff --git a/smolagents.md b/smolagents.md new file mode 100644 index 0000000000..5cdb55bbe4 --- /dev/null +++ b/smolagents.md @@ -0,0 +1,198 @@ +--- +title: "Introducing smolagents: simple agents that write actions in code." +thumbnail: /blog/assets/beating-gaia/thumbnail.jpeg +authors: + - user: m-ric + - user: merve +--- + +# Introducing `smolagents`, a simple library to build agents + +Today we are launching `smolagents`, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: + +```swift +from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel + +agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) + +agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?") +``` + +[smolagents.mp4](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/smolagents.mp4) + +### 🤔 What are agents? + +Any efficient system using AI will need to provide LLMs some kind of access to the real world: for instance the possibility to call a search tool to get external information, or to act on certain programs in order to solve a task. In other words, LLMs should have ***agency***. Agentic programs are the gateway to the outside world for LLMs. + +AI Agents are *“programs where LLM outputs control the workflow”*. + +Any system leveraging LLMs will integrate the LLM outputs into code. The influence of the LLM's input on the code workflow is the level of agency of LLMs in the system. + +Note that with this definition, "agent" is not a discrete, 0 or 1 definition: instead, "agency" evolves on a continuous spectrum, as you give more or less power to the LLM on your workflow. + +The table below illustrates how agency varies across systems: + +| Agency Level | Description | How that's called | Example Pattern | +| --- | --- | --- | --- | +| ☆☆☆ | LLM output has no impact on program flow | Simple Processor | `process_llm_output(llm_response)` | +| ★☆☆ | LLM output determines basic control flow | Router | `if llm_decision(): path_a() else: path_b()` | +| ★★☆ | LLM output determines function execution | Tool Caller | `run_function(llm_chosen_tool, llm_chosen_args)` | +| ★★★ | LLM output controls iteration and program continuation | Multi-step Agent | `while llm_should_continue(): execute_next_step()` | +| ★★★ | One agentic workflow can start another agentic workflow | Multi-Agent | `if llm_trigger(): execute_agent()` | + +The multi-step agent has this code structure: + +```python +memory = [user_defined_task] +while llm_should_continue(memory): # this loop is the multi-step part + action = llm_get_next_action(memory) # this is the tool-calling part + observations = execute_action(action) + memory += [action, observations] +``` + +So this system runs in a loop, executing a new action at each step (the action can involve calling some pre-determined *tools* that are just functions), until its observations make it apparent that a satisfactory state has been reached to solve the given task. Here’s an example of how a multi-step agent can solve a simple math question: + +
+ +
+ +![image.png](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/image.png) + +### ✅ When to use agents / ⛔ when to avoid them + +Agents are useful when you need an LLM to determine the workflow of an app. But they’re often overkill. The question is: do I really need flexibility in the workflow to efficiently solve the task at hand? + +Let's take an example: say you're making an app that handles customer requests on a surfing trip website. + +You could know in advance that the requests will can belong to either of 2 buckets (based on user choice), and you have a predefined workflow for each of these 2 cases. + +1. Want some knowledge on the trips? ⇒ give them access to a search bar to search your knowledge base +2. Wants to talk to sales? ⇒ let them type in a contact form. + +If that deterministic workflow fits all queries, by all means just code everything! This will give you a 100% reliable system with no risk of error introduced by letting unpredictable LLMs meddle in your workflow. For the sake of simplicity and robustness, it's advised to regularize towards not using any agentic behaviour. + +But what if the workflow can't be determined that well in advance? + +For instance, a user wants to ask : `"I can come on Monday, but I forgot my passport so risk being delayed to Wednesday, is it possible to take me and my stuff to surf on Tuesday morning, with a cancellation insurance?"` This question hinges on many factors, and probably none of the predetermined criteria above will suffice for this request. + +If the pre-determined workflow falls short too often, that means you need more flexibility,. + +That is where an agentic setup helps. + +In the above example, you could just make a multi-step agent that has access to a weather API for weather forecasts, Google Maps API to compute travel distance, an employee availability dashboard and a RAG system on your knowledge base. + +Until recently, computer programs were restricted to pre-determined workflows, trying to handle complexity by piling up if/else switches. They focused on extremely narrow tasks, like "compute the sum of these numbers" or "find the shortest path in this graph". But actually, most real-life tasks, like our trip example above, do not fit in pre-determined workflows. Agentic systems open up the vast world of real-world tasks to programs! + +### Code agents + +In a multi-step agent, at each step, the LLM can write an action, in the form of some calls to external tools. A common format (used by Anthropic, OpenAI, and many others) for writing these actions is generally different shades of "writing actions as a JSON of tools names and arguments to use, which you then parse to know which tool to execute and with which arguments". + +[Multiple](https://huggingface.co/papers/2402.01030) [research](https://huggingface.co/papers/2411.01747) [papers](https://huggingface.co/papers/2401.00812) have shown that having the tool calling LLMs in code is much better. + +The reason for this simply that *we crafted our code languages specifically to be the best possible way to express actions performed by a computer*. If JSON snippets were a better expression, JSON would be the top programming language and programming would be hell on earth. + +The figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030), illustrate some advantages of writing actions in code: + + + +![image.png](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/image%201.png) + +Writing actions in code rather than JSON-like snippets provides better: + +- **Composability:** could you nest JSON actions within each other, or define a set of JSON actions to re-use later, the same way you could just define a python function? +- **Object management:** how do you store the output of an action like `generate_image` in JSON? +- **Generality:** code is built to express simply anything you can have a computer do. +- **Representation in LLM training data:** plenty of quality code actions is already included in LLMs’ training data which means they’re already trained for this! + +### Introducing `smolagents`: making agents simple 🥳 + +We built `smolagents` with these objectives: + +✨ **Simplicity**: the logic for agents fits in ~thousand lines of code (see [this file](https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py)). We kept abstractions to their minimal shape above raw code! + +🧑‍💻 **First-class support for Code Agents**, i.e. agents that write their actions in code (as opposed to "agents being used to write code"), [a](https://www.notion.so/huggingface2/tutorials/secure_code_execution)s explained above. To make it secure, we support executing in sandboxed environments via [E2B](https://e2b.dev/). + +- On top of this `CodeAgent` class, we still support the standard `ToolCallingAgent` that writes actions as JSON/text blobs. + +🤗 **Hub integrations**: you can share and load tools to/from the Hub, and more is to come! + +🌐 **Support for any LLM**: it supports models hosted on the Hub loaded in their `transformers` version or through our inference API, but also models from OpenAI, Anthropic... it's really easy to power an agent with any LLM. + +`smolagents` is the successor to [`transformers.agents`](https://huggingface.co/blog/agents), and will be replacing it as `transformers.agents` will be deprecated in the future. + +### Building an agent + +To build an agent, you need at least two elements: + +- `tools`: a list of that the agent has access to +- `model`: a LLM that will be the engine of your agent. + +For the `model`, you can use any LLM, either open models with our `HfApiModel` class shown in the leopard example above that leverages Hugging Face’s free inference API, or use `LiteLLMModel` leveraging [litellm](https://github.com/BerriAI/litellm) to pick from a list of 100+ different LLMs . + +For the tool, you can just make a function with type hints on inputs and outputs, and docstrings giving descriptions for inputs, and use the `@tool` decorator to make it a tool. + +Here’s how to make a custom tool that gets travel times Google Maps, and uses it into a travel planner agent: + +```python +from typing import Optional +from smolagents import CodeAgent, HfApiModel, tool + +@tool +def get_travel_duration(start_location: str, destination_location: str, departure_time: Optional[int] = None) -> str: + """Gets the travel time in car between two places. + + Args: + start_location: the place from which you start your ride + destination_location: the place of arrival + departure_time: the departure time, provide only a `datetime.datetime` if you want to specify this + """ + import googlemaps + import os + + gmaps = googlemaps.Client(os.getenv("GMAPS_API_KEY")) + + if departure_time is None: + from datetime import datetime + monday = datetime(2025, 1, 6, 11, 0) + + directions_result = gmaps.directions( + start_location, + destination_location, + mode="transit", + departure_time=departure_time + ) + return directions_result[0]["legs"][0]["duration"]["text"] + +agent = CodeAgent(tools=[get_travel_duration], model=HfApiModel(), additional_authorized_imports=["datetime"]) + +agent.run("Can you give me a nice one-day trip around Paris with a few locations and the times? Could be in the city or outside, but should fit in one day. I'm travelling only via public transportation.") +``` + +After thoroughly calculating travel times, the agent returns this final report: + +``` +Out - Final answer: Here's a suggested one-day itinerary for Paris: +Visit Eiffel Tower at 9:00 AM - 10:30 AM +Visit Louvre Museum at 11:00 AM - 12:30 PM +Visit Notre-Dame Cathedral at 1:00 PM - 2:30 PM +Visit Palace of Versailles at 3:30 PM - 5:00 PM +Note: The travel time to the Palace of Versailles is approximately 59 +minutes from Notre-Dame Cathedral, so be sure to plan your day accordingly. +``` + +After building a tool, sharing it to the Hub is as simple as: + +```python +get_travel_duration.push_to_hub("{your_username}/get-travel-duration-tool") +``` + +You can see the result under [this space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool) on my account. Running “submit” won’t work, since the tool needs an API key that’s not on the space. You can check the logic for the tool under the file [tool.py in the space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool/blob/main/tool.py). As you can see, the tool was actually exported to a class inheriting from class `Tool`, which is the underlying structure for all our tools. + +### Next steps 🚀 + +- Start with the [guided tour](https://huggingface.co/docs/smolagents/guided_tour) to familiarize yourself with the library. +- Study more in-depth tutorials to learn more on [tools](https://huggingface.co/docs/smolagents/tutorials/tools) or [general best practices](https://huggingface.co/docs/smolagents/tutorials/building_good_agents). +- Dive into examples to set up specific systems: [text-to-SQL](https://huggingface.co/docs/smolagents/examples/text_to_sql), [agentic RAG](https://huggingface.co/docs/smolagents/examples/rag) or [multi-agent orchestration](https://huggingface.co/docs/smolagents/examples/multiagents). +- Read more on agents: + - [This excellent blog post](https://www.anthropic.com/research/building-effective-agents) by Anthropic gives solid general knowledge. + - [This collection](https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e) gathers the most impactful research papers on agents. \ No newline at end of file From 66a4d34aae7daac234944fb7178ee767867d52b5 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 13:48:08 +0100 Subject: [PATCH 02/12] Fix image links --- smolagents.md | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/smolagents.md b/smolagents.md index 5cdb55bbe4..f71abd10ec 100644 --- a/smolagents.md +++ b/smolagents.md @@ -5,8 +5,7 @@ authors: - user: m-ric - user: merve --- - -# Introducing `smolagents`, a simple library to build agents +# Introducing `smolagents`, a simple library to build agents Today we are launching `smolagents`, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: @@ -18,13 +17,15 @@ agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?") ``` -[smolagents.mp4](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/smolagents.mp4) +
+ +
### 🤔 What are agents? Any efficient system using AI will need to provide LLMs some kind of access to the real world: for instance the possibility to call a search tool to get external information, or to act on certain programs in order to solve a task. In other words, LLMs should have ***agency***. Agentic programs are the gateway to the outside world for LLMs. -AI Agents are *“programs where LLM outputs control the workflow”*. +AI Agents are **programs where LLM outputs control the workflow**. Any system leveraging LLMs will integrate the LLM outputs into code. The influence of the LLM's input on the code workflow is the level of agency of LLMs in the system. @@ -34,9 +35,9 @@ The table below illustrates how agency varies across systems: | Agency Level | Description | How that's called | Example Pattern | | --- | --- | --- | --- | -| ☆☆☆ | LLM output has no impact on program flow | Simple Processor | `process_llm_output(llm_response)` | +| ☆☆☆ | LLM output has no impact on program flow | Simple processor | `process_llm_output(llm_response)` | | ★☆☆ | LLM output determines basic control flow | Router | `if llm_decision(): path_a() else: path_b()` | -| ★★☆ | LLM output determines function execution | Tool Caller | `run_function(llm_chosen_tool, llm_chosen_args)` | +| ★★☆ | LLM output determines function execution | Tool call | `run_function(llm_chosen_tool, llm_chosen_args)` | | ★★★ | LLM output controls iteration and program continuation | Multi-step Agent | `while llm_should_continue(): execute_next_step()` | | ★★★ | One agentic workflow can start another agentic workflow | Multi-Agent | `if llm_trigger(): execute_agent()` | @@ -45,19 +46,17 @@ The multi-step agent has this code structure: ```python memory = [user_defined_task] while llm_should_continue(memory): # this loop is the multi-step part - action = llm_get_next_action(memory) # this is the tool-calling part - observations = execute_action(action) - memory += [action, observations] + action = llm_get_next_action(memory) # this is the tool-calling part + observations = execute_action(action) + memory += [action, observations] ``` So this system runs in a loop, executing a new action at each step (the action can involve calling some pre-determined *tools* that are just functions), until its observations make it apparent that a satisfactory state has been reached to solve the given task. Here’s an example of how a multi-step agent can solve a simple math question:
- +
-![image.png](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/image.png) - ### ✅ When to use agents / ⛔ when to avoid them Agents are useful when you need an LLM to determine the workflow of an app. But they’re often overkill. The question is: do I really need flexibility in the workflow to efficiently solve the task at hand? @@ -93,9 +92,7 @@ The reason for this simply that *we crafted our code languages specifically to b The figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030), illustrate some advantages of writing actions in code: - - -![image.png](Blog%20post%2016c1384ebcac801b9ffef753951f4d8b/image%201.png) + Writing actions in code rather than JSON-like snippets provides better: From 79b926a80f01589a7166cd96d99c44fc2250a414 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 14:02:19 +0100 Subject: [PATCH 03/12] Wtf is even swift --- smolagents.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/smolagents.md b/smolagents.md index f71abd10ec..989bde2174 100644 --- a/smolagents.md +++ b/smolagents.md @@ -9,7 +9,7 @@ authors: Today we are launching `smolagents`, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: -```swift +```python from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) From e5e176e50357cb0ede7ebdc62037948740b44134 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 14:05:02 +0100 Subject: [PATCH 04/12] Formatting changes --- smolagents.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/smolagents.md b/smolagents.md index 989bde2174..c32e38be72 100644 --- a/smolagents.md +++ b/smolagents.md @@ -5,7 +5,7 @@ authors: - user: m-ric - user: merve --- -# Introducing `smolagents`, a simple library to build agents +# Introducing *smolagents*, a simple library to build agents Today we are launching `smolagents`, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: @@ -101,7 +101,7 @@ Writing actions in code rather than JSON-like snippets provides better: - **Generality:** code is built to express simply anything you can have a computer do. - **Representation in LLM training data:** plenty of quality code actions is already included in LLMs’ training data which means they’re already trained for this! -### Introducing `smolagents`: making agents simple 🥳 +### Introducing *smolagents*: making agents simple 🥳 We built `smolagents` with these objectives: From 0d3cf02400c2db5461ea7fe64b4da92e99a3b838 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 14:06:04 +0100 Subject: [PATCH 05/12] Link to smolagents --- smolagents.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/smolagents.md b/smolagents.md index c32e38be72..dc25192c3f 100644 --- a/smolagents.md +++ b/smolagents.md @@ -7,7 +7,7 @@ authors: --- # Introducing *smolagents*, a simple library to build agents -Today we are launching `smolagents`, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: +Today we are launching [smolagents](https://github.com/huggingface/smolagents), a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: ```python from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel From 3f2e20393f7d40851d0c4834782b603c4ab36003 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 14:09:26 +0100 Subject: [PATCH 06/12] Linking --- smolagents.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/smolagents.md b/smolagents.md index dc25192c3f..7b817a9053 100644 --- a/smolagents.md +++ b/smolagents.md @@ -7,7 +7,7 @@ authors: --- # Introducing *smolagents*, a simple library to build agents -Today we are launching [smolagents](https://github.com/huggingface/smolagents), a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: +Today we are launching [`smolagents`](https://github.com/huggingface/smolagents), a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: ```python from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel @@ -103,7 +103,7 @@ Writing actions in code rather than JSON-like snippets provides better: ### Introducing *smolagents*: making agents simple 🥳 -We built `smolagents` with these objectives: +We built [`smolagents`](https://github.com/huggingface/smolagents) with these objectives: ✨ **Simplicity**: the logic for agents fits in ~thousand lines of code (see [this file](https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py)). We kept abstractions to their minimal shape above raw code! @@ -113,9 +113,9 @@ We built `smolagents` with these objectives: 🤗 **Hub integrations**: you can share and load tools to/from the Hub, and more is to come! -🌐 **Support for any LLM**: it supports models hosted on the Hub loaded in their `transformers` version or through our inference API, but also models from OpenAI, Anthropic... it's really easy to power an agent with any LLM. +🌐 **Support for any LLM**: it supports models hosted on the Hub loaded in their `transformers` version or through our inference API, but also supports models from OpenAI, Anthropic and many others via our [LiteLLM](https://www.litellm.ai/) integration. -`smolagents` is the successor to [`transformers.agents`](https://huggingface.co/blog/agents), and will be replacing it as `transformers.agents` will be deprecated in the future. +[`smolagents`](https://github.com/huggingface/smolagents) is the successor to [`transformers.agents`](https://huggingface.co/blog/agents), and will be replacing it as [`transformers.agents`](https://huggingface.co/blog/agents) gets deprecated in the future. ### Building an agent From b7db1053c66424e50d47b1bc2965b89218669887 Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 14:13:12 +0100 Subject: [PATCH 07/12] Linking fixes --- smolagents.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/smolagents.md b/smolagents.md index 7b817a9053..18ea40cbd1 100644 --- a/smolagents.md +++ b/smolagents.md @@ -107,9 +107,9 @@ We built [`smolagents`](https://github.com/huggingface/smolagents) with these ob ✨ **Simplicity**: the logic for agents fits in ~thousand lines of code (see [this file](https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py)). We kept abstractions to their minimal shape above raw code! -🧑‍💻 **First-class support for Code Agents**, i.e. agents that write their actions in code (as opposed to "agents being used to write code"), [a](https://www.notion.so/huggingface2/tutorials/secure_code_execution)s explained above. To make it secure, we support executing in sandboxed environments via [E2B](https://e2b.dev/). +🧑‍💻 **First-class support for Code Agents**, i.e. agents that write their actions in code (as opposed to "agents being used to write code"). To make it secure, we support executing in sandboxed environments via [E2B](https://e2b.dev/). -- On top of this `CodeAgent` class, we still support the standard `ToolCallingAgent` that writes actions as JSON/text blobs. +- On top of this [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) class, we still support the standard [`ToolCallingAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.ToolCallingAgent) that writes actions as JSON/text blobs. 🤗 **Hub integrations**: you can share and load tools to/from the Hub, and more is to come! @@ -143,7 +143,7 @@ def get_travel_duration(start_location: str, destination_location: str, departur destination_location: the place of arrival departure_time: the departure time, provide only a `datetime.datetime` if you want to specify this """ - import googlemaps + import googlemaps # All imports are placed within the function, to allow for sharing to Hub. import os gmaps = googlemaps.Client(os.getenv("GMAPS_API_KEY")) @@ -183,7 +183,7 @@ After building a tool, sharing it to the Hub is as simple as: get_travel_duration.push_to_hub("{your_username}/get-travel-duration-tool") ``` -You can see the result under [this space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool) on my account. Running “submit” won’t work, since the tool needs an API key that’s not on the space. You can check the logic for the tool under the file [tool.py in the space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool/blob/main/tool.py). As you can see, the tool was actually exported to a class inheriting from class `Tool`, which is the underlying structure for all our tools. +You can see the result under [this space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool). CLicking “submit” won’t work, since the tool needs an API key that’s not on the space. You can check the logic for the tool under the file [tool.py in the space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool/blob/main/tool.py). As you can see, the tool was actually exported to a class inheriting from class [`Tool`](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool), which is the underlying structure for all our tools. ### Next steps 🚀 From c419d762680f3a95605e05b4f6a46bd4208cb3da Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 15:56:56 +0100 Subject: [PATCH 08/12] Add benchmark details --- smolagents.md | 44 +++++++++++++++++++++++++++++++++++--------- 1 file changed, 35 insertions(+), 9 deletions(-) diff --git a/smolagents.md b/smolagents.md index 18ea40cbd1..3f4f03c92d 100644 --- a/smolagents.md +++ b/smolagents.md @@ -21,7 +21,21 @@ agent.run("How many seconds would it take for a leopard at full speed to run thr -### 🤔 What are agents? + +## Table of Contents + +- [Introducing *smolagents*, a simple library to build agents](#introducing-smolagents-a-simple-library-to-build-agents) + - [Table of Contents](#table-of-contents) + - [🤔 What are agents?](#what-are-agents) + - [✅ When to use agents / ⛔ when to avoid them](#when-to-use-agents--when-to-avoid-them) + - [Code agents](#code-agents) + - [Introducing *smolagents*: making agents simple 🥳](#introducing-smolagents-making-agents-simple-) + - [Building an agent](#building-an-agent) + - [How strong are open models for agentic workflows?](#how-strong-are-open-models-for-agentic-workflows) + - [Next steps 🚀](#next-steps-) + + +## 🤔 What are agents? Any efficient system using AI will need to provide LLMs some kind of access to the real world: for instance the possibility to call a search tool to get external information, or to act on certain programs in order to solve a task. In other words, LLMs should have ***agency***. Agentic programs are the gateway to the outside world for LLMs. @@ -57,10 +71,10 @@ So this system runs in a loop, executing a new action at each step (the action c -### ✅ When to use agents / ⛔ when to avoid them +## ✅ When to use agents / ⛔ when to avoid them Agents are useful when you need an LLM to determine the workflow of an app. But they’re often overkill. The question is: do I really need flexibility in the workflow to efficiently solve the task at hand? - +If the pre-determined workflow falls short too often, that means you need more flexibility. Let's take an example: say you're making an app that handles customer requests on a surfing trip website. You could know in advance that the requests will can belong to either of 2 buckets (based on user choice), and you have a predefined workflow for each of these 2 cases. @@ -74,7 +88,7 @@ But what if the workflow can't be determined that well in advance? For instance, a user wants to ask : `"I can come on Monday, but I forgot my passport so risk being delayed to Wednesday, is it possible to take me and my stuff to surf on Tuesday morning, with a cancellation insurance?"` This question hinges on many factors, and probably none of the predetermined criteria above will suffice for this request. -If the pre-determined workflow falls short too often, that means you need more flexibility,. +If the pre-determined workflow falls short too often, that means you need more flexibility. That is where an agentic setup helps. @@ -82,7 +96,7 @@ In the above example, you could just make a multi-step agent that has access to Until recently, computer programs were restricted to pre-determined workflows, trying to handle complexity by piling up if/else switches. They focused on extremely narrow tasks, like "compute the sum of these numbers" or "find the shortest path in this graph". But actually, most real-life tasks, like our trip example above, do not fit in pre-determined workflows. Agentic systems open up the vast world of real-world tasks to programs! -### Code agents +## Code agents In a multi-step agent, at each step, the LLM can write an action, in the form of some calls to external tools. A common format (used by Anthropic, OpenAI, and many others) for writing these actions is generally different shades of "writing actions as a JSON of tools names and arguments to use, which you then parse to know which tool to execute and with which arguments". @@ -101,7 +115,7 @@ Writing actions in code rather than JSON-like snippets provides better: - **Generality:** code is built to express simply anything you can have a computer do. - **Representation in LLM training data:** plenty of quality code actions is already included in LLMs’ training data which means they’re already trained for this! -### Introducing *smolagents*: making agents simple 🥳 +## Introducing *smolagents*: making agents simple 🥳 We built [`smolagents`](https://github.com/huggingface/smolagents) with these objectives: @@ -165,7 +179,7 @@ agent = CodeAgent(tools=[get_travel_duration], model=HfApiModel(), additional_au agent.run("Can you give me a nice one-day trip around Paris with a few locations and the times? Could be in the city or outside, but should fit in one day. I'm travelling only via public transportation.") ``` -After thoroughly calculating travel times, the agent returns this final report: +After a few steps of gathering travel times and running calculations, the agent returns this final proposition: ``` Out - Final answer: Here's a suggested one-day itinerary for Paris: @@ -183,9 +197,21 @@ After building a tool, sharing it to the Hub is as simple as: get_travel_duration.push_to_hub("{your_username}/get-travel-duration-tool") ``` -You can see the result under [this space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool). CLicking “submit” won’t work, since the tool needs an API key that’s not on the space. You can check the logic for the tool under the file [tool.py in the space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool/blob/main/tool.py). As you can see, the tool was actually exported to a class inheriting from class [`Tool`](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool), which is the underlying structure for all our tools. +You can see the result under [this space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool). +You can check the logic for the tool under the file [tool.py in the space](https://huggingface.co/spaces/m-ric/get-travel-duration-tool/blob/main/tool.py). As you can see, the tool was actually exported to a class inheriting from class [`Tool`](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool), which is the underlying structure for all our tools. + +### How strong are open models for agentic workflows? + +We've created [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) instances with some leading models, and compared them on [this benchmark](https://huggingface.co/datasets/m-ric/agents_medium_benchmark_2) that gathers questions from a few different benchmarks to propose a varied blend of challenges. +[Find the benchmark here](https://github.com/huggingface/smolagents/blob/main/examples/benchmark.ipynb) for more detail on the agentic setup used, and see a comparison of code agents versus tool calling agents (spoilers: code works better). + +

+ benchmark of different models on agentic workflows +

+ +This comparison shows that open source models can now take on the best closed models! -### Next steps 🚀 +## Next steps 🚀 - Start with the [guided tour](https://huggingface.co/docs/smolagents/guided_tour) to familiarize yourself with the library. - Study more in-depth tutorials to learn more on [tools](https://huggingface.co/docs/smolagents/tutorials/tools) or [general best practices](https://huggingface.co/docs/smolagents/tutorials/building_good_agents). 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From e406aa6ae9ed3373ede8541717808941e8fce07d Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 16:09:39 +0100 Subject: [PATCH 11/12] Add thom in authors --- smolagents.md | 1 + 1 file changed, 1 insertion(+) diff --git a/smolagents.md b/smolagents.md index f0bb871dfe..4eb1eb08ec 100644 --- a/smolagents.md +++ b/smolagents.md @@ -4,6 +4,7 @@ thumbnail: /blog/assets/beating-gaia/thumbnail.jpeg authors: - user: m-ric - user: merve + - user: thomwolf --- # Introducing *smolagents*, a simple library to build agents From 1b67b4042c172f78ca4de1697c7f05838c964b5c Mon Sep 17 00:00:00 2001 From: Aymeric Date: Tue, 31 Dec 2024 16:11:14 +0100 Subject: [PATCH 12/12] Remove ds store --- assets/smolagents/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 assets/smolagents/.DS_Store diff --git a/assets/smolagents/.DS_Store b/assets/smolagents/.DS_Store deleted file mode 100644 index a9b1f9c2c8964113fd0ed488cd854efa72169df3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jx;?w5QX1Bibx14CFPzV5;rhWD5$vr7$iV4go5<$I5xcb0bv;m8VdAAnti+D znces+JXrv?+25W5D*#KnD_(t=n?HA-*+E5&O6MLuo^Xu^3|P;yzYlorF(tR|HL!;`YntznFxr02#A0Ph`{d&c<-exk5V;?fCz}dCjtLHG`ee7 zI;O^_gF{>as0)U}IFGvowRwQrm5!;5&@4--S!#8~uq

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Contents -- [Introducing *smolagents*, a simple library to build agents](#introducing-smolagents-a-simple-library-to-build-agents) - - [Table of Contents](#table-of-contents) - - [🤔 What are agents?](#what-are-agents) - - [✅ When to use agents / ⛔ when to avoid them](#when-to-use-agents--when-to-avoid-them) - - [Code agents](#code-agents) - - [Introducing *smolagents*: making agents simple 🥳](#introducing-smolagents-making-agents-simple-) - - [Building an agent](#building-an-agent) - - [How strong are open models for agentic workflows?](#how-strong-are-open-models-for-agentic-workflows) - - [Next steps 🚀](#next-steps-) +- [🤔 What are agents?](#🤔-what-are-agents) +- [✅ When to use agents / ⛔ when to avoid them](#✅-when-to-use-agents--⛔-when-to-avoid-them) +- [Code agents](#code-agents) +- [Introducing *smolagents*: making agents simple 🥳](#introducing-smolagents-making-agents-simple-🥳) + - [Building an agent](#building-an-agent) + - [How strong are open models for agentic workflows?](#how-strong-are-open-models-for-agentic-workflows) +- [Next steps 🚀](#next-steps-🚀) ## 🤔 What are agents? @@ -203,6 +201,7 @@ You can check the logic for the tool under the file [tool.py in the space](https ### How strong are open models for agentic workflows? We've created [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) instances with some leading models, and compared them on [this benchmark](https://huggingface.co/datasets/m-ric/agents_medium_benchmark_2) that gathers questions from a few different benchmarks to propose a varied blend of challenges. + [Find the benchmark here](https://github.com/huggingface/smolagents/blob/main/examples/benchmark.ipynb) for more detail on the agentic setup used, and see a comparison of code agents versus tool calling agents (spoilers: code works better).