ldp


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SummaryAgent framework for constructing language model agents and training on constructive tasks.
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requires_python>=3.11
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            # ldp

An framework for constructing language model agents and training on constructive tasks.

This repo models agent-environment interactions using a
[Partially Observable Markov Decision Process][pomdp] (POMDP).
Inspired by POMDP, this repo's name `ldp` stands for Language Decision Processes.

[pomdp]: https://en.wikipedia.org/wiki/Partially_observable_Markov_decision_process

## Installation

To install `ldp`:

```bash
pip install -e .
```

If you plan to export Graphviz visualizations,
make sure you also install the `graphviz` library into your OS via:

- Linux: `apt install graphviz`
- macOS: `brew install graphviz`

## Agent

An agent is something that interacts with an environment (defined in our other GitHub repo [Future-House/aviary](https://github.com/Future-House/aviary)).

An agent uses tools in response to observations, which are just natural language observations. An agent has two functions:

```py
agent_state = await agent.init_state(tools=tools)
new_action, new_agent_state, value = await agent.get_asv(agent_state, obs)
```

`get_asv(agent_state, obs)` chooses an action (`a`) conditioned on the observation messages,
and returns the next agent state (`s`) and a value estimate (`v`).
The first argument, `agent_state`, is a state specific for the agent.
The state is outside of the agent so agents are functional, enabling batching across environments.
You can make the state `None` if you aren't using it. It could contain things like memory, as a list of previous observations and actions.

The `obs` are not the complete list of all prior observations, but rather the return of `env.step`.
Usually the state should keep track of these.

Value is the agent's state-action value estimate; it can default to 0.
This is used for training with reinforcement learning.

## Computing Actions

You can just emit actions directly if you want:

```py
from aviary.core import ToolCall


def get_asv(agent_state, obs):
    action = ToolCall.from_name("calculator_tool", x="3 * 2")
    return action, agent_state, 0
```

but likely you want to do something more sophisticated.
Here's how our `SimpleAgent` - which just relies on a single LLM call - works (typing omitted):

```py
from ldp.agent import Agent
from ldp.graph import LLMCallOp


class AgentState:
    def __init__(self, messages, tools):
        self.messages = messages
        self.tools = tools


class SimpleAgent(Agent):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.llm_call_op = LLMCallOp()

    async def init_state(self, tools):
        return AgentState([], tools)

    async def get_asv(self, agent_state, obs):
        action = await self.llm_call_op(
            config={"model": "gpt-4o", "temperature": 0.1},
            msgs=agent_state.messages + obs,
            tools=agent_state.tools,
        )
        new_state = AgentState(
            messages=agent_state.messages + obs + [action], tools=agent_state.tools
        )
        return action, new_state, 0.0
```

Notice how it's pretty simple. We have to do some bookkeeping - namely appending messages as they come and passing tools. There is no magic here.

### Compute Graph

We do have a compute graph - which helps if you want to differentiate with respect to parameters inside your agent (including possibly the LLM). If your compute graph looks like the above example - where all you do is call an LLM directly, then don't worry about this.

If you want to do more complex agents and train them, then read on. Let's start with an example compute graph

```py
from ldp.graph import FxnOp, LLMCallOp, PromptOp, compute_graph

op_a = FxnOp(lambda x: 2 * x)

async with compute_graph():
    op_result = op_a(3)
```

This creates a compute graph and executes it. The compute graph is silly - just doubles the input. The compute graph executions and gradients are saved in a context for later use, like training updates. For example:

```py
print(op_result.compute_grads())
```

Now, inside the `SimpleAgent` example above, you can see some of the compute graph. Let's see a more complex example for an agent that has a memory it can draw upon.

```py
@compute_graph()
async def get_asv(self, agent_state, obs):
    # Update state with new observations
    next_state = agent_state.get_next_state(obs)

    # Retrieve relevant memories
    query = await self._query_factory_op(next_state.messages)
    memories = await self._memory_op(query, matches=self.num_memories)

    # Format memories and package messages
    formatted_memories = await self._format_memory_op(self.memory_prompt, memories)
    memory_prompt = await self._prompt_op(memories=formatted_memories)
    packaged_messages = await self._package_op(
        next_state.messages, memory_prompt=memory_prompt, use_memories=bool(memories)
    )

    # Make LLM call and update state
    config = await self._config_op()
    result = await self._llm_call_op(
        config, msgs=packaged_messages, tools=next_state.tools
    )
    next_state.messages.extend([result])

    return result, next_state, 0.0
```

You can see in this example that we use differentiable ops to ensure there is a connection in the compute graph from the LLM result (action) back to things like the memory retrieval and the query used to retrieve the memory.

Why use a compute graph? Aside from a gradient, using the compute graph enables the tracking of all inputs/outputs to the ops and serialization/deserialization of the compute graph so that you can easily save/load them. The tracking of input/outputs also makes it easier to do things like fine-tuning or reinforcement learning on the underlying LLMs.

## Generic Support

The `Agent` (as well as classes in `agent.ops`)
are [generics](https://en.wikipedia.org/wiki/Generic_programming),
which means:

- `Agent` is designed to support arbitrary types
- Subclasses can exactly specify state types, making the code more readable

If you are new to Python generics (`typing.Generic`),
please read about them in [Python typing](https://docs.python.org/3/library/typing.html#generics).

Below is how to specify an agent with a custom state type.

```py
from dataclasses import dataclass, field
from datetime import datetime

from ldp.agents import Agent


@dataclass
class MyComplexState:
    vector: list[float]
    timestamp: datetime = field(default_factory=datetime.now)


class MyAgent(Agent[MyComplexState]):
    """Some agent who is now type checked to match the custom state."""
```

## Complete Example

```py
from ldp.agent import SimpleAgent
from aviary.env import DummyEnv

env = DummyEnv()
agent = SimpleAgent()

obs, tools = await env.reset()
agent_state = await agent.init_state(tools=tools)

done = False
while not done:
    action, agent_state, _ = await agent.get_asv(agent_state, obs)
    obs, reward, done, truncated = await env.step(action.value)
```

## Tutorial

See a tutorial of building and [running an agent for GSM8K](docs/agent_tutorial.ipynb)

            

Raw data

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    "author_email": "FutureHouse technical staff <hello@futurehouse.org>",
    "download_url": "https://files.pythonhosted.org/packages/89/e9/366ae99956c88a7cd02dddbada029c4497297d3403e64d3b42190de8a166/ldp-0.18.0.tar.gz",
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    "description": "# ldp\n\nAn framework for constructing language model agents and training on constructive tasks.\n\nThis repo models agent-environment interactions using a\n[Partially Observable Markov Decision Process][pomdp] (POMDP).\nInspired by POMDP, this repo's name `ldp` stands for Language Decision Processes.\n\n[pomdp]: https://en.wikipedia.org/wiki/Partially_observable_Markov_decision_process\n\n## Installation\n\nTo install `ldp`:\n\n```bash\npip install -e .\n```\n\nIf you plan to export Graphviz visualizations,\nmake sure you also install the `graphviz` library into your OS via:\n\n- Linux: `apt install graphviz`\n- macOS: `brew install graphviz`\n\n## Agent\n\nAn agent is something that interacts with an environment (defined in our other GitHub repo [Future-House/aviary](https://github.com/Future-House/aviary)).\n\nAn agent uses tools in response to observations, which are just natural language observations. An agent has two functions:\n\n```py\nagent_state = await agent.init_state(tools=tools)\nnew_action, new_agent_state, value = await agent.get_asv(agent_state, obs)\n```\n\n`get_asv(agent_state, obs)` chooses an action (`a`) conditioned on the observation messages,\nand returns the next agent state (`s`) and a value estimate (`v`).\nThe first argument, `agent_state`, is a state specific for the agent.\nThe state is outside of the agent so agents are functional, enabling batching across environments.\nYou can make the state `None` if you aren't using it. It could contain things like memory, as a list of previous observations and actions.\n\nThe `obs` are not the complete list of all prior observations, but rather the return of `env.step`.\nUsually the state should keep track of these.\n\nValue is the agent's state-action value estimate; it can default to 0.\nThis is used for training with reinforcement learning.\n\n## Computing Actions\n\nYou can just emit actions directly if you want:\n\n```py\nfrom aviary.core import ToolCall\n\n\ndef get_asv(agent_state, obs):\n    action = ToolCall.from_name(\"calculator_tool\", x=\"3 * 2\")\n    return action, agent_state, 0\n```\n\nbut likely you want to do something more sophisticated.\nHere's how our `SimpleAgent` - which just relies on a single LLM call - works (typing omitted):\n\n```py\nfrom ldp.agent import Agent\nfrom ldp.graph import LLMCallOp\n\n\nclass AgentState:\n    def __init__(self, messages, tools):\n        self.messages = messages\n        self.tools = tools\n\n\nclass SimpleAgent(Agent):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.llm_call_op = LLMCallOp()\n\n    async def init_state(self, tools):\n        return AgentState([], tools)\n\n    async def get_asv(self, agent_state, obs):\n        action = await self.llm_call_op(\n            config={\"model\": \"gpt-4o\", \"temperature\": 0.1},\n            msgs=agent_state.messages + obs,\n            tools=agent_state.tools,\n        )\n        new_state = AgentState(\n            messages=agent_state.messages + obs + [action], tools=agent_state.tools\n        )\n        return action, new_state, 0.0\n```\n\nNotice how it's pretty simple. We have to do some bookkeeping - namely appending messages as they come and passing tools. There is no magic here.\n\n### Compute Graph\n\nWe do have a compute graph - which helps if you want to differentiate with respect to parameters inside your agent (including possibly the LLM). If your compute graph looks like the above example - where all you do is call an LLM directly, then don't worry about this.\n\nIf you want to do more complex agents and train them, then read on. Let's start with an example compute graph\n\n```py\nfrom ldp.graph import FxnOp, LLMCallOp, PromptOp, compute_graph\n\nop_a = FxnOp(lambda x: 2 * x)\n\nasync with compute_graph():\n    op_result = op_a(3)\n```\n\nThis creates a compute graph and executes it. The compute graph is silly - just doubles the input. The compute graph executions and gradients are saved in a context for later use, like training updates. For example:\n\n```py\nprint(op_result.compute_grads())\n```\n\nNow, inside the `SimpleAgent` example above, you can see some of the compute graph. Let's see a more complex example for an agent that has a memory it can draw upon.\n\n```py\n@compute_graph()\nasync def get_asv(self, agent_state, obs):\n    # Update state with new observations\n    next_state = agent_state.get_next_state(obs)\n\n    # Retrieve relevant memories\n    query = await self._query_factory_op(next_state.messages)\n    memories = await self._memory_op(query, matches=self.num_memories)\n\n    # Format memories and package messages\n    formatted_memories = await self._format_memory_op(self.memory_prompt, memories)\n    memory_prompt = await self._prompt_op(memories=formatted_memories)\n    packaged_messages = await self._package_op(\n        next_state.messages, memory_prompt=memory_prompt, use_memories=bool(memories)\n    )\n\n    # Make LLM call and update state\n    config = await self._config_op()\n    result = await self._llm_call_op(\n        config, msgs=packaged_messages, tools=next_state.tools\n    )\n    next_state.messages.extend([result])\n\n    return result, next_state, 0.0\n```\n\nYou can see in this example that we use differentiable ops to ensure there is a connection in the compute graph from the LLM result (action) back to things like the memory retrieval and the query used to retrieve the memory.\n\nWhy use a compute graph? Aside from a gradient, using the compute graph enables the tracking of all inputs/outputs to the ops and serialization/deserialization of the compute graph so that you can easily save/load them. The tracking of input/outputs also makes it easier to do things like fine-tuning or reinforcement learning on the underlying LLMs.\n\n## Generic Support\n\nThe `Agent` (as well as classes in `agent.ops`)\nare [generics](https://en.wikipedia.org/wiki/Generic_programming),\nwhich means:\n\n- `Agent` is designed to support arbitrary types\n- Subclasses can exactly specify state types, making the code more readable\n\nIf you are new to Python generics (`typing.Generic`),\nplease read about them in [Python typing](https://docs.python.org/3/library/typing.html#generics).\n\nBelow is how to specify an agent with a custom state type.\n\n```py\nfrom dataclasses import dataclass, field\nfrom datetime import datetime\n\nfrom ldp.agents import Agent\n\n\n@dataclass\nclass MyComplexState:\n    vector: list[float]\n    timestamp: datetime = field(default_factory=datetime.now)\n\n\nclass MyAgent(Agent[MyComplexState]):\n    \"\"\"Some agent who is now type checked to match the custom state.\"\"\"\n```\n\n## Complete Example\n\n```py\nfrom ldp.agent import SimpleAgent\nfrom aviary.env import DummyEnv\n\nenv = DummyEnv()\nagent = SimpleAgent()\n\nobs, tools = await env.reset()\nagent_state = await agent.init_state(tools=tools)\n\ndone = False\nwhile not done:\n    action, agent_state, _ = await agent.get_asv(agent_state, obs)\n    obs, reward, done, truncated = await env.step(action.value)\n```\n\n## Tutorial\n\nSee a tutorial of building and [running an agent for GSM8K](docs/agent_tutorial.ipynb)\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\"  \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.  2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:  (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and  (b) You must cause any modified files to carry prominent notices stating that You changed the files; and  (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and  (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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