{%- if cookiecutter.project_metadata.license == "MIT" %}
MIT License

Copyright (c) {{ cookiecutter.project_metadata.year }} {{ cookiecutter.project_metadata.author_name }}

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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{%- endif %}

{%- if cookiecutter.project_metadata.license == "Apache-2.0" %}
                               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,
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      "Licensor" shall mean the copyright owner or entity authorized by
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   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

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   Copyright {{ cookiecutter.project_metadata.year }} {{ cookiecutter.project_metadata.author_name }}

   Licensed under the Apache License, Version 2.0 (the "License");
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   You may obtain a copy of the License at

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{%- endif %}

{%- if cookiecutter.project_metadata.license == "GNU" %}
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007

Copyright © 2007 Free Software Foundation, Inc. <https://fsf.org/>

Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

Preamble
The GNU General Public License is a free, copyleft license for software and other kinds of works.

The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.

When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.

To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.

For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.

Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.

For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.

Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.

Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.

The precise terms and conditions for copying, distribution and modification follow.

TERMS AND CONDITIONS
0. Definitions.
“This License” refers to version 3 of the GNU General Public License.

“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.

“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.

To “modify” a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a “modified version” of the earlier work or a work “based on” the earlier work.

A “covered work” means either the unmodified Program or a work based on the Program.

To “propagate” a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well.

To “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.

An interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.

1. Source Code.
The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.

A “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.

The “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.

The “Corresponding Source” for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work's System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work.

The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.

The Corresponding Source for a work in source code form is that same work.

2. Basic Permissions.
All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law.

You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you.

Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary.

3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures.

When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work's users, your or third parties' legal rights to forbid circumvention of technological measures.

4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.

You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.

5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:

a) The work must carry prominent notices stating that you modified it, and giving a relevant date.
b) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to “keep intact all notices”.
c) You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so.
A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an “aggregate” if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.

6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:

a) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b.
d) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d.
A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.

A “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.

“Installation Information” for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.

If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).

The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.

Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.

7. Additional Terms.
“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.

When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.

Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:

a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or
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f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors.
All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.

If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.

Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.

8. Termination.
You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).

However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.

Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.

Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.

9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.

10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.

An “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.

You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.

11. Patents.
A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's “contributor version”.

A contributor's “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.

Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.

In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.

If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.

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Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

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END OF TERMS AND CONDITIONS
{%- endif %}

agentstack/frameworks/templates/autogen/{{cookiecutter.project_metadata.project_slug}}/LICENSE.md


# {{ cookiecutter.project_metadata.project_name }}
{{ cookiecutter.project_metadata.description }}

agentstack/frameworks/templates/autogen/{{cookiecutter.project_metadata.project_slug}}/README.md

The fastest way to build robust AI agents


agentstack/frameworks/templates/crewai/{{cookiecutter.project_metadata.project_slug}}/LICENSE.md


# {{ cookiecutter.project_metadata.project_name }}
{{ cookiecutter.project_metadata.description }}

## How to build your Crew Agent
### With the CLI
Add an agent using AgentStack with the CLI:
`agentstack generate agent <agent_name>`
You can also shorten this to `agentstack g a <agent_name>`
For wizard support use `agentstack g a <agent_name> --wizard`
Finally for creation in the CLI alone, use `agentstack g a <agent_name> --role/-r <role> --goal/-g <goal> --backstory/-b <backstory> --model/-m <provider/model>`

This will automatically create a new agent in the `agents.yaml` config as well as in your code. Either placeholder strings will be used, or data included in the wizard.

Similarly, tasks can be created with `agentstack g t <tool_name>`

Add tools with `agentstack tools add` and view tools available with `agentstack tools list`

## How to use your Agent
In this directory, run `uv pip install --requirements pyproject.toml`

To run your project, use the following command:
`agentstack run`

This will initialize your crew of AI agents and begin task execution as defined in your configuration in the main.py file.

#### Replay Tasks from Latest Crew Kickoff:

CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run:
`crewai replay <task_id>`
Replace <task_id> with the ID of the task you want to replay.

#### Reset Crew Memory
If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
`crewai reset-memory`
This will clear the crew's memory, allowing for a fresh start.

> 🪩 Project built with [AgentStack](https://github.com/AgentOps-AI/AgentStack)

agentstack/frameworks/templates/crewai/{{cookiecutter.project_metadata.project_slug}}/README.md


agentstack/frameworks/templates/langgraph/{{cookiecutter.project_metadata.project_slug}}/LICENSE.md


# {{ cookiecutter.project_metadata.project_name }}
{{ cookiecutter.project_metadata.description }}

## How to build your LangGraph Agent
### With the CLI
Add an agent using AgentStack with the CLI:
`agentstack generate agent <agent_name>`
You can also shorten this to `agentstack g a <agent_name>`
For wizard support use `agentstack g a <agent_name> --wizard`
Finally for creation in the CLI alone, use `agentstack g a <agent_name> --role/-r <role> --goal/-g <goal> --backstory/-b <backstory> --model/-m <provider/model>`

This will automatically create a new agent in the `agents.yaml` config as well as in your code. Either placeholder strings will be used, or data included in the wizard.

Similarly, tasks can be created with `agentstack g t <tool_name>`

Add tools with `agentstack tools add` and view tools available with `agentstack tools list`

## How to use your Agent
In this directory, run `uv pip install --requirements pyproject.toml`

To run your project, use the following command:
`agentstack run`

This will initialize your AI agent project and begin task execution as defined in your configuration in the main.py file.

> 🪩 Project built with [AgentStack](https://github.com/AgentOps-AI/AgentStack)

agentstack/frameworks/templates/langgraph/{{cookiecutter.project_metadata.project_slug}}/README.md


agentstack/frameworks/templates/llamaindex/{{cookiecutter.project_metadata.project_slug}}/LICENSE.md


# {{ cookiecutter.project_metadata.project_name }}
{{ cookiecutter.project_metadata.description }}

## How to build your LlamaIndex Agent
### With the CLI
Add an agent using AgentStack with the CLI:
`agentstack generate agent <agent_name>`
You can also shorten this to `agentstack g a <agent_name>`
For wizard support use `agentstack g a <agent_name> --wizard`
Finally for creation in the CLI alone, use `agentstack g a <agent_name> --role/-r <role> --goal/-g <goal> --backstory/-b <backstory> --model/-m <provider/model>`

This will automatically create a new agent in the `agents.yaml` config as well as in your code. Either placeholder strings will be used, or data included in the wizard.

Similarly, tasks can be created with `agentstack g t <tool_name>`

Add tools with `agentstack tools add` and view tools available with `agentstack tools list`

## How to use your Agent
In this directory, run `uv pip install --requirements pyproject.toml`

To run your project, use the following command:
`agentstack run`

This will initialize your AI agent project and begin task execution as defined in your configuration in the main.py file.

> 🪩 Project built with [AgentStack](https://github.com/AgentOps-AI/AgentStack)

agentstack/frameworks/templates/llamaindex/{{cookiecutter.project_metadata.project_slug}}/README.md


agentstack/frameworks/templates/openai_swarm/{{cookiecutter.project_metadata.project_slug}}/LICENSE.md


# {{ cookiecutter.project_metadata.project_name }}
{{ cookiecutter.project_metadata.description }}

## How to build your OpenAI Swarm Agent
### With the CLI
Add an agent using AgentStack with the CLI:
`agentstack generate agent <agent_name>`
You can also shorten this to `agentstack g a <agent_name>`
For wizard support use `agentstack g a <agent_name> --wizard`
Finally for creation in the CLI alone, use `agentstack g a <agent_name> --role/-r <role> --goal/-g <goal> --backstory/-b <backstory> --model/-m <provider/model>`

This will automatically create a new agent in the `agents.yaml` config as well as in your code. Either placeholder strings will be used, or data included in the wizard.

Similarly, tasks can be created with `agentstack g t <tool_name>`

Add tools with `agentstack tools add` and view tools available with `agentstack tools list`

## How to use your Agent
In this directory, run `uv pip install --requirements pyproject.toml`

To run your project, use the following command:
`agentstack run`

This will initialize your AI agent project and begin task execution as defined in your configuration in the main.py file.

> 🪩 Project built with [AgentStack](https://github.com/AgentOps-AI/AgentStack)

agentstack/frameworks/templates/openai_swarm/{{cookiecutter.project_metadata.project_slug}}/README.md


# Contribute to *OpenR*

> Every contribution, no matter how small, is valuable to the community.

Thank you for your interest in ***OpenR*** ! 🥰 We are deeply committed to the open-source community,
and we welcome contributions from everyone. Your efforts, whether big or small, help us grow and improve.
Contributions aren’t limited to code—answering questions, helping others, enhancing our
documentation, and sharing the project are equally impactful.

## Ways to contribute

There are several ways you can contribute to ***OpenR***:

- Fix existing issues in the codebase.
- Report bugs or suggest new features by submitting issues.
- Implement new models or features.
- Improve our examples or contribute to the documentation.

The best way to do that is to open a **Pull Request** and link it to the **issue** that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over.

## Create a Pull Request

> This guide was heavily inspired by [huggingface guide to contribution](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request)

You will need basic `git` proficiency to contribute to ***OpenR***. While `git` is not the easiest tool to use, it has the greatest
manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro
Git](https://git-scm.com/book/en/v2) is a very good reference.

Follow the steps below to start contributing:

1. Fork the [repository](https://github.com/openreasoner/openr) by
   clicking on the **[Fork](https://github.com/openreasoner/openr/fork)** button on the repository's page. This creates a copy of the code
   under your GitHub user account.

2. Clone your fork to your local disk, and add the base repository as a remote:

   ```bash
   git clone git@github.com:<your Github handle>/openr.git
   cd openr
   git remote add upstream https://github.com/openreasoner/openr.git
   ```

3. Create a new branch to hold your development changes:

   ```bash
   git checkout -b a-descriptive-name-for-my-changes
   ```

   🚨 **Do not** work on the `main` branch!

4. Set up a development environment by following the [README](https://github.com/openreasoner/openr/blob/main/README.md) file.

5. Develop the features in your branch.

   Once you're happy with your changes, add the changed files with `git add` and
   record your changes locally with `git commit`:

   ```bash
   git add modified_file.py
   git commit
   ```

   Please remember to write [good commit
   messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made!

   To keep your copy of the code up to date with the original
   repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer:

   ```bash
   git fetch upstream
   git rebase upstream/main
   ```

   Push your changes to your branch:

   ```bash
   git push -u origin a-descriptive-name-for-my-changes
   ```

   If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally.

6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request.
When you're ready, you can send your changes to the project maintainers for review.

7. It's ok if maintainers request changes, it happens to our core contributors
   too! So everyone can see the changes in the pull request, work in your local
   branch and push the changes to your fork. They will automatically appear in
   the pull request.

CONTRIBUTING.md

OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models


OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

    Paper
    ·
    Tutorial
    ·
    Code
    ·
    Docs
    ·
    Data
    ·
    Model
    ·
    Issue
    ·
    Demo

     [ English ][ 中文 ]

---
[![GitHub contributors](https://img.shields.io/github/contributors/openreasoner/openr)][contributors-url]
[![arXiv](https://img.shields.io/badge/ArXiv-2410.09671-b31b1b.svg)](https://arxiv.org/pdf/2410.09671)
![GitHub License](https://img.shields.io/github/license/openreasoner/openr)
[![GitHub Issues or Pull Requests](https://img.shields.io/github/issues/openreasoner/openr)][issues-url]
[![GitHub forks](https://img.shields.io/github/forks/openreasoner/openr)][forks-url]
[![GitHub Repo stars](https://img.shields.io/github/stars/openreasoner/openr)][stars-url]
[![HuggingFace Dataset](https://img.shields.io/badge/Hugging%20Face-FFD21E?logo=huggingface&logoColor=000)](https://huggingface.co/openreasoner)
[![X](https://img.shields.io/badge/openreasoner-%23000000.svg?logo=X&logoColor=white)](https://x.com/openreasoner)
[![WeChat](https://img.shields.io/badge/WeChat_Group-07C160?logo=wechat&logoColor=white)](#community)

  Table of Contents 📖

News and Updates

Features

TODO

Benchmark

Plots

Datasets and Models

      Getting Started

Installation

Quick Start

Usage

Join Us

Contact

Response Examples

Community

Reference

## News and Updates
- **[29/11/2024]** We have now added a [**demo**](https://www.modelscope.cn/studios/modelscope/OpenR_Inference) page on *ModelScope*. Many thanks to [@wangxingjun778](https://github.com/wangxingjun778) !
- **[24/10/2024]** ***OpenR*** now supports **MCTS** reasoning ([#24](https://github.com/openreasoner/openr/pull/24))! 🌲
- **[15/10/2024]** Our report is on [**Arxiv**](https://arxiv.org/abs/2410.09671)!
- **[12/10/2024]** ***OpenR*** has been released! 🚀

## Features

| Feature                                | Contents                                                                                |
|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ✅ Process-supervision Data Generation | - [**OmegaPRM**](https://arxiv.org/abs/2406.06592): Improve Mathematical Reasoning in Language Models by Automated Process Supervision                                                                                |
| ✅ Online Policy Training              | - [**RL Training**](train/mat/trainers): APPO, GRPO, TPPO;                                                                                |
| ✅ Generative and Discriminative PRM Training | - [**PRM Training**](prm/code): Supervised Training for PRMs
 - **Generative RM Training**: [Direct GenRM](gen_rm/)                                                                                |
| ✅ Multiple Search Strategies          | - **Greedy Search**
 - **Best-of-N**
 - **Beam Search**
 - **MCTS**
 - [**rStar**](https://arxiv.org/pdf/2408.06195v1): Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
 - **Critic-MCTS**: [Under Review](https://github.com/openreasoner/openr/pull/44) |
| ✅ Test-time Computation and Scaling Law | TBA, see [benchmark](#benchmark)                                                                                |

## TODO

| Feature                                 | TODO (High Priority, We value you contribution!)                                                                                |
|-----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 👨💻Data                                    | - Re-implement [**Journey Learning**](https://arxiv.org/abs/2410.18982)                                                                                |
| 👨💻RL Training                             | - Distributed Training
- Reinforcement Fine-Tuning (RFT) [#80](https://github.com/openreasoner/openr/issues/80)                                                                                |
| 👨💻PRM                                     | - Larger-scale training
 - GenRM-CoT implementation
- Soft-label training [#57](https://github.com/openreasoner/openr/issues/57)                                                                                |
| 👨💻Reasoning                               | - Optimize code structure [#53](https://github.com/openreasoner/openr/pull/53)
 - More tasks on reasoning (AIME, etc.) [#53](https://github.com/openreasoner/openr/pull/53)
 - Multi-modal reasoning [#82](https://github.com/openreasoner/openr/issues/82)
 - Reasoning in code generation [#68](https://github.com/openreasoner/openr/pull/68)
 - Dots [#75](https://github.com/openreasoner/openr/pull/75)
 - Consistency check
 - Benchmarking |

## Benchmark

See [Benchmark](benchmark) !

## Plots

## Provided Datasets and Models

[//]: # ([PRM800K](https://github.com/openai/prm800k) (Process Supervision Dataset))

[MATH-APS](https://huggingface.co/datasets/mengfang/MATH-APS) (Our Dataset)

[MATH-psa](https://huggingface.co/openreasoner/Math-psa) (Our Process Reward Model)

## Getting Started

### Installation

```
conda create -n open_reasoner python=3.10
conda activate open_reasoner
pip install -r requirements.txt
pip3 install  "fschat[model_worker,webui]"
pip install -U pydantic
cd envs/MATH/latex2sympy
pip install -e .
cd -
```

### Download Base Models

Before running the project, please ensure that all required base models are downloaded. The models used in this project include:

- `Qwen2.5-Math-1.5B-Instruct`, `Qwen2.5-Math-7B-Instruct`
- `peiyi9979/mistral-7b-sft`
- `peiyi9979/math-shepherd-mistral-7b-prm`

To download these models, please refer to the [Hugging Face model downloading tutorial](https://huggingface.co/docs/hub/models-downloading) for step-by-step guidance on downloading models from the Hugging Face Hub.

Please make sure that all models are saved in their directories according to the project setup before proceeding.

### Quickstart

Before running inference, please modify the following variables in the scripts under `reason/llm_service/` to set the appropriate base models for your usage:

- `$MODEL_BASE`: Set this to the directory where your models are stored.
- `$POLICY_MODEL_NAME`: Set this to the name of the policy model you wish to use.
- `$VALUE_MODEL_NAME`: Set this to the name of the value model you wish to use.
- `$NUM_LM_WORKER`: Set this to the number of language model (LM) workers to start.
- `$NUM_RM_WORKER`: Set this to the number of reward model (RM) workers to start.

Then it prepares and runs inference using different techniques.

#### Start LM & RM Services
For example, to start the LM and RM services for the Math Shepherd model, run the following command:
```bash
sh reason/llm_service/create_service_math_shepherd.sh
```

To kill the server processes, recommend using the following command:
```bash
tmux kill-session -t {Your Session Name} # default is `FastChat`
```

## Usage

#### Run Inference

⚠ Make sure the input (`--LM`, `--RM`) in the script aligns with the variables (`$POLICY_MODEL_NAME`, `$VALUE_MODEL_NAME`) in the pending worker!

```bash
export PYTHONPATH=$(pwd)
sh scripts/eval/cot_greedy.sh

# Method: cot. Average result: ({'majority_vote': 0.734, 'total_completion_tokens': 559.13},)

sh scripts/eval/cot_rerank.sh

# Method: best_of_n. Average result: ({'majority_vote': 0.782,
#                                       'prm_min_max': 0.772,
#                                       'prm_min_vote': 0.792,
#                                       'prm_last_max': 0.776,
#                                       'prm_last_vote': 0.792,
#                                       'total_completion_tokens': 4431.268},)

sh scripts/eval/beam_search.sh

# Method: beam_search. Average result: ({'majority_vote': 0.74, 'total_completion_tokens': 2350.492},)

sh scripts/eval/vanila_mcts.sh

```

#### Run Training

⚠ Before training, please modify the `$dataset_path`, `$model_name_or_path` and `$prm_name_or_path` in `train/mat/scripts/train_llm.sh`.

```bash
cd train/mat/scripts
bash train_llm.sh
```

#### Run PRM Learning

```bash
cd prm/code

\\ single gpu
python finetune_qwen_single_gpu.py --model_path $YOUR_MODEL_PATH \
                                   --train_data_path $TRAIN_DATA_PATH \
                                   --test_data_path $TEST_DATA_PATH

\\ multi gpu
torchrun --nproc_per_node=2 finetune_qwen.py --model_path $YOUR_MODEL_PATH \
                                             --data_path $YOUR_DATA_FOLDER_PATH \
                                             --datasets both \
```

## Join Us

> Every contribution is valuable to the community.

Thank you for your interest in ***OpenR*** ! 🥰 We are deeply committed to the open-source community,
and we welcome contributions from everyone. Your efforts, whether big or small, help us grow and improve.
Contributions aren’t limited to code—answering questions, helping others, enhancing our
documentation, and sharing the project are equally impactful.

Feel free to checkout the [contribution guidance](CONTRIBUTING.md) !

### Future Plan

- Add More Comprehensive Evaluations on RL Training and Search Strategies

- Scaling the Prove-Verifier Model Size

- Support Self-improvement Training

## Contact

The ***OpenR*** community is maintained by:

- **Openreasoner Team** (openreasoner@gmail.com)

## License
OpenR is released under the MIT License.

## Citation

If you do find our resources helpful, please cite our paper:

```
@misc{wang2024tutorial,
  author = {Jun Wang},
  title = {A Tutorial on LLM Reasoning: Relevant Methods Behind ChatGPT o1},
  year = {2024},
  url = {https://github.com/openreasoner/openr/blob/main/reports/tutorial.pdf},
  note = {Available on GitHub}
}
```

```
@article{wang2024openr,
  title={OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models},
  author={Wang, Jun and Fang, Meng and Wan, Ziyu and Wen, Muning and Zhu, Jiachen and Liu, Anjie and Gong, Ziqin and Song, Yan and Chen, Lei and Ni, Lionel M and others},
  journal={arXiv preprint arXiv:2410.09671},
  year={2024}
}
```

## Response Examples

### Comparing PRM, Math-psa (Ours) V.S. Math-Shepherd

### Justifing RL Training

### Exploring Test-time Computation

## Community

**WeChat**:

## Reference

### Inference-time Computing
[1] [Alphazero-like tree-search can guide large language model decoding and training.](https://arxiv.org/pdf/2309.17179)

[2] [Reasoning with language model is planning with world model.](https://arxiv.org/pdf/2305.14992)

[3] [Scaling LLM test-time compute optimally can be more effective than scaling model parameters](https://arxiv.org/pdf/2408.03314?)

[4] [Think before you speak: Training language models with pause tokens](https://arxiv.org/pdf/2310.02226)

### From Outcome Supervision to Process Supervision

[1] [Training verifiers to solve math word problems](https://arxiv.org/pdf/2110.14168)

[2] [Solving math word problems with process-and outcome-based feedback](https://arxiv.org/pdf/2211.14275)

[3] [Let’s verify step by step](https://arxiv.org/pdf/2305.20050)

[4] [Making large language models better reasoners with step-aware verifier](https://arxiv.org/pdf/2206.02336)

[5] [Ovm, outcome-supervised value models for planning in
mathematical reasoning](https://aclanthology.org/2024.findings-naacl.55.pdf)

[6] [Generative verifiers: Reward modeling as next-token prediction](https://arxiv.org/pdf/2408.15240)

### Data Acquisition

[1] [Star: Bootstrapping reasoning with reasoning](https://proceedings.neurips.cc/paper_files/paper/2022/file/639a9a172c044fbb64175b5fad42e9a5-Paper-Conference.pdf)

[2] [Quiet-star: Language models can teach themselves to think before speaking](https://arxiv.org/pdf/2403.09629)

[3] [Improve mathematical reasoning in language models by automated
process supervision](https://arxiv.org/pdf/2406.06592)

[4] [Shepherd: A critic for language model generation](https://arxiv.org/abs/2308.04592)

[5] [Math-shepherd: Verify and reinforce llms step-by-step without human annotations](https://aclanthology.org/2024.acl-long.510.pdf)

[contributors-shield]: https://img.shields.io/github/contributors/openreasoner/openr.svg?style=for-the-badge
[contributors-url]: https://github.com/openreasoner/openr/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/openreasoner/openr.svg?style=for-the-badge
[forks-url]: https://github.com/openreasoner/openr/network/members
[stars-shield]: https://img.shields.io/github/stars/openreasoner/openr.svg?style=for-the-badge
[stars-url]: https://github.com/openreasoner/openr/stargazers
[issues-shield]: https://img.shields.io/github/issues/openreasoner/openr.svg?style=for-the-badge
[issues-url]: https://github.com/openreasoner/openr/issues

[license-shield]: https://img.shields.io/github/license/openreasoner/openr.svg?style=for-the-badge
[license-url]: https://github.com/openreasoner/openr/blob/main/LICENSE.txt

README.md


OpenR: 专注大型语言模型进阶推理能力的开源框架

    技术报告
    ·
    指南
    ·
    代码库
    ·
    文档
    ·
    数据集
    ·
    模型文件
    ·
    问答
    ·
    推理

     [ English ][ 中文 ]

---
[![GitHub contributors](https://img.shields.io/github/contributors/openreasoner/openr)][contributors-url]
[![arXiv](https://img.shields.io/badge/ArXiv-2410.09671-b31b1b.svg)](https://arxiv.org/pdf/2410.09671)
![GitHub License](https://img.shields.io/github/license/openreasoner/openr)
[![GitHub Issues or Pull Requests](https://img.shields.io/github/issues/openreasoner/openr)][issues-url]
[![GitHub forks](https://img.shields.io/github/forks/openreasoner/openr)][forks-url]
[![GitHub Repo stars](https://img.shields.io/github/stars/openreasoner/openr)][stars-url]
[![HuggingFace Dataset](https://img.shields.io/badge/Hugging%20Face-FFD21E?logo=huggingface&logoColor=000)](https://huggingface.co/openreasoner)
[![X](https://img.shields.io/badge/openreasoner-%23000000.svg?logo=X&logoColor=white)](https://x.com/openreasoner)
[![WeChat](https://img.shields.io/badge/WeChat_Group-07C160?logo=wechat&logoColor=white)](#community)

  目录 📖

新闻与更新

功能

TODO

Benchmark

图表

数据集与模型

      快速入门

安装

快速开始

用法

加入我们

联系方式

问答示例

社区

参考引用

## 新闻与更新
- **[29/11/2024]** 已添加 [**demo**](https://www.modelscope.cn/studios/modelscope/OpenR_Inference) 页面至 *ModelScope*. 感谢 [@wangxingjun778](https://github.com/wangxingjun778) !
- **[24/10/2024]** ***OpenR*** 现已支持 **MCTS** 推理 ([#24](https://github.com/openreasoner/openr/pull/24))! 🌲
- **[15/10/2024]** 我们的报告已发布在 [**Arxiv**](https://arxiv.org/abs/2410.09671) 上!
- **[12/10/2024]** ***OpenR*** 已经发布！ 🚀

## 功能

✅ 过程监督的数据生成

✅ 在线策略训练

✅ Generative 和 Discriminative 过程奖励模型的训练

✅ 多种搜索策略

✅ Test-time 计算和 Scaling Law

| 功能                                      | 内容                                                                                |
|-----------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ✅ 过程监督的数据生成                             | - [**OmegaPRM**](https://arxiv.org/abs/2406.06592): Improve Mathematical Reasoning in Language Models by Automated Process Supervision                                                                                |
| ✅ 在线策略训练                                | - **强化学习训练**: 使用PRM进行在线RL训练                                                                                |
| ✅ PRM奖励模型的训练                            | - **PRM 训练**: Supervised Training for PRMs
 - **生成式奖励模型训练**: Direct GenRM                                                                                |
| ✅ 多种搜索策略            | - **Greedy Search**
 - **Best-of-N**
 - **Beam Search**
 - **MCTS**
 - [**rStar**](https://arxiv.org/pdf/2408.06195v1): Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
 - **Critic-MCTS** |
| ✅ Test-time Computation and Scaling Law | 详见 [benchmark](#benchmark)                                                                                |

## TODO

| 功能        | TODO (高优先级, 欢迎加入开发！)                                                                                |
|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 👨💻数据   | - 复现 [**Journey Learning**](https://arxiv.org/abs/2410.18982)                                                                                |
| 👨💻RL训练 | - 分布式训练
- Reinforcement Fine-Tuning (RFT) [#80](https://github.com/openreasoner/openr/issues/80)                                                                                |
| 👨💻PRM  | - 更大规模训练
 - GenRM-CoT 的训练实现
- Soft-label training [#57](https://github.com/openreasoner/openr/issues/57)                                                                                |
| 👨💻推理   | - 优化代码结构 [#53](https://github.com/openreasoner/openr/pull/53)
 - 添加更多推理任务 (AIME, etc.) [#53](https://github.com/openreasoner/openr/pull/53)
 - 多模态推理 [#82](https://github.com/openreasoner/openr/issues/82)
 - 代码生成推理 [#68](https://github.com/openreasoner/openr/pull/68)
 - Dots [#75](https://github.com/openreasoner/openr/pull/75)
 - 推理精度检查
 - Benchmarking |

## Benchmark

详见 [Benchmark](benchmark) !

## 图表

## 数据集与模型

[//]: # ([PRM800K](https://github.com/openai/prm800k) (Process Supervision Dataset))

[MATH-APS](https://huggingface.co/datasets/mengfang/MATH-APS) (我们发布的数据集)

[MATH-psa](https://huggingface.co/openreasoner/Math-psa) (我们发布的过程奖励模型)

## 快速入门

### 安装

```
conda create -n open_reasoner python=3.10
conda activate open_reasoner
pip install -r requirements.txt
pip3 install  "fschat[model_worker,webui]"
pip install -U pydantic
cd envs/MATH/latex2sympy
pip install -e .
cd -
```

### 下载基座模型

在运行项目之前，请确保已下载所有所需的基础模型。本项目使用的模型包括：

- `Qwen2.5-Math-1.5B-Instruct`, `Qwen2.5-Math-7B-Instruct`
- `peiyi9979/mistral-7b-sft`
- `peiyi9979/math-shepherd-mistral-7b-prm`

Huggingface 具体下载方式可参考 [Huggingface 下载教程](https://huggingface.co/docs/hub/models-downloading)

在继续之前，请确保所有模型已根据项目设置保存在各自的目录中。

### 快速开始

在运行推理之前，请修改`reason/llm_service/`目录下脚本中的以下变量，以设置适合您使用的基座模型：

- `$MODEL_BASE`: 设置为存储模型的目录路径。
- `$POLICY_MODEL_NAME`: 设置为您希望使用的策略模型的名称。
- `$VALUE_MODEL_NAME`: 设置为您希望使用的Value模型的名称。
- `$NUM_LM_WORKER`: 设置为要启动的语言模型（LM）进程的数量
- `$NUM_RM_WORKER`: 设置为要启动的奖励模型（RM）进程的数量。

接下来，我们将使用不同的技术运行推理。

#### 启动 LM 和 RM 服务

例如，要启动 Math Shepherd 模型的 LM 和 RM 服务，请运行以下命令：

```bash
sh reason/llm_service/create_service_math_shepherd.sh
```

关闭服务进程可以参考以下命令:
```bash
tmux kill-session -t {Your Session Name} # default is `FastChat`
```

## 用法

#### 运行 推理(Inference)

⚠ 确保脚本中的输入参数(`--LM`, `--RM`)与待运行的进程中的变量(`$POLICY_MODEL_NAME`, `$VALUE_MODEL_NAME`)保持一致！

```bash
export PYTHONPATH=$(pwd)
sh scripts/eval/cot_greedy.sh

# Method: cot. Average result: ({'majority_vote': 0.734, 'total_completion_tokens': 559.13},)

sh scripts/eval/cot_rerank.sh

# Method: best_of_n. Average result: ({'majority_vote': 0.782,
#                                       'prm_min_max': 0.772,
#                                       'prm_min_vote': 0.792,
#                                       'prm_last_max': 0.776,
#                                       'prm_last_vote': 0.792,
#                                       'total_completion_tokens': 4431.268},)

sh scripts/eval/beam_search.sh

# Method: beam_search. Average result: ({'majority_vote': 0.74, 'total_completion_tokens': 2350.492},)

sh scripts/eval/vanila_mcts.sh

```

#### 运行 训练(Training)

⚠ 运行训练之前，请修改 `train/mat/scripts/train_llm.sh` 文件中的 `$dataset_path`, `$model_name_or_path` 和 `$prm_name_or_path` 项。

```bash
cd train/mat/scripts
bash train_llm.sh
```

#### 运行 PRM学习

```bash
cd prm/code

\\ single gpu
python finetune_qwen_single_gpu.py --model_path $YOUR_MODEL_PATH \
                                   --train_data_path $TRAIN_DATA_PATH \
                                   --test_data_path $TEST_DATA_PATH

\\ multi gpu
torchrun --nproc_per_node=2 finetune_qwen.py --model_path $YOUR_MODEL_PATH \
                                             --data_path $YOUR_DATA_FOLDER_PATH \
                                             --datasets both \
```

## 加入我们

> 您的每一份贡献对社区来说都是宝贵的。

感谢您对 ***OpenR*** 的关注！🥰 我们致力于发展开源社区，并十分欢迎大家的contribution。无论大小，您的努力都将帮助我们成长和进步。贡献不仅限于代码——解答问题、帮助他人、改进我们的文档、分享项目同样具有深远的影响。

欢迎查阅 [贡献指南](CONTRIBUTING.md) !

### 未来计划

- 更全面的强化学习训练和搜索方法的实验

- 更大规模的Prove-Verifier模型

- 支持自我提升训练功能

## 联系方式

***OpenR*** 社区由以下团队维护：

- **Openreasoner Team** (openreasoner@gmail.com)

## License

***OpenR*** is released under the MIT License.

## 欢迎引用

如果您觉得我们的资源对您有帮助，请引用我们的论文：

```
@article{wang2024openr,
  title={OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models},
  author={Wang, Jun and Fang, Meng and Wan, Ziyu and Wen, Muning and Zhu, Jiachen and Liu, Anjie and Gong, Ziqin and Song, Yan and Chen, Lei and Ni, Lionel M and others},
  journal={arXiv preprint arXiv:2410.09671},
  year={2024}
}
```
十分感谢！

## 问答示例

### 对比 过程奖励模型（PRM）：Math-psa (Ours) V.S. Math-Shepherd

### 验证强化学习训练 （RL Training）

### 探索 Test-time Computation

## 社区

**微信群聊**:

## 参考引用

### Inference-time Computing
[1] [Alphazero-like tree-search can guide large language model decoding and training.](https://arxiv.org/pdf/2309.17179)

[2] [Reasoning with language model is planning with world model.](https://arxiv.org/pdf/2305.14992)

[3] [Scaling LLM test-time compute optimally can be more effective than scaling model parameters](https://arxiv.org/pdf/2408.03314?)

[4] [Think before you speak: Training language models with pause tokens](https://arxiv.org/pdf/2310.02226)

### From Outcome Supervision to Process Supervision

[1] [Training verifiers to solve math word problems](https://arxiv.org/pdf/2110.14168)

[2] [Solving math word problems with process-and outcome-based feedback](https://arxiv.org/pdf/2211.14275)

[3] [Let’s verify step by step](https://arxiv.org/pdf/2305.20050)

[4] [Making large language models better reasoners with step-aware verifier](https://arxiv.org/pdf/2206.02336)

[5] [Ovm, outcome-supervised value models for planning in
mathematical reasoning](https://aclanthology.org/2024.findings-naacl.55.pdf)

[6] [Generative verifiers: Reward modeling as next-token prediction](https://arxiv.org/pdf/2408.15240)

### Data Acquisition

[1] [Star: Bootstrapping reasoning with reasoning](https://proceedings.neurips.cc/paper_files/paper/2022/file/639a9a172c044fbb64175b5fad42e9a5-Paper-Conference.pdf)

[2] [Quiet-star: Language models can teach themselves to think before speaking](https://arxiv.org/pdf/2403.09629)

[3] [Improve mathematical reasoning in language models by automated
process supervision](https://arxiv.org/pdf/2406.06592)

[4] [Shepherd: A critic for language model generation](https://arxiv.org/abs/2308.04592)

[5] [Math-shepherd: Verify and reinforce llms step-by-step without human annotations](https://aclanthology.org/2024.acl-long.510.pdf)

[contributors-shield]: https://img.shields.io/github/contributors/openreasoner/openr.svg?style=for-the-badge
[contributors-url]: https://github.com/openreasoner/openr/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/openreasoner/openr.svg?style=for-the-badge
[forks-url]: https://github.com/openreasoner/openr/network/members
[stars-shield]: https://img.shields.io/github/stars/openreasoner/openr.svg?style=for-the-badge
[stars-url]: https://github.com/openreasoner/openr/stargazers
[issues-shield]: https://img.shields.io/github/issues/openreasoner/openr.svg?style=for-the-badge
[issues-url]: https://github.com/openreasoner/openr/issues

[license-shield]: https://img.shields.io/github/license/openreasoner/openr.svg?style=for-the-badge
[license-url]: https://github.com/openreasoner/openr/blob/main/LICENSE.txt

README_zh.md


## ***OpenR*** Benchmark Results

Here we present some preliminary results in the following tables.

      Generator
      Reward Model
      Search
      Budget
      MATH

        Qwen2.5-Math-1.5B-Instruct

      Math-Sphered-Mistral-7B-PRM
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Math-psa-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Skywork-o1-PRM-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

        Qwen2.5-Math-7B-Instruct

      Math-Sphered-Mistral-7B-PRM
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Math-psa-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Skywork-o1-PRM-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

        Skywork-o1-Open-Llama-3.1-8B

      Math-Sphered-Mistral-7B-PRM
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Math-psa-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

      Skywork-o1-PRM-7B
      Greedy
      $2^0$

      Majority Vote
      $2^6$

      Best-of-N
      $2^6$

      Beam Search
      $2^6$

      MCTS
      $2^6$

benchmark/tables.md


# Open O1 dev

A simple implementation of data generation of [Improve Mathematical Reasoning in Language Models by Automated Process Supervision](https://arxiv.org/pdf/2406.06592)

## Generating data

Edit the `config.yaml` file to set the model and other parameters. Input json file should be a list of problems and answers with key `problem` and `final_answer`.

```yaml
input:
  json_file_path: 'extracted_problems_and_answers.json'

output:
  file_prefix: 'math'
  log_file_path: 'processing_log.log'

processing:
  initial_rollouts: 20
  num_rollouts: 20
  max_iterations: 100

model:
  # supported model_types: "hf", "openai", "anthropic"
  model_type: "hf"
  model_name: "Qwen/Qwen2.5-Math-7B-Instruct"
  model_args:
    max_tokens: 200
    temperature_range: [0.7, 1.0]
```

```bash
python gen_data.py
```

## Data

Download the [**MATH dataset**](https://people.eecs.berkeley.edu/~hendrycks/MATH.tar) here.

data/README.md


# Open O1 dev

## Environment
```
conda create -n open_reasoner python=3.10
conda activate open_reasoner
pip install -r requirements.txt
pip3 install  "fschat[model_worker,webui]"
```

## Running
`export PYTHONPATH=$(pwd)`
### Start LM & RM Services
Set `NUM_LM_WORKER` and `NUM_RM_WORKER`, as well as the model_path config, in `reason/llm_service/create_service_math_shepherd.sh`
```
sh reason/llm_service/create_service_math_shepherd.sh
```

### Run Inference
```
export PYTHONPATH=$(pwd)
sh scripts/eval/cot_greedy.sh
sh scripts/eval/cot_rerank.sh
sh scripts/eval/beam_search.sh
```

### Run Experiment for me @(anjie)
see `scripts/eval/exp/`, run scripts in it.

reason/README.md


## How to run
Step 1: cd mat/scripts

Step 2: modify the dataset_path, model_name_or_path and prm_name_or_path in train_llm.sh

Step 3: bash train_llm.sh

train/README.md


# OmegaPRM Multi-GPU Runner

This script runs `OmegaPRM` on multiple GPUs, with each GPU handling a different part of the dataset for parallel processing.

## Steps to Use

1. **Split the Input Data**

   Use `process_json.py` to split your input JSON file into multiple parts for each GPU:

   ```bash
   python process_json.py --input_file questions.json --output_dir output_directory --num_splits 8
   ```

   2. **Run the Script**

      Use `run_omegaprm_multi_gpu.sh` to start processing with OmegaPRM on each GPU:
       ``` bash
      run_omegaprm_multi_gpu.sh
       ```
      Results are saved in `output_results`.

      **Note**: Before running, make sure to set the correct values for parameters in the script. Important parameters include:

        -`MODEL_NAME`: Path to the model (e.g., Hugging Face or vLLM model).

        -`MODEL_TYPE`: Set to "hf" for Hugging Face or "vllm" for vLLM support.

        -`Other parameters` like `MAX_NEW_TOKENS`, `TEMPERATURE`, `TOP_K`, and other hyperparameters according to your needs.
## Run on a Single GPU

```bash
CUDA_VISIBLE_DEVICES=0 python run_omegaprm.py \
    --question_file ../extracted_problems_and_answers.json \
    --output_dir output_results \
    --model_name "Your model name or path " \
    --model_type $MODEL_TYPE \
    --device "cuda" \
    --max_new_tokens 2048 \
    --temperature 0.7 \
    --top_k 30 \
    --top_p 0.9 \
    --c_puct 0.125 \
    --alpha 0.5 \
    --beta 0.9 \
    --length_scale 500 \
    --num_rollouts 16 \
    --max_search_count 20 \
    --rollout_budget 200 \
    --save_data_tree True\
    --log_file_prefix "log/omega_prm_single_gpu"

```

# Generated Data Format

For data generated by `OmegaPRM_v2`, two formats are available:

1. Flat Format (`save_data_tree=False`):
   Each entry is structured as:

   { "solution_prefix": [Q, x_1:x_i], "mc_value": 0.5 }

   where `i` is a variable representing the number of reasoning steps. This format provides a linear view of the reasoning process without hierarchical structure.

2. Tree Format (`save_data_tree=True`):
   In this format, data is organized as a tree structure, aligned with the figure presented in the paper. Each reasoning step (or node) includes:

   - text: The cumulative reasoning from the root node up to this specific step.
   - mc_value: The Monte Carlo score computed for the reasoning progression up to this step.
   - children: A list of child nodes branching from the current node.


## Re-implementing rStar MCTS

Original Paper: [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195)
Original Repo: [https://github.com/zhentingqi/rStar](https://github.com/zhentingqi/rStar)

For the current version of rStar, we use a separated environment `rstar_env.py`. In the future,
we hope to rewrite it in a better way.

For inference, set up your LLM and RM, then run `scripts/rstar_mcts.sh`.


# Deploy LMs and RMs via FastChat
The code about model workers are adapted from [FastChat official repo](https://github.com/lm-sys/FastChat).
## Requirements
```
pip3 install "fschat[model_worker,webui]"
sudo apt install -y tmux
```
## Examples
For example run Qwen2.5-Math LMs and PRMs we trained.
Set `NUM_LM_WORKER` and `NUM_RM_WORKER`, as well as the model_path config, see details in `create_service_qwen2.5_math_vllm.sh`
```
sh reason/llm_service/create_service_qwen2.5_math_vllm.sh
```


![Logo](https://picgo-1258602555.cos.ap-nanjing.myqcloud.com/icon.png)

# [latex2sympy2](https://github.com/OrangeX4/latex2sympy)

## About

`latex2sympy2` parses **LaTeX math expressions** and converts it into the equivalent **SymPy form**. The latex2sympy2 is adapted from [augustt198/latex2sympy](https://github.com/augustt198/latex2sympy) and [purdue-tlt / latex2sympy](https://github.com/purdue-tlt/latex2sympy).

This project is a part of a VS Code extension called [Latex Sympy Calculator](https://marketplace.visualstudio.com/items?itemName=OrangeX4.latex-sympy-calculator). It is designed for providing people writing in latex or markdown a ability to calculate something when writing math expression.

[ANTLR](http://www.antlr.org/) is used to generate the parser.

## Features

* **Arithmetic:** Add (+), Sub (-), Dot Mul (·), Cross Mul (×), Frac (/), Power (^), Abs (|x|), Sqrt (√), etc...
* **Alphabet:** a - z, A - Z, α - ω, Subscript (x_1), Accent Bar(ā), etc...
* **Common Functions:** gcd, lcm, floor, ceil, max, min, log, ln, exp, sin, cos, tan, csc, sec, cot, arcsin, sinh, arsinh, etc...
* **Funcion Symbol:** f(x), f(x-1,), g(x,y), etc...
* **Calculous:** Limit ($lim_{n\to\infty}$), Derivation ($\frac{d}{dx}(x^2+x)$), Integration ($\int xdx$), etc...
* **Linear Algebra:** Matrix, Determinant, Transpose, Inverse, Elementary Transformation, etc...
* **Other:** Binomial...

**NOTICE:** It will do some irreversible calculations when converting determinants, transposed matrixes and elementary transformations...

## Installation

```
pip install latex2sympy2
```

**Requirements:** `sympy` and `antlr4-python3-runtime` packages.

## Usage

### Basic

In Python:

```python
from latex2sympy2 import latex2sympy, latex2latex

tex = r"\frac{d}{dx}(x^{2}+x)"
# Or you can use '\mathrm{d}' to replace 'd'
latex2sympy(tex)
# => "Derivative(x**2 + x, x)"
latex2latex(tex)
# => "2 x + 1"
```

### Examples

|LaTeX|Converted SymPy|Calculated Latex|
|-----|-----|---------------|
|`x^{3}` $x^{3}$| `x**3`|`x^{3}` $x^{3}$|
|`\frac{d}{dx} tx` $\frac{d}{dx}tx$|`Derivative(x*t, x)`|`t` $t$|
|`\sum_{i = 1}^{n} i` $\sum_{i = 1}^{n} i$|`Sum(i, (i, 1, n))`|`\frac{n \left(n + 1\right)}{2}` $\frac{n \left(n + 1\right)}{2}$|
|`\int_{a}^{b} \frac{dt}{t}`|`Integral(1/t, (t, a, b))`|`-\log{(a)} + \log{(b)}` $-\log{(a)} + \log{(b)}$|
|`(2x^3 - x + z)|_{x=3}` $(2x^3 - x + z)\|_{x=3}$|`z + 51`| `z + 51` $z + 51$ |

If you want to read the math formula, you can click [GitNotes](https://notes.orangex4.cool/?git=github&github=OrangeX4/latex2sympy).

### Solve Equation

``` latex
# Before
x + y = 1

# After
[ y = 1 - x, \  x = 1 - y]
```

### Eval At

``` latex
# Before
(x+2)|_{x=y+1}

# After
y + 3
```

### Matrix

#### Identity matrix

```
tex = r"\bm{I}_3"
latex2sympy(tex)
# => "Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])"
```

#### Determinant

``` python
from latex2sympy2 import latex2sympy

tex = r"\begin{vmatrix} x & 0 & 0 \\ 0 & x & 0 \\ 0 & 0 & x \end{vmatrix}"
latex2sympy(tex)
# => "x^{3}"
```

#### Transpose

``` python
from latex2sympy2 import latex2sympy

tex = r"\begin{pmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \end{pmatrix}^T"
# Or you can use "\begin{pmatrix}1&2&3\\4&5&6\\7&8&9\end{pmatrix}'"
latex2sympy(tex)
# => "Matrix([[1, 4, 7], [2, 5, 8], [3, 6, 9]])"
```

#### Elementary Transformation

``` python
from latex2sympy2 import latex2sympy

matrix = r'''
    \begin{pmatrix}
        1 & 2 & 3 \\
        4 & 5 & 6 \\
        7 & 8 & 9 \\
    \end{pmatrix}
'''

# Scale the row with grammar "\xrightarrow{kr_n}"
tex = matrix + r'\xrightarrow{3r_1}'
latex2sympy(tex)
# => "Matrix([[3, 6, 9], [4, 5, 6], [7, 8, 9]])"

# Swap the cols with grammar "\xrightarrow{c_1<=>c_2}"
# Of course, you can use "\leftrightarrow" to replace "<=>"
tex = matrix + r'\xrightarrow{c_1<=>c_2}'
latex2sympy(tex)
# => "Matrix([[2, 1, 3], [5, 4, 6], [8, 7, 9]])"

# Scale the second row and add it to the first row
# with grammar "\xrightarrow{r_1+kr_2}"
tex = matrix + r'\xrightarrow{r_1+kr_2}'
latex2sympy(tex)
# => "Matrix([[4*k + 1, 5*k + 2, 6*k + 3], [4, 5, 6], [7, 8, 9]])"

# You can compose the transform with comma ","
# and grammar "\xrightarrow[4r_3]{2r_1, 3r_2}"
# Remember the priority of "{}" is higher than "[]"
tex = matrix + r'\xrightarrow[4r_3]{2r_1, 3r_2}'
latex2sympy(tex)
# => "Matrix([[2, 4, 6], [12, 15, 18], [28, 32, 36]])"
```

### Variances

``` python
from latex2sympy2 import latex2sympy, variances, var, set_variances

# Assign x a value of 1
latex2sympy(r"x = 1")

# Assign x a matrix symbol with dimension of n x m
latex2sympy(r"x \in \mathbb{R}^{n \times m}")

# Calculate x + y
latex2sympy(r"x + y")
# => "y + 1"

# Get all variances
print(variances)
# => "{x: 1}"

# Get variance of "x"
print(var["x"])
# => "1"

# Reset all variances
set_variances({})
latex2sympy(r"x + y")
# => "x + y"
```

### Complex Number Support

``` python
from latex2sympy2 import set_real

set_real(False)
```

## Contributing

If you want to add a new grammar, you can fork the code from [OrangeX4/latex2sympy](https://github.com/OrangeX4/latex2sympy).

* To modify parser grammar, view the existing structure in `PS.g4`.
* To modify the action associated with each grammar, look into `latex2sympy.py`.

Contributors are welcome! Feel free to open a pull request or an issue.