Comparison
lagent vs ReAct
Verdict
Pick lagent if lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents; pick ReAct if reAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3.
Markdown twin · lagent alternatives · ReAct alternatives
GraphCanon updated 4d
Trust & integrity
| Signal | lagent | ReAct |
|---|---|---|
| Maintenance | Active (12d since push) As of 6d · github_public_v1 | Dormant (923d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 6d · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- lagent
- A lightweight framework for building LLM-based agents
- ReAct
- ReAct Prompting for decision-making with language models
Stars
- lagent
- 2.3k
- ReAct
- 4.1k
Forks
- lagent
- 238
- ReAct
- 396
Open issues
- lagent
- 24
- ReAct
- 5
Language
- lagent
- Python
- ReAct
- Jupyter Notebook
Adopt for
- lagent
- lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents.
- ReAct
- ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3.
Persona
- lagent
- -
- ReAct
- -
Runtime
- lagent
- -
- ReAct
- -
License
- lagent
- lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution.
- ReAct
- MIT
Last pushed
- lagent
- Aug 3, 2026
- ReAct
- Feb 6, 2024
Categories
- lagent
- AI Agents, LLM Frameworks
- ReAct
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- lagent
- Active (82%)
- ReAct
- Dormant (18%)
Days since push
- lagent
- 12d
- ReAct
- 923d
Open issues (now)
- lagent
- 24
- ReAct
- 5
Stars delta
- lagent
- +8 (30d)
- ReAct
- +50 (30d)
Open issues delta
- lagent
- +1 (30d)
- ReAct
- 0 (30d)
Owner type
- lagent
- Organization
- ReAct
- User
Full report
- lagent
- Trust report
- ReAct
- Trust report
Shared compatibility
- Python · lagent: Python runtime · ReAct: Python runtime
Choose lagent if…
- lagent is primarily Python; ReAct is Jupyter Notebook.
- License: lagent is Apache-2.0, ReAct is MIT.
- Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs..
- Tags unique to lagent: agent, gpt, transformers.
- When you need a streamlined approach to develop LLM-based agents with minimal overhead, lagent can be particularly advantageous due to its lightweight design.
When NOT to use lagent
- Avoid using lagent if your project necessitates integration with a broader set of tools that are not natively supported by this framework, as it offers limited out-of-the-box extensibility.
- Steer clear if you need robust scalability features right from the start. While lightweight, lagent may require additional custom work to handle more demanding scaling requirements.
Choose ReAct if…
- ReAct is primarily Jupyter Notebook; lagent is Python.
- License: ReAct is MIT, lagent is Apache-2.0.
- Tags unique to ReAct: decision-making, large language models, prompting, reasoning.
- When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3
When NOT to use ReAct
- If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable
- When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (InternLM/lagent) · observed Aug 16, 2026
- GitHub forks (InternLM/lagent) · observed Aug 16, 2026
- Last push (InternLM/lagent) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ysymyth/ReAct) · observed Aug 17, 2026
- GitHub forks (ysymyth/ReAct) · observed Aug 17, 2026
- Last push (ysymyth/ReAct) · observed Feb 6, 2024
- License file (MIT) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lagent 2.3k · ReAct 4.1k (synced Aug 16, 2026).
Common questions
- What is the difference between lagent and ReAct?
- lagent: A lightweight framework for building LLM-based agents. ReAct: ReAct Prompting for decision-making with language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose lagent over ReAct?
- Choose lagent over ReAct when lagent is primarily Python; ReAct is Jupyter Notebook; License: lagent is Apache-2.0, ReAct is MIT; Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.; Tags unique to lagent: agent, gpt, transformers; When you need a streamlined approach to develop LLM-based agents with minimal overhead, lagent can be particularly advantageous due to its lightweight design.
- When should I choose ReAct over lagent?
- Choose ReAct over lagent when ReAct is primarily Jupyter Notebook; lagent is Python; License: ReAct is MIT, lagent is Apache-2.0; Tags unique to ReAct: decision-making, large language models, prompting, reasoning; When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3.
- When should I avoid lagent?
- Avoid using lagent if your project necessitates integration with a broader set of tools that are not natively supported by this framework, as it offers limited out-of-the-box extensibility. Steer clear if you need robust scalability features right from the start. While lightweight, lagent may require additional custom work to handle more demanding scaling requirements.
- When should I avoid ReAct?
- If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred
- Is lagent or ReAct more popular on GitHub?
- ReAct has more GitHub stars (4,109 vs 2,276). Stars measure visibility, not whether either tool fits your constraints.
- Are lagent and ReAct open source?
- Yes - both are open-source projects on GitHub (lagent: Apache-2.0, ReAct: MIT).
- Where can I find alternatives to lagent or ReAct?
- GraphCanon lists graph-backed alternatives at lagent alternatives and ReAct alternatives (lagent markdown twin, ReAct markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, lagent or ReAct?
- lagent: Active. ReAct: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for lagent and ReAct?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lagent trust report; ReAct trust report.