Comparison
llm_agents vs agency
Verdict
Pick llm_agents if llm_agents is a Python library enabling users to build simple agents directed by large language models, featuring functions like executing Python code and using Google search; pick agency if agency is a fast and minimal Python-based framework for developing agentic systems that excels in its streamlined approach to creating autonomous agents.
Markdown twin · llm_agents alternatives · agency alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | llm_agents | agency |
|---|---|---|
| Maintenance | Dormant (418d since push) As of 6d · github_public_v1 | Steady (71d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 6d · github_public_v1 | Not a fork · Personal account As of 1d · github_public_v1 |
| OSV dependency advisories | Published findings 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
- llm_agents
- Library to build agents controlled by LLMs
- agency
- A fast and minimal framework for building agentic systems
Stars
- llm_agents
- 1.1k
- agency
- 489
Forks
- llm_agents
- 85
- agency
- 28
Open issues
- llm_agents
- 3
- agency
- 19
Language
- llm_agents
- Python
- agency
- Python
Adopt for
- llm_agents
- llm_agents is a Python library enabling users to build simple agents directed by large language models, featuring functions like executing Python code and using Google search.
- agency
- Agency is a fast and minimal Python-based framework for developing agentic systems that excels in its streamlined approach to creating autonomous agents.
Persona
- llm_agents
- -
- agency
- -
Runtime
- llm_agents
- -
- agency
- -
License
- llm_agents
- MIT
- agency
- MIT
Last pushed
- llm_agents
- Jun 23, 2025
- agency
- Jun 10, 2026
Categories
- llm_agents
- AI Agents
- agency
- AI Agents
Trust and health
Maintenance
- llm_agents
- Dormant (18%)
- agency
- Steady (60%)
Days since push
- llm_agents
- 418d
- agency
- 71d
Open issues (now)
- llm_agents
- 3
- agency
- 19
Stars delta
- llm_agents
- +3 (30d)
- agency
- +2 (30d)
OSV dependency advisories
- llm_agents
- Published findings
- agency
- No lockfile (source not queried)
Full report
- llm_agents
- Trust report
- agency
- Trust report
Shared compatibility
- Python · llm_agents: Python runtime · agency: Python runtime
Choose llm_agents if…
- Requirements: Min 1 GB RAM; Requires the installation of requirements specified by running `pip install -r requirements.txt` followed by `pip install -e .`.; Dependencies include setting up environment variables for `OPENAI_API_KEY` to use OpenAI API and optionally `SERPAPI_API_KEY` if Google search tool is utilized..
- Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning.
- Use llm_agents when you require a lightweight solution for building agents controlled by LLMs with specific tools such as Python REPL execution or Hacker News search.
When NOT to use llm_agents
- Avoid llm_agents if you need a more robust and feature-rich product for complex tasks that require extensive integration capabilities beyond Python REPL, Google search, and Hacker News.
- Do not choose it when you are looking for advanced abstraction layers like those found in LangChain, as llm_agents aims to remain simple with fewer files and a straightforward core.
Choose agency if…
- Tags unique to agency: actor-model, agent-framework, autonomous-agents.
- When you prefer a lightweight solution for building autonomous agent systems without the need for extensive configuration or complex dependencies.
- More recently updated (last pushed Jun 10, 2026).
When NOT to use agency
- If your development requires deeply integrated functionalities that would necessitate a heavier, more feature-rich framework.
- In scenarios where you need robust tooling for large-scale deployment and management as Agency does not offer extensive LLMOps (LLM Operations) capabilities beyond its minimalistic design.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mpaepper/llm_agents) · observed Aug 15, 2026
- GitHub forks (mpaepper/llm_agents) · observed Aug 15, 2026
- Last push (mpaepper/llm_agents) · observed Jun 23, 2025
- License file (MIT) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (operand/agency) · observed Aug 21, 2026
- GitHub forks (operand/agency) · observed Aug 21, 2026
- Last push (operand/agency) · observed Jun 10, 2026
- License file (MIT) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llm_agents 1.1k · agency 489 (synced Aug 15, 2026).
Common questions
- What is the difference between llm_agents and agency?
- llm_agents: Library to build agents controlled by LLMs. agency: A fast and minimal framework for building agentic systems. See the comparison table for live GitHub stats and shared categories.
- When should I choose llm_agents over agency?
- Choose llm_agents over agency when Requirements: Min 1 GB RAM; Requires the installation of requirements specified by running
pip install -r requirements.txtfollowed bypip install -e ..; Dependencies include setting up environment variables forOPENAI_API_KEYto use OpenAI API and optionallySERPAPI_API_KEYif Google search tool is utilized.; Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning; Use llm_agents when you require a lightweight solution for building agents controlled by LLMs with specific tools such as Python REPL execution or Hacker News search. - When should I choose agency over llm_agents?
- Choose agency over llm_agents when Tags unique to agency: actor-model, agent-framework, autonomous-agents; When you prefer a lightweight solution for building autonomous agent systems without the need for extensive configuration or complex dependencies; More recently updated (last pushed Jun 10, 2026).
- When should I avoid llm_agents?
- Avoid llm_agents if you need a more robust and feature-rich product for complex tasks that require extensive integration capabilities beyond Python REPL, Google search, and Hacker News. Do not choose it when you are looking for advanced abstraction layers like those found in LangChain, as llm_agents aims to remain simple with fewer files and a straightforward core.
- When should I avoid agency?
- If your development requires deeply integrated functionalities that would necessitate a heavier, more feature-rich framework. In scenarios where you need robust tooling for large-scale deployment and management as Agency does not offer extensive LLMOps (LLM Operations) capabilities beyond its minimalistic design.
- Is llm_agents or agency more popular on GitHub?
- llm_agents has more GitHub stars (1,053 vs 489). Stars measure visibility, not whether either tool fits your constraints.
- Are llm_agents and agency open source?
- Yes - both are open-source projects on GitHub (llm_agents: MIT, agency: MIT).
- Where can I find alternatives to llm_agents or agency?
- GraphCanon lists graph-backed alternatives at llm_agents alternatives and agency alternatives (llm_agents markdown twin, agency 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, llm_agents or agency?
- llm_agents: Dormant. agency: Steady. 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 llm_agents and agency?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm_agents trust report; agency trust report.