Comparison
llm_agents vs TradingAgents
Verdict
Pick llm_agents when license: llm_agents is MIT, TradingAgents is Apache-2.0; pick TradingAgents when license: TradingAgents is Apache-2.0, llm_agents is MIT.
Markdown twin · llm_agents alternatives · TradingAgents alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | llm_agents | TradingAgents |
|---|---|---|
| Maintenance | Dormant (382d since push) As of today · github_public_v1 | Very active (5d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | 32 low (32 low) As of today · osv@v1 | No lockfile As of today · none |
Tagline
- llm_agents
- Build agents which are controlled by LLMs
- TradingAgents
- Multi-Agents LLM Financial Trading Framework
Stars
- llm_agents
- 1.1k
- TradingAgents
- 92k
Forks
- llm_agents
- 85
- TradingAgents
- 18k
Open issues
- llm_agents
- 3
- TradingAgents
- 292
Language
- llm_agents
- Python
- TradingAgents
- Python
Adopt for
- llm_agents
- -
- TradingAgents
- Use TradingAgents for projects requiring a sophisticated framework to develop and deploy AI agents in financial market transactions leveraging Large Language Models. Avoid it if you need simpler tools or frameworks thatだ
Persona
- llm_agents
- -
- TradingAgents
- -
Runtime
- llm_agents
- -
- TradingAgents
- -
License
- llm_agents
- MIT
- TradingAgents
- Apache-2.0
Last pushed
- llm_agents
- Jun 23, 2025
- TradingAgents
- Jul 5, 2026
Categories
- llm_agents
- AI Agents, LLM Frameworks
- TradingAgents
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- llm_agents
- Dormant (18%)
- TradingAgents
- Very active (96%)
Days since push
- llm_agents
- 382d
- TradingAgents
- 5d
Open issues (now)
- llm_agents
- 3
- TradingAgents
- 292
Owner type
- llm_agents
- User
- TradingAgents
- Organization
Security scan
- llm_agents
- 32 low (32 low)
- TradingAgents
- No lockfile
Full report
- llm_agents
- Trust report
- TradingAgents
- Trust report
Choose llm_agents if…
- License: llm_agents is MIT, TradingAgents is Apache-2.0.
- Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning.
- Leaner open-issue backlog (3).
When NOT to use llm_agents
- Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose TradingAgents if…
- License: TradingAgents is Apache-2.0, llm_agents is MIT.
- Requirements: Min 8 GB RAM; Python environment setup is required.; Deep understanding of finance and LLMs will enhance the utilization of this framework..
- Tags unique to TradingAgents: agent, finance, llm, multiagent.
- When your project involves complex multi-agent interactions specifically in the finance domain, utilizing LLMs to manage trading strategies.
When NOT to use TradingAgents
- If simplicity and ease of deployment are prioritized over advanced AI capabilities; TradingAgents' complexity might introduce unnecessary overhead.
- When the focus is on non-financial applications or when LLM integration isn't necessary, as this framework specializes in financial market trading with a multi-agent approach.
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 Jul 11, 2026
- GitHub forks (mpaepper/llm_agents) · observed Jul 11, 2026
- Last push (mpaepper/llm_agents) · observed Jun 23, 2025
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (TauricResearch/TradingAgents) · observed Jul 11, 2026
- GitHub forks (TauricResearch/TradingAgents) · observed Jul 11, 2026
- Last push (TauricResearch/TradingAgents) · observed Jul 5, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llm_agents 1.1k · TradingAgents 92k (synced Jul 11, 2026).
Common questions
- What is the difference between llm_agents and TradingAgents?
- llm_agents: Build agents which are controlled by LLMs. TradingAgents: Multi-Agents LLM Financial Trading Framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose llm_agents over TradingAgents?
- Choose llm_agents over TradingAgents when License: llm_agents is MIT, TradingAgents is Apache-2.0; Tags unique to llm_agents: deep-learning, langchain, llms, machine-learning; Leaner open-issue backlog (3).
- When should I choose TradingAgents over llm_agents?
- Choose TradingAgents over llm_agents when License: TradingAgents is Apache-2.0, llm_agents is MIT; Requirements: Min 8 GB RAM; Python environment setup is required.; Deep understanding of finance and LLMs will enhance the utilization of this framework.; Tags unique to TradingAgents: agent, finance, llm, multiagent; When your project involves complex multi-agent interactions specifically in the finance domain, utilizing LLMs to manage trading strategies.
- When should I avoid llm_agents?
- Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- When should I avoid TradingAgents?
- If simplicity and ease of deployment are prioritized over advanced AI capabilities; TradingAgents' complexity might introduce unnecessary overhead. When the focus is on non-financial applications or when LLM integration isn't necessary, as this framework specializes in financial market trading with a multi-agent approach.
- Is llm_agents or TradingAgents more popular on GitHub?
- TradingAgents has more GitHub stars (92,290 vs 1,050). Stars measure visibility, not whether either tool fits your constraints.
- Are llm_agents and TradingAgents open source?
- Yes - both are open-source projects on GitHub (llm_agents: MIT, TradingAgents: Apache-2.0).
- Where can I find alternatives to llm_agents or TradingAgents?
- GraphCanon lists graph-backed alternatives at llm_agents alternatives and TradingAgents alternatives (llm_agents markdown twin, TradingAgents 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 TradingAgents?
- llm_agents: Dormant. TradingAgents: Very active. 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 TradingAgents?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm_agents trust report; TradingAgents trust report.