Comparison
agents-from-scratch vs TradingAgents
Verdict
Pick agents-from-scratch when license: agents-from-scratch is MIT, TradingAgents is Apache-2.0; pick TradingAgents when license: TradingAgents is Apache-2.0, agents-from-scratch is MIT.
Markdown twin · agents-from-scratch alternatives · TradingAgents alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | agents-from-scratch | TradingAgents |
|---|---|---|
| Maintenance | Slowing (182d since push) As of today · github_public_v1 | Very active (5d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | No lockfile (source not queried) As of 4d · 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
- agents-from-scratch
- Build AI agents from first principles using a local LLM - no frameworks, no cloud APIs, no hidden reasoning.
- TradingAgents
- Multi-Agents LLM Financial Trading Framework
Stars
- agents-from-scratch
- 901
- TradingAgents
- 92k
Forks
- agents-from-scratch
- 226
- TradingAgents
- 18k
Open issues
- agents-from-scratch
- 6
- TradingAgents
- 292
Language
- agents-from-scratch
- Python
- TradingAgents
- Python
Adopt for
- agents-from-scratch
- -
- 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
- agents-from-scratch
- -
- TradingAgents
- -
Runtime
- agents-from-scratch
- -
- TradingAgents
- -
License
- agents-from-scratch
- MIT
- TradingAgents
- Apache-2.0
Last pushed
- agents-from-scratch
- Jan 14, 2026
- TradingAgents
- Jul 5, 2026
Categories
- agents-from-scratch
- AI Agents, LLM Frameworks
- TradingAgents
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- agents-from-scratch
- Slowing (36%)
- TradingAgents
- Very active (96%)
Days since push
- agents-from-scratch
- 182d
- TradingAgents
- 5d
Open issues (now)
- agents-from-scratch
- 6
- TradingAgents
- 292
Owner type
- agents-from-scratch
- User
- TradingAgents
- Organization
Full report
- agents-from-scratch
- Trust report
- TradingAgents
- Trust report
Choose agents-from-scratch if…
- License: agents-from-scratch is MIT, TradingAgents is Apache-2.0.
- Tags unique to agents-from-scratch: agent-architecture, ai-agents, ai-education, ai-from-scratch.
- Leaner open-issue backlog (6).
When NOT to use agents-from-scratch
- Last GitHub push was 182 days ago (slowing maintenance, Jan 14, 2026). Validate activity before betting a new project on agents-from-scratch.
- 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, agents-from-scratch 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, multiagent, trading.
- 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 (pguso/agents-from-scratch) · observed Jul 15, 2026
- GitHub forks (pguso/agents-from-scratch) · observed Jul 15, 2026
- Last push (pguso/agents-from-scratch) · observed Jan 14, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 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: agents-from-scratch 901 · TradingAgents 92k (synced Jul 15, 2026).
Common questions
- What is the difference between agents-from-scratch and TradingAgents?
- agents-from-scratch: Build AI agents from first principles using a local LLM - no frameworks, no cloud APIs, no hidden reasoning.. TradingAgents: Multi-Agents LLM Financial Trading Framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose agents-from-scratch over TradingAgents?
- Choose agents-from-scratch over TradingAgents when License: agents-from-scratch is MIT, TradingAgents is Apache-2.0; Tags unique to agents-from-scratch: agent-architecture, ai-agents, ai-education, ai-from-scratch; Leaner open-issue backlog (6).
- When should I choose TradingAgents over agents-from-scratch?
- Choose TradingAgents over agents-from-scratch when License: TradingAgents is Apache-2.0, agents-from-scratch 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, multiagent, trading; When your project involves complex multi-agent interactions specifically in the finance domain, utilizing LLMs to manage trading strategies.
- When should I avoid agents-from-scratch?
- Last GitHub push was 182 days ago (slowing maintenance, Jan 14, 2026). Validate activity before betting a new project on agents-from-scratch. 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 agents-from-scratch or TradingAgents more popular on GitHub?
- TradingAgents has more GitHub stars (92,290 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are agents-from-scratch and TradingAgents open source?
- Yes - both are open-source projects on GitHub (agents-from-scratch: MIT, TradingAgents: Apache-2.0).
- Where can I find alternatives to agents-from-scratch or TradingAgents?
- GraphCanon lists graph-backed alternatives at agents-from-scratch alternatives and TradingAgents alternatives (agents-from-scratch 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, agents-from-scratch or TradingAgents?
- agents-from-scratch: Slowing. 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 agents-from-scratch and TradingAgents?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agents-from-scratch trust report; TradingAgents trust report.