Comparison
forge vs llm-strategy
Verdict
Pick forge if developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge; pick llm-strategy if llm-strategy is a Python library promoting type safety in interactions with language models through its use of strongly typed functions and dataclasses.
Markdown twin · forge alternatives · llm-strategy alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | forge | llm-strategy |
|---|---|---|
| Maintenance | Very active (0d since push) As of 5d · github_public_v1 | Dormant (522d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 5d · github_public_v1 | Not a fork · Personal account As of 1w · 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
- forge
- A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows
- llm-strategy
- Python library for strongly typed interaction with LLMs
Stars
- forge
- 2.2k
- llm-strategy
- 400
Forks
- forge
- 173
- llm-strategy
- 22
Open issues
- forge
- 4
- llm-strategy
- 5
Language
- forge
- Python
- llm-strategy
- Python
Adopt for
- forge
- Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge.
- llm-strategy
- llm-strategy is a Python library promoting type safety in interactions with language models through its use of strongly typed functions and dataclasses.
Persona
- forge
- -
- llm-strategy
- -
Runtime
- forge
- -
- llm-strategy
- -
License
- forge
- MIT
- llm-strategy
- MIT
Last pushed
- forge
- Aug 13, 2026
- llm-strategy
- Mar 3, 2025
Categories
- forge
- AI Agents, LLM Frameworks
- llm-strategy
- LLM Frameworks
Trust and health
Maintenance
- forge
- Very active (96%)
- llm-strategy
- Dormant (18%)
Days since push
- forge
- 0d
- llm-strategy
- 522d
Open issues (now)
- forge
- 4
- llm-strategy
- 5
Full report
- forge
- Trust report
- llm-strategy
- Trust report
Choose forge if…
- Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case..
- Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework.
- Also covers AI Agents.
- - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.
When NOT to use forge
- - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup.
- - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management.
- - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.
Choose llm-strategy if…
- Tags unique to llm-strategy: gpt, langchain, llm, openai.
- You need to enforce strict type safety when working with LLMs
When NOT to use llm-strategy
- If loose or dynamic typing offers better flexibility for your application
- When you prefer frameworks that do not have a steep learning curve due to advanced type annotations
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (antoinezambelli/forge) · observed Aug 14, 2026
- GitHub forks (antoinezambelli/forge) · observed Aug 14, 2026
- Last push (antoinezambelli/forge) · observed Aug 13, 2026
- License file (MIT) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (BlackHC/llm-strategy) · observed Aug 8, 2026
- GitHub forks (BlackHC/llm-strategy) · observed Aug 8, 2026
- Last push (BlackHC/llm-strategy) · observed Mar 3, 2025
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: forge 2.2k · llm-strategy 400 (synced Aug 14, 2026).
Common questions
- What is the difference between forge and llm-strategy?
- forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. llm-strategy: Python library for strongly typed interaction with LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose forge over llm-strategy?
- Choose forge over llm-strategy when Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.; Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework; Also covers AI Agents; - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.
- When should I choose llm-strategy over forge?
- Choose llm-strategy over forge when Tags unique to llm-strategy: gpt, langchain, llm, openai; You need to enforce strict type safety when working with LLMs.
- When should I avoid forge?
- - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup. - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management. - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.
- When should I avoid llm-strategy?
- If loose or dynamic typing offers better flexibility for your application When you prefer frameworks that do not have a steep learning curve due to advanced type annotations
- Is forge or llm-strategy more popular on GitHub?
- forge has more GitHub stars (2,217 vs 400). Stars measure visibility, not whether either tool fits your constraints.
- Are forge and llm-strategy open source?
- Yes - both are open-source projects on GitHub (forge: MIT, llm-strategy: MIT).
- Where can I find alternatives to forge or llm-strategy?
- GraphCanon lists graph-backed alternatives at forge alternatives and llm-strategy alternatives (forge markdown twin, llm-strategy 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, forge or llm-strategy?
- forge: Very active. llm-strategy: 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 forge and llm-strategy?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: forge trust report; llm-strategy trust report.