Comparison
forge vs LLFn
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 LLFn if lightweight, MIT-licensed Python framework for developing with Language Models.
Markdown twin · forge alternatives · LLFn alternatives
GraphCanon updated 4d
Trust & integrity
| Signal | forge | LLFn |
|---|---|---|
| Maintenance | Very active (0d since push) As of 6d · github_public_v1 | Dormant (1112d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 6d · github_public_v1 | Not a fork · Organization 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
- forge
- A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows
- LLFn
- A lightweight framework for creating applications using LLMs
Stars
- forge
- 2.2k
- LLFn
- 96
Forks
- forge
- 173
- LLFn
- 7
Open issues
- forge
- 4
- LLFn
- 1
Language
- forge
- Python
- LLFn
- 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.
- LLFn
- Lightweight, MIT-licensed Python framework for developing with Language Models
Persona
- forge
- -
- LLFn
- -
Runtime
- forge
- -
- LLFn
- -
License
- forge
- MIT
- LLFn
- MIT
Last pushed
- forge
- Aug 13, 2026
- LLFn
- Jul 30, 2023
Categories
- forge
- AI Agents, LLM Frameworks
- LLFn
- LLM Frameworks
Trust and health
Maintenance
- forge
- Very active (96%)
- LLFn
- Dormant (18%)
Days since push
- forge
- 0d
- LLFn
- 1112d
Open issues (now)
- forge
- 4
- LLFn
- 1
Stars delta
- forge
- Unknown
- LLFn
- 0 (30d)
Open issues delta
- forge
- Unknown
- LLFn
- 0 (30d)
Owner type
- forge
- User
- LLFn
- Organization
Full report
- forge
- Trust report
- LLFn
- Trust report
Shared compatibility
- Python · forge: Python runtime · LLFn: Python runtime
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.
- forge ships Docker support for self-hosted deployment.
- - 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 LLFn if…
- Tags unique to LLFn: applications with llms, lightweight, python.
- Ideal for prototyping and small-scale projects needing quick development cycles.
- Leaner open-issue backlog (1).
When NOT to use LLFn
- Avoid if requiring extensive customization or large-scale applications with complex scaling needs.
- Not recommended for teams prioritizing enterprise-level support and service features.
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 (orgexyz/LLFn) · observed Aug 16, 2026
- GitHub forks (orgexyz/LLFn) · observed Aug 16, 2026
- Last push (orgexyz/LLFn) · observed Jul 30, 2023
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: forge 2.2k · LLFn 96 (synced Aug 14, 2026).
Common questions
- What is the difference between forge and LLFn?
- forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. LLFn: A lightweight framework for creating applications using LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose forge over LLFn?
- Choose forge over LLFn 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; forge ships Docker support for self-hosted deployment; - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.
- When should I choose LLFn over forge?
- Choose LLFn over forge when Tags unique to LLFn: applications with llms, lightweight, python; Ideal for prototyping and small-scale projects needing quick development cycles; Leaner open-issue backlog (1).
- 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 LLFn?
- Avoid if requiring extensive customization or large-scale applications with complex scaling needs. Not recommended for teams prioritizing enterprise-level support and service features.
- Is forge or LLFn more popular on GitHub?
- forge has more GitHub stars (2,217 vs 96). Stars measure visibility, not whether either tool fits your constraints.
- Are forge and LLFn open source?
- Yes - both are open-source projects on GitHub (forge: MIT, LLFn: MIT).
- Where can I find alternatives to forge or LLFn?
- GraphCanon lists graph-backed alternatives at forge alternatives and LLFn alternatives (forge markdown twin, LLFn 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 LLFn?
- forge: Very active. LLFn: 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 LLFn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: forge trust report; LLFn trust report.