Comparison
forge vs langchain-decorators
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 langchain-decorators if langchain-decorators: syntax enhancer for better LangChain prompt engineering.
Markdown twin · forge alternatives · langchain-decorators alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | forge | langchain-decorators |
|---|---|---|
| Maintenance | Active (19d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (142d since push) As of Sep 8, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 8, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- langchain-decorators
- syntactic sugar for langchain
Stars
- forge
- 2.2k
- langchain-decorators
- 232
Forks
- forge
- 173
- langchain-decorators
- 13
Open issues
- forge
- 3
- langchain-decorators
- 8
Language
- forge
- Python
- langchain-decorators
- 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.
- langchain-decorators
- langchain-decorators: syntax enhancer for better LangChain prompt engineering.
Persona
- forge
- -
- langchain-decorators
- -
Runtime
- forge
- -
- langchain-decorators
- -
License
- forge
- MIT
- langchain-decorators
- MIT
Last pushed
- forge
- Sep 1, 2026
- langchain-decorators
- Apr 18, 2026
Categories
- forge
- AI Agents, LLM Frameworks
- langchain-decorators
- Developer Tools, LLM Frameworks
Trust and health
Maintenance
- forge
- Active (82%)
- langchain-decorators
- Slowing (36%)
Days since push
- forge
- 19d
- langchain-decorators
- 142d
Open issues (now)
- forge
- 3
- langchain-decorators
- 8
Stars delta
- forge
- +31 (30d)
- langchain-decorators
- -1 (30d)
Open issues delta
- forge
- -1 (30d)
- langchain-decorators
- +2 (30d)
OSV dependency advisories
- forge
- No lockfile (source not queried)
- langchain-decorators
- Published findings
Full report
- forge
- Trust report
- langchain-decorators
- Trust report
Shared compatibility
- Python · forge: Python runtime · langchain-decorators: 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 langchain-decorators if…
- Tags unique to langchain-decorators: langchain, llm, prompt-engineering.
- Also covers Developer Tools.
- Wants to streamline and simplify prompt engineering with LangChain.
When NOT to use langchain-decorators
- Needs tools without the added layer of abstraction decorators provide.
- Seeks lower-level control over each function call without syntactic sugar.
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 Sep 20, 2026
- GitHub forks (antoinezambelli/forge) · observed Sep 20, 2026
- Last push (antoinezambelli/forge) · observed Sep 1, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (ju-bezdek/langchain-decorators) · observed Sep 20, 2026
- GitHub forks (ju-bezdek/langchain-decorators) · observed Sep 20, 2026
- Last push (ju-bezdek/langchain-decorators) · observed Apr 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: forge 2.2k · langchain-decorators 232 (synced Sep 20, 2026).
Common questions
- What is the difference between forge and langchain-decorators?
- forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. langchain-decorators: syntactic sugar for langchain. See the comparison table for live GitHub stats and shared categories.
- When should I choose forge over langchain-decorators?
- Choose forge over langchain-decorators 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 langchain-decorators over forge?
- Choose langchain-decorators over forge when Tags unique to langchain-decorators: langchain, llm, prompt-engineering; Also covers Developer Tools; Wants to streamline and simplify prompt engineering with LangChain.
- 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 langchain-decorators?
- Needs tools without the added layer of abstraction decorators provide. Seeks lower-level control over each function call without syntactic sugar.
- Is forge or langchain-decorators more popular on GitHub?
- forge has more GitHub stars (2,248 vs 232). Stars measure visibility, not whether either tool fits your constraints.
- Are forge and langchain-decorators open source?
- Yes - both are open-source projects on GitHub (forge: MIT, langchain-decorators: MIT).
- Where can I find alternatives to forge or langchain-decorators?
- GraphCanon lists graph-backed alternatives at forge alternatives and langchain-decorators alternatives (forge markdown twin, langchain-decorators 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 langchain-decorators?
- forge: Active. langchain-decorators: Slowing. 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 langchain-decorators?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: forge trust report; langchain-decorators trust report.