Comparison
autogen vs auto-evaluator
Verdict
Pick autogen when requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure.; pick auto-evaluator when tags unique to auto-evaluator: python.
Markdown twin · autogen alternatives · auto-evaluator alternatives
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Trust & integrity
| Signal | autogen | auto-evaluator |
|---|---|---|
| Maintenance | Steady (87d since push) As of today · github_public_v1 | Dormant (1158d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | 118 low (118 low) As of 1d · osv@v1 |
Tagline
- autogen
- A programming framework for agentic AI
- auto-evaluator
- Evaluation tool for LLM QA chains
Stars
- autogen
- 60k
- auto-evaluator
- 1.1k
Forks
- autogen
- 9.0k
- auto-evaluator
- 92
Open issues
- autogen
- 945
- auto-evaluator
- 3
Language
- autogen
- Python
- auto-evaluator
- Python
Adopt for
- autogen
- AutoGen is a Python-based framework for developing and managing agentic AI systems. It includes the AutoGen Studio for no-code GUI setup, integrating with various models.
- auto-evaluator
- -
Persona
- autogen
- -
- auto-evaluator
- -
Runtime
- autogen
- -
- auto-evaluator
- -
License
- autogen
- CC-BY-4.0
- auto-evaluator
- -
Last pushed
- autogen
- Apr 15, 2026
- auto-evaluator
- May 10, 2023
Categories
- autogen
- AI Agents, LLM Frameworks
- auto-evaluator
- Data & Retrieval, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- autogen
- Steady (60%)
- auto-evaluator
- Dormant (18%)
Days since push
- autogen
- 87d
- auto-evaluator
- 1158d
Open issues (now)
- autogen
- 945
- auto-evaluator
- 3
Owner type
- autogen
- Organization
- auto-evaluator
- User
Security scan
- autogen
- No lockfile
- auto-evaluator
- 118 low (118 low)
Full report
- autogen
- Trust report
- auto-evaluator
- Trust report
Shared compatibility
- Python · autogen: Python runtime · auto-evaluator: Python runtime
Choose autogen if…
- Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure..
- Tags unique to autogen: agentic-agi, agents, ai, autogen.
- Also covers AI Agents.
- You need a framework that supports integration with multiple AI models via OpenAI's chat completion client.
When NOT to use autogen
- If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework.
- When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited.
- You need solutions which do not involve additional installations for server components such as `playwright/mcp`, as AutoGen requires this setup for certain functionalities.
Choose auto-evaluator if…
- Tags unique to auto-evaluator: python.
- Also covers Data & Retrieval, Vector Databases.
- Leaner open-issue backlog (3).
When NOT to use auto-evaluator
- Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (microsoft/autogen) · observed Jul 11, 2026
- GitHub forks (microsoft/autogen) · observed Jul 11, 2026
- Last push (microsoft/autogen) · observed Apr 15, 2026
- License file (CC-BY-4.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (rlancemartin/auto-evaluator) · observed Jul 11, 2026
- GitHub forks (rlancemartin/auto-evaluator) · observed Jul 11, 2026
- Last push (rlancemartin/auto-evaluator) · observed May 10, 2023
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autogen 60k · auto-evaluator 1.1k (synced Jul 11, 2026).
Common questions
- What is the difference between autogen and auto-evaluator?
- autogen: A programming framework for agentic AI. auto-evaluator: Evaluation tool for LLM QA chains. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogen over auto-evaluator?
- Choose autogen over auto-evaluator when Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure.; Tags unique to autogen: agentic-agi, agents, ai, autogen; Also covers AI Agents; You need a framework that supports integration with multiple AI models via OpenAI's chat completion client.
- When should I choose auto-evaluator over autogen?
- Choose auto-evaluator over autogen when Tags unique to auto-evaluator: python; Also covers Data & Retrieval, Vector Databases; Leaner open-issue backlog (3).
- When should I avoid autogen?
- If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework. When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited. You need solutions which do not involve additional installations for server components such as
playwright/mcp, as AutoGen requires this setup for certain functionalities. - When should I avoid auto-evaluator?
- Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is autogen or auto-evaluator more popular on GitHub?
- autogen has more GitHub stars (59,658 vs 1,102). Stars measure visibility, not whether either tool fits your constraints.
- Are autogen and auto-evaluator open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to autogen or auto-evaluator?
- GraphCanon lists graph-backed alternatives at autogen alternatives and auto-evaluator alternatives (autogen markdown twin, auto-evaluator 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, autogen or auto-evaluator?
- autogen: Steady. auto-evaluator: 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 autogen and auto-evaluator?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogen trust report; auto-evaluator trust report.