Comparison
AgentGuard vs control-layer
Verdict
Pick AgentGuard if agentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic; pick control-layer if controlLayer offers robust interaction management with LLMs through validation, schema enforcement, circuit breaking, retry mechanisms, and audit logging.
Markdown twin · AgentGuard alternatives · control-layer alternatives
GraphCanon updated Sep 20, 2026
10views this month
vs
Trust & integrity
| Signal | AgentGuard | control-layer |
|---|---|---|
| Maintenance | Dormant (407d since push) As of Sep 12, 2026 · github_public_v1 | Slowing (111d since push) As of Sep 14, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 12, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 14, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 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
- AgentGuard
- Real-time guardrail that monitors token spend and manages LLM/agent loops in real time
- control-layer
- A production-grade control layer for LLM interaction
Stars
- AgentGuard
- 173
- control-layer
- 62
Forks
- AgentGuard
- 11
- control-layer
- 8
Open issues
- AgentGuard
- 2
- control-layer
- 0
Language
- AgentGuard
- JavaScript
- control-layer
- Python
Adopt for
- AgentGuard
- AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic.
- control-layer
- ControlLayer offers robust interaction management with LLMs through validation, schema enforcement, circuit breaking, retry mechanisms, and audit logging.
Persona
- AgentGuard
- -
- control-layer
- -
Runtime
- AgentGuard
- -
- control-layer
- -
License
- AgentGuard
- MIT
- control-layer
- MIT
Last pushed
- AgentGuard
- Jul 31, 2025
- control-layer
- May 25, 2026
Categories
- AgentGuard
- Evaluation & Observability, Inference & Serving
- control-layer
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- AgentGuard
- Dormant (18%)
- control-layer
- Slowing (36%)
Days since push
- AgentGuard
- 407d
- control-layer
- 111d
Open issues (now)
- AgentGuard
- 2
- control-layer
- 0
Stars delta
- AgentGuard
- +2 (30d)
- control-layer
- 0 (30d)
Open issues delta
- AgentGuard
- +1 (30d)
- control-layer
- 0 (30d)
OSV dependency advisories
- AgentGuard
- Published findings
- control-layer
- No lockfile (source not queried)
Full report
- AgentGuard
- Trust report
- control-layer
- Trust report
Choose AgentGuard if…
- AgentGuard is primarily JavaScript; control-layer is Python.
- Tags unique to AgentGuard: ai-agents, cost-monitoring, observability.
- Also covers Inference & Serving.
- When you need precise control over spend and want live updates on token prices
When NOT to use AgentGuard
- If you prioritize a different language for your project and cannot use JavaScript
- In cases requiring more elaborate fallback mechanisms than what AgentGuard offers
Choose control-layer if…
- control-layer is primarily Python; AgentGuard is JavaScript.
- Requirements: The tool runs without ML libraries or GPU requirements. It relies solely on Python standard library and four additional packages.; Installation involves pip installing tiktoken, tenacity, pydantic, and structlog..
- Tags unique to control-layer: circuit breaker, generative-ai, input-validation, llm-guardrails.
- Also covers LLM Frameworks.
- When your application requires strict input validation and schema enforcement to ensure consistent interactions with LLMs.
When NOT to use control-layer
- If your project does not require Python-based middleware between the app logic and LLM, or if working exclusively within another language ecosystem.
- For scenarios where minimal dependencies are a hard requirement, as ControlLayer depends on tiktoken, tenacity, pydantic, structlog.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dipampaul17/AgentGuard) · observed Sep 20, 2026
- GitHub forks (dipampaul17/AgentGuard) · observed Sep 20, 2026
- Last push (dipampaul17/AgentGuard) · observed Jul 31, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (Emmimal/control-layer) · observed Sep 20, 2026
- GitHub forks (Emmimal/control-layer) · observed Sep 20, 2026
- Last push (Emmimal/control-layer) · observed May 25, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: AgentGuard 173 · control-layer 62 (synced Sep 20, 2026).
Common questions
- What is the difference between AgentGuard and control-layer?
- AgentGuard: Real-time guardrail that monitors token spend and manages LLM/agent loops in real time. control-layer: A production-grade control layer for LLM interaction. See the comparison table for live GitHub stats and shared categories.
- When should I choose AgentGuard over control-layer?
- Choose AgentGuard over control-layer when AgentGuard is primarily JavaScript; control-layer is Python; Tags unique to AgentGuard: ai-agents, cost-monitoring, observability; Also covers Inference & Serving; When you need precise control over spend and want live updates on token prices.
- When should I choose control-layer over AgentGuard?
- Choose control-layer over AgentGuard when control-layer is primarily Python; AgentGuard is JavaScript; Requirements: The tool runs without ML libraries or GPU requirements. It relies solely on Python standard library and four additional packages.; Installation involves pip installing tiktoken, tenacity, pydantic, and structlog.; Tags unique to control-layer: circuit breaker, generative-ai, input-validation, llm-guardrails; Also covers LLM Frameworks; When your application requires strict input validation and schema enforcement to ensure consistent interactions with LLMs.
- When should I avoid AgentGuard?
- If you prioritize a different language for your project and cannot use JavaScript In cases requiring more elaborate fallback mechanisms than what AgentGuard offers
- When should I avoid control-layer?
- If your project does not require Python-based middleware between the app logic and LLM, or if working exclusively within another language ecosystem. For scenarios where minimal dependencies are a hard requirement, as ControlLayer depends on tiktoken, tenacity, pydantic, structlog.
- Is AgentGuard or control-layer more popular on GitHub?
- AgentGuard has more GitHub stars (173 vs 62). Stars measure visibility, not whether either tool fits your constraints.
- Are AgentGuard and control-layer open source?
- Yes - both are open-source projects on GitHub (AgentGuard: MIT, control-layer: MIT).
- Where can I find alternatives to AgentGuard or control-layer?
- GraphCanon lists graph-backed alternatives at AgentGuard alternatives and control-layer alternatives (AgentGuard markdown twin, control-layer 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, AgentGuard or control-layer?
- AgentGuard: Dormant. control-layer: 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 AgentGuard and control-layer?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AgentGuard trust report; control-layer trust report.