Core Use Cases
Assist Human Developers
Cut onboarding time and reduce internal support load.- Faster onboarding and fewer interruptions
- Immediate API/service reference inside IDEs
- Always-up-to-date documentation context
- Reduced Slack/Teams dependency
Power Coding Agents With Correct Documentation
Let your agents write correct code on the first try.- Supply agents with accurate repo + docs context
- Reduce hallucinations and incorrect API calls
- Enable autonomous generation, refactoring, and analysis
- Feed structured context into LangGraph/agent frameworks
Engineering Management & Knowledge Governance
One source of truth. Zero stale docs.- Enforce documentation and context rules across projects
- Automatically parse APIs, RST/Sphinx, MDX, OpenAPI
- Detect outdated or inconsistent sections
- Maintain a single source of truth across engineering
Enterprise Features
- SOC 2 certified cloud infrastructure
- SSO (SAML / OIDC)
- Unlimited seats and teams
- Custom volume expansions and high-usage plans
- Private GitHub / GitLab / Bitbucket repository ingestion
- Professional support with response time SLA
- Self-hosted deployment options available
Security & Compliance
Context7 runs on SOC 2 Type II compliant infrastructure provided by Upstash. Key security highlights include:- Privacy-first architecture — your original prompts and code never leave your AI assistant. Only MCP-formulated search queries reach the Context7 API, and sensitive data is stripped before transmission.
- Encryption at rest and in transit (TLS 1.2+)
- VPC isolation, RBAC, DDoS protection, and 24/7 monitoring
- API key security — cryptographically generated, hashed, encrypted, and rate-limited
- Enterprise SSO — supports SAML 2.0, OAuth 2.0, and OpenID Connect (OIDC)
- GDPR compliant with data access, deletion, and portability rights
- Data stored in the US and EU with cross-border transfers following GDPR and EU-U.S. Data Privacy Framework
- ISO 27001 certification in progress
- Open-source MCP server — publicly auditable at github.com/upstash/context7
Quality & Safety
Context7 is built with retrieval quality and trust at its core:- Benchmark-driven retrieval — a library benchmark system generates developer-style questions and measures how effectively each library answers them. Scores are publicly visible and help prioritize better-performing libraries during retrieval.
- Trust scores — every library receives a trust score based on repository signals (stars, activity, account age) and website signals (TLS, domain authority, backlinks). This ensures reliable, well-established libraries are surfaced first.
- Deduplication — exact match checking and cosine similarity filtering remove redundant code snippets and overlapping documentation content.
- Version-aware parsing — a version analyzer detects multi-version documentation structures, ensuring only current docs are stored by default while older versions remain accessible.
- Prompt injection protection — a two-pass detection pipeline combining complementary stages blocks malicious documentation content while minimizing false positives. Pipelines are regularly updated to address evolving attack methods.
- Minimal data ingestion — Context7 does not ingest user code, conversation history, or other sensitive data.