cie
CIE (Code Intelligence Engine) is a local CLI that indexes a codebase and exposes semantic code search, call-graph/path analysis, and code/HTTP endpoint discovery to AI agents via the Model Context Protocol (MCP). It stores an embedded CozoDB/RocksDB index locally and can optionally use local or hosted embedding/LLM providers for semantic search and narrative analysis.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Security posture is positioned as local-only data storage (code never leaves the machine) and embedded DB use. However, the provided content does not document MCP transport security/auth controls, threat model, or rate limiting. Secrets for embedding/LLM providers are configured via YAML/env vars; guidance on preventing logging/accidental exposure is not provided in the README. TLS cannot be meaningfully assessed for the MCP server because no network/auth details are described.
⚡ Reliability
Best When
You want an offline/local code-knowledge layer for an AI coding agent, especially when you need call graphs and structured code search across a large repository.
Avoid When
You need strict RBAC/authN/authZ guarantees for multi-tenant remote access, or you require guaranteed semantic results without any embedding model/providers.
Use Cases
- • Semantic search for functions/types by intent (e.g., “where is auth middleware” )
- • Tracing call graphs and execution paths to understand how a function is reached
- • Discovering HTTP/REST endpoints (Go framework conventions) and gRPC services (from .proto)
- • Providing agents with structured code intelligence to reduce tool round-trips
- • Auditing/verification tasks via pattern absence checks (cie_verify_absence)
Not For
- • A hosted SaaS for running on remote code (it is positioned as local-only)
- • High-availability production APIs for end-user traffic (it’s primarily a local indexing/querying tool)
- • Security/compliance systems that require formally documented threat models and guarantees beyond local storage claims
Interface
Authentication
No auth mechanism for the MCP server is described in the provided README; access is implied to be local-process based. For embedding/LLM features, credentials are configured via env vars in YAML (e.g., OpenAI API key / Ollama base_url).
Pricing
Open source (AGPL v3) is available via releases/binaries. Enterprise is offered commercially, but no pricing details are included in the provided content.
Agent Metadata
Known Gotchas
- ⚠ Semantic search may require embeddings; without configuring an embedding provider (e.g., Ollama/OpenAI/Nomic) the semantic tool’s results may be unavailable or degraded while structural tools still work.
- ⚠ Because the index is local, agents need to ensure the correct project_id and that indexing has been run before querying.
- ⚠ No documented auth/rate-limit/error-contract details for the MCP server in the provided README; agents should be prepared for tool failures without standardized guidance.
Alternatives
Full Evaluation Report
Comprehensive deep-dive: security analysis, reliability audit, agent experience review, cost modeling, competitive positioning, and improvement roadmap for cie.
AI-powered analysis · PDF + markdown · Delivered within 30 minutes
Package Brief
Quick verdict, integration guide, cost projections, gotchas with workarounds, and alternatives comparison.
Delivered within 10 minutes
Score Monitoring
Get alerted when this package's AF, security, or reliability scores change significantly. Stay ahead of regressions.
Continuous monitoring
Scores are editorial opinions as of 2026-03-30.