Integration Guide
Logito + Claude Code
Claude Code can write code, deploy services, and modify configuration. Logito tells it whether those changes actually worked at runtime. Together, they create a closed-loop development workflow where every change is validated against actual system behavior.
Why this matters
AI agents need runtime feedback
When Claude Code modifies your API handler or updates a database migration, it has no way to verify whether the change worked correctly at runtime. Unit tests validate code correctness. But they don't tell you whether system behavior changed in ways that matter — latency regressions, new error codes, missing response fields, or cascading failures.
Logito closes this gap. It captures actual runtime behavior, compares it against a known baseline, and produces a structured verdict that Claude Code can read and act on.
Setup
Step 1: Install Logito CLI
brew tap progadigital/tap brew install logito
No account required. The CLI works locally with no cloud dependency.
Step 2: Install the MCP bridge
logito codex install
This installs the Logito MCP bridge, which exposes runtime intelligence tools to Claude Code. The bridge reads your CLI profile and authenticates automatically.
Step 3: Use in Claude Code
Once installed, Claude Code can use Logito's MCP tools directly:
# Claude Code can now use these tools: logito.get_latest_run # Get the most recent run summary review_run # Review a run for drift logito.get_run_diff # Get structured diff vs baseline logito.explain_issue # Investigate a specific issue logito.get_recommended_action # Get the next recommended action logito.get_system_status # Check overall system health
Workflow
The closed-loop pattern
Claude Code makes a change
Modifies an API handler, updates a migration, changes configuration.
Tests run and Logito captures behavior
During the test run, Logito records HTTP requests/responses, latency, error rates, and response shapes.
Claude Code checks the verdict
Calls review_run to get a structured assessment: PASS (no drift), WARNING (minor changes), or FAIL (regression detected).
If regression: investigate and fix
Claude Code calls logito.explain_issue to understand the specific regression, then modifies the code to fix it.
Re-run and verify
The cycle repeats until the review returns PASS. The agent has validated its own work against actual runtime behavior.
Example
Claude Code fixing a regression
# 1. Start capture
logito dev start --project my-api
# 2. Run tests (Claude Code does this after making changes)
npm test
# 3. Claude Code reviews via MCP
> review_run
Status: FAIL
Regression: POST /checkout latency increased 120ms -> 450ms
Confidence: high (8 endpoints verified, 3 observations on weakest edge)
# 4. Claude Code investigates
> logito.explain_issue { "service": "api" }
Driver: New database query in checkout handler missing index
Evidence: 3 slow queries > 300ms in the last 2 minutes
# 5. Claude Code fixes and re-runs
# ... adds database index ...
npm test
# 6. Review passes
> review_run
Status: PASS
All 8 endpoints verified stable vs baseline. Advanced
Local sidecar for air-gapped environments
For environments where the CLI can't reach the cloud API, use the self-hosted sidecar:
docker run -p 3000:3000 -v logito-data:/data logito-local # The CLI and MCP bridge auto-detect the sidecar at localhost:3000
All analysis runs locally with deterministic-only intelligence. No cloud dependency, no data leaves the network.
Schema stability
MCP tool contract guarantee
Logito's MCP tools follow semantic versioning. The current contract version is 1.1.0 with stability level stable. This means:
- Tool names and argument schemas will not change within version 1.x
- New tools and optional arguments may be added in minor versions
- Breaking changes require a major version bump with 90-day migration period
- The
contract_versionandcontract_stabilityfields are included in everytools/listresponse
Install