Inferra - Autonomous Debugging via Trace-to-Code Correlation

Turn production traces into code-level diagnoses. Automatically.

Inferra bridges the gap between observability tools (which show what happened) and source code (which shows why it happened). It ingests standard OpenTelemetry traces, maps each span to the exact function and line in your codebase via AST analysis, and produces a structured root cause analysis, powered by your choice of LLM.

Your App --> OTLP Traces --> Inferra --> Code-Aware Diagnosis  (routes.py:195, cve_extractor.py:43)

Quick Start

Install

pip install inferra

Option A: OTLP Mode (Production Debugging)

Point Inferra at your codebase, send it traces from your running app:

# Terminal 1: Start the OTLP receiver + code indexer
inferra serve --project /path/to/your/app/src --llm groq

# Terminal 2: Your app sends OTel traces to Inferra
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
python my_app.py

# Terminal 3: Trigger analysis after some traffic
curl -X POST http://localhost:4318/v1/analyze | python3 -m json.tool

Option B: Static Analysis (Quick Code Review)

Analyze any project without running it:

inferra analyze /path/to/any/project --llm groq

Python API

import inferra

report = inferra.analyze("./my_project", llm="groq")
print(report.confidence)   # "92%"
print(report.root_cause)   # "Sequential pipeline with blocking I/O..."
report.open()              # Opens HTML report in browser

The Problem

When a production API is slow, observability tools tell you:

"POST /api/analyze took 3.5s"

But they don't tell you which function handles that endpoint, what the code does, or where the bottleneck is. You're left grep-ing through source code, matching route decorators to trace names by hand.

Without Inferra

"The endpoint is slow. Consider optimizing."

With Inferra

"POST /api/analyze at routes.py:195 calls graph.invoke() which runs 3 agents sequentially: cve_extractor.py:43 (1.2s), attack_classifier.py:110 (1.1s), playbook_generator.py:39 (1.2s). Use asyncio.gather() to parallelize for ~3x speedup."

Multi-Language Code Indexing

Inferra indexes codebases across 4 languages:

Language	Parser	Features
Python	AST (ast.parse)	Functions, classes, decorators, routes, docstrings, call graphs
JavaScript/TypeScript	Regex + heuristics	Functions, classes, arrow functions, generics (<T>), interfaces, type aliases, generators
Go	Regex + heuristics	Functions, methods, structs, interfaces, Go 1.18+ generics ([T any]), routes (Gin/Echo/Chi)
Java	Regex + heuristics	Classes, methods, records, sealed classes, generic return types (ResponseEntity<UserDTO>), Spring/JAX-RS routes

The Python parser is production-grade (full AST). The JS/TS, Go, and Java parsers use regex with generics support and handle real-world codebases (tested on 270+ file MERN projects).

Multi-LLM Support

Inferra supports 3 backends. Swap with one environment variable:

Backend	Models	Speed	Quality	Cost
Groq	Kimi-K2, GPT-OSS-120b, Llama-3.3-70b	~2-4s	85-92%	Free tier
Claude	Sonnet 4	~8s	93%	API key
Ollama	Qwen 3:8b (local)	~15s	70%	Free, fully local

# Groq (fastest)
export GROQ_API_KEY=your-key
inferra serve --project ./my-app --llm groq --model moonshotai/kimi-k2-instruct

# Claude (highest quality)
export ANTHROPIC_API_KEY=your-key
inferra serve --project ./my-app --llm claude

# Ollama (free, runs locally)
ollama pull qwen3:8b
inferra serve --project ./my-app --llm ollama

Architecture

+----------------------------------------------------------------------+
|                              Inferra                                  |
|                                                                       |
|  +--------------+   +---------------+   +----------------------+     |
|  | OTLP Receiver|   | Code Indexer  |   | Multi-Language       |     |
|  |              |   |               |   | Parsers              |     |
|  | Protobuf     |   | AST Parser    |   |                      |     |
|  | HTTP/JSON    |   | TF-IDF Index  |   | Python (AST)         |     |
|  | Span Buffer  |   | Route Extract |   | JS/TS, Go, Java      |     |
|  +------+-------+   +-------+-------+   +----------+-----------+     |
|         |                   |                       |                 |
|         v                   v                       v                 |
|  +---------------------------------------------------------------+   |
|  |                    Span <-> Code Correlator                    |   |
|  |  1. code.filepath attribute match                              |   |
|  |  2. Route match (GET /articles/{slug} -> articles.get)         |   |
|  |  3. Function name match                                        |   |
|  |  4. TF-IDF fuzzy search fallback                               |   |
|  +------------------------------+--------------------------------+   |
|                                 |                                     |
|        +------------------------+------------------------+            |
|        v                        v                        v            |
|  +-------------+  +-------------------+  +--------------------+      |
|  | Multi-Agent |  | LLM Synthesis     |  | Persistence &      |      |
|  | Analyzers   |  |                   |  | Streaming          |      |
|  |             |  | Claude / Groq     |  |                    |      |
|  | Metrics     |  | / Ollama          |  | SQLite history     |      |
|  | Dependency  |  |                   |  | Anomaly detection  |      |
|  | Security    |  | Agentic RAG       |  | Regression alerts  |      |
|  +-------------+  +-------------------+  +--------------------+      |
|                                                                       |
|  +---------------------------------------------------------------+   |
|  |                    Output Layer                                |   |
|  |  HTML Reports | Service Topology | Follow-up API | PR Suggest |   |
|  +---------------------------------------------------------------+   |
+----------------------------------------------------------------------+

Multi-Agent Analysis

Inferra uses 5 specialized agents that run before the LLM:

Agent	Purpose	Example Finding
MetricsCorrelation	Latency anomalies, critical path, P95 stats	"8 slow spans detected (>100ms)"
DeepReasoning	LLM-powered root cause analysis with code context	"Sequential pipeline bottleneck in graph.invoke()"
Dependency	Call graph analysis, circular deps, dead code	"High fan-out: api.routes calls 12 functions"
Security	SQL injection, hardcoded secrets, SSRF patterns	"Potential SQL injection in query builder"
Coordinator	Synthesizes all findings into a final RCA report	Severity, confidence, ranked recommendations

API Endpoints

Endpoint	Method	Description
/v1/traces	POST	Accept OTLP spans
/v1/traces	GET	View buffered spans
/v1/analyze	POST	Trigger RCA on buffered spans
/v1/ask	POST	Follow-up questions on last analysis
/v1/topology	POST	Generate service topology (Mermaid + D3.js)
/v1/history	GET	View past analysis results
/v1/regressions	GET	Detect performance regressions
/healthz	GET	Health check

Tested On Real Projects

Project	Language	Files	Code Units	Correlations	Finding
Cybersec Threat Analyst	Python	35	284	29	Sequential pipeline bottleneck
MERN Ecommerce	JavaScript	130	338	10	MongoDB aggregation bottleneck (2190ms)
Goldman Sachs gs-quant	Python	52	1,460+	45+	Synchronous computation chains
RealWorld Conduit	Python	37	104	6/22	JWT auth + bcrypt bottleneck
PharmaSight	Python	25+	80+	route-based	Batch processing latency

CLI Reference

# OTLP receiver mode (production debugging)
inferra serve --project <path> [--llm groq|claude|ollama] [--model <name>] [--port 4318]

# Static analysis mode (code review)
inferra analyze <path> [--llm groq|claude|ollama] [--model <name>] [--output report.html]

# Help
inferra --help
inferra analyze --help
inferra serve --help

Environment Variables

Variable	Required	Description
GROQ_API_KEY	For Groq backend	Get from console.groq.com
ANTHROPIC_API_KEY	For Claude backend	Get from console.anthropic.com
INFERRA_LLM_BACKEND	Optional	Force backend: groq, claude, ollama
INFERRA_LLM_MODEL	Optional	Specific model name
OTEL_EXPORTER_OTLP_ENDPOINT	For your app	Set to http://localhost:4318

Project Structure

inferra/
├── __init__.py           # Package exports + inferra.analyze() API
├── __main__.py           # CLI entry point (inferra analyze / inferra serve)
├── api.py                # Public Python API
├── indexer.py            # AST-based code indexer + TF-IDF search
├── otlp_receiver.py      # OTLP/HTTP trace receiver + correlator
├── rca_engine.py         # Root cause analysis orchestration
├── agents.py             # Rule-based analyzers (latency, errors, patterns)
├── llm_agent.py          # Multi-LLM backend (Claude, Groq, Ollama)
├── rag.py                # Context-aware code retrieval
├── embeddings.py         # TF-IDF code search index
├── parsers/
│   ├── base.py           # Parser registry + abstract base
│   ├── javascript_parser.py  # JS/TS parser (generics, interfaces, types)
│   ├── go_parser.py      # Go parser (1.18+ generics)
│   └── java_parser.py    # Java parser (records, sealed, generics)
├── dependency_agent.py   # Call graph + dependency analysis
├── security_agent.py     # Security vulnerability scanner
├── storage.py            # SQLite persistence + regression detection
├── streaming.py          # Real-time anomaly detection
├── topology.py           # Service topology (Mermaid + D3.js)
├── pr_generator.py       # PR fix suggestions
├── auto_instrument.py    # OTel auto-instrumentation generator
├── config_indexer.py     # YAML/TOML/.env config parser
├── sql_indexer.py        # SQL file indexer
└── aws_integration.py    # S3 report upload + CloudWatch ingestion

How It Compares

Tool	Trace Collection	Code Correlation	Multi-Language	LLM Root Cause	Persistence	Cost
Jaeger	Yes	No	N/A	No	Yes	Free
Datadog	Yes	Partial (APM)	Yes	Partial (Watchdog)	Yes	$15-35/host/mo
Sentry	Yes	Yes (requires SDK)	Yes	No	Yes	$26+/mo
Inferra	Yes (standard OTLP)	Yes (AST, zero SDK)	4 languages	Yes (multi-LLM)	Yes (SQLite)	Free

The key difference: Inferra uses standard OTLP (no vendor SDK), correlates via AST analysis (no runtime agent), and runs fully local with Ollama.

Limitations

• Regex-based parsers for JS/TS, Go, Java: The Python indexer uses full AST parsing. Other languages use regex with generics support, which covers most real-world code but can miss deeply nested constructs. Tree-sitter migration is on the roadmap.
• Not continuous monitoring: Analysis is triggered manually via /v1/analyze. The streaming module provides anomaly detection on ingested spans, but Inferra is not a 24/7 monitoring agent.
• LLM-dependent analysis quality: Root cause analysis quality scales with the LLM backend. Claude scores 93% accuracy while Ollama/Qwen scores around 70%.
• Simulated traces for testing: The OTLP receiver requires externally instrumented apps or simulated spans. Auto-instrumentation generates scripts but requires manual setup.