Observed Signal · Jul 26, 2026 · Product Launch · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
AgentWire: Observability for the Agentic Stack
AgentWire is an open-source observability, security, and cost-analytics project for AI agents, LLMs, and MCP servers. Built as an event-driven system on ASP.NET Core and licensed under Apache 2.0, the MVP provides trace ingestion (POST /v1/traces), packet inspection (GET /v1/packets), and cost rollups (GET /v1/analytics/costs), plus an optional Next.js dashboard. The roadmap includes a YARP-based gateway, ClickHouse analytics, a prompt-injection scanner, and a multi-tenant managed edition. The repository is available on GitHub and the project emphasizes free core functionality with paid hosted services for heavy operational components.
Introduces an open-source observability and security layer tailored to agentic AI workflows, addressing visibility, cost attribution, and prompt-injection risks—useful but not industry-shifting on its own.
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Key Takeaways & Evidence Grounding
- AgentWire is an open-source observability, security, and cost-analytics layer for AI agents, LLMs, and MCP servers.
- The project is implemented on ASP.NET Core and licensed under the Apache 2.0 license.
- Current MVP features: trace ingestion via POST /v1/traces; packet inspection via GET /v1/packets; cost rollups via GET /v1/analytics/costs; optional Next.js dashboard.
- Planned roadmap includes a YARP-based gateway, ClickHouse as the analytical store, a prompt injection scanner, and a multi-tenant enterprise edition.
- Source code is published at github.com/qmmughal/AgentWire and the project describes itself as an active MVP (alpha).
Connected Companies & Entities
4 Entities mapped“SDKs for .NET, Python, and JavaScript wrap standard OpenTelemetry SDKs, and a plugin system lets providers (OpenAI, Anthropic) and MCP serve...”
“SDKs for .NET, Python, and JavaScript wrap standard OpenTelemetry SDKs, and a plugin system lets providers (OpenAI, Anthropic) and MCP serve...”
“Repo: github.com/qmmughal/AgentWire...”
“The project's own tagline sums up the ambition well: OpenTelemetry + Wireshark + Cloudflare for AI agents....”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Real-Time Monitoring for AI Agents
A DEV Community post (Apr 30, 2026) by Albert Zhang describes AgentForge’s approach to observability for agentic AI pipelines. The article argues that raw log streaming is inadequate and defines needed capabilities: live execution views, state inspection, failure forensics, and per-agent performance metrics. AgentForge’s monitoring stack includes structured execution traces (JSON), a real-time WebSocket dashboard showing active agents, queue depth, error rates and cost-per-run, and declarative alert rules (examples shown). The post links to an open-source AgentForge MVP repository on GitHub and explains why proactive, structured monitoring is necessary for production agent pipelines running at scale.
Four Pillars of AI Agent Observability
The article describes a production incident where an autonomous AI agent entered a reasoning loop and generated $2,847 in token charges, and cites broader runaway-agent billing reports. It argues that traditional APM is insufficient for probabilistic AI agents and presents an observability stack built around four pillars: Cost Observability (per-run token ledgers and real-time anomaly detection), Quality Observability (production canary evaluations and semantic drift detection), Behavioral Observability (structured agent logs and reasoning tracing), and Dependency Observability (dependency health maps and agent-to-agent distributed tracing). The piece provides code examples, recommends OpenTelemetry GenAI semantic conventions for portability, and highlights platforms (Nebula, Grafana Cloud) and practices for enforcing budgets, instrumenting agent reasoning, and surfacing root causes before monthly bills arrive.
Agentic Ledger: Open-source flight recorder for AI agents
Agentic Ledger is an open-source, MIT-licensed transparent proxy that records HTTP traffic between AI agents and LLM providers to provide request/response logging, per-call cost accounting, loop detection, budgeting/rate-limits, and a local dashboard/API. The project (version 0.4.0) runs as a proxy that stores data in SQLite or Postgres, supports OTLP ingest, and integrates with many agent frameworks. The solo-maintained project is seeking testers and contributors for integrations, pricing updates, loop-detection heuristics, frontend work, and Postgres scale testing. The repo and documentation are public and the proxy can be installed via pip or run with Docker.
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