Open, modular infrastructure for building, observing, evaluating, and governing AI agents in production.
Website · Specification · Hosted MCP · Roadmap
DeepAgentLabs is a specification-first ecosystem for operating AI agents: a shared AI Operations Specification (AIOS) defines a vendor-neutral contract for what happens when an agent runs, and each project below is a reference implementation of that contract — observing, evaluating, supervising, stress-testing, or exposing it through a common interface.
Every component is independently usable. None of them require each other, and any team can implement the specification without depending on our Python packages.
| Project | What it does |
|---|---|
| ai-operations-spec | The AIOS specification: a vendor-neutral contract and shared vocabulary for runs, steps, model/tool calls, retrieval, memory, evaluations, and incidents. |
AgenticLens (agenticlens) |
Observability, evaluation, and operational intelligence for production AI systems — what ran, why, at what cost, and how to improve it, with failure diagnosis and faulty-step attribution (0.7.0). |
Agentic Evals (agentic-evals) |
A standalone, framework-agnostic scoring and evaluation engine for LLM and agent outputs. |
Agentic Sidecar (agentic-sidecar) |
A real-time supervision layer that checks an agent's plans and actions against intent, policy, risk, and budget before they execute. |
Agentic Chaos (agentic-chaos) |
A fault-injection toolkit that deliberately breaks agent workflows to measure resilience and recovery, with circuit breakers and dry-run previews (0.5.2), AgenticLens diagnosis span markers (0.5.3), and a pytest plugin with fail-closed resilience contracts (0.6.0). |
MCP Server (deep-agentic-core-mcp) |
A single MCP interface that exposes workflow inspection, evaluation, and chaos/resilience tooling to AI clients. |
| AgenticOps Control Tower | An operations control plane for inventory, evidence posture, cited MCP investigation and approved verified staging recovery (0.6.0). |
See the roadmap for how these pieces fit together and what's being built next.
Sidecar v0.8: Shared & Exclusive context/intent and guardrails. Users may choose context only, guardrails only, or both, independently Shared across a system/task or Exclusive to designated agent(s). Each agent receives shared plus assigned exclusive context; shared guardrails remain mandatory alongside exclusive rules. v0.8 provides scoped configuration, delivery and composition; v0.9 validates privacy, authority and lifecycle behavior; v1.0 stabilizes contracts. Explicit host context delivery is implemented; automatic prompt rewriting and end-to-end private-context isolation are not claimed. Lens 0.7.1 reads correlated decisions; Semantica export remains conditional.
See the Sidecar acceptance criteria.
- Specification-first — the contract comes before SDK ergonomics, dashboards, or integrations.
- Modular — each project is independently installable and useful on its own.
- Runtime and framework agnostic — works alongside LangGraph, CrewAI, the OpenAI Agents SDK, AutoGen, LlamaIndex, and custom agent stacks.
- Open source — released under permissive licenses; see each repository for its license.
Found a bug or have an idea? Open an issue or discussion on the relevant repository above. Most projects also publish to PyPI — search for the package name shown next to each project.
Phase 6 and Phase 6A ship as one release, paired with MCP 0.3.6. The supported first runtime is a deterministic staging dependency fixture: detect, investigate with cited Lens evidence, preview, obtain separate human approval, execute once and verify fresh Lens/Evals evidence plus a probe. Failed recovery escalates. Thin Sidecar stop/restart and native MCP revocation adapters preserve runtime ownership. Production orchestration and hosted tenant credential binding remain planned. Walkthrough and scope.
