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One YAML file. One method call. A production AI agent. Stop rebuilding the same agent plumbing. Define it once, deploy anywhere, let it learn. |
Overview • Architecture • Features • Getting Started • Use Cases • Config • CLI • Deployment • FAQ
Every AI team builds the same stack: task decomposition, parallel tool execution, streaming, retries, session management, quality scoring, multi-provider routing, deployment. Most teams rebuild it two or three times before shipping.
Cortex is that stack. Pre-built. Battle-tested. Driven by config, not code.
pip install cortex-agent-framework
cortex setup # visual wizard at localhost:7799
cortex publish ui # chat UI at localhost:8090You now have a working agent with a professional web interface, file upload support, streaming responses, and persistent chat history. No frontend to build. No backend to wire. No infrastructure to manage.
agent:
name: ResearchAgent
description: Searches the web and writes reports
llm_access:
default:
provider: anthropic
model: claude-sonnet-4-5
api_key_env_var: ANTHROPIC_API_KEY
task_types:
- name: web_research
capability_hint: web_search
output_format: md
- name: write_report
capability_hint: document_generation
depends_on: [web_research]from cortex.framework import CortexFramework
framework = CortexFramework("cortex.yaml")
await framework.initialize()
result = await framework.run_session(
user_id="user_1",
request="Research the latest vector DB benchmarks and write a report",
)
print(result.response)Fan-out, tool calls, dependency resolution, synthesis, validation — all handled.
The orchestration, parallelism, tool integration, streaming, retries, validation, session persistence, and deployment pipeline — it's all in the box. Your Python code stays a thin wrapper. The agent's behavior lives in cortex.yaml, versioned, diffable, reviewable.
Any Cortex agent becomes an MCP server in one command. Other agents consume it as a tool. That's the entire inter-agent protocol — standard MCP, nothing custom.
Orchestrator → Research Agent (MCP :8081) → brave-search, wikipedia
→ Code Review Agent (MCP :8082) → github, filesystem
→ Writing Agent (MCP :8083) → document-gen
Each agent scales, deploys, and configures independently. Compose them by adding YAML lines.
Every response passes through a Validation Agent that scores intent match, completeness, and coherence. You set a floor (default: 0.75); responses below it are flagged and remediated. Bad intermediate outputs are retried with feedback before the pipeline moves on.
Catch regressions before users do, not after.
The Learning Engine observes task patterns across sessions. When patterns recur, it stages a delta proposal — a concrete config change you review with cortex delta review and apply in one command. The agent doesn't silently drift; it surfaces what it learned and asks for approval.
| 8 LLM providers | Anthropic, OpenAI, Gemini, Grok, Mistral, DeepSeek, AWS Bedrock, Azure AI — swap with one YAML line |
| Fan-out / fan-in | LLM-generated DAG with parallel execution; independent tasks run simultaneously |
| MCP-native tools | First-class SSE, stdio, and streamable-HTTP MCP tool servers |
| Multi-agent mesh | Publish any agent as an MCP server — compose specialist agents into an orchestrator |
| Identity & delegation | First-class Principal model — human, system, or agent-to-agent calls with full delegation chains in audit logs |
| Quality validation | Every response scored and gated; per-task validation inside the execution loop |
| Delta learning | Agent proposes improvements; human-in-the-loop review before apply |
| Blueprints | Reusable workflow knowledge loaded into context, auto-updated with consent |
| Streaming | Typed event classes (StatusEvent, ResultEvent, ClarificationEvent) for any UI |
| Per-task LLM routing | Route decomposition to a fast model, synthesis to flagship |
| Session persistence | Memory / SQLite / Redis with WAL replay and resumable sessions |
| Built-in chat UI | Web frontend with file uploads, streaming, and conversation history |
| 4 deploy targets | publish docker, publish package, publish mcp, publish ui |
| Visual setup wizard | Configure everything from a browser — cortex setup |
| Security | Input sanitisation, credential scrubbing, sandboxed code execution, MCP output guard |
| Observability | OpenTelemetry, audit logs, anomaly detection, token budgets |
| Capability | Cortex | Typical agent frameworks |
|---|---|---|
| Configuration | Single cortex.yaml drives everything |
Scattered code, env vars, multiple config files |
| Task orchestration | LLM-generated DAG with parallel fan-out/fan-in | Sequential chain or hand-coded state machine |
| Tool protocol | Native MCP (SSE, stdio, streamable-HTTP) | Custom tool wrappers per integration |
| Multi-agent | Any agent becomes an MCP tool in one command | Bespoke inter-agent protocols |
| Quality gates | Built-in validation with scoring + remediation | Manual testing or nothing |
| Learning | Delta proposals + blueprints with human review | Prompt tweaking by hand |
| LLM providers | 8 built-in, swap via config | Usually 1-2, hard-coded |
| Deployment | 4 targets, one command each | Write your own Dockerfile |
| You are... | Cortex gives you... |
|---|---|
| Startup founder shipping an AI product | A production agent runtime in an afternoon — skip 3-6 months of plumbing |
| Platform team at a larger company | A governed agent runtime with audit trails, quality gates, and per-user isolation |
| Enterprise architect | Multi-agent meshes with independent scaling and compliance-friendly history encryption |
| Solo developer | Prototype to production with one YAML file |
| Researcher | Swap providers, models, and tools from config — run experiments without touching code |
| MLOps engineer | Validation scores, session replay, token accounting, and OpenTelemetry out of the box |
- Not a low-code builder. It's a Python library. The config replaces boilerplate, not code.
- Not an LLM gateway. Bring your own API key.
- Not a vector database. It calls MCP tools that do RAG — it doesn't implement retrieval itself.
- Not a web framework. Cortex runs inside FastAPI/Django/Flask/Click.
| Document | Read this if you want to... |
|---|---|
| Overview | ...understand what Cortex is, who it's for, and why it exists |
| Architecture | ...see the internals: primary agent, task graph, MCP agents, validation |
| Features | ...scan the full feature matrix |
| Getting Started | ...build your first agent with working code |
| Use Cases | ...see real-world scenarios and reference architectures |
| Configuration | ...look up every cortex.yaml field |
| CLI Reference | ...look up every cortex subcommand |
| Deployment | ...ship to production |
| FAQ | ...find answers to common gotchas |
| Contributing | ...report bugs or submit PRs |
- Issues: file bugs and feature requests on GitHub Issues
- Discussions: ask questions on GitHub Discussions
- Security: report vulnerabilities privately, not in public issues
MIT — see LICENSE. Use it commercially, fork it, ship it.
Define once. Deploy anywhere. Let it learn.