pragmatiks-agno-provider 0.119.0

Agno agent provider for Pragmatiks

Agno Provider for Pragmatiks

Deploys Agno AI agents and teams to Kubernetes with reactive dependency management. Define models, tools, knowledge bases, and memory as declarative resources -- the platform handles wiring, deployment, and change propagation automatically.

Architecture

The Agno provider follows a spec pattern: most resources are stateless configuration wrappers that produce serializable specifications. Only the runner resource creates actual infrastructure.

models/openai ──┐
models/anthropic┤
                ├─→ agent ──┐
tools/mcp ──────┤           ├─→ runner ──→ Kubernetes Deployment + Service
tools/websearch─┤           │
knowledge ──────┤     team ─┘
  vectordb/qdrant       │
  knowledge/content     │
  knowledge/embedder    │
memory/manager ─┤       │
db/postgres ────┤       │
prompt ─────────┘       │
                        │
config (kubernetes/config) ─┘
namespace (kubernetes) ─────┘

How it works:

1. Configuration resources (models, tools, knowledge, etc.) resolve their dependencies and produce a serializable Spec
2. The agent or team resource aggregates all specs from its dependencies into a single AgentSpec or TeamSpec
3. The runner resource deploys one or more agents and teams to Kubernetes by passing the combined specs as a JSON environment variable
4. The container image reconstructs every agent/team from the spec payload at startup using from_spec() factory methods and registers them with a single AgentOS instance
5. When any dependency changes (e.g., a model API key rotates), Pragma propagates the change through the dependency graph and redeploys automatically

Prerequisites

• A kubernetes/config resource providing cluster access (in-cluster, a GKE cluster, or an external kubeconfig)
• A Kubernetes namespace managed by the kubernetes provider
• API keys for your chosen model provider (OpenAI, Anthropic)
• An Agno runner container image (default: ghcr.io/pragmatiks/agno-runner:v2)
• For knowledge/RAG: a Qdrant vector database instance
• For memory/sessions: a PostgreSQL database instance

Installation

pragma providers install agno

Resources

Resource	Type Slug	Description
Agent	agent	AI agent definition with model, tools, knowledge, and memory
Team	team	Coordinated group of agents with shared resources
Runner	runner	Deploys one or more agents/teams to Kubernetes as a Deployment + Service
Prompt	prompt	Reusable instruction template with variable interpolation
OpenAI Model	models/openai	OpenAI model configuration (GPT-4o, etc.)
Anthropic Model	models/anthropic	Anthropic model configuration (Claude, etc.)
MCP Tools	tools/mcp	Model Context Protocol server integration (stdio, SSE, streamable-http)
Web Search Tools	tools/websearch	Web and news search toolkit (DuckDuckGo, Google, Bing, etc.)
Knowledge	knowledge	Semantic search configuration backed by a vector database
Content	knowledge/content	Content source (URL or text) for ingestion into a knowledge base
OpenAI Embedder	knowledge/embedder/openai	OpenAI embedding model configuration
Qdrant VectorDB	vectordb/qdrant	Qdrant vector database adapter for Agno knowledge
Memory Manager	memory/manager	Agent memory management with PostgreSQL storage
PostgreSQL DB	db/postgres	PostgreSQL database connection for sessions, memory, and storage

Example: Full Agent Deployment

A realistic example showing a model, tools, knowledge base, and agent deployed to Kubernetes.

# 1. Model
provider: agno
resource: models/anthropic
name: claude
config:
  id: claude-sonnet-4-20250514
  api_key:
    ref: secrets/anthropic-key
    field: value

---
# 2. MCP tool server
provider: agno
resource: tools/mcp
name: search-tool
config:
  url: http://mcp-search.tools.svc.cluster.local/sse
  transport: sse

---
# 3. Web search tool
provider: agno
resource: tools/websearch
name: web-search
config:
  backend: duckduckgo
  enable_news: true

---
# 4. Embedder for knowledge
provider: agno
resource: knowledge/embedder/openai
name: embedder
config:
  id: text-embedding-3-small
  api_key:
    ref: secrets/openai-key
    field: value

---
# 5. Vector database adapter
provider: agno
resource: vectordb/qdrant
name: doc-vectors
config:
  url:
    ref: qdrant/database/main
    field: url
  collection:
    ref: qdrant/collection/docs
    field: name
  api_key:
    ref: qdrant/database/main
    field: api_key
  search_type: hybrid
  embedder:
    ref: agno/knowledge/embedder/openai/embedder

---
# 6. Knowledge base
provider: agno
resource: knowledge
name: docs-kb
config:
  vector_db:
    ref: agno/vectordb/qdrant/doc-vectors
  max_results: 5

---
# 7. Content ingestion
provider: agno
resource: knowledge/content
name: product-docs
config:
  knowledge:
    ref: agno/knowledge/docs-kb
  url: https://docs.example.com/product
  description: Product documentation

---
# 8. Database for sessions and memory
provider: agno
resource: db/postgres
name: agent-db
config:
  connection_url:
    ref: cloudsql/instance/main
    field: connection_url
  username:
    ref: secrets/db-creds
    field: username
  password:
    ref: secrets/db-creds
    field: password

---
# 9. Memory manager
provider: agno
resource: memory/manager
name: agent-memory
config:
  db:
    ref: agno/db/postgres/agent-db
  add_memories: true
  update_memories: true

---
# 10. Prompt template
provider: agno
resource: prompt
name: system-prompt
config:
  template: |
    You are {{role}}, a helpful assistant for {{company}}.
    Always be concise and accurate.
  variables:
    role: Senior Support Engineer
    company: Acme Corp

---
# 11. Agent definition
provider: agno
resource: agent
name: support-agent
config:
  model:
    ref: agno/models/anthropic/claude
  tools:
    - ref: agno/tools/mcp/search-tool
    - ref: agno/tools/websearch/web-search
  knowledge:
    ref: agno/knowledge/docs-kb
  memory:
    ref: agno/memory/manager/agent-memory
  db:
    ref: agno/db/postgres/agent-db
  prompt:
    ref: agno/prompt/system-prompt
  markdown: true
  enable_agentic_memory: true

---
# 12. Deploy to Kubernetes
provider: agno
resource: runner
name: support-agent
config:
  agents:
    - ref: agno/agent/support-agent
  config:
    ref: kubernetes/config/main-cluster
  namespace:
    ref: kubernetes/namespace/agents
  replicas: 2
  cpu: 500m
  memory: 2Gi
  security_key:
    ref: secrets/runner-security-key
    field: value

Common Patterns

Team of Agents

Compose multiple specialized agents into a coordinated team.

provider: agno
resource: agent
name: researcher
config:
  model:
    ref: agno/models/openai/gpt4o
  tools:
    - ref: agno/tools/websearch/search
  instructions:
    - "You are a research specialist. Find and summarize information."

---
provider: agno
resource: agent
name: writer
config:
  model:
    ref: agno/models/anthropic/claude
  instructions:
    - "You are a technical writer. Create clear, structured content."

---
provider: agno
resource: team
name: content-team
config:
  members:
    - ref: agno/agent/researcher
    - ref: agno/agent/writer
  model:
    ref: agno/models/anthropic/claude
  delegate_to_all_members: true
  markdown: true

---
provider: agno
resource: runner
name: content-team
config:
  teams:
    - ref: agno/team/content-team
  config:
    ref: kubernetes/config/main-cluster
  namespace:
    ref: kubernetes/namespace/agents

Knowledge-Augmented Agent (RAG)

An agent with access to a vector knowledge base for semantic search.

provider: agno
resource: vectordb/qdrant
name: kb-vectors
config:
  url: http://qdrant.databases.svc.cluster.local:6333
  collection: knowledge
  search_type: hybrid

---
provider: agno
resource: knowledge
name: product-kb
config:
  vector_db:
    ref: agno/vectordb/qdrant/kb-vectors
  max_results: 10

---
provider: agno
resource: knowledge/content
name: faq
config:
  knowledge:
    ref: agno/knowledge/product-kb
  text_content: |
    Q: What are the supported regions?
    A: US-East, EU-West, and APAC.

---
provider: agno
resource: agent
name: support-bot
config:
  model:
    ref: agno/models/openai/gpt4o
  knowledge:
    ref: agno/knowledge/product-kb
  instructions:
    - "Answer questions using the knowledge base. Cite sources."

Multi-Tool Agent

An agent with multiple tool integrations.

provider: agno
resource: tools/mcp
name: github-mcp
config:
  command: npx -y @modelcontextprotocol/server-github
  env:
    GITHUB_TOKEN:
      ref: secrets/github-token
      field: value

---
provider: agno
resource: tools/mcp
name: slack-mcp
config:
  url: http://mcp-slack.tools.svc.cluster.local/sse
  transport: sse
  headers:
    Authorization: "Bearer my-token"
  include_tools:
    - send_message
    - read_channel

---
provider: agno
resource: tools/websearch
name: web
config:
  backend: auto
  enable_news: true

---
provider: agno
resource: agent
name: ops-agent
config:
  model:
    ref: agno/models/anthropic/claude
  tools:
    - ref: agno/tools/mcp/github-mcp
    - ref: agno/tools/mcp/slack-mcp
    - ref: agno/tools/websearch/web
  instructions:
    - "You are an operations assistant with access to GitHub, Slack, and web search."