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Your First Agent

In this tutorial, you'll scaffold a new agent from a template, explore the generated code, build the container, and run it interactively.

Time: ~10 minutes

Prerequisites: Getting Started complete. Podman and Ollama running.

Step 1: Scaffold the Agent

Use vlinder agent new to create a project from a template. Pick any language you're comfortable with:

vlinder agent new python my-agent

Available languages: python, golang, js, ts, java, dotnet.

This creates a my-agent/ directory with everything you need:

my-agent/
├── agent.toml   # Agent manifest
├── agent.py     # Agent source code
├── Dockerfile   # Container image definition
├── build.sh     # Build script
└── README.md    # Template documentation

Step 2: Explore agent.toml

cd my-agent

Open agent.toml — the manifest that tells Vlinder what your agent needs:

agent.toml
name = "my-agent"
description = "A starter agent — greets users, remembers names, and fetches fun facts."
runtime = "container"
executable = "localhost/my-agent:latest"
object_storage = "sqlite://data/objects.db"

[requirements.models]
phi3 = "phi3"

[requirements.services.infer]
provider = "ollama"
protocol = "openai"

Key fields:

  • name — identifies the agent in the registry
  • runtime — execution runtime ("container" for OCI containers via Podman)
  • executable — the container image reference
  • object_storage — key-value storage for persisting data
  • requirements.models — maps model aliases to registry names
  • requirements.services — declares which platform services and providers the agent uses

Step 3: Explore the Agent Code

The generated source file implements an HTTP server on port 8080. Vlinder sends user messages as POST requests and reads the plain-text response.

The template includes a bridge helper library — one function per platform service:

Function Endpoint Description
infer(prompt, max_tokens) /infer LLM completion
embed(text) /embed Text embedding
kv_get(key) /kv/get Read from storage
kv_put(key, content) /kv/put Write to storage (base64)
kv_list(prefix) /kv/list List storage keys
kv_delete(key) /kv/delete Delete from storage
vector_store(key, vector, metadata) /vector/store Store vector
vector_search(vector, limit) /vector/search Search vectors
vector_delete(key) /vector/delete Delete vector
delegate(agent, input) /delegate Delegate to another agent
wait(handle) /wait Wait for delegation result

The agent calls these helpers to talk to the bridge — a sidecar HTTP server that Vlinder runs alongside your container. The bridge URL is provided via the VLINDER_BRIDGE_URL environment variable.

The template's main logic:

  • "my name is Alice" → stores the name in KV storage
  • "who am i?" → retrieves the name from KV storage
  • "surprise me" → asks the LLM for a fun fact via inference

Step 4: Build the Container

./build.sh

This runs podman build to create the container image at localhost/my-agent:latest.

Step 5: Deploy and Run the Agent

vlinder agent deploy
vlinder agent run my-agent

Vlinder loads agent.toml, validates that phi3 is available via Ollama, starts the container, and opens an interactive REPL.

Try it out:

> my name is Alice
Nice to meet you, Alice! I'll remember that.

> who am i?
You're Alice!

> surprise me
The shortest war in recorded history lasted 38 minutes, between Britain and Zanzibar in 1896.

Step 6: Inspect the Session

After running your agent, check that interactions were recorded:

vlinder session list my-agent

Every conversation turn is stored in a content-addressed DAG, enabling time-travel debugging.

Step 7: Make It Your Own

  1. Edit the source file — change the message handler to implement your logic
  2. Update agent.toml — change the name, description, models, and services
  3. Rebuild with ./build.sh
  4. Deploy and run again

What You Learned

  • How to scaffold a new agent with vlinder agent new
  • The structure of an agent.toml manifest
  • How agents communicate with platform services via bridge helpers
  • How to build, deploy, and run an agent container
  • How interactions are recorded for time-travel debugging

Next Steps