agent
Skill6 starsChanged 23 days agoApache-2.0View on GitHub
Build streaming LLM agent loops in Go with typed parts and part deltas, tool execution, media input, context compaction, sub-agent delegation, and RLHF feedback. Use when building AI agents, integrating LLM providers, or implementing tool-use patterns.
We split this skill into the rules it gives an agent
1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 50
Every constructor takes a Config and options and returns an error.
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
agent
Build LLM agent loops using saige/agent.
Quick Start
A catalog preset builds the adapters. default serves the cheapest model of each vendor whose credentials are set, then a model pulled into a local Ollama.
import (
"github.com/urmzd/saige/agent"
"github.com/urmzd/saige/agent/provider/catalog"
"github.com/urmzd/saige/agent/provider/preset"
"github.com/urmzd/saige/agent/types"
)
bundle, err := preset.Build(ctx, catalog.Default(), "default", nil, preset.Options{})
if err != nil {
return err
}
defer bundle.Close(ctx)
a, err := agent.New(agent.Config{SystemPrompt: "Answer in one sentence."}, agent.WithPreset(bundle))
if err != nil {
return err
}
stream := a.Invoke(ctx, []types.Message{types.UserMsg(types.Text("Hello!"))})
for delta := range stream.Deltas() {
if d, ok := delta.(types.PartDelta); ok {
fmt.Print(d.Text)
}
}
if err := stream.Wait(); err != nil {
return err
}agent.CollectText(a.Invoke(...)) returns the final text instead.
One adapter
Every constructor takes a Config and options and returns an error. Types that own resources (bundles, routers, MCP pools, stores) close with Close(ctx); types.CloseProvider(ctx, p) closes a provider stack that owns any.
import "github.com/urmzd/saige/agent/provider/ollama"
adapter, err := ollama.New(ollama.Config{Host: "http://localhost:11434", Model: "qwen3.5:4b"})
if err != nil {
return err
}
a, err := agent.New(agent.Config{Provider: adapter, MaxIter: 10})The other adapters: anthropic.New(anthropic.Config{APIKey: key, Model: "claude-haiku-5-5"}), openai.New (Chat Completions) and openai.NewResponses (Responses API) with openai.Config, and google.New(ctx, google.Config{...}). provider.Build(ctx, provider.Config{Provider: "openai", Model: "gpt-6-luna"}) picks the adapter and serving API from the catalog.
Key Concepts
| Concept | Description |
| Messages | Ordered typed parts: types.UserMsg(types.Text("hi")), types.SystemMsg, types.AssistantMsg, types.ToolResults. The role seals which part kinds a message holds |
| Media | A part with a Source: types.Image(types.Bytes(types.MediaPNG, png)), types.Document(types.URL("https://...", types.MediaPDF)); types.Media(src) picks the kind by media type |
| Output | Streams as PartStart, PartDelta and PartEnd, each with the part's Index in the final message. types.NewPartAssembler() rebuilds the turn |
| Provider | Implement Stream(ctx, types.Request) (<-chan types.Delta, error); Request carries messages, tools, an optional schema and options |
| Tools | agent.Func derives a schema from a struct; types.ToolFunc for an inline tool; register them in types.NewToolRegistry |
| Conversion | Media the serving model cannot take is rejected unless a modality action is permitted (extract, describe, transcribe, omit) |
| Targets | Typed IDs (types.ModelID, ProfileID, PresetName, ProviderName); switch with types.ConfigPart{Target: types.PresetTarget("fast")} or p.WithTarget(types.ModelTarget(m)) |
| Compaction | CompactCfg: &types.CompactConfig{Strategy: types.CompactSummarize}; see the strategies in docs/guides/context-management.md |
| Sub-agents | SubAgents: []agent.SubAgentDef{...} gives the model delegate_to_<name> tools; handoffs with agent.WithHandoffs |
| Embeddings | types.Embedder; ollama.NewEmbedder(client) for Ollama-backed vectors |
| Feedback | a.Feedback(ctx, nodeID, types.RatingPositive, "comment") attaches an RLHF rating as a leaf node |
Adding a Tool
type GreetIn struct {
Name string `json:"name" description:"Person's name"`
}
greet := agent.Func("greet", "Greet a person",
func(rc agent.RunContext[agent.NoDeps], in GreetIn) (string, error) {
return "Hello, " + in.Name + "!", nil
})
a, err := agent.New(agent.Config{Provider: adapter, Tools: types.NewToolRegistry(greet)})An untyped tool:
tool := &types.ToolFunc{
Def: types.ToolDef{
Name: "greet", Description: "Greet a person",
Parameters: types.ParameterSchema{
Type: "object", Required: []string{"name"},
Properties: map[string]types.PropertyDef{
"name": {Type: "string", Description: "Person's name"},
},
},
},
Fn: func(ctx context.Context, args map[string]any) (string, error) {
return fmt.Sprintf("Hello, %s!", args["name"]), nil
},
}A tool that returns media or JSON implements types.RichTool, whose ExecuteRich returns types.ToolResult{Parts: []types.ToolOutputPart{types.Text(s), img}}.
Sending media
png, err := os.ReadFile("chart.png")
if err != nil {
return err
}
msg := types.UserMsg(
types.Text("What does this chart show?"),
types.Image(types.Bytes(types.MediaPNG, png)),
)To send a PDF to a model that takes no documents, permit extraction:
a, err := agent.New(agent.Config{Provider: adapter},
agent.WithConversion(types.ConversionPolicy{
Dial: types.ModalityDial{Per: map[types.Modality][]types.ModalityAction{types.ModalityDocument: {types.ActExtract}}},
Converters: []types.Converter{convert.Documents()},
}))Without a permitted action the request fails with an error matching types.ErrModalityUnsupported that names the part.
Implementing a Provider
type echo struct{}
func (echo) Stream(ctx context.Context, req types.Request) (<-chan types.Delta, error) {
ch := make(chan types.Delta, 4)
go func() {
defer close(ch)
text := "echo: " + types.TextOf(req.Messages[len(req.Messages)-1])
for _, d := range types.PartDeltas(0, types.Text(text)) {
ch <- d
}
ch <- types.DoneDelta{}
}()
return ch, nil
}A decorator embeds wrapper.Base (wrapper.NewBase(inner, rewrap)) so it forwards names, capabilities, WithTarget and Close without writing them.
Feedback (RLHF)
// Rate an assistant response: creates a dead-end branch off the target node.
tip, err := a.Tree().Tip(a.Tree().Active())
if err != nil {
return err
}
if _, err := a.Feedback(ctx, tip.ID, types.RatingPositive, "Clear and helpful"); err != nil {
return err
}
// Collect all feedback across the tree.
for _, entry := range a.FeedbackSummary() {
fmt.Printf("node=%s rating=%d comment=%q\n",
entry.TargetNodeID, entry.Rating, entry.Comment)
}Further reading
docs/guides/parts.md (parts, sources, part deltas, wire versions), docs/guides/modality-conversion.md, docs/reference/catalog.md, docs/guides/dials.md, docs/architecture/tool-calling.md, docs/guides/delegation.md and docs/guides/upgrading.md in the saige repository.
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 8
- atopile/atopile★ 4ksame name
- kstonekuan/webskills★ 8same name
- aihu-project/aihu★ 4same name
- gabriellst/codm★ 4same name
- venom444556/StoryFlow★ 4same name
- anthonymaley/Kerd★ 3same name
- SprintsAI/lightsprint-claude-code-plugin★ 2same name
- lyoneel/ly-agent-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository4 other files it gives its agents.
- AGENTS.mdAGENTS.md
- llms.txtllms.txt
- knowledge-graphSkill
- ragSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

