https://github.com/urmzd/saige

saige — Super AI Graph Ecosystem. A unified Go SDK and CLI for streaming AI agents, knowledge graphs, and RAG pipelines.
https://github.com/urmzd/saige
ai-agents ai-sdk anthropic cli conversation-tree go golang knowledge-graph llm ollama openai pgvector rag retrieval-augmented-generation rlhf streaming tool-use tui vector-search
Last synced: 2 months ago
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saige — Super AI Graph Ecosystem. A unified Go SDK and CLI for streaming AI agents, knowledge graphs, and RAG pipelines.
Host: GitHub
URL: https://github.com/urmzd/saige
Owner: urmzd
License: apache-2.0
Created: 2026-03-19T05:42:31.000Z (3 months ago)
Default Branch: main
Last Pushed: 2026-04-01T01:58:43.000Z (2 months ago)
Last Synced: 2026-04-01T04:27:50.346Z (2 months ago)
Topics: ai-agents, ai-sdk, anthropic, cli, conversation-tree, go, golang, knowledge-graph, llm, ollama, openai, pgvector, rag, retrieval-augmented-generation, rlhf, streaming, tool-use, tui, vector-search
Language: Go
Size: 5.41 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 6
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Agents: AGENTS.md
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README

          


  
saige

  

    Super Artificial Intelligence Graph Environment

    


    A unified Go SDK for streaming AI agents, knowledge graphs, and RAG pipelines.

    



    Install

    ·

    Report Bug

    ·

    Go Docs

  




  

  

  



## Showcase



  



## Features

- **Streaming-first agent loop** with 15 typed delta events and parallel tool execution

- **Functional options** — compose agents incrementally with `AgentOption` functions

- **Conversation tree** with branching, checkpoints, rewind, and RLHF feedback — all context-aware

- **Sub-agent delegation** — stateless child agents as tools, deltas forwarded with attribution

- **Human-in-the-loop markers** — gate tool execution pending approval

- **Structured tool errors** — `IsError` flag on tool results, distinguishable from successful output

- **Knowledge graph construction** — LLM-powered entity extraction, fuzzy dedup, temporal tracking

- **Multi-retriever RAG** — vector + BM25 + graph retrieval fused via Reciprocal Rank Fusion

- **Reranking** — MMR diversity and cross-encoder scoring built in

- **4 LLM providers** (Ollama, OpenAI, Anthropic, Google) behind one `Provider` interface

- **Provider resilience** — retry + fallback composition out of the box

- **Structured output** — constrain LLM responses to JSON schema

### Why one SDK?

Agent orchestration, knowledge graphs, and RAG pipelines are deeply interconnected — RAG benefits from graph retrieval, agents need both for grounded responses, and all three share providers and embedders. **saige** unifies them under shared `Provider`, `Embedder`, and `Tool` interfaces, eliminating the wiring complexity of combining separate libraries.

## Quick Start

```bash

go get github.com/urmzd/saige

```

### CLI

The `saige` CLI provides two interaction modes plus standalone RAG/KG operations:

```bash

# Interactive multi-turn chat (Bubble Tea TUI)

saige chat

saige chat --provider anthropic --model claude-sonnet-4-6-20250514

saige chat --verbose  # plain-text mode for pipes/CI

# Single-shot question (pipe-friendly)

saige ask "What is retrieval-augmented generation?"

echo "Explain transformers" | saige ask --raw

# With RAG/KG tools attached to the agent

saige chat --rag-db "postgres://localhost/mydb" --kg-db "postgres://localhost/mydb"

saige ask --rag-db "$SAIGE_RAG_DB" "What does the paper say about attention?"

# Standalone RAG operations (JSON output)

saige rag ingest --db "$SAIGE_RAG_DB" --file paper.pdf --mime application/pdf

saige rag search --db "$SAIGE_RAG_DB" --query "attention mechanism"

saige rag lookup --db "$SAIGE_RAG_DB" --uuid 

saige rag delete --db "$SAIGE_RAG_DB" --uuid 

# Standalone KG operations (JSON output)

saige kg ingest --db "$SAIGE_KG_DB" --name "meeting" --text "Alice presented the roadmap."

saige kg search --db "$SAIGE_KG_DB" --query "Who presented?"

saige kg graph  --db "$SAIGE_KG_DB" --limit 50

saige kg node   --db "$SAIGE_KG_DB" --id  --depth 2

```

**Provider auto-detection:** The CLI checks for `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `GOOGLE_API_KEY` in order, falling back to Ollama (no key needed). Override with `--provider` or `SAIGE_PROVIDER`.

### Build an Agent

```go

import (

    "github.com/urmzd/saige/agent"

    "github.com/urmzd/saige/agent/types"

    "github.com/urmzd/saige/agent/provider/ollama"

)

client := ollama.NewClient("http://localhost:11434", "qwen2.5", "nomic-embed-text")

a := agent.NewAgent(agent.AgentConfig{

    Name:         "assistant",

    SystemPrompt: "You are a helpful assistant.",

    Provider:     ollama.NewAdapter(client),

    Tools:        types.NewToolRegistry(myTool),

})

// Or compose incrementally with functional options:

a := agent.NewAgent(agent.AgentConfig{

    Name:         "assistant",

    SystemPrompt: "You are a helpful assistant.",

    Provider:     ollama.NewAdapter(client),

    Tools:        types.NewToolRegistry(myTool),

},

    agent.WithMaxIter(20),

    agent.WithLogger(slog.Default()),

    agent.WithMetrics(myMetrics),

)

stream := a.Invoke(ctx, []types.Message{types.NewUserMessage("Hello!")})

for delta := range stream.Deltas() {

    switch d := delta.(type) {

    case types.TextContentDelta:

        fmt.Print(d.Content)

    }

}

```

### Build a Knowledge Graph

```go

import (

    "github.com/urmzd/saige/knowledge"

    "github.com/urmzd/saige/knowledge/types"

    "github.com/urmzd/saige/postgres"

    "github.com/urmzd/saige/agent/provider/ollama"

)

// Connect to PostgreSQL (requires pgvector extension).

pool, _ := postgres.NewPool(ctx, postgres.Config{URL: "postgres://localhost:5432/mydb"})

postgres.RunMigrations(ctx, pool, postgres.MigrationOptions{})

client := ollama.NewClient("http://localhost:11434", "qwen2.5", "nomic-embed-text")

graph, _ := knowledge.NewGraph(ctx,

    knowledge.WithPostgres(pool),

    knowledge.WithExtractor(knowledge.NewOllamaExtractor(client)),

    knowledge.WithEmbedder(knowledge.NewOllamaEmbedder(client)),

)

defer graph.Close(ctx)

graph.IngestEpisode(ctx, &types.EpisodeInput{

    Name: "meeting-notes",

    Body: "Alice presented the Q4 roadmap. Bob raised concerns about the timeline.",

})

results, _ := graph.SearchFacts(ctx, "Who presented the roadmap?")

```

### Build a RAG Pipeline

```go

import (

    "github.com/urmzd/saige/rag"

    "github.com/urmzd/saige/rag/types"

    "github.com/urmzd/saige/rag/pgstore"

    "github.com/urmzd/saige/postgres"

)

// Reuse the same PostgreSQL pool (or create a new one).

pool, _ := postgres.NewPool(ctx, postgres.Config{URL: "postgres://localhost:5432/mydb"})

postgres.RunMigrations(ctx, pool, postgres.MigrationOptions{})

pipe, _ := rag.NewPipeline(

    rag.WithStore(pgstore.NewStore(pool, nil)),

    rag.WithContentExtractor(myExtractor),

    rag.WithEmbedders(myEmbedderRegistry),

    rag.WithRecursiveChunker(512, 50),

    rag.WithBM25(nil),

    rag.WithMMR(0.7),

)

defer pipe.Close(ctx)

pipe.Ingest(ctx, &types.RawDocument{

    SourceURI: "https://example.com/paper.pdf",

    Data:      pdfBytes,

})

result, _ := pipe.Search(ctx, "attention mechanism", types.WithLimit(5))

fmt.Println(result.AssembledContext.Prompt) // context with citations

```

---

## Table of Contents

- [CLI](#cli)

- [agent — AI Agent Framework](#agent--ai-agent-framework) (providers, deltas, tools, sub-agents, markers, feedback/RLHF, compaction, tree, TUI)

- [kg — Knowledge Graph SDK](#kg--knowledge-graph-sdk)

- [rag — RAG Pipeline SDK](#rag--rag-pipeline-sdk)

- [Examples](#examples)

- [Agent Skill](#agent-skill)

---

## agent — AI Agent Framework

Streaming-first agent loop with parallel tool execution, sub-agent delegation, human-in-the-loop markers, conversation tree persistence, and multi-provider resilience.

### Provider Interface

Implement one method to integrate any LLM backend:

```go

type Provider interface {

    ChatStream(ctx context.Context, messages []Message, tools []ToolDef) (<-chan Delta, error)

}

```

**Built-in providers:**

| Provider | Package | Structured Output | Content Negotiation | Embedder |

|----------|---------|:-:|:-:|:-:|

| Ollama | `agent/provider/ollama` | yes | JPEG, PNG | yes |

| OpenAI | `agent/provider/openai` | yes | JPEG, PNG, GIF, WebP, PDF | yes |

| Anthropic | `agent/provider/anthropic` | yes | JPEG, PNG, GIF, WebP, PDF | — |

| Google | `agent/provider/google` | yes | JPEG, PNG, GIF, WebP, PDF | yes |

### Messages

Three roles. Tool results are content blocks, not a separate role.

| Type | Role | Content Types |

|------|------|---------------|

| `SystemMessage` | system | `TextContent`, `ToolResultContent`, `ConfigContent` |

| `UserMessage` | user | `TextContent`, `ToolResultContent`, `ConfigContent`, `FileContent` |

| `AssistantMessage` | assistant | `TextContent`, `ToolUseContent` |

`ToolResultContent` carries an `IsError` field that signals whether the text represents an error or a successful result. This distinction is preserved through to the LLM — Anthropic passes it natively, Google uses an `error` key in the function response, and OpenAI/Ollama prefix the text with `[TOOL ERROR]`.

### Deltas

15 concrete types across five categories — LLM-side, execution-side, marker, feedback, and metadata:

| Type | Category | Purpose |

|------|----------|---------|

| `TextStartDelta` | LLM | Text block opened |

| `TextContentDelta` | LLM | Text chunk |

| `TextEndDelta` | LLM | Text block closed |

| `ToolCallStartDelta` | LLM | Tool call generation started |

| `ToolCallArgumentDelta` | LLM | JSON argument chunk |

| `ToolCallEndDelta` | LLM | Tool call complete |

| `ToolExecStartDelta` | Execution | Tool began executing |

| `ToolExecDelta` | Execution | Streaming delta from tool/sub-agent |

| `ToolExecEndDelta` | Execution | Tool finished |

| `MarkerDelta` | Marker | Tool gated pending approval |

| `FeedbackDelta` | Feedback | RLHF rating recorded on a node |

| `UsageDelta` | Metadata | Token usage + wall-clock timing |

| `ErrorDelta` | Terminal | Provider or tool error |

| `DoneDelta` | Terminal | Stream complete |

### Tools

```go

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

    },

}

```

When the LLM requests multiple tool calls, all tools execute **concurrently**.

### Sub-Agents

Sub-agents are registered as tools and execute within parallel tool dispatch. Their deltas are forwarded through the parent's stream. **Sub-agents are stateless** — a fresh agent is constructed for each delegation, so conversation history is not preserved between calls. This is intentional: sub-agents are task executors, not persistent conversational partners.

```go

a := agent.NewAgent(agent.AgentConfig{

    Provider: adapter,

    SubAgents: []agent.SubAgentDef{

        {

            Name:         "researcher",

            Description:  "Searches the web for information",

            SystemPrompt: "You are a research assistant.",

            Provider:     adapter,

            Tools:        types.NewToolRegistry(searchTool),

        },

    },

})

```

### Markers (Human-in-the-Loop)

Gate tool execution pending consumer approval:

```go

safeTool := types.WithMarkers(myTool,

    types.Marker{Kind: "human_approval", Message: "This modifies production data."},

)

// Consumer resolves:

stream.ResolveMarker(d.ToolCallID, approved, nil)

```

### Structured Output

Constrain LLM responses to a JSON schema:

```go

schema := types.SchemaFrom[MyResponse]()

a := agent.NewAgent(agent.AgentConfig{

    Provider: adapter,

}, agent.WithResponseSchema(schema))

```

### Provider Resilience

```go

import (

    "github.com/urmzd/saige/agent/provider/retry"

    "github.com/urmzd/saige/agent/provider/fallback"

)

provider := fallback.New(

    retry.New(primary, retry.DefaultConfig()),

    retry.New(backup, retry.DefaultConfig()),

)

```

### Compaction

Data-driven context management:

| Strategy | Behavior |

|----------|----------|

| `CompactNone` | No compaction |

| `CompactSlidingWindow` | Keep system prompt + last N messages |

| `CompactSummarize` | Summarize older messages via the provider |

### Conversation Tree

Persistent branching conversation graph with checkpoints, rewind, and archive. All mutation methods (`AddChild`, `Branch`, `UpdateUserMessage`, `AddFeedback`) accept a `context.Context` for cancellation, deadlines, and tracing — including WAL writes:

```go

tr := a.Tree()

tr.AddChild(ctx, parentID, msg)

tr.Branch(ctx, nodeID, "experiment", msg)

tr.UpdateUserMessage(ctx, nodeID, newMsg)

tr.Checkpoint(branchID, "before-refactor")

tr.Rewind(checkpointID)

```

### Feedback (RLHF)

Attach positive/negative ratings and comments to any node in the conversation tree. Feedback is stored as permanent leaf nodes branching off the target — never sent to the LLM, available for post-analysis and training.

```go

// Rate an assistant response.

tip, _ := a.Tree().Tip(a.Tree().Active())

a.Feedback(ctx, tip.ID, types.RatingPositive, "Clear and helpful")

a.Feedback(ctx, tip.ID, types.RatingNegative, "Too verbose")

// 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)

}

```

Feedback nodes have `NodeFeedback` state — they cannot have children added, forming dead-end branches that don't interfere with the conversation flow. During `Replay`, feedback emits `FeedbackDelta` for consumers that track ratings.

### File Pipeline

Automatic URI resolution and content negotiation for multi-modal input:

```go

a := agent.NewAgent(agent.AgentConfig{

    Provider: adapter,

},

    agent.WithResolvers(map[string]types.Resolver{

        "file": myFileResolver,

        "s3":   myS3Resolver,

    }),

    agent.WithExtractors(map[types.MediaType]types.Extractor{

        types.MediaPDF: myPDFExtractor,

    }),

)

```

### TUI

Three modes for streaming agent interaction:

```go

import "github.com/urmzd/saige/agent/tui"

// Non-interactive (works in pipes/CI)

result := tui.StreamVerbose(header, stream.Deltas(), os.Stdout)

// Interactive single-stream (bubbletea)

model := tui.NewStreamModel(header, stream.Deltas())

tea.NewProgram(model).Run()

// Multi-turn conversation loop (reads input, resolves markers, loops until /quit)

runner := &tui.Runner{Title: "My Agent"}

runner.Run(ctx, myAgent)

```

### Testing

```go

import "github.com/urmzd/saige/agent/agenttest"

provider := &agenttest.ScriptedProvider{

    Responses: [][]types.Delta{

        agenttest.ToolCallResponse("id-1", "greet", map[string]any{"name": "Alice"}),

        agenttest.TextResponse("Hello, Alice!"),

    },

}

```

---

## knowledge — Knowledge Graph SDK

Build and query knowledge graphs with LLM-powered entity extraction, fuzzy deduplication, and hybrid search.

### Graph Interface

```go

type Graph interface {

    ApplyOntology(ctx, ontology) error

    IngestEpisode(ctx, episode) (*IngestResult, error)

    GetEntity(ctx, uuid) (*Entity, error)

    SearchFacts(ctx, query, opts...) (*SearchFactsResult, error)

    GetGraph(ctx) (*GraphData, error)

    GetNode(ctx, uuid, depth) (*NodeDetail, error)

    GetFactProvenance(ctx, factID) ([]Episode, error)

    Close(ctx) error

}

```

### Core Types

| Type | Purpose |

|------|---------|

| `Entity` | Node — UUID, Name, Type, Summary, Embedding |

| `Relation` | Edge — Source/Target UUID, Type, Fact, ValidAt/InvalidAt |

| `Fact` | Relation with resolved source/target entities |

| `Episode` | Text input with Name, Body, Source, GroupID, Metadata |

| `Ontology` | Schema constraints — EntityTypes, RelationTypes |

### Hybrid Search

Combines vector similarity (HNSW) and full-text (BM25) via **Reciprocal Rank Fusion**:

```go

results, _ := graph.SearchFacts(ctx, "Who works at Acme?",

    types.WithLimit(10),

    types.WithGroupID("project-alpha"),

)

for _, fact := range knowledge.FactsToStrings(results.Facts) {

    fmt.Println(fact) // "Alice -> Acme Corp: works at"

}

```

### Deduplication

- **Exact match** by (name, type) pair

- **Fuzzy match** via Levenshtein distance (threshold 0.8)

- **Relation dedup** by text similarity (threshold 0.92)

### Graph Traversal

```go

detail, _ := graph.GetNode(ctx, entityUUID, 2) // BFS to depth 2

sub := knowledge.Subgraph(detail)                      // extract visualization data

```

### PostgreSQL Backend

Automatic schema provisioning via `postgres.RunMigrations` with pgvector HNSW index (configurable dimension, cosine distance), tsvector fulltext search, pg_trgm fuzzy matching, unique constraints, and temporal relation tracking.

---

## rag — RAG Pipeline SDK

Multi-modal document ingestion with pluggable chunking, retrieval, reranking, and context assembly.

### Data Model

```

Document (fingerprint for dedup, metadata, source URI)

  └── Section[] (ordered by index, optional heading)

        └── ContentVariant[] (text, image, table, audio — each with bytes, embedding, MIME)

```

Every `ContentVariant` has a `.Text` field that is always populated, enabling uniform search and entity extraction.

### Pipeline Interface

```go

type Pipeline interface {

    Ingest(ctx, raw) (*IngestResult, error)

    Search(ctx, query, opts...) (*SearchPipelineResult, error)

    Lookup(ctx, variantUUID) (*SearchHit, error)

    Update(ctx, documentUUID, raw) (*IngestResult, error)

    Delete(ctx, documentUUID) error

    Reconstruct(ctx, documentUUID) (*Document, error)

    Close(ctx) error

}

```

### Chunking

| Strategy | Description |

|----------|-------------|

| Recursive | Tries separators (`\n\n`, `\n`, `. `, ` `) with configurable overlap |

| Semantic | Splits where embedding similarity drops below threshold |

```go

rag.WithRecursiveChunker(512, 50)     // maxSize, overlap

rag.WithSemanticChunker(0.1, 100, 1000) // threshold, minSize, maxSize

```

### Retrieval

| Retriever | Description |

|-----------|-------------|

| Vector | Embed query, cosine similarity search |

| BM25 | In-memory inverted index with configurable K1/B |

| Graph | Knowledge graph facts resolved to document variants via episode provenance |

| Parent | Wraps any retriever, expands hits to full parent section context |

Multiple retrievers are combined via **Reciprocal Rank Fusion**.

```go

rag.WithBM25(nil)          // default K1=1.2, B=0.75

rag.WithParentContext()    // expand to parent sections

```

### Reranking

| Reranker | Description |

|----------|-------------|

| MMR | Maximal Marginal Relevance — balances relevance and diversity |

| Cross-Encoder | Pair-wise scoring via custom `Scorer` interface |

```go

rag.WithMMR(0.7)                    // lambda=0.7

rag.WithCrossEncoder(myScorer)      // custom scorer

```

### Context Assembly

Built-in citation support:

```go

// Default: numbered citations with source URIs

// Compressing: LLM-based extraction of relevant sentences

rag.WithCompression(myLLM)

```

### Query Transformation

**HyDE** (Hypothetical Document Embeddings) — generates hypothetical documents via LLM for better retrieval:

```go

rag.WithHyDE(myLLM, 3) // generate 3 hypothetical docs

```

### Evaluation Metrics

9 metrics across retrieval, generation, and end-to-end evaluation:

| Metric | Type | Description |

|--------|------|-------------|

| `ContextPrecision` | Retrieval | Average Precision over relevant UUIDs |

| `ContextRecall` | Retrieval | Fraction of relevant UUIDs in results |

| `NDCG` | Retrieval | Normalized Discounted Cumulative Gain at rank k |

| `MRR` | Retrieval | Reciprocal Rank of first relevant result |

| `HitRate` | Retrieval | Binary: any relevant doc in top-k? |

| `Faithfulness` | Generation | Claim decomposition + verification against context |

| `AnswerRelevancy` | Generation | RAGAS-style synthetic question similarity |

| `AnswerCorrectness` | Generation | LLM-judged comparison to ground truth |

| `LLMJudge` | Generation | Pointwise scoring with custom rubric |

```go

import "github.com/urmzd/saige/rag/eval"

// Retrieval metrics (pure functions, no LLM needed).

precision := eval.ContextPrecision(hits, relevantUUIDs)

recall := eval.ContextRecall(hits, relevantUUIDs)

ndcg := eval.NDCG(hits, relevantUUIDs, 10)

mrr := eval.MRR(hits, relevantUUIDs)

hitRate := eval.HitRate(hits, relevantUUIDs, 10)

// Generation metrics (require LLM and/or embedders).

faith, detail, _ := eval.Faithfulness(ctx, response, contextText, llm)

relevancy, _ := eval.AnswerRelevancy(ctx, query, response, llm, embedders, 3)

correctness, _ := eval.AnswerCorrectness(ctx, response, groundTruth, llm)

score, reason, _ := eval.LLMJudge(ctx, query, response, contextText, rubric, llm)

// Full evaluation pipeline with functional options.

results, _ := eval.Evaluate(ctx, cases, pipeline,

    eval.WithLLM(llm),

    eval.WithEmbedders(embedders),

    eval.WithK(10),

    eval.WithJudgeRubric("Score helpfulness, accuracy, and completeness."),

)

```

### Agent Tool Bindings

5 RAG tools and 2 KG tools for integrating into agent workflows:

```go

import (

    ragtool "github.com/urmzd/saige/rag/tool"

    kgtool "github.com/urmzd/saige/knowledge/tool"

)

ragTools := ragtool.NewTools(pipeline)

// rag_search, rag_lookup, rag_update, rag_delete, rag_reconstruct

kgTools := kgtool.NewTools(graph)

// kg_search, kg_ingest

```

---

## Examples

| Example | Path | Description |

|---------|------|-------------|

| Basic Agent | `examples/agent/basic/` | Single tool with Ollama |

| Sub-agents | `examples/agent/subagents/` | Parent delegating to researcher |

| Resilient | `examples/agent/resilient/` | Retry + fallback composition |

| Streaming | `examples/agent/streaming/` | All delta types with ANSI output |

| Multimodal | `examples/agent/multimodal/` | File pipeline with `file://` resolver |

| TUI | `examples/agent/tui/` | Interactive and verbose modes |

| Runner | `examples/agent/runner/` | Multi-turn conversation loop |

| Concurrent | `examples/agent/concurrent-subagents/` | Parallel sub-agent execution |

| Knowledge Graph | `examples/knowledge/basic/` | Build and query a knowledge graph |

| RAG | `examples/rag/arxiv/` | Full pipeline with arXiv papers |

```bash

go run ./examples/agent/basic/

go run ./examples/knowledge/basic/

go run ./examples/rag/arxiv/

```

## Agent Skill

This repo's conventions are available as portable agent skills in [`skills/`](skills/).

## License

Apache 2.0 — see [LICENSE](LICENSE).
ecosyste.ms

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