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Add multi-pattern orchestrator with plan-then-execute and supervisor (#739)
Introduce an agent orchestrator service that supports three execution patterns (ReAct, plan-then-execute, supervisor) with LLM-based meta-routing to select the appropriate pattern and task type per request. Update the agent schema to support orchestration fields (correlation, sub-agents, plan steps) and remove legacy response fields (answer, thought, observation).
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939
docs/tech-specs/agent-orchestration.md
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docs/tech-specs/agent-orchestration.md
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# TrustGraph Agent Orchestration — Technical Specification
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## Overview
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This specification describes the extension of TrustGraph's agent architecture
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from a single ReACT execution pattern to a multi-pattern orchestration
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model. The existing Pulsar-based self-queuing loop is pattern-agnostic — the
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same infrastructure supports ReACT, Plan-then-Execute, Supervisor/Subagent
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fan-out, and other execution strategies without changes to the message
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transport. The extension adds a routing layer that selects the appropriate
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pattern for each task, a set of pattern implementations that share common
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iteration infrastructure, and a fan-out/fan-in mechanism for multi-agent
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coordination.
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The central design principle is that
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**trust and explainability are structural properties of the architecture**,
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achieved by constraining LLM decisions to
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graph-defined option sets and recording those constraints in the execution
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trace.
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---
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## Background
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### Existing Architecture
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The current agent manager is built on the ReACT pattern (Reasoning + Acting)
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with these properties:
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- **Self-queuing loop**: Each iteration emits a new Pulsar message carrying
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the accumulated history. The agent manager picks this up and runs the next
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iteration.
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- **Stateless agent manager**: No in-process state. All state lives in the
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message payload.
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- **Natural parallelism**: Multiple independent agent requests are handled
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concurrently across Pulsar consumers.
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- **Durability**: Crash recovery is inherent — the message survives process
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failure.
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- **Real-time feedback**: Streaming thought, action, observation and answer
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chunks are emitted as iterations complete.
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- **Tool calling and MCP invocation**: Tool calls into knowledge graphs,
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external services, and MCP-connected systems.
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- **Decision traces written to the knowledge graph**: Every iteration records
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PROV-O triples — session, analysis, and conclusion entities — forming the
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basis of explainability.
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### Current Message Flow
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```
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AgentRequest arrives (question, history=[], state, group, session_id)
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│
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▼
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Filter tools by group/state
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│
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▼
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AgentManager.react() → LLM call → parse → Action or Final
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│ │
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│ [Action] │ [Final]
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▼ ▼
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Execute tool, capture observation Emit conclusion triples
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Emit iteration triples Send AgentResponse
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Append to history (end_of_dialog=True)
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Emit new AgentRequest → "next" topic
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│
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└── (picked up again by consumer, loop continues)
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```
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The key insight is that this loop structure is not ReACT-specific. The
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plumbing — receive message, do work, emit next message — is the same
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regardless of what the "work" step does. The payload and the pattern logic
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define the behaviour; the infrastructure remains constant.
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### Current Limitations
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- Only one execution pattern (ReACT) is available regardless of task
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characteristics.
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- No mechanism for one agent to spawn and coordinate subagents.
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- Pattern selection is implicit — every task gets the same treatment.
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- The provenance model assumes a linear iteration chain (analysis N derives
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from analysis N-1), with no support for parallel branches.
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---
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## Design Goals
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- **Pattern-agnostic iteration infrastructure**: The self-queuing loop, tool
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filtering, provenance emission, and streaming feedback should be shared
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across all patterns.
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- **Graph-constrained pattern selection**: The LLM selects patterns from a
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graph-defined set, not from unconstrained reasoning. This makes the
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selection auditable and explainable.
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- **Genuinely parallel fan-out**: Subagent tasks execute concurrently on the
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Pulsar queue, not sequentially in a single process.
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- **Stateless coordination**: Fan-in uses the knowledge graph as coordination
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substrate. The agent manager remains stateless.
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- **Additive change**: The existing ReACT flow continues to work
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unchanged. New patterns are added alongside it, not in place of it.
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---
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## Patterns
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### ReACT as One Pattern Among Many
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ReACT is one point in a wider space of agent execution strategies:
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| Pattern | Structure | Strengths |
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|---|---|---|
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| **ReACT** | Interleaved reasoning and action | Adaptive, good for open-ended tasks |
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| **Plan-then-Execute** | Decompose into a step DAG, then execute | More predictable, auditable plan |
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| **Reflexion** | ReACT + self-critique after each action | Agents improve within the episode |
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| **Supervisor/Subagent** | One agent orchestrates others | Parallel decomposition, synthesis |
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| **Debate/Ensemble** | Multiple agents reason independently | Diverse perspectives, reconciliation |
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| **LLM-as-router** | No reasoning loop, pure dispatch | Fast classification and routing |
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Not all of these need to be implemented at once. The architecture should
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support them; the initial implementation delivers ReACT (already exists),
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Plan-then-Execute, and Supervisor/Subagent.
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### Pattern Storage
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Patterns are stored as configuration items via the config API. They are
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finite in number, mechanically well-defined, have enumerable properties,
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and change slowly. Each pattern is a JSON object stored under the
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`agent-pattern` config type.
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```json
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Config type: "agent-pattern"
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Config key: "react"
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Value: {
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"name": "react",
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"description": "ReACT — Reasoning + Acting",
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"when_to_use": "Adaptive, good for open-ended tasks"
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}
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```
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These are written at deployment time and change rarely. If the architecture
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later benefits from graph-based pattern storage (e.g. for richer ontological
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relationships), the config items can be migrated to graph nodes — the
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meta-router's selection logic is the same regardless of backend.
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---
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## Task Types
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### What a Task Type Represents
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A **task type** characterises the problem domain — what the agent is being
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asked to accomplish, and how a domain expert would frame it analytically.
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- Carries domain-specific methodology (e.g. "intelligence analysis always
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applies structured analytic techniques")
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- Pre-populates initial reasoning context via a framing prompt
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- Constrains which patterns are valid for this class of problem
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- Can define domain-specific termination criteria
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### Identification
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Task types are identified from plain-text task descriptions by the
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LLM. Building a formal ontology over task descriptions is premature — natural
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language is too varied and context-dependent. The LLM reads the description;
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the graph provides the structure downstream.
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### Task Type Storage
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Task types are stored as configuration items via the config API under the
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`agent-task-type` config type. Each task type is a JSON object that
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references valid patterns by name.
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```json
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Config type: "agent-task-type"
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Config key: "risk-assessment"
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Value: {
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"name": "risk-assessment",
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"description": "Due Diligence / Risk Assessment",
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"framing_prompt": "Analyse across financial, reputational, legal and operational dimensions using structured analytic techniques.",
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"valid_patterns": ["supervisor", "plan-then-execute", "react"],
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"when_to_use": "Multi-dimensional analysis requiring structured assessment"
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}
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```
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The `valid_patterns` list defines the constrained decision space — the LLM
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can only select patterns that the task type's configuration says are valid.
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This is the many-to-many relationship between task types and patterns,
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expressed as configuration rather than graph edges.
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### Selection Flow
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```
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Task Description (plain text, from AgentRequest.question)
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│
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│ [LLM interprets, constrained by available task types from config]
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▼
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Task Type (config item — domain framing and methodology)
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│
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│ [config lookup — valid_patterns list]
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▼
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Pattern Candidates (config items)
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│
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│ [LLM selects within constrained set,
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│ informed by task description signals:
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│ complexity, urgency, scope]
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▼
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Selected Pattern
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```
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The task description may carry modulating signals (complexity, urgency, scope)
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that influence which pattern is selected within the constrained set. But the
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raw description never directly selects a pattern — it always passes through
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the task type layer first.
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---
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## Explainability Through Constrained Decision Spaces
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A central principle of TrustGraph's explainability architecture is that
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**explainability comes from constrained decision spaces**.
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When a decision is made from an unconstrained space — a raw LLM call with no
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guardrails — the reasoning is opaque even if the LLM produces a rationale,
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because that rationale is post-hoc and unverifiable.
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When a decision is made from a **constrained set defined in configuration**,
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you can always answer:
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- What valid options were available
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- What criteria narrowed the set
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- What signal made the final selection within that set
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This principle already governs the existing decision trace architecture and
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extends naturally to pattern selection. The routing decision — which task type
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and which pattern — is itself recorded as a provenance node, making the first
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decision in the execution trace auditable.
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**Trust becomes a structural property of the architecture, not a claimed
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property of the model.**
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---
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## Orchestration Architecture
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### The Meta-Router
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The meta-router is the entry point for all agent requests. It runs as a
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pre-processing step before the pattern-specific iteration loop begins. Its
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job is to determine the task type and select the execution pattern.
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**When it runs**: On receipt of an `AgentRequest` with empty history (i.e. a
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new task, not a continuation). Requests with non-empty history are already
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mid-iteration and bypass the meta-router.
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**What it does**:
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1. Lists all available task types from the config API
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(`config.list("agent-task-type")`).
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2. Presents these to the LLM alongside the task description. The LLM
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identifies which task type applies (or "general" as a fallback).
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3. Reads the selected task type's configuration to get the `valid_patterns`
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list.
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4. Loads the candidate pattern definitions from config and presents them to
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the LLM. The LLM selects one, influenced by signals in the task
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description (complexity, number of independent dimensions, urgency).
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5. Records the routing decision as a provenance node (see Provenance Model
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below).
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6. Populates the `AgentRequest` with the selected pattern, task type framing
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prompt, and any pattern-specific configuration, then emits it onto the
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queue.
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**Where it lives**: The meta-router is a phase within the agent-orchestrator,
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not a separate service. The agent-orchestrator is a new executable that
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uses the same service identity as the existing agent-manager-react, making
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it a drop-in replacement on the same Pulsar queues. It includes the full
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ReACT implementation alongside the new orchestration patterns. The
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distinction between "route" and "iterate" is determined by whether the
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request already has a pattern set.
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### Pattern Dispatch
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Once the meta-router has annotated the request with a pattern, the agent
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manager dispatches to the appropriate pattern implementation. This is a
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straightforward branch on the pattern field:
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```
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request arrives
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│
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├── history is empty → meta-router → annotate with pattern → re-emit
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│
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└── history is non-empty (or pattern is set)
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│
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├── pattern = "react" → ReACT iteration
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├── pattern = "plan-then-execute" → PtE iteration
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├── pattern = "supervisor" → Supervisor iteration
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└── (no pattern) → ReACT iteration (default)
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```
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Each pattern implementation follows the same contract: receive a request, do
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one iteration of work, then either emit a "next" message (continue) or emit a
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response (done). The self-queuing loop doesn't change.
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### Pattern Implementations
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#### ReACT (Existing)
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No changes. The existing `AgentManager.react()` path continues to work
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as-is.
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#### Plan-then-Execute
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Two-phase pattern:
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**Planning phase** (first iteration):
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- LLM receives the question plus task type framing.
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- Produces a structured plan: an ordered list of steps, each with a goal,
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expected tool, and dependencies on prior steps.
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- The plan is recorded in the history as a special "plan" step.
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- Emits a "next" message to begin execution.
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**Execution phase** (subsequent iterations):
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- Reads the plan from history.
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- Identifies the next unexecuted step.
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- Executes that step (tool call + observation), similar to a single ReACT
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action.
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- Records the result against the plan step.
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- If all steps complete, synthesises a final answer.
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- If a step fails or produces unexpected results, the LLM can revise the
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remaining plan (bounded re-planning, not a full restart).
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The plan lives in the history, so it travels with the message. No external
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state is needed.
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#### Supervisor/Subagent
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The supervisor pattern introduces fan-out and fan-in. This is the most
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architecturally significant addition.
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**Supervisor planning iteration**:
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- LLM receives the question plus task type framing.
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- Decomposes the task into independent subagent goals.
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- For each subagent, emits a new `AgentRequest` with:
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- A focused question (the subagent's specific goal)
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- A shared correlation ID tying it to the parent task
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- The subagent's own pattern (typically ReACT, but could be anything)
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- Relevant context sliced from the parent request
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**Subagent execution**:
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- Each subagent request is picked up by an agent manager instance and runs its
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own independent iteration loop.
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- Subagents are ordinary agent executions — they self-queue, use tools, emit
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provenance, stream feedback.
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- When a subagent reaches a Final answer, it writes a completion record to the
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knowledge graph under the shared correlation ID.
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**Fan-in and synthesis**:
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- An aggregator detects when all sibling subagents for a correlation ID have
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completed.
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- It emits a synthesis request to the supervisor carrying the correlation ID.
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- The supervisor queries the graph for subagent results, reasons across them,
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and decides whether to emit a final answer or iterate again.
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**Supervisor re-iteration**:
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- After synthesis, the supervisor may determine that the results are
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incomplete, contradictory, or reveal gaps requiring further investigation.
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- Rather than emitting a final answer, it can fan out again with new or
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refined subagent goals under a new correlation ID. This is the same
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self-queuing loop — the supervisor emits new subagent requests and stops,
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the aggregator detects completion, and synthesis runs again.
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- The supervisor's iteration count (planning + synthesis rounds) is bounded
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to prevent unbounded looping.
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This is detailed further in the Fan-Out / Fan-In section below.
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---
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## Message Schema Evolution
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### Shared Schema Principle
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The `AgentRequest` and `AgentResponse` schemas are the shared contract
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between the agent-manager (existing ReACT execution) and the
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agent-orchestrator (meta-routing, supervisor, plan-then-execute). Both
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services consume from the same *agent request* topic using the same
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schema. Any schema changes must be reflected in both — the schema is
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the integration point, not the service implementation.
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This means the orchestrator does not introduce separate message types for
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its own use. Subagent requests, synthesis triggers, and meta-router
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outputs are all `AgentRequest` messages with different field values. The
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agent-manager ignores orchestration fields it doesn't use.
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### New Fields
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The `AgentRequest` schema needs new fields to carry orchestration
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metadata.
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```python
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@dataclass
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class AgentRequest:
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# Existing fields (unchanged)
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question: str = ""
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state: str = ""
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group: list[str] | None = None
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history: list[AgentStep] = field(default_factory=list)
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user: str = ""
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collection: str = "default"
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streaming: bool = False
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session_id: str = ""
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# New orchestration fields
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conversation_id: str = "" # Optional caller-generated ID grouping related requests
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pattern: str = "" # "react", "plan-then-execute", "supervisor", ""
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task_type: str = "" # Identified task type name
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framing: str = "" # Task type framing prompt injected into LLM context
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correlation_id: str = "" # Shared ID linking subagents to parent
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parent_session_id: str = "" # Parent's session_id (for subagents)
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subagent_goal: str = "" # Focused goal for this subagent
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expected_siblings: int = 0 # How many sibling subagents exist
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```
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The `AgentStep` schema also extends to accommodate non-ReACT iteration types:
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```python
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@dataclass
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class AgentStep:
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# Existing fields (unchanged)
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thought: str = ""
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action: str = ""
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arguments: dict[str, str] = field(default_factory=dict)
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observation: str = ""
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user: str = ""
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# New fields
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step_type: str = "" # "react", "plan", "execute", "supervise", "synthesise"
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plan: list[PlanStep] | None = None # For plan-then-execute: the structured plan
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subagent_results: dict | None = None # For supervisor: collected subagent outputs
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```
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The `PlanStep` structure for Plan-then-Execute:
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```python
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@dataclass
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class PlanStep:
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goal: str = "" # What this step should accomplish
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tool_hint: str = "" # Suggested tool (advisory, not binding)
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depends_on: list[int] = field(default_factory=list) # Indices of prerequisite steps
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status: str = "pending" # "pending", "complete", "failed", "revised"
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result: str = "" # Observation from execution
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```
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---
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## Fan-Out and Fan-In
|
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|
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### Why This Matters
|
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|
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Fan-out is the mechanism that makes multi-agent coordination genuinely
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parallel rather than simulated. With Pulsar, emitting multiple messages means
|
||||
multiple consumers can pick them up concurrently. This is not threading or
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async simulation — it is real distributed parallelism across agent manager
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instances.
|
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|
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### Fan-Out: Supervisor Emits Subagent Requests
|
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When a supervisor iteration decides to decompose a task, it:
|
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1. Generates a **correlation ID** — a UUID that groups the sibling subagents.
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2. For each subagent, constructs a new `AgentRequest`:
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- `question` = the subagent's focused goal (from `subagent_goal`)
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- `correlation_id` = the shared correlation ID
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- `parent_session_id` = the supervisor's session_id
|
||||
- `pattern` = typically "react", but the supervisor can specify any pattern
|
||||
- `session_id` = a new unique ID for this subagent's own provenance chain
|
||||
- `expected_siblings` = total number of sibling subagents
|
||||
- `history` = empty (fresh start, but framing context inherited)
|
||||
- `group`, `user`, `collection` = inherited from parent
|
||||
3. Emits each subagent request onto the agent request topic.
|
||||
4. Records the fan-out decision in the provenance graph (see below).
|
||||
|
||||
The supervisor then **stops**. It does not wait. It does not poll. It has
|
||||
emitted its messages and its iteration is complete. The graph and the
|
||||
aggregator handle the rest.
|
||||
|
||||
### Fan-In: Graph-Based Completion Detection
|
||||
|
||||
When a subagent reaches its Final answer, it writes a **completion node** to
|
||||
the knowledge graph:
|
||||
|
||||
```
|
||||
Completion node:
|
||||
rdf:type tg:SubagentCompletion
|
||||
tg:correlationId <shared correlation ID>
|
||||
tg:subagentSessionId <this subagent's session_id>
|
||||
tg:parentSessionId <supervisor's session_id>
|
||||
tg:subagentGoal <what this subagent was asked to do>
|
||||
tg:result → <document URI in librarian>
|
||||
prov:wasGeneratedBy → <this subagent's conclusion entity>
|
||||
```
|
||||
|
||||
The **aggregator** is a component that watches for completion nodes. When it
|
||||
detects that all expected siblings for a correlation ID have written
|
||||
completion nodes, it:
|
||||
|
||||
1. Collects all sibling results from the graph and librarian.
|
||||
2. Constructs a **synthesis request** — a new `AgentRequest` addressed to the supervisor flow:
|
||||
- `session_id` = the original supervisor's session_id
|
||||
- `pattern` = "supervisor"
|
||||
- `step_type` = "synthesise" (carried in history)
|
||||
- `subagent_results` = the collected findings
|
||||
- `history` = the supervisor's history up to the fan-out point, plus the synthesis step
|
||||
3. Emits this onto the agent request topic.
|
||||
|
||||
The supervisor picks this up, reasons across the aggregated findings, and
|
||||
produces its final answer.
|
||||
|
||||
### Aggregator Design
|
||||
|
||||
The aggregator is event-driven, consistent with TrustGraph's Pulsar-based
|
||||
architecture. Polling would be an anti-pattern in a system where all
|
||||
coordination is message-driven.
|
||||
|
||||
**Mechanism**: The aggregator is a Pulsar consumer on the explainability
|
||||
topic. Subagent completion nodes are emitted as triples on this topic as
|
||||
part of the existing provenance flow. When the aggregator receives a
|
||||
`tg:SubagentCompletion` triple, it:
|
||||
|
||||
1. Extracts the `tg:correlationId` from the completion node.
|
||||
2. Queries the graph to count how many siblings for that correlation ID
|
||||
have completed.
|
||||
3. If all `expected_siblings` are present, triggers fan-in immediately —
|
||||
collects results and emits the synthesis request.
|
||||
|
||||
**State**: The aggregator is stateless in the same sense as the agent
|
||||
manager — it holds no essential in-memory state. The graph is the source
|
||||
of truth for completion counts. If the aggregator restarts, it can
|
||||
re-process unacknowledged completion messages from Pulsar and re-check the
|
||||
graph. No coordination state is lost.
|
||||
|
||||
**Consistency**: Because the completion check queries the graph rather than
|
||||
relying on an in-memory counter, the aggregator is tolerant of duplicate
|
||||
messages, out-of-order delivery, and restarts. The graph query is
|
||||
idempotent — asking "are all siblings complete?" gives the same answer
|
||||
regardless of how many times or in what order the events arrive.
|
||||
|
||||
### Timeout and Failure
|
||||
|
||||
- **Subagent timeout**: The aggregator records the timestamp of the first
|
||||
sibling completion (from the graph). A periodic timeout check (the one
|
||||
concession to polling — but over local state, not the graph) detects
|
||||
stalled correlation IDs. If `expected_siblings` completions are not
|
||||
reached within a configurable timeout, the aggregator emits a partial
|
||||
synthesis request with whatever results are available, flagging the
|
||||
incomplete subagents.
|
||||
- **Subagent failure**: If a subagent errors out, it writes an error
|
||||
completion node (with `tg:status = "error"` and an error message). The
|
||||
aggregator treats this as a completion — the supervisor receives the error
|
||||
in its synthesis input and can reason about partial results.
|
||||
- **Supervisor iteration limit**: The supervisor's own iteration count
|
||||
(planning + synthesis) is bounded by `max_iterations` just like any other
|
||||
pattern.
|
||||
|
||||
---
|
||||
|
||||
## Provenance Model Extensions
|
||||
|
||||
### Routing Decision
|
||||
|
||||
The meta-router's task type and pattern selection is recorded as the first
|
||||
provenance node in the session:
|
||||
|
||||
```
|
||||
Routing node:
|
||||
rdf:type prov:Entity, tg:RoutingDecision
|
||||
prov:wasGeneratedBy → session (Question) activity
|
||||
tg:taskType → TaskType node URI
|
||||
tg:selectedPattern → Pattern node URI
|
||||
tg:candidatePatterns → [Pattern node URIs] (what was available)
|
||||
tg:routingRationale → document URI in librarian (LLM's reasoning)
|
||||
```
|
||||
|
||||
This captures the constrained decision space: what candidates existed, which
|
||||
was selected, and why. The candidates are graph-derived; the rationale is
|
||||
LLM-generated but verifiable against the candidates.
|
||||
|
||||
### Fan-Out Provenance
|
||||
|
||||
When a supervisor fans out, the provenance records the decomposition:
|
||||
|
||||
```
|
||||
FanOut node:
|
||||
rdf:type prov:Entity, tg:FanOut
|
||||
prov:wasDerivedFrom → supervisor's routing or planning iteration
|
||||
tg:correlationId <correlation ID>
|
||||
tg:subagentGoals → [document URIs for each subagent goal]
|
||||
tg:expectedSiblings <count>
|
||||
```
|
||||
|
||||
Each subagent's provenance chain is independent (its own session, iterations,
|
||||
conclusion) but linked back to the parent via:
|
||||
|
||||
```
|
||||
Subagent session:
|
||||
rdf:type prov:Activity, tg:Question, tg:AgentQuestion
|
||||
tg:parentCorrelationId <correlation ID>
|
||||
tg:parentSessionId <supervisor session URI>
|
||||
```
|
||||
|
||||
### Fan-In Provenance
|
||||
|
||||
The synthesis step links back to all subagent conclusions:
|
||||
|
||||
```
|
||||
Synthesis node:
|
||||
rdf:type prov:Entity, tg:Synthesis
|
||||
prov:wasDerivedFrom → [all subagent Conclusion entities]
|
||||
tg:correlationId <correlation ID>
|
||||
```
|
||||
|
||||
This creates a DAG in the provenance graph: the supervisor's routing fans out
|
||||
to N parallel subagent chains, which fan back in to a synthesis node. The
|
||||
entire multi-agent execution is traceable from a single correlation ID.
|
||||
|
||||
### URI Scheme
|
||||
|
||||
Extending the existing `urn:trustgraph:agent:{session_id}` pattern:
|
||||
|
||||
| Entity | URI Pattern |
|
||||
|---|---|
|
||||
| Session (existing) | `urn:trustgraph:agent:{session_id}` |
|
||||
| Iteration (existing) | `urn:trustgraph:agent:{session_id}/i{n}` |
|
||||
| Conclusion (existing) | `urn:trustgraph:agent:{session_id}/answer` |
|
||||
| Routing decision | `urn:trustgraph:agent:{session_id}/routing` |
|
||||
| Fan-out record | `urn:trustgraph:agent:{session_id}/fanout/{correlation_id}` |
|
||||
| Subagent completion | `urn:trustgraph:agent:{session_id}/completion` |
|
||||
|
||||
---
|
||||
|
||||
## Storage Responsibilities
|
||||
|
||||
Pattern and task type definitions live in the config API. Runtime state and
|
||||
provenance live in the knowledge graph. The division is:
|
||||
|
||||
| Role | Storage | When Written | Content |
|
||||
|---|---|---|---|
|
||||
| Pattern definitions | Config API | At design time | Pattern properties, descriptions |
|
||||
| Task type definitions | Config API | At design time | Domain framing, valid pattern lists |
|
||||
| Routing decision trace | Knowledge graph | At request arrival | Why this task type and pattern were selected |
|
||||
| Iteration decision trace | Knowledge graph | During execution | Each think/act/observe cycle, per existing model |
|
||||
| Fan-out coordination | Knowledge graph | During fan-out | Subagent goals, correlation ID, expected count |
|
||||
| Subagent completion | Knowledge graph | During fan-in | Per-subagent results under shared correlation ID |
|
||||
| Execution audit trail | Knowledge graph | Post-execution | Full multi-agent reasoning trace as a DAG |
|
||||
|
||||
The config API holds the definitions that constrain decisions. The knowledge
|
||||
graph holds the runtime decisions and their provenance. The fan-in
|
||||
coordination state is part of the provenance automatically — subagent
|
||||
completion nodes are both coordination signals and audit trail entries.
|
||||
|
||||
---
|
||||
|
||||
## Worked Example: Partner Risk Assessment
|
||||
|
||||
**Request**: "Assess the risk profile of Company X as a potential partner"
|
||||
|
||||
**1. Request arrives** on the *agent request* topic with empty history.
|
||||
The agent manager picks it up.
|
||||
|
||||
**2. Meta-router**:
|
||||
- Queries config API, finds task types: *Risk Assessment*, *Research*,
|
||||
*Summarisation*, *General*.
|
||||
- LLM identifies *Risk Assessment*. Framing prompt loaded: "analyse across
|
||||
financial, reputational, legal and operational dimensions using structured
|
||||
analytic techniques."
|
||||
- Valid patterns for *Risk Assessment*: [*Supervisor/Subagent*,
|
||||
*Plan-then-Execute*, *ReACT*].
|
||||
- LLM selects *Supervisor/Subagent* — task has four independent investigative
|
||||
dimensions, well-suited to parallel decomposition.
|
||||
- Routing decision written to graph. Request re-emitted on the
|
||||
*agent request* topic with `pattern="supervisor"`, framing populated.
|
||||
|
||||
**3. Supervisor iteration** (picked up from *agent request* topic):
|
||||
- LLM receives question + framing. Reasons that four independent investigative
|
||||
threads are required.
|
||||
- Generates correlation ID `corr-abc123`.
|
||||
- Emits four subagent requests on the *agent request* topic:
|
||||
- Financial analysis (pattern="react", subagent_goal="Analyse financial
|
||||
health and stability of Company X")
|
||||
- Legal analysis (pattern="react", subagent_goal="Review regulatory filings,
|
||||
sanctions, and legal exposure for Company X")
|
||||
- Reputational analysis (pattern="react", subagent_goal="Analyse news
|
||||
sentiment and public reputation of Company X")
|
||||
- Operational analysis (pattern="react", subagent_goal="Assess supply chain
|
||||
dependencies and operational risks for Company X")
|
||||
- Fan-out node written to graph.
|
||||
|
||||
**4. Four subagents run in parallel** (each picked up from the *agent
|
||||
request* topic by agent manager instances), each as an independent ReACT
|
||||
loop:
|
||||
- Financial — queries financial data services and knowledge graph
|
||||
relationships
|
||||
- Legal — searches regulatory filings and sanctions lists
|
||||
- Reputational — searches news, analyses sentiment
|
||||
- Operational — queries supply chain databases
|
||||
|
||||
Each self-queues its iterations on the *agent request* topic. Each writes
|
||||
its own decision trace to the graph as it progresses. Each completes
|
||||
independently.
|
||||
|
||||
**5. Fan-in**:
|
||||
- Each subagent writes a `tg:SubagentCompletion` node to the graph on
|
||||
completion, emitted on the *explainability* topic. The completion node
|
||||
references the subagent's result document in the librarian.
|
||||
- Aggregator (consuming the *explainability* topic) sees each completion
|
||||
event. It queries the graph for the fan-out node to get the expected
|
||||
sibling count, then checks how many completions exist for
|
||||
`corr-abc123`.
|
||||
- When all four siblings are complete, the aggregator emits a synthesis
|
||||
request on the *agent request* topic with the correlation ID. It does
|
||||
not fetch or bundle subagent results — the supervisor will query the
|
||||
graph for those.
|
||||
|
||||
**6. Supervisor synthesis** (picked up from *agent request* topic):
|
||||
- Receives the synthesis trigger carrying the correlation ID.
|
||||
- Queries the graph for `tg:SubagentCompletion` nodes under
|
||||
`corr-abc123`, retrieving each subagent's goal and result document
|
||||
reference.
|
||||
- Fetches the result documents from the librarian.
|
||||
- Reasons across all four dimensions, produces a structured risk
|
||||
assessment with confidence scores.
|
||||
- Emits final answer on the *agent response* topic and writes conclusion
|
||||
provenance to the graph.
|
||||
|
||||
**7. Response delivered** — the supervisor's synthesis streams on the
|
||||
*agent response* topic as the LLM generates it, with `end_of_dialog`
|
||||
on the final chunk. The collated answer is saved to the librarian and
|
||||
referenced from conclusion provenance in the graph. The graph now holds
|
||||
a complete, human-readable trace of the entire multi-agent execution —
|
||||
from pattern selection through four parallel investigations to final
|
||||
synthesis.
|
||||
|
||||
---
|
||||
|
||||
## Class Hierarchy
|
||||
|
||||
The agent-orchestrator executable (`agent-orchestrator`) uses the same
|
||||
service identity as agent-manager-react, making it a drop-in replacement.
|
||||
The pattern dispatch model suggests a class hierarchy where shared iteration
|
||||
infrastructure lives in a base class and pattern-specific logic is in
|
||||
subclasses:
|
||||
|
||||
```
|
||||
AgentService (base — Pulsar consumer/producer specs, request handling)
|
||||
│
|
||||
└── Processor (agent-orchestrator service)
|
||||
│
|
||||
├── MetaRouter — task type identification, pattern selection
|
||||
│
|
||||
├── PatternBase — shared: tool filtering, provenance, streaming, history
|
||||
│ ├── ReactPattern — existing ReACT logic (extract from current AgentManager)
|
||||
│ ├── PlanThenExecutePattern — plan phase + execute phase
|
||||
│ └── SupervisorPattern — fan-out, synthesis
|
||||
│
|
||||
└── Aggregator — fan-in completion detection
|
||||
```
|
||||
|
||||
`PatternBase` captures what is currently spread across `Processor` and
|
||||
`AgentManager`: tool filtering, LLM invocation, provenance triple emission,
|
||||
streaming callbacks, history management. The pattern subclasses implement only
|
||||
the decision logic specific to their execution strategy — what to do with the
|
||||
LLM output, when to terminate, whether to fan out.
|
||||
|
||||
This refactoring is not strictly necessary for the first iteration — the
|
||||
meta-router and pattern dispatch could be added as branches within the
|
||||
existing `Processor.agent_request()` method. But the class hierarchy clarifies
|
||||
where shared vs. pattern-specific logic lives and will prevent duplication as
|
||||
more patterns are added.
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Config API Seeding
|
||||
|
||||
Pattern and task type definitions are stored via the config API and need to
|
||||
be seeded at deployment time. This is analogous to how flow blueprints and
|
||||
parameter types are loaded — a bootstrap step that writes the initial
|
||||
configuration.
|
||||
|
||||
The initial seed includes:
|
||||
|
||||
**Patterns** (config type `agent-pattern`):
|
||||
- `react` — interleaved reasoning and action
|
||||
- `plan-then-execute` — structured plan followed by step execution
|
||||
- `supervisor` — decomposition, fan-out to subagents, synthesis
|
||||
|
||||
**Task types** (config type `agent-task-type`, initial set, expected to grow):
|
||||
- `general` — no specific domain framing, all patterns valid
|
||||
- `research` — open-ended investigation, valid patterns: react, plan-then-execute
|
||||
- `risk-assessment` — multi-dimensional analysis, valid patterns: supervisor,
|
||||
plan-then-execute, react
|
||||
- `summarisation` — condense information, valid patterns: react
|
||||
|
||||
The seed data is configuration, not code. It can be extended via the config
|
||||
API (or the configuration UI) without redeploying the agent manager.
|
||||
|
||||
### Migration Path
|
||||
|
||||
The config API provides a practical starting point. If richer ontological
|
||||
relationships between patterns, task types, and domain knowledge become
|
||||
valuable, the definitions can be migrated to graph storage. The meta-router's
|
||||
selection logic queries an abstract set of task types and patterns — the
|
||||
storage backend is an implementation detail.
|
||||
|
||||
### Fallback Behaviour
|
||||
|
||||
If the config contains no patterns or task types:
|
||||
- Task type defaults to `general`.
|
||||
- Pattern defaults to `react`.
|
||||
- The system degrades gracefully to existing behaviour.
|
||||
|
||||
---
|
||||
|
||||
## Design Decisions
|
||||
|
||||
| Decision | Resolution | Rationale |
|
||||
|---|---|---|
|
||||
| Task type identification | LLM interprets from plain text | Natural language too varied to formalise prematurely |
|
||||
| Pattern/task type storage | Config API initially, graph later if needed | Avoids graph model complexity upfront; config API already has UI support; migration path is straightforward |
|
||||
| Meta-router location | Phase within agent manager, not separate service | Avoids an extra network hop; routing is fast |
|
||||
| Fan-in mechanism | Event-driven via explainability topic | Consistent with Pulsar-based architecture; graph query for completion count is idempotent and restart-safe |
|
||||
| Aggregator deployment | Separate lightweight process | Decoupled from agent manager lifecycle |
|
||||
| Subagent pattern selection | Supervisor specifies per-subagent | Supervisor has task context to make this choice |
|
||||
| Plan storage | In message history | No external state needed; plan travels with message |
|
||||
| Default pattern | Empty pattern field → ReACT | Sensible default when meta-router is not configured |
|
||||
|
||||
---
|
||||
|
||||
## Streaming Protocol
|
||||
|
||||
### Current Model
|
||||
|
||||
The existing agent response schema has two levels:
|
||||
|
||||
- **`end_of_message`** — marks the end of a complete thought, observation,
|
||||
or answer. Chunks belonging to the same message arrive sequentially.
|
||||
- **`end_of_dialog`** — marks the end of the entire agent execution. No
|
||||
more messages will follow.
|
||||
|
||||
This works because the current system produces messages serially — one
|
||||
thought at a time, one agent at a time.
|
||||
|
||||
### Fan-Out Breaks Serial Assumptions
|
||||
|
||||
With supervisor/subagent fan-out, multiple subagents stream chunks
|
||||
concurrently on the same *agent response* topic. The caller receives
|
||||
interleaved chunks from different sources and needs to demultiplex them.
|
||||
|
||||
### Resolution: Message ID
|
||||
|
||||
Each chunk carries a `message_id` — a per-message UUID generated when
|
||||
the agent begins streaming a new thought, observation, or answer. The
|
||||
caller groups chunks by `message_id` and assembles each message
|
||||
independently.
|
||||
|
||||
```
|
||||
Response chunk fields:
|
||||
message_id UUID for this message (groups chunks)
|
||||
session_id Which agent session produced this chunk
|
||||
chunk_type "thought" | "observation" | "answer" | ...
|
||||
content The chunk text
|
||||
end_of_message True on the final chunk of this message
|
||||
end_of_dialog True on the final message of the entire execution
|
||||
```
|
||||
|
||||
A single subagent emits multiple messages (thought, observation, thought,
|
||||
answer), each with a distinct `message_id`. The `session_id` identifies
|
||||
which subagent the message belongs to. The caller can display, group, or
|
||||
filter by either.
|
||||
|
||||
### Provenance Trigger
|
||||
|
||||
`end_of_message` is the trigger for provenance storage. When a complete
|
||||
message has been assembled from its chunks:
|
||||
|
||||
1. The collated text is saved to the librarian as a single document.
|
||||
2. A provenance node is written to the graph referencing the document URI.
|
||||
|
||||
This follows the pattern established by GraphRAG, where streaming synthesis
|
||||
chunks are delivered live but the stored provenance references the collated
|
||||
answer text. Streaming is for the caller; provenance needs complete messages.
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
- **Re-planning depth** (resolved): Runtime parameter on the
|
||||
agent-orchestrator executable, default 2. Bounds how many times
|
||||
Plan-then-Execute can revise its plan before forcing termination.
|
||||
- **Nested fan-out** (phase B): A subagent can itself be a supervisor
|
||||
that fans out further. The architecture supports this — correlation IDs
|
||||
are independent and the aggregator is stateless. The protocols and
|
||||
message schema should not preclude nested fan-out, but implementation
|
||||
is deferred. Depth limits will need to be enforced to prevent runaway
|
||||
decomposition.
|
||||
- **Task type evolution** (resolved): Manually curated for now. See
|
||||
Future Directions below for automated discovery.
|
||||
- **Cost attribution** (deferred): Costs are measured at the
|
||||
text-completion queue level as they are today. Per-request attribution
|
||||
across subagents is not yet implemented and is not a blocker for
|
||||
orchestration.
|
||||
- **Conversation ID** (resolved): An optional `conversation_id` field on
|
||||
`AgentRequest`, generated by the caller. When present, all objects
|
||||
created during the execution (provenance nodes, librarian documents,
|
||||
subagent completion records) are tagged with the conversation ID. This
|
||||
enables querying all interactions in a conversation with a single
|
||||
lookup, and provides the foundation for conversation-scoped memory.
|
||||
No explicit open/close — the first request with a new conversation ID
|
||||
implicitly starts the conversation. Omit for one-shot queries.
|
||||
- **Tool scoping per subagent** (resolved): Subagents inherit the
|
||||
parent's tool group by default. The supervisor can optionally override
|
||||
the group per subagent to constrain capabilities (e.g. financial
|
||||
subagent gets only financial tools). The `group` field on
|
||||
`AgentRequest` already supports this — the supervisor just sets it
|
||||
when constructing subagent requests.
|
||||
|
||||
---
|
||||
|
||||
## Future Directions
|
||||
|
||||
### Automated Task Type Discovery
|
||||
|
||||
Task types are manually curated in the initial implementation. However,
|
||||
the architecture is well-suited to automated discovery because all agent
|
||||
requests and their execution traces flow through Pulsar topics. A
|
||||
learning service could consume these messages and analyse patterns in
|
||||
how tasks are framed, which patterns are selected, and how successfully
|
||||
they execute. Over time, it could propose new task types based on
|
||||
clusters of similar requests that don't map well to existing types, or
|
||||
suggest refinements to framing prompts based on which framings lead to
|
||||
better outcomes. This service would write proposed task types to the
|
||||
config API for human review — automated discovery, manual approval. The
|
||||
agent-orchestrator does not need to change; it always reads task types
|
||||
from config regardless of how they got there.
|
||||
|
|
@ -87,10 +87,11 @@ def sample_message_data():
|
|||
"history": []
|
||||
},
|
||||
"AgentResponse": {
|
||||
"answer": "Machine learning is a subset of AI.",
|
||||
"chunk_type": "answer",
|
||||
"content": "Machine learning is a subset of AI.",
|
||||
"end_of_message": True,
|
||||
"end_of_dialog": True,
|
||||
"error": None,
|
||||
"thought": "I need to provide information about machine learning.",
|
||||
"observation": None
|
||||
},
|
||||
"Metadata": {
|
||||
"id": "test-doc-123",
|
||||
|
|
|
|||
|
|
@ -212,10 +212,11 @@ class TestAgentMessageContracts:
|
|||
|
||||
# Test required fields
|
||||
response = AgentResponse(**response_data)
|
||||
assert hasattr(response, 'answer')
|
||||
assert hasattr(response, 'chunk_type')
|
||||
assert hasattr(response, 'content')
|
||||
assert hasattr(response, 'end_of_message')
|
||||
assert hasattr(response, 'end_of_dialog')
|
||||
assert hasattr(response, 'error')
|
||||
assert hasattr(response, 'thought')
|
||||
assert hasattr(response, 'observation')
|
||||
|
||||
def test_agent_step_schema_contract(self):
|
||||
"""Test AgentStep schema contract"""
|
||||
|
|
|
|||
|
|
@ -188,12 +188,10 @@ class TestAgentTranslatorCompletionFlags:
|
|||
# Arrange
|
||||
translator = TranslatorRegistry.get_response_translator("agent")
|
||||
response = AgentResponse(
|
||||
answer="4",
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None,
|
||||
chunk_type="answer",
|
||||
content="4",
|
||||
end_of_message=True,
|
||||
end_of_dialog=True
|
||||
end_of_dialog=True,
|
||||
)
|
||||
|
||||
# Act
|
||||
|
|
@ -201,7 +199,7 @@ class TestAgentTranslatorCompletionFlags:
|
|||
|
||||
# Assert
|
||||
assert is_final is True, "is_final must be True when end_of_dialog=True"
|
||||
assert response_dict["answer"] == "4"
|
||||
assert response_dict["content"] == "4"
|
||||
assert response_dict["end_of_dialog"] is True
|
||||
|
||||
def test_agent_translator_is_final_with_end_of_dialog_false(self):
|
||||
|
|
@ -212,12 +210,10 @@ class TestAgentTranslatorCompletionFlags:
|
|||
# Arrange
|
||||
translator = TranslatorRegistry.get_response_translator("agent")
|
||||
response = AgentResponse(
|
||||
answer=None,
|
||||
error=None,
|
||||
thought="I need to solve this.",
|
||||
observation=None,
|
||||
chunk_type="thought",
|
||||
content="I need to solve this.",
|
||||
end_of_message=True,
|
||||
end_of_dialog=False
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
||||
# Act
|
||||
|
|
@ -225,31 +221,9 @@ class TestAgentTranslatorCompletionFlags:
|
|||
|
||||
# Assert
|
||||
assert is_final is False, "is_final must be False when end_of_dialog=False"
|
||||
assert response_dict["thought"] == "I need to solve this."
|
||||
assert response_dict["content"] == "I need to solve this."
|
||||
assert response_dict["end_of_dialog"] is False
|
||||
|
||||
def test_agent_translator_is_final_fallback_with_answer(self):
|
||||
"""
|
||||
Test that AgentResponseTranslator returns is_final=True
|
||||
when answer is present (fallback for legacy responses).
|
||||
"""
|
||||
# Arrange
|
||||
translator = TranslatorRegistry.get_response_translator("agent")
|
||||
# Legacy response without end_of_dialog flag
|
||||
response = AgentResponse(
|
||||
answer="4",
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None
|
||||
)
|
||||
|
||||
# Act
|
||||
response_dict, is_final = translator.from_response_with_completion(response)
|
||||
|
||||
# Assert
|
||||
assert is_final is True, "is_final must be True when answer is present (legacy fallback)"
|
||||
assert response_dict["answer"] == "4"
|
||||
|
||||
def test_agent_translator_intermediate_message_is_not_final(self):
|
||||
"""
|
||||
Test that intermediate messages (thought/observation) return is_final=False.
|
||||
|
|
@ -259,12 +233,10 @@ class TestAgentTranslatorCompletionFlags:
|
|||
|
||||
# Test thought message
|
||||
thought_response = AgentResponse(
|
||||
answer=None,
|
||||
error=None,
|
||||
thought="Processing...",
|
||||
observation=None,
|
||||
chunk_type="thought",
|
||||
content="Processing...",
|
||||
end_of_message=True,
|
||||
end_of_dialog=False
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
||||
# Act
|
||||
|
|
@ -275,12 +247,10 @@ class TestAgentTranslatorCompletionFlags:
|
|||
|
||||
# Test observation message
|
||||
observation_response = AgentResponse(
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation="Result found",
|
||||
chunk_type="observation",
|
||||
content="Result found",
|
||||
end_of_message=True,
|
||||
end_of_dialog=False
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
||||
# Act
|
||||
|
|
@ -302,10 +272,6 @@ class TestAgentTranslatorCompletionFlags:
|
|||
content="",
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None
|
||||
)
|
||||
|
||||
# Act
|
||||
|
|
|
|||
|
|
@ -82,16 +82,16 @@ class TestAgentServiceNonStreaming:
|
|||
# Check thought message
|
||||
thought_response = sent_responses[0]
|
||||
assert isinstance(thought_response, AgentResponse)
|
||||
assert thought_response.thought == "I need to solve this."
|
||||
assert thought_response.answer is None
|
||||
assert thought_response.chunk_type == "thought"
|
||||
assert thought_response.content == "I need to solve this."
|
||||
assert thought_response.end_of_message is True, "Thought message must have end_of_message=True"
|
||||
assert thought_response.end_of_dialog is False, "Thought message must have end_of_dialog=False"
|
||||
|
||||
# Check observation message
|
||||
observation_response = sent_responses[1]
|
||||
assert isinstance(observation_response, AgentResponse)
|
||||
assert observation_response.observation == "The answer is 4."
|
||||
assert observation_response.answer is None
|
||||
assert observation_response.chunk_type == "observation"
|
||||
assert observation_response.content == "The answer is 4."
|
||||
assert observation_response.end_of_message is True, "Observation message must have end_of_message=True"
|
||||
assert observation_response.end_of_dialog is False, "Observation message must have end_of_dialog=False"
|
||||
|
||||
|
|
@ -161,9 +161,8 @@ class TestAgentServiceNonStreaming:
|
|||
# Check final answer message
|
||||
answer_response = sent_responses[0]
|
||||
assert isinstance(answer_response, AgentResponse)
|
||||
assert answer_response.answer == "4"
|
||||
assert answer_response.thought is None
|
||||
assert answer_response.observation is None
|
||||
assert answer_response.chunk_type == "answer"
|
||||
assert answer_response.content == "4"
|
||||
assert answer_response.end_of_message is True, "Final answer must have end_of_message=True"
|
||||
assert answer_response.end_of_dialog is True, "Final answer must have end_of_dialog=True"
|
||||
|
||||
|
|
|
|||
|
|
@ -402,23 +402,6 @@ class SocketClient:
|
|||
content=resp.get("content", ""),
|
||||
end_of_message=resp.get("end_of_message", False)
|
||||
)
|
||||
# Non-streaming agent format: chunk_type is empty but has thought/observation/answer fields
|
||||
elif resp.get("thought"):
|
||||
return AgentThought(
|
||||
content=resp.get("thought", ""),
|
||||
end_of_message=resp.get("end_of_message", False)
|
||||
)
|
||||
elif resp.get("observation"):
|
||||
return AgentObservation(
|
||||
content=resp.get("observation", ""),
|
||||
end_of_message=resp.get("end_of_message", False)
|
||||
)
|
||||
elif resp.get("answer"):
|
||||
return AgentAnswer(
|
||||
content=resp.get("answer", ""),
|
||||
end_of_message=resp.get("end_of_message", False),
|
||||
end_of_dialog=resp.get("end_of_dialog", False)
|
||||
)
|
||||
else:
|
||||
content = resp.get("response", resp.get("chunk", resp.get("text", "")))
|
||||
return RAGChunk(
|
||||
|
|
|
|||
|
|
@ -57,8 +57,7 @@ class AgentClient(RequestResponse):
|
|||
await self.request(
|
||||
AgentRequest(
|
||||
question = question,
|
||||
plan = plan,
|
||||
state = state,
|
||||
state = state or "",
|
||||
history = history,
|
||||
),
|
||||
recipient=recipient,
|
||||
|
|
|
|||
|
|
@ -90,9 +90,6 @@ class AgentService(FlowProcessor):
|
|||
type = "agent-error",
|
||||
message = str(e),
|
||||
),
|
||||
thought = None,
|
||||
observation = None,
|
||||
answer = None,
|
||||
end_of_message = True,
|
||||
end_of_dialog = True,
|
||||
),
|
||||
|
|
|
|||
|
|
@ -16,6 +16,14 @@ class AgentRequestTranslator(MessageTranslator):
|
|||
collection=data.get("collection", "default"),
|
||||
streaming=data.get("streaming", False),
|
||||
session_id=data.get("session_id", ""),
|
||||
conversation_id=data.get("conversation_id", ""),
|
||||
pattern=data.get("pattern", ""),
|
||||
task_type=data.get("task_type", ""),
|
||||
framing=data.get("framing", ""),
|
||||
correlation_id=data.get("correlation_id", ""),
|
||||
parent_session_id=data.get("parent_session_id", ""),
|
||||
subagent_goal=data.get("subagent_goal", ""),
|
||||
expected_siblings=data.get("expected_siblings", 0),
|
||||
)
|
||||
|
||||
def from_pulsar(self, obj: AgentRequest) -> Dict[str, Any]:
|
||||
|
|
@ -28,6 +36,14 @@ class AgentRequestTranslator(MessageTranslator):
|
|||
"collection": getattr(obj, "collection", "default"),
|
||||
"streaming": getattr(obj, "streaming", False),
|
||||
"session_id": getattr(obj, "session_id", ""),
|
||||
"conversation_id": getattr(obj, "conversation_id", ""),
|
||||
"pattern": getattr(obj, "pattern", ""),
|
||||
"task_type": getattr(obj, "task_type", ""),
|
||||
"framing": getattr(obj, "framing", ""),
|
||||
"correlation_id": getattr(obj, "correlation_id", ""),
|
||||
"parent_session_id": getattr(obj, "parent_session_id", ""),
|
||||
"subagent_goal": getattr(obj, "subagent_goal", ""),
|
||||
"expected_siblings": getattr(obj, "expected_siblings", 0),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -40,24 +56,15 @@ class AgentResponseTranslator(MessageTranslator):
|
|||
def from_pulsar(self, obj: AgentResponse) -> Dict[str, Any]:
|
||||
result = {}
|
||||
|
||||
# Check if this is a streaming response (has chunk_type)
|
||||
if hasattr(obj, 'chunk_type') and obj.chunk_type:
|
||||
if obj.chunk_type:
|
||||
result["chunk_type"] = obj.chunk_type
|
||||
if obj.content:
|
||||
result["content"] = obj.content
|
||||
result["end_of_message"] = getattr(obj, "end_of_message", False)
|
||||
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
|
||||
else:
|
||||
# Legacy format (non-streaming)
|
||||
if obj.answer:
|
||||
result["answer"] = obj.answer
|
||||
if obj.thought:
|
||||
result["thought"] = obj.thought
|
||||
if obj.observation:
|
||||
result["observation"] = obj.observation
|
||||
# Include completion flags for legacy format too
|
||||
result["end_of_message"] = getattr(obj, "end_of_message", False)
|
||||
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
|
||||
if obj.content:
|
||||
result["content"] = obj.content
|
||||
result["end_of_message"] = getattr(obj, "end_of_message", False)
|
||||
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
|
||||
|
||||
if getattr(obj, "message_id", ""):
|
||||
result["message_id"] = obj.message_id
|
||||
|
||||
# Include explainability fields if present
|
||||
explain_id = getattr(obj, "explain_id", None)
|
||||
|
|
@ -76,11 +83,5 @@ class AgentResponseTranslator(MessageTranslator):
|
|||
|
||||
def from_response_with_completion(self, obj: AgentResponse) -> Tuple[Dict[str, Any], bool]:
|
||||
"""Returns (response_dict, is_final)"""
|
||||
# For streaming responses, check end_of_dialog
|
||||
if hasattr(obj, 'chunk_type') and obj.chunk_type:
|
||||
is_final = getattr(obj, 'end_of_dialog', False)
|
||||
else:
|
||||
# For legacy responses, check if answer is present
|
||||
is_final = (obj.answer is not None)
|
||||
|
||||
is_final = getattr(obj, 'end_of_dialog', False)
|
||||
return self.from_pulsar(obj), is_final
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional
|
||||
|
||||
from ..core.topic import topic
|
||||
from ..core.primitives import Error
|
||||
|
|
@ -8,6 +9,14 @@ from ..core.primitives import Error
|
|||
|
||||
# Prompt services, abstract the prompt generation
|
||||
|
||||
@dataclass
|
||||
class PlanStep:
|
||||
goal: str = ""
|
||||
tool_hint: str = "" # Suggested tool for this step
|
||||
depends_on: list[int] = field(default_factory=list) # Indices of prerequisite steps
|
||||
status: str = "pending" # pending, running, completed, failed
|
||||
result: str = "" # Result of step execution
|
||||
|
||||
@dataclass
|
||||
class AgentStep:
|
||||
thought: str = ""
|
||||
|
|
@ -15,6 +24,9 @@ class AgentStep:
|
|||
arguments: dict[str, str] = field(default_factory=dict)
|
||||
observation: str = ""
|
||||
user: str = "" # User context for the step
|
||||
step_type: str = "" # "react", "plan", "execute", "decompose", "synthesise"
|
||||
plan: list[PlanStep] = field(default_factory=list) # Plan steps (for plan-then-execute)
|
||||
subagent_results: dict[str, str] = field(default_factory=dict) # Subagent results keyed by goal
|
||||
|
||||
@dataclass
|
||||
class AgentRequest:
|
||||
|
|
@ -27,6 +39,16 @@ class AgentRequest:
|
|||
streaming: bool = False # Enable streaming response delivery (default false)
|
||||
session_id: str = "" # For provenance tracking across iterations
|
||||
|
||||
# Orchestration fields
|
||||
conversation_id: str = "" # Groups related requests into a conversation
|
||||
pattern: str = "" # Selected pattern: "react", "plan-then-execute", "supervisor"
|
||||
task_type: str = "" # Task type from config: "general", "research", etc.
|
||||
framing: str = "" # Domain framing text injected into prompts
|
||||
correlation_id: str = "" # Links fan-out subagents to parent for fan-in
|
||||
parent_session_id: str = "" # Session ID of the supervisor that spawned this subagent
|
||||
subagent_goal: str = "" # Specific goal for a subagent (set by supervisor)
|
||||
expected_siblings: int = 0 # Number of sibling subagents in this fan-out
|
||||
|
||||
@dataclass
|
||||
class AgentResponse:
|
||||
# Streaming-first design
|
||||
|
|
@ -39,11 +61,10 @@ class AgentResponse:
|
|||
explain_id: str | None = None # Provenance URI (announced as created)
|
||||
explain_graph: str | None = None # Named graph where explain was stored
|
||||
|
||||
# Legacy fields (deprecated but kept for backward compatibility)
|
||||
answer: str = ""
|
||||
# Orchestration fields
|
||||
message_id: str = "" # Unique ID for this response message
|
||||
|
||||
error: Error | None = None
|
||||
thought: str = ""
|
||||
observation: str = ""
|
||||
|
||||
############################################################################
|
||||
|
||||
|
|
|
|||
|
|
@ -56,6 +56,7 @@ Homepage = "https://github.com/trustgraph-ai/trustgraph"
|
|||
|
||||
[project.scripts]
|
||||
agent-manager-react = "trustgraph.agent.react:run"
|
||||
agent-orchestrator = "trustgraph.agent.orchestrator:run"
|
||||
api-gateway = "trustgraph.gateway:run"
|
||||
chunker-recursive = "trustgraph.chunking.recursive:run"
|
||||
chunker-token = "trustgraph.chunking.token:run"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
from . service import *
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
from . service import run
|
||||
|
||||
if __name__ == '__main__':
|
||||
run()
|
||||
157
trustgraph-flow/trustgraph/agent/orchestrator/aggregator.py
Normal file
157
trustgraph-flow/trustgraph/agent/orchestrator/aggregator.py
Normal file
|
|
@ -0,0 +1,157 @@
|
|||
"""
|
||||
Aggregator — monitors the explainability topic for subagent completions
|
||||
and triggers synthesis when all siblings in a fan-out have completed.
|
||||
|
||||
The aggregator watches for tg:Conclusion triples that carry a
|
||||
correlation_id. When it detects that all expected siblings have
|
||||
completed, it emits a synthesis AgentRequest on the agent request topic.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
|
||||
from ... schema import AgentRequest, AgentStep
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# How long to wait for stalled correlations before giving up (seconds)
|
||||
DEFAULT_TIMEOUT = 300
|
||||
|
||||
|
||||
class Aggregator:
|
||||
"""
|
||||
Tracks in-flight fan-out correlations and triggers synthesis
|
||||
when all subagents complete.
|
||||
|
||||
State is held in-memory; if the process restarts, in-flight
|
||||
correlations are lost (acceptable for v1).
|
||||
"""
|
||||
|
||||
def __init__(self, timeout=DEFAULT_TIMEOUT):
|
||||
self.timeout = timeout
|
||||
|
||||
# correlation_id -> {
|
||||
# "parent_session_id": str,
|
||||
# "expected": int,
|
||||
# "results": {goal: answer},
|
||||
# "request_template": AgentRequest or None,
|
||||
# "created_at": float,
|
||||
# }
|
||||
self.correlations = {}
|
||||
|
||||
def register_fanout(self, correlation_id, parent_session_id,
|
||||
expected_siblings, request_template=None):
|
||||
"""
|
||||
Register a new fan-out. Called by the supervisor after emitting
|
||||
subagent requests.
|
||||
"""
|
||||
self.correlations[correlation_id] = {
|
||||
"parent_session_id": parent_session_id,
|
||||
"expected": expected_siblings,
|
||||
"results": {},
|
||||
"request_template": request_template,
|
||||
"created_at": time.time(),
|
||||
}
|
||||
logger.info(
|
||||
f"Aggregator: registered fan-out {correlation_id}, "
|
||||
f"expecting {expected_siblings} subagents"
|
||||
)
|
||||
|
||||
def record_completion(self, correlation_id, subagent_goal, result):
|
||||
"""
|
||||
Record a subagent completion.
|
||||
|
||||
Returns:
|
||||
True if all siblings are now complete, False otherwise.
|
||||
Returns None if the correlation_id is unknown.
|
||||
"""
|
||||
if correlation_id not in self.correlations:
|
||||
logger.warning(
|
||||
f"Aggregator: unknown correlation_id {correlation_id}"
|
||||
)
|
||||
return None
|
||||
|
||||
entry = self.correlations[correlation_id]
|
||||
entry["results"][subagent_goal] = result
|
||||
|
||||
completed = len(entry["results"])
|
||||
expected = entry["expected"]
|
||||
|
||||
logger.info(
|
||||
f"Aggregator: {correlation_id} — "
|
||||
f"{completed}/{expected} subagents complete"
|
||||
)
|
||||
|
||||
return completed >= expected
|
||||
|
||||
def get_results(self, correlation_id):
|
||||
"""Get all results for a correlation and remove the tracking entry."""
|
||||
entry = self.correlations.pop(correlation_id, None)
|
||||
if entry is None:
|
||||
return None, None, None
|
||||
return (
|
||||
entry["results"],
|
||||
entry["parent_session_id"],
|
||||
entry["request_template"],
|
||||
)
|
||||
|
||||
def build_synthesis_request(self, correlation_id, original_question,
|
||||
user, collection):
|
||||
"""
|
||||
Build the AgentRequest that triggers the synthesis phase.
|
||||
"""
|
||||
results, parent_session_id, template = self.get_results(correlation_id)
|
||||
|
||||
if results is None:
|
||||
raise RuntimeError(
|
||||
f"No results for correlation_id {correlation_id}"
|
||||
)
|
||||
|
||||
# Build history with decompose step + results
|
||||
synthesis_step = AgentStep(
|
||||
thought="All subagents completed",
|
||||
action="aggregate",
|
||||
arguments={},
|
||||
observation=json.dumps(results),
|
||||
step_type="synthesise",
|
||||
subagent_results=results,
|
||||
)
|
||||
|
||||
history = []
|
||||
if template and template.history:
|
||||
history = list(template.history)
|
||||
history.append(synthesis_step)
|
||||
|
||||
return AgentRequest(
|
||||
question=original_question,
|
||||
state="",
|
||||
group=template.group if template else [],
|
||||
history=history,
|
||||
user=user,
|
||||
collection=collection,
|
||||
streaming=template.streaming if template else False,
|
||||
session_id=parent_session_id,
|
||||
conversation_id=template.conversation_id if template else "",
|
||||
pattern="supervisor",
|
||||
task_type=template.task_type if template else "",
|
||||
framing=template.framing if template else "",
|
||||
correlation_id=correlation_id,
|
||||
parent_session_id="",
|
||||
subagent_goal="",
|
||||
expected_siblings=0,
|
||||
)
|
||||
|
||||
def cleanup_stale(self):
|
||||
"""Remove correlations that have timed out."""
|
||||
now = time.time()
|
||||
stale = [
|
||||
cid for cid, entry in self.correlations.items()
|
||||
if now - entry["created_at"] > self.timeout
|
||||
]
|
||||
for cid in stale:
|
||||
logger.warning(f"Aggregator: timing out stale correlation {cid}")
|
||||
self.correlations.pop(cid, None)
|
||||
return stale
|
||||
168
trustgraph-flow/trustgraph/agent/orchestrator/meta_router.py
Normal file
168
trustgraph-flow/trustgraph/agent/orchestrator/meta_router.py
Normal file
|
|
@ -0,0 +1,168 @@
|
|||
"""
|
||||
MetaRouter — selects the task type and execution pattern for a query.
|
||||
|
||||
Uses the config API to look up available task types and patterns, then
|
||||
asks the LLM to classify the query and select the best pattern.
|
||||
Falls back to ("react", "general", "") if config is empty.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_PATTERN = "react"
|
||||
DEFAULT_TASK_TYPE = "general"
|
||||
DEFAULT_FRAMING = ""
|
||||
|
||||
|
||||
class MetaRouter:
|
||||
|
||||
def __init__(self, config=None):
|
||||
"""
|
||||
Args:
|
||||
config: The full config dict from the config service.
|
||||
May contain "agent-pattern" and "agent-task-type" keys.
|
||||
"""
|
||||
self.patterns = {}
|
||||
self.task_types = {}
|
||||
|
||||
if config:
|
||||
# Load from config API
|
||||
if "agent-pattern" in config:
|
||||
for pid, pval in config["agent-pattern"].items():
|
||||
try:
|
||||
self.patterns[pid] = json.loads(pval)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
self.patterns[pid] = {"name": pid}
|
||||
|
||||
if "agent-task-type" in config:
|
||||
for tid, tval in config["agent-task-type"].items():
|
||||
try:
|
||||
self.task_types[tid] = json.loads(tval)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
self.task_types[tid] = {"name": tid}
|
||||
|
||||
# If config has no patterns/task-types, default to react/general
|
||||
if not self.patterns:
|
||||
self.patterns = {
|
||||
"react": {"name": "react", "description": "Interleaved reasoning and action"},
|
||||
}
|
||||
if not self.task_types:
|
||||
self.task_types = {
|
||||
"general": {"name": "general", "description": "General queries", "valid_patterns": ["react"], "framing": ""},
|
||||
}
|
||||
|
||||
async def identify_task_type(self, question, context):
|
||||
"""
|
||||
Use the LLM to classify the question into one of the known task types.
|
||||
|
||||
Args:
|
||||
question: The user's query.
|
||||
context: UserAwareContext (flow wrapper).
|
||||
|
||||
Returns:
|
||||
(task_type_id, framing) tuple.
|
||||
"""
|
||||
if len(self.task_types) <= 1:
|
||||
tid = next(iter(self.task_types), DEFAULT_TASK_TYPE)
|
||||
framing = self.task_types.get(tid, {}).get("framing", DEFAULT_FRAMING)
|
||||
return tid, framing
|
||||
|
||||
try:
|
||||
client = context("prompt-request")
|
||||
response = await client.prompt(
|
||||
id="task-type-classify",
|
||||
variables={
|
||||
"question": question,
|
||||
"task_types": [
|
||||
{"name": tid, "description": tdata.get("description", tid)}
|
||||
for tid, tdata in self.task_types.items()
|
||||
],
|
||||
},
|
||||
)
|
||||
selected = response.strip().lower().replace('"', '').replace("'", "")
|
||||
|
||||
if selected in self.task_types:
|
||||
framing = self.task_types[selected].get("framing", DEFAULT_FRAMING)
|
||||
logger.info(f"MetaRouter: identified task type '{selected}'")
|
||||
return selected, framing
|
||||
else:
|
||||
logger.warning(
|
||||
f"MetaRouter: LLM returned unknown task type '{selected}', "
|
||||
f"falling back to '{DEFAULT_TASK_TYPE}'"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"MetaRouter: task type classification failed: {e}")
|
||||
|
||||
framing = self.task_types.get(DEFAULT_TASK_TYPE, {}).get(
|
||||
"framing", DEFAULT_FRAMING
|
||||
)
|
||||
return DEFAULT_TASK_TYPE, framing
|
||||
|
||||
async def select_pattern(self, question, task_type, context):
|
||||
"""
|
||||
Use the LLM to select the best execution pattern for this task type.
|
||||
|
||||
Args:
|
||||
question: The user's query.
|
||||
task_type: The identified task type ID.
|
||||
context: UserAwareContext (flow wrapper).
|
||||
|
||||
Returns:
|
||||
Pattern ID string.
|
||||
"""
|
||||
task_config = self.task_types.get(task_type, {})
|
||||
valid_patterns = task_config.get("valid_patterns", list(self.patterns.keys()))
|
||||
|
||||
if len(valid_patterns) <= 1:
|
||||
return valid_patterns[0] if valid_patterns else DEFAULT_PATTERN
|
||||
|
||||
try:
|
||||
client = context("prompt-request")
|
||||
response = await client.prompt(
|
||||
id="pattern-select",
|
||||
variables={
|
||||
"question": question,
|
||||
"task_type": task_type,
|
||||
"task_type_description": task_config.get("description", task_type),
|
||||
"patterns": [
|
||||
{"name": pid, "description": self.patterns.get(pid, {}).get("description", pid)}
|
||||
for pid in valid_patterns
|
||||
if pid in self.patterns
|
||||
],
|
||||
},
|
||||
)
|
||||
selected = response.strip().lower().replace('"', '').replace("'", "")
|
||||
|
||||
if selected in valid_patterns:
|
||||
logger.info(f"MetaRouter: selected pattern '{selected}'")
|
||||
return selected
|
||||
else:
|
||||
logger.warning(
|
||||
f"MetaRouter: LLM returned invalid pattern '{selected}', "
|
||||
f"falling back to '{valid_patterns[0]}'"
|
||||
)
|
||||
return valid_patterns[0]
|
||||
except Exception as e:
|
||||
logger.warning(f"MetaRouter: pattern selection failed: {e}")
|
||||
return valid_patterns[0] if valid_patterns else DEFAULT_PATTERN
|
||||
|
||||
async def route(self, question, context):
|
||||
"""
|
||||
Full routing pipeline: identify task type, then select pattern.
|
||||
|
||||
Args:
|
||||
question: The user's query.
|
||||
context: UserAwareContext (flow wrapper).
|
||||
|
||||
Returns:
|
||||
(pattern, task_type, framing) tuple.
|
||||
"""
|
||||
task_type, framing = await self.identify_task_type(question, context)
|
||||
pattern = await self.select_pattern(question, task_type, context)
|
||||
logger.info(
|
||||
f"MetaRouter: route result — "
|
||||
f"pattern={pattern}, task_type={task_type}, framing={framing!r}"
|
||||
)
|
||||
return pattern, task_type, framing
|
||||
428
trustgraph-flow/trustgraph/agent/orchestrator/pattern_base.py
Normal file
428
trustgraph-flow/trustgraph/agent/orchestrator/pattern_base.py
Normal file
|
|
@ -0,0 +1,428 @@
|
|||
"""
|
||||
Base class for agent patterns.
|
||||
|
||||
Provides shared infrastructure used by all patterns: tool filtering,
|
||||
provenance emission, streaming callbacks, history management, and
|
||||
librarian integration.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
|
||||
from ... schema import AgentRequest, AgentResponse, AgentStep, Error
|
||||
from ... schema import Triples, Metadata
|
||||
|
||||
from trustgraph.provenance import (
|
||||
agent_session_uri,
|
||||
agent_iteration_uri,
|
||||
agent_thought_uri,
|
||||
agent_observation_uri,
|
||||
agent_final_uri,
|
||||
agent_session_triples,
|
||||
agent_iteration_triples,
|
||||
agent_final_triples,
|
||||
set_graph,
|
||||
GRAPH_RETRIEVAL,
|
||||
)
|
||||
|
||||
from ..react.types import Action, Final
|
||||
from ..tool_filter import filter_tools_by_group_and_state, get_next_state
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class UserAwareContext:
|
||||
"""Wraps flow interface to inject user context for tools that need it."""
|
||||
|
||||
def __init__(self, flow, user):
|
||||
self._flow = flow
|
||||
self._user = user
|
||||
|
||||
def __call__(self, service_name):
|
||||
client = self._flow(service_name)
|
||||
if service_name in (
|
||||
"structured-query-request",
|
||||
"row-embeddings-query-request",
|
||||
):
|
||||
client._current_user = self._user
|
||||
return client
|
||||
|
||||
|
||||
class PatternBase:
|
||||
"""
|
||||
Shared infrastructure for all agent patterns.
|
||||
|
||||
Subclasses implement iterate() to perform one iteration of their
|
||||
pattern-specific logic.
|
||||
"""
|
||||
|
||||
def __init__(self, processor):
|
||||
self.processor = processor
|
||||
|
||||
def filter_tools(self, tools, request):
|
||||
"""Apply group/state filtering to the tool set."""
|
||||
return filter_tools_by_group_and_state(
|
||||
tools=tools,
|
||||
requested_groups=getattr(request, 'group', None),
|
||||
current_state=getattr(request, 'state', None),
|
||||
)
|
||||
|
||||
def make_context(self, flow, user):
|
||||
"""Create a user-aware context wrapper."""
|
||||
return UserAwareContext(flow, user)
|
||||
|
||||
def build_history(self, request):
|
||||
"""Convert AgentStep history into Action objects."""
|
||||
if not request.history:
|
||||
return []
|
||||
return [
|
||||
Action(
|
||||
thought=h.thought,
|
||||
name=h.action,
|
||||
arguments=h.arguments,
|
||||
observation=h.observation,
|
||||
)
|
||||
for h in request.history
|
||||
]
|
||||
|
||||
# ---- Streaming callbacks ------------------------------------------------
|
||||
|
||||
def make_think_callback(self, respond, streaming):
|
||||
"""Create the think callback for streaming/non-streaming."""
|
||||
async def think(x, is_final=False):
|
||||
logger.debug(f"Think: {x} (is_final={is_final})")
|
||||
if streaming:
|
||||
r = AgentResponse(
|
||||
chunk_type="thought",
|
||||
content=x,
|
||||
end_of_message=is_final,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
else:
|
||||
r = AgentResponse(
|
||||
chunk_type="thought",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
await respond(r)
|
||||
return think
|
||||
|
||||
def make_observe_callback(self, respond, streaming):
|
||||
"""Create the observe callback for streaming/non-streaming."""
|
||||
async def observe(x, is_final=False):
|
||||
logger.debug(f"Observe: {x} (is_final={is_final})")
|
||||
if streaming:
|
||||
r = AgentResponse(
|
||||
chunk_type="observation",
|
||||
content=x,
|
||||
end_of_message=is_final,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
else:
|
||||
r = AgentResponse(
|
||||
chunk_type="observation",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
await respond(r)
|
||||
return observe
|
||||
|
||||
def make_answer_callback(self, respond, streaming):
|
||||
"""Create the answer callback for streaming/non-streaming."""
|
||||
async def answer(x):
|
||||
logger.debug(f"Answer: {x}")
|
||||
if streaming:
|
||||
r = AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=x,
|
||||
end_of_message=False,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
else:
|
||||
r = AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
await respond(r)
|
||||
return answer
|
||||
|
||||
# ---- Provenance emission ------------------------------------------------
|
||||
|
||||
async def emit_session_triples(self, flow, session_uri, question, user,
|
||||
collection, respond, streaming):
|
||||
"""Emit provenance triples for a new session."""
|
||||
timestamp = datetime.utcnow().isoformat() + "Z"
|
||||
triples = set_graph(
|
||||
agent_session_triples(session_uri, question, timestamp),
|
||||
GRAPH_RETRIEVAL,
|
||||
)
|
||||
await flow("explainability").send(Triples(
|
||||
metadata=Metadata(
|
||||
id=session_uri,
|
||||
user=user,
|
||||
collection=collection,
|
||||
),
|
||||
triples=triples,
|
||||
))
|
||||
logger.debug(f"Emitted session triples for {session_uri}")
|
||||
|
||||
if streaming:
|
||||
await respond(AgentResponse(
|
||||
chunk_type="explain",
|
||||
content="",
|
||||
explain_id=session_uri,
|
||||
explain_graph=GRAPH_RETRIEVAL,
|
||||
))
|
||||
|
||||
async def emit_iteration_triples(self, flow, session_id, iteration_num,
|
||||
session_uri, act, request, respond,
|
||||
streaming):
|
||||
"""Emit provenance triples for an iteration and save to librarian."""
|
||||
iteration_uri = agent_iteration_uri(session_id, iteration_num)
|
||||
|
||||
if iteration_num > 1:
|
||||
iter_question_uri = None
|
||||
iter_previous_uri = agent_iteration_uri(session_id, iteration_num - 1)
|
||||
else:
|
||||
iter_question_uri = session_uri
|
||||
iter_previous_uri = None
|
||||
|
||||
# Save thought to librarian
|
||||
thought_doc_id = None
|
||||
if act.thought:
|
||||
thought_doc_id = (
|
||||
f"urn:trustgraph:agent:{session_id}/i{iteration_num}/thought"
|
||||
)
|
||||
try:
|
||||
await self.processor.save_answer_content(
|
||||
doc_id=thought_doc_id,
|
||||
user=request.user,
|
||||
content=act.thought,
|
||||
title=f"Agent Thought: {act.name}",
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to save thought to librarian: {e}")
|
||||
thought_doc_id = None
|
||||
|
||||
# Save observation to librarian
|
||||
observation_doc_id = None
|
||||
if act.observation:
|
||||
observation_doc_id = (
|
||||
f"urn:trustgraph:agent:{session_id}/i{iteration_num}/observation"
|
||||
)
|
||||
try:
|
||||
await self.processor.save_answer_content(
|
||||
doc_id=observation_doc_id,
|
||||
user=request.user,
|
||||
content=act.observation,
|
||||
title=f"Agent Observation: {act.name}",
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to save observation to librarian: {e}")
|
||||
observation_doc_id = None
|
||||
|
||||
thought_entity_uri = agent_thought_uri(session_id, iteration_num)
|
||||
observation_entity_uri = agent_observation_uri(session_id, iteration_num)
|
||||
|
||||
iter_triples = set_graph(
|
||||
agent_iteration_triples(
|
||||
iteration_uri,
|
||||
question_uri=iter_question_uri,
|
||||
previous_uri=iter_previous_uri,
|
||||
action=act.name,
|
||||
arguments=act.arguments,
|
||||
thought_uri=thought_entity_uri if thought_doc_id else None,
|
||||
thought_document_id=thought_doc_id,
|
||||
observation_uri=observation_entity_uri if observation_doc_id else None,
|
||||
observation_document_id=observation_doc_id,
|
||||
),
|
||||
GRAPH_RETRIEVAL,
|
||||
)
|
||||
await flow("explainability").send(Triples(
|
||||
metadata=Metadata(
|
||||
id=iteration_uri,
|
||||
user=request.user,
|
||||
collection=getattr(request, 'collection', 'default'),
|
||||
),
|
||||
triples=iter_triples,
|
||||
))
|
||||
logger.debug(f"Emitted iteration triples for {iteration_uri}")
|
||||
|
||||
if streaming:
|
||||
await respond(AgentResponse(
|
||||
chunk_type="explain",
|
||||
content="",
|
||||
explain_id=iteration_uri,
|
||||
explain_graph=GRAPH_RETRIEVAL,
|
||||
))
|
||||
|
||||
async def emit_final_triples(self, flow, session_id, iteration_num,
|
||||
session_uri, answer_text, request, respond,
|
||||
streaming):
|
||||
"""Emit provenance triples for the final answer and save to librarian."""
|
||||
final_uri = agent_final_uri(session_id)
|
||||
|
||||
if iteration_num > 1:
|
||||
final_question_uri = None
|
||||
final_previous_uri = agent_iteration_uri(session_id, iteration_num - 1)
|
||||
else:
|
||||
final_question_uri = session_uri
|
||||
final_previous_uri = None
|
||||
|
||||
# Save answer to librarian
|
||||
answer_doc_id = None
|
||||
if answer_text:
|
||||
answer_doc_id = f"urn:trustgraph:agent:{session_id}/answer"
|
||||
try:
|
||||
await self.processor.save_answer_content(
|
||||
doc_id=answer_doc_id,
|
||||
user=request.user,
|
||||
content=answer_text,
|
||||
title=f"Agent Answer: {request.question[:50]}...",
|
||||
)
|
||||
logger.debug(f"Saved answer to librarian: {answer_doc_id}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to save answer to librarian: {e}")
|
||||
answer_doc_id = None
|
||||
|
||||
final_triples = set_graph(
|
||||
agent_final_triples(
|
||||
final_uri,
|
||||
question_uri=final_question_uri,
|
||||
previous_uri=final_previous_uri,
|
||||
document_id=answer_doc_id,
|
||||
),
|
||||
GRAPH_RETRIEVAL,
|
||||
)
|
||||
await flow("explainability").send(Triples(
|
||||
metadata=Metadata(
|
||||
id=final_uri,
|
||||
user=request.user,
|
||||
collection=getattr(request, 'collection', 'default'),
|
||||
),
|
||||
triples=final_triples,
|
||||
))
|
||||
logger.debug(f"Emitted final triples for {final_uri}")
|
||||
|
||||
if streaming:
|
||||
await respond(AgentResponse(
|
||||
chunk_type="explain",
|
||||
content="",
|
||||
explain_id=final_uri,
|
||||
explain_graph=GRAPH_RETRIEVAL,
|
||||
))
|
||||
|
||||
# ---- Response helpers ---------------------------------------------------
|
||||
|
||||
async def prompt_as_answer(self, client, prompt_id, variables,
|
||||
respond, streaming):
|
||||
"""Call a prompt template, forwarding chunks as answer
|
||||
AgentResponse messages when streaming is enabled.
|
||||
|
||||
Returns the full accumulated answer text (needed for provenance).
|
||||
"""
|
||||
if streaming:
|
||||
accumulated = []
|
||||
|
||||
async def on_chunk(text, end_of_stream):
|
||||
if text:
|
||||
accumulated.append(text)
|
||||
await respond(AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=text,
|
||||
end_of_message=False,
|
||||
end_of_dialog=False,
|
||||
))
|
||||
|
||||
await client.prompt(
|
||||
id=prompt_id,
|
||||
variables=variables,
|
||||
streaming=True,
|
||||
chunk_callback=on_chunk,
|
||||
)
|
||||
|
||||
return "".join(accumulated)
|
||||
else:
|
||||
return await client.prompt(
|
||||
id=prompt_id,
|
||||
variables=variables,
|
||||
)
|
||||
|
||||
async def send_final_response(self, respond, streaming, answer_text,
|
||||
already_streamed=False):
|
||||
"""Send the answer content and end-of-dialog marker.
|
||||
|
||||
Args:
|
||||
already_streamed: If True, answer chunks were already sent
|
||||
via streaming callbacks (e.g. ReactPattern). Only the
|
||||
end-of-dialog marker is emitted.
|
||||
"""
|
||||
if streaming and not already_streamed:
|
||||
# Answer wasn't streamed yet — send it as a chunk first
|
||||
if answer_text:
|
||||
await respond(AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=answer_text,
|
||||
end_of_message=False,
|
||||
end_of_dialog=False,
|
||||
))
|
||||
if streaming:
|
||||
# End-of-dialog marker
|
||||
await respond(AgentResponse(
|
||||
chunk_type="answer",
|
||||
content="",
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
))
|
||||
else:
|
||||
await respond(AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=answer_text,
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
))
|
||||
|
||||
def build_next_request(self, request, history, session_id, collection,
|
||||
streaming, next_state):
|
||||
"""Build the AgentRequest for the next iteration."""
|
||||
return AgentRequest(
|
||||
question=request.question,
|
||||
state=next_state,
|
||||
group=getattr(request, 'group', []),
|
||||
history=[
|
||||
AgentStep(
|
||||
thought=h.thought,
|
||||
action=h.name,
|
||||
arguments={k: str(v) for k, v in h.arguments.items()},
|
||||
observation=h.observation,
|
||||
)
|
||||
for h in history
|
||||
],
|
||||
user=request.user,
|
||||
collection=collection,
|
||||
streaming=streaming,
|
||||
session_id=session_id,
|
||||
# Preserve orchestration fields
|
||||
conversation_id=getattr(request, 'conversation_id', ''),
|
||||
pattern=getattr(request, 'pattern', ''),
|
||||
task_type=getattr(request, 'task_type', ''),
|
||||
framing=getattr(request, 'framing', ''),
|
||||
correlation_id=getattr(request, 'correlation_id', ''),
|
||||
parent_session_id=getattr(request, 'parent_session_id', ''),
|
||||
subagent_goal=getattr(request, 'subagent_goal', ''),
|
||||
expected_siblings=getattr(request, 'expected_siblings', 0),
|
||||
)
|
||||
|
||||
async def iterate(self, request, respond, next, flow):
|
||||
"""
|
||||
Perform one iteration of this pattern.
|
||||
|
||||
Must be implemented by subclasses.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
349
trustgraph-flow/trustgraph/agent/orchestrator/plan_pattern.py
Normal file
349
trustgraph-flow/trustgraph/agent/orchestrator/plan_pattern.py
Normal file
|
|
@ -0,0 +1,349 @@
|
|||
"""
|
||||
PlanThenExecutePattern — structured planning followed by step execution.
|
||||
|
||||
Phase 1 (planning): LLM produces a structured plan of steps.
|
||||
Phase 2 (execution): Each step is executed via single-shot tool call.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
|
||||
from ... schema import AgentRequest, AgentResponse, AgentStep, PlanStep
|
||||
|
||||
from ..react.types import Action
|
||||
|
||||
from . pattern_base import PatternBase
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PlanThenExecutePattern(PatternBase):
|
||||
"""
|
||||
Plan-then-Execute pattern.
|
||||
|
||||
History tracks the current phase via AgentStep.step_type:
|
||||
- "plan" step: contains the plan in step.plan
|
||||
- "execute" step: a normal react iteration executing a plan step
|
||||
|
||||
On the first call (empty history), a planning iteration is run.
|
||||
Subsequent calls execute the next pending plan step via ReACT.
|
||||
"""
|
||||
|
||||
async def iterate(self, request, respond, next, flow):
|
||||
|
||||
streaming = getattr(request, 'streaming', False)
|
||||
session_id = getattr(request, 'session_id', '') or str(uuid.uuid4())
|
||||
collection = getattr(request, 'collection', 'default')
|
||||
|
||||
history = self.build_history(request)
|
||||
iteration_num = len(request.history) + 1
|
||||
session_uri = self.processor.provenance_session_uri(session_id)
|
||||
|
||||
# Emit session provenance on first iteration
|
||||
if iteration_num == 1:
|
||||
await self.emit_session_triples(
|
||||
flow, session_uri, request.question,
|
||||
request.user, collection, respond, streaming,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"PlanThenExecutePattern iteration {iteration_num}: "
|
||||
f"{request.question}"
|
||||
)
|
||||
|
||||
if iteration_num >= self.processor.max_iterations:
|
||||
raise RuntimeError("Too many agent iterations")
|
||||
|
||||
# Determine current phase by checking history for a plan step
|
||||
plan = self._extract_plan(request.history)
|
||||
|
||||
if plan is None:
|
||||
await self._planning_iteration(
|
||||
request, respond, next, flow,
|
||||
session_id, collection, streaming, session_uri,
|
||||
iteration_num,
|
||||
)
|
||||
else:
|
||||
await self._execution_iteration(
|
||||
request, respond, next, flow,
|
||||
session_id, collection, streaming, session_uri,
|
||||
iteration_num, plan,
|
||||
)
|
||||
|
||||
def _extract_plan(self, history):
|
||||
"""Find the most recent plan from history.
|
||||
|
||||
Checks execute steps first (they carry the updated plan with
|
||||
completion statuses), then falls back to the original plan step.
|
||||
"""
|
||||
if not history:
|
||||
return None
|
||||
for step in reversed(history):
|
||||
if step.plan:
|
||||
return list(step.plan)
|
||||
return None
|
||||
|
||||
def _find_next_pending_step(self, plan):
|
||||
"""Return index of the next pending step, or None if all done."""
|
||||
for i, step in enumerate(plan):
|
||||
if getattr(step, 'status', 'pending') == 'pending':
|
||||
return i
|
||||
return None
|
||||
|
||||
async def _planning_iteration(self, request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num):
|
||||
"""Ask the LLM to produce a structured plan."""
|
||||
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
|
||||
tools = self.filter_tools(self.processor.agent.tools, request)
|
||||
framing = getattr(request, 'framing', '')
|
||||
|
||||
context = self.make_context(flow, request.user)
|
||||
client = context("prompt-request")
|
||||
|
||||
# Use the plan-create prompt template
|
||||
plan_steps = await client.prompt(
|
||||
id="plan-create",
|
||||
variables={
|
||||
"question": request.question,
|
||||
"framing": framing,
|
||||
"tools": [
|
||||
{"name": t.name, "description": t.description}
|
||||
for t in tools.values()
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
# Validate we got a list
|
||||
if not isinstance(plan_steps, list) or not plan_steps:
|
||||
logger.warning("plan-create returned invalid result, falling back to single step")
|
||||
plan_steps = [{"goal": "Answer the question directly", "tool_hint": "", "depends_on": []}]
|
||||
|
||||
# Emit thought about the plan
|
||||
thought_text = f"Created plan with {len(plan_steps)} steps"
|
||||
await think(thought_text, is_final=True)
|
||||
|
||||
# Build PlanStep objects
|
||||
plan_agent_steps = [
|
||||
PlanStep(
|
||||
goal=ps.get("goal", ""),
|
||||
tool_hint=ps.get("tool_hint", ""),
|
||||
depends_on=ps.get("depends_on", []),
|
||||
status="pending",
|
||||
result="",
|
||||
)
|
||||
for ps in plan_steps
|
||||
]
|
||||
|
||||
# Create a plan step in history
|
||||
plan_history_step = AgentStep(
|
||||
thought=thought_text,
|
||||
action="plan",
|
||||
arguments={},
|
||||
observation=json.dumps(plan_steps),
|
||||
step_type="plan",
|
||||
plan=plan_agent_steps,
|
||||
)
|
||||
|
||||
# Build next request with plan in history
|
||||
new_history = list(request.history) + [plan_history_step]
|
||||
r = AgentRequest(
|
||||
question=request.question,
|
||||
state=request.state,
|
||||
group=getattr(request, 'group', []),
|
||||
history=new_history,
|
||||
user=request.user,
|
||||
collection=collection,
|
||||
streaming=streaming,
|
||||
session_id=session_id,
|
||||
conversation_id=getattr(request, 'conversation_id', ''),
|
||||
pattern=getattr(request, 'pattern', ''),
|
||||
task_type=getattr(request, 'task_type', ''),
|
||||
framing=getattr(request, 'framing', ''),
|
||||
correlation_id=getattr(request, 'correlation_id', ''),
|
||||
parent_session_id=getattr(request, 'parent_session_id', ''),
|
||||
subagent_goal=getattr(request, 'subagent_goal', ''),
|
||||
expected_siblings=getattr(request, 'expected_siblings', 0),
|
||||
)
|
||||
await next(r)
|
||||
|
||||
async def _execution_iteration(self, request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num, plan):
|
||||
"""Execute the next pending plan step via single-shot tool call."""
|
||||
|
||||
pending_idx = self._find_next_pending_step(plan)
|
||||
|
||||
if pending_idx is None:
|
||||
# All steps done — synthesise final answer
|
||||
await self._synthesise(
|
||||
request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num, plan,
|
||||
)
|
||||
return
|
||||
|
||||
current_step = plan[pending_idx]
|
||||
goal = getattr(current_step, 'goal', '') or str(current_step)
|
||||
|
||||
logger.info(f"Executing plan step {pending_idx}: {goal}")
|
||||
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
observe = self.make_observe_callback(respond, streaming)
|
||||
|
||||
# Gather results from dependencies
|
||||
previous_results = []
|
||||
depends_on = getattr(current_step, 'depends_on', [])
|
||||
if depends_on:
|
||||
for dep_idx in depends_on:
|
||||
if 0 <= dep_idx < len(plan):
|
||||
dep_step = plan[dep_idx]
|
||||
dep_result = getattr(dep_step, 'result', '')
|
||||
if dep_result:
|
||||
previous_results.append({
|
||||
"index": dep_idx,
|
||||
"result": dep_result,
|
||||
})
|
||||
|
||||
tools = self.filter_tools(self.processor.agent.tools, request)
|
||||
context = self.make_context(flow, request.user)
|
||||
client = context("prompt-request")
|
||||
|
||||
# Single-shot: ask LLM which tool + arguments to use for this goal
|
||||
tool_call = await client.prompt(
|
||||
id="plan-step-execute",
|
||||
variables={
|
||||
"goal": goal,
|
||||
"previous_results": previous_results,
|
||||
"tools": [
|
||||
{
|
||||
"name": t.name,
|
||||
"description": t.description,
|
||||
"arguments": [
|
||||
{"name": a.name, "type": a.type, "description": a.description}
|
||||
for a in t.arguments
|
||||
],
|
||||
}
|
||||
for t in tools.values()
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
tool_name = tool_call.get("tool", "")
|
||||
tool_arguments = tool_call.get("arguments", {})
|
||||
|
||||
await think(
|
||||
f"Step {pending_idx}: {goal} → calling {tool_name}",
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
# Invoke the tool directly
|
||||
if tool_name in tools:
|
||||
tool = tools[tool_name]
|
||||
resp = await tool.implementation(context).invoke(**tool_arguments)
|
||||
step_result = resp.strip() if isinstance(resp, str) else str(resp).strip()
|
||||
else:
|
||||
logger.warning(
|
||||
f"Plan step {pending_idx}: LLM selected unknown tool "
|
||||
f"'{tool_name}', available: {list(tools.keys())}"
|
||||
)
|
||||
step_result = f"Error: tool '{tool_name}' not found"
|
||||
|
||||
await observe(step_result, is_final=True)
|
||||
|
||||
# Update plan step status
|
||||
plan[pending_idx] = PlanStep(
|
||||
goal=goal,
|
||||
tool_hint=getattr(current_step, 'tool_hint', ''),
|
||||
depends_on=getattr(current_step, 'depends_on', []),
|
||||
status="completed",
|
||||
result=step_result,
|
||||
)
|
||||
|
||||
# Emit iteration provenance
|
||||
prov_act = Action(
|
||||
thought=f"Plan step {pending_idx}: {goal}",
|
||||
name=tool_name,
|
||||
arguments=tool_arguments,
|
||||
observation=step_result,
|
||||
)
|
||||
await self.emit_iteration_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
prov_act, request, respond, streaming,
|
||||
)
|
||||
|
||||
# Build execution step for history
|
||||
exec_step = AgentStep(
|
||||
thought=f"Executing plan step {pending_idx}: {goal}",
|
||||
action=tool_name,
|
||||
arguments={k: str(v) for k, v in tool_arguments.items()},
|
||||
observation=step_result,
|
||||
step_type="execute",
|
||||
plan=plan,
|
||||
)
|
||||
|
||||
new_history = list(request.history) + [exec_step]
|
||||
|
||||
r = AgentRequest(
|
||||
question=request.question,
|
||||
state=request.state,
|
||||
group=getattr(request, 'group', []),
|
||||
history=new_history,
|
||||
user=request.user,
|
||||
collection=collection,
|
||||
streaming=streaming,
|
||||
session_id=session_id,
|
||||
conversation_id=getattr(request, 'conversation_id', ''),
|
||||
pattern=getattr(request, 'pattern', ''),
|
||||
task_type=getattr(request, 'task_type', ''),
|
||||
framing=getattr(request, 'framing', ''),
|
||||
correlation_id=getattr(request, 'correlation_id', ''),
|
||||
parent_session_id=getattr(request, 'parent_session_id', ''),
|
||||
subagent_goal=getattr(request, 'subagent_goal', ''),
|
||||
expected_siblings=getattr(request, 'expected_siblings', 0),
|
||||
)
|
||||
await next(r)
|
||||
|
||||
async def _synthesise(self, request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num, plan):
|
||||
"""Synthesise a final answer from all completed plan step results."""
|
||||
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
framing = getattr(request, 'framing', '')
|
||||
|
||||
context = self.make_context(flow, request.user)
|
||||
client = context("prompt-request")
|
||||
|
||||
# Use the plan-synthesise prompt template
|
||||
steps_data = []
|
||||
for i, step in enumerate(plan):
|
||||
steps_data.append({
|
||||
"index": i,
|
||||
"goal": getattr(step, 'goal', f'Step {i}'),
|
||||
"result": getattr(step, 'result', ''),
|
||||
})
|
||||
|
||||
await think("Synthesising final answer from plan results", is_final=True)
|
||||
|
||||
response_text = await self.prompt_as_answer(
|
||||
client, "plan-synthesise",
|
||||
variables={
|
||||
"question": request.question,
|
||||
"framing": framing,
|
||||
"steps": steps_data,
|
||||
},
|
||||
respond=respond,
|
||||
streaming=streaming,
|
||||
)
|
||||
|
||||
await self.emit_final_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
response_text, request, respond, streaming,
|
||||
)
|
||||
await self.send_final_response(
|
||||
respond, streaming, response_text, already_streamed=streaming,
|
||||
)
|
||||
134
trustgraph-flow/trustgraph/agent/orchestrator/react_pattern.py
Normal file
134
trustgraph-flow/trustgraph/agent/orchestrator/react_pattern.py
Normal file
|
|
@ -0,0 +1,134 @@
|
|||
"""
|
||||
ReactPattern — extracted from the existing agent_manager.py.
|
||||
|
||||
Implements the ReACT (Reasoning + Acting) loop: think, select a tool,
|
||||
observe the result, repeat until a final answer is produced.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
|
||||
from ... schema import AgentRequest, AgentResponse, AgentStep
|
||||
|
||||
from ..react.agent_manager import AgentManager
|
||||
from ..react.types import Action, Final
|
||||
from ..tool_filter import get_next_state
|
||||
|
||||
from . pattern_base import PatternBase
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ReactPattern(PatternBase):
|
||||
"""
|
||||
ReACT pattern: interleaved reasoning and action.
|
||||
|
||||
Each iterate() call performs one reason/act cycle. If the LLM
|
||||
produces a Final answer the dialog completes; otherwise the action
|
||||
result is appended to history and a next-request is emitted.
|
||||
"""
|
||||
|
||||
async def iterate(self, request, respond, next, flow):
|
||||
|
||||
streaming = getattr(request, 'streaming', False)
|
||||
session_id = getattr(request, 'session_id', '') or str(uuid.uuid4())
|
||||
collection = getattr(request, 'collection', 'default')
|
||||
|
||||
history = self.build_history(request)
|
||||
iteration_num = len(history) + 1
|
||||
session_uri = self.processor.provenance_session_uri(session_id)
|
||||
|
||||
# Emit session provenance on first iteration
|
||||
if iteration_num == 1:
|
||||
await self.emit_session_triples(
|
||||
flow, session_uri, request.question,
|
||||
request.user, collection, respond, streaming,
|
||||
)
|
||||
|
||||
logger.info(f"ReactPattern iteration {iteration_num}: {request.question}")
|
||||
|
||||
if len(history) >= self.processor.max_iterations:
|
||||
raise RuntimeError("Too many agent iterations")
|
||||
|
||||
# Build callbacks
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
observe = self.make_observe_callback(respond, streaming)
|
||||
answer_cb = self.make_answer_callback(respond, streaming)
|
||||
|
||||
# Filter tools
|
||||
filtered_tools = self.filter_tools(
|
||||
self.processor.agent.tools, request,
|
||||
)
|
||||
logger.info(
|
||||
f"Filtered from {len(self.processor.agent.tools)} "
|
||||
f"to {len(filtered_tools)} available tools"
|
||||
)
|
||||
|
||||
# Create temporary agent with filtered tools and optional framing
|
||||
additional_context = self.processor.agent.additional_context
|
||||
framing = getattr(request, 'framing', '')
|
||||
if framing:
|
||||
if additional_context:
|
||||
additional_context = f"{additional_context}\n\n{framing}"
|
||||
else:
|
||||
additional_context = framing
|
||||
|
||||
temp_agent = AgentManager(
|
||||
tools=filtered_tools,
|
||||
additional_context=additional_context,
|
||||
)
|
||||
|
||||
context = self.make_context(flow, request.user)
|
||||
|
||||
act = await temp_agent.react(
|
||||
question=request.question,
|
||||
history=history,
|
||||
think=think,
|
||||
observe=observe,
|
||||
answer=answer_cb,
|
||||
context=context,
|
||||
streaming=streaming,
|
||||
)
|
||||
|
||||
logger.debug(f"Action: {act}")
|
||||
|
||||
if isinstance(act, Final):
|
||||
|
||||
if isinstance(act.final, str):
|
||||
f = act.final
|
||||
else:
|
||||
f = json.dumps(act.final)
|
||||
|
||||
# Emit final provenance
|
||||
await self.emit_final_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
f, request, respond, streaming,
|
||||
)
|
||||
|
||||
await self.send_final_response(
|
||||
respond, streaming, f, already_streamed=streaming,
|
||||
)
|
||||
return
|
||||
|
||||
# Not final — emit iteration provenance and send next request
|
||||
await self.emit_iteration_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
act, request, respond, streaming,
|
||||
)
|
||||
|
||||
history.append(act)
|
||||
|
||||
# Handle state transitions
|
||||
next_state = request.state
|
||||
if act.name in filtered_tools:
|
||||
executed_tool = filtered_tools[act.name]
|
||||
next_state = get_next_state(executed_tool, request.state or "undefined")
|
||||
|
||||
r = self.build_next_request(
|
||||
request, history, session_id, collection,
|
||||
streaming, next_state,
|
||||
)
|
||||
await next(r)
|
||||
|
||||
logger.debug("ReactPattern iteration complete")
|
||||
511
trustgraph-flow/trustgraph/agent/orchestrator/service.py
Normal file
511
trustgraph-flow/trustgraph/agent/orchestrator/service.py
Normal file
|
|
@ -0,0 +1,511 @@
|
|||
"""
|
||||
Agent orchestrator service — multi-pattern drop-in replacement for
|
||||
agent-manager-react.
|
||||
|
||||
Uses the same service identity and Pulsar queues. Adds meta-routing
|
||||
to select between ReactPattern, PlanThenExecutePattern, and
|
||||
SupervisorPattern at runtime.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import functools
|
||||
import logging
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
|
||||
from ... base import AgentService, TextCompletionClientSpec, PromptClientSpec
|
||||
from ... base import GraphRagClientSpec, ToolClientSpec, StructuredQueryClientSpec
|
||||
from ... base import RowEmbeddingsQueryClientSpec, EmbeddingsClientSpec
|
||||
from ... base import ProducerSpec
|
||||
from ... base import Consumer, Producer
|
||||
from ... base import ConsumerMetrics, ProducerMetrics
|
||||
|
||||
from ... schema import AgentRequest, AgentResponse, AgentStep, Error
|
||||
from ... schema import Triples, Metadata
|
||||
from ... schema import LibrarianRequest, LibrarianResponse, DocumentMetadata
|
||||
from ... schema import librarian_request_queue, librarian_response_queue
|
||||
|
||||
from trustgraph.provenance import (
|
||||
agent_session_uri,
|
||||
GRAPH_RETRIEVAL,
|
||||
)
|
||||
|
||||
from ..react.tools import (
|
||||
KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl, PromptImpl,
|
||||
StructuredQueryImpl, RowEmbeddingsQueryImpl, ToolServiceImpl,
|
||||
)
|
||||
from ..react.agent_manager import AgentManager
|
||||
from ..tool_filter import validate_tool_config
|
||||
from ..react.types import Final, Action, Tool, Argument
|
||||
|
||||
from . meta_router import MetaRouter
|
||||
from . pattern_base import PatternBase, UserAwareContext
|
||||
from . react_pattern import ReactPattern
|
||||
from . plan_pattern import PlanThenExecutePattern
|
||||
from . supervisor_pattern import SupervisorPattern
|
||||
from . aggregator import Aggregator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
default_ident = "agent-manager"
|
||||
default_max_iterations = 10
|
||||
default_librarian_request_queue = librarian_request_queue
|
||||
default_librarian_response_queue = librarian_response_queue
|
||||
|
||||
|
||||
class Processor(AgentService):
|
||||
|
||||
def __init__(self, **params):
|
||||
|
||||
id = params.get("id")
|
||||
|
||||
self.max_iterations = int(
|
||||
params.get("max_iterations", default_max_iterations)
|
||||
)
|
||||
|
||||
self.config_key = params.get("config_type", "agent")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"id": id,
|
||||
"max_iterations": self.max_iterations,
|
||||
"config_type": self.config_key,
|
||||
}
|
||||
)
|
||||
|
||||
self.agent = AgentManager(
|
||||
tools={},
|
||||
additional_context="",
|
||||
)
|
||||
|
||||
self.tool_service_clients = {}
|
||||
|
||||
# Patterns
|
||||
self.react_pattern = ReactPattern(self)
|
||||
self.plan_pattern = PlanThenExecutePattern(self)
|
||||
self.supervisor_pattern = SupervisorPattern(self)
|
||||
|
||||
# Aggregator for supervisor fan-in
|
||||
self.aggregator = Aggregator()
|
||||
|
||||
# Meta-router (initialised on first config load)
|
||||
self.meta_router = None
|
||||
|
||||
self.config_handlers.append(self.on_tools_config)
|
||||
|
||||
self.register_specification(
|
||||
TextCompletionClientSpec(
|
||||
request_name="text-completion-request",
|
||||
response_name="text-completion-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
GraphRagClientSpec(
|
||||
request_name="graph-rag-request",
|
||||
response_name="graph-rag-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
PromptClientSpec(
|
||||
request_name="prompt-request",
|
||||
response_name="prompt-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
ToolClientSpec(
|
||||
request_name="mcp-tool-request",
|
||||
response_name="mcp-tool-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
StructuredQueryClientSpec(
|
||||
request_name="structured-query-request",
|
||||
response_name="structured-query-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
EmbeddingsClientSpec(
|
||||
request_name="embeddings-request",
|
||||
response_name="embeddings-response",
|
||||
)
|
||||
)
|
||||
|
||||
self.register_specification(
|
||||
RowEmbeddingsQueryClientSpec(
|
||||
request_name="row-embeddings-query-request",
|
||||
response_name="row-embeddings-query-response",
|
||||
)
|
||||
)
|
||||
|
||||
# Explainability producer
|
||||
self.register_specification(
|
||||
ProducerSpec(
|
||||
name="explainability",
|
||||
schema=Triples,
|
||||
)
|
||||
)
|
||||
|
||||
# Librarian client
|
||||
librarian_request_q = params.get(
|
||||
"librarian_request_queue", default_librarian_request_queue
|
||||
)
|
||||
librarian_response_q = params.get(
|
||||
"librarian_response_queue", default_librarian_response_queue
|
||||
)
|
||||
|
||||
librarian_request_metrics = ProducerMetrics(
|
||||
processor=id, flow=None, name="librarian-request"
|
||||
)
|
||||
|
||||
self.librarian_request_producer = Producer(
|
||||
backend=self.pubsub,
|
||||
topic=librarian_request_q,
|
||||
schema=LibrarianRequest,
|
||||
metrics=librarian_request_metrics,
|
||||
)
|
||||
|
||||
librarian_response_metrics = ConsumerMetrics(
|
||||
processor=id, flow=None, name="librarian-response"
|
||||
)
|
||||
|
||||
self.librarian_response_consumer = Consumer(
|
||||
taskgroup=self.taskgroup,
|
||||
backend=self.pubsub,
|
||||
flow=None,
|
||||
topic=librarian_response_q,
|
||||
subscriber=f"{id}-librarian",
|
||||
schema=LibrarianResponse,
|
||||
handler=self.on_librarian_response,
|
||||
metrics=librarian_response_metrics,
|
||||
)
|
||||
|
||||
self.pending_librarian_requests = {}
|
||||
|
||||
async def start(self):
|
||||
await super(Processor, self).start()
|
||||
await self.librarian_request_producer.start()
|
||||
await self.librarian_response_consumer.start()
|
||||
|
||||
async def on_librarian_response(self, msg, consumer, flow):
|
||||
response = msg.value()
|
||||
request_id = msg.properties().get("id")
|
||||
|
||||
if request_id in self.pending_librarian_requests:
|
||||
future = self.pending_librarian_requests.pop(request_id)
|
||||
future.set_result(response)
|
||||
|
||||
async def save_answer_content(self, doc_id, user, content, title=None,
|
||||
timeout=120):
|
||||
request_id = str(uuid.uuid4())
|
||||
|
||||
doc_metadata = DocumentMetadata(
|
||||
id=doc_id,
|
||||
user=user,
|
||||
kind="text/plain",
|
||||
title=title or "Agent Answer",
|
||||
document_type="answer",
|
||||
)
|
||||
|
||||
request = LibrarianRequest(
|
||||
operation="add-document",
|
||||
document_id=doc_id,
|
||||
document_metadata=doc_metadata,
|
||||
content=base64.b64encode(content.encode("utf-8")).decode("utf-8"),
|
||||
user=user,
|
||||
)
|
||||
|
||||
future = asyncio.get_event_loop().create_future()
|
||||
self.pending_librarian_requests[request_id] = future
|
||||
|
||||
try:
|
||||
await self.librarian_request_producer.send(
|
||||
request, properties={"id": request_id}
|
||||
)
|
||||
response = await asyncio.wait_for(future, timeout=timeout)
|
||||
|
||||
if response.error:
|
||||
raise RuntimeError(
|
||||
f"Librarian error saving answer: "
|
||||
f"{response.error.type}: {response.error.message}"
|
||||
)
|
||||
return doc_id
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
self.pending_librarian_requests.pop(request_id, None)
|
||||
raise RuntimeError(f"Timeout saving answer document {doc_id}")
|
||||
|
||||
def provenance_session_uri(self, session_id):
|
||||
return agent_session_uri(session_id)
|
||||
|
||||
async def on_tools_config(self, config, version):
|
||||
|
||||
logger.info(f"Loading configuration version {version}")
|
||||
|
||||
try:
|
||||
tools = {}
|
||||
|
||||
# Load tool-service configurations
|
||||
tool_services = {}
|
||||
if "tool-service" in config:
|
||||
for service_id, service_value in config["tool-service"].items():
|
||||
service_data = json.loads(service_value)
|
||||
tool_services[service_id] = service_data
|
||||
logger.debug(f"Loaded tool-service config: {service_id}")
|
||||
|
||||
logger.info(
|
||||
f"Loaded {len(tool_services)} tool-service configurations"
|
||||
)
|
||||
|
||||
# Load tool configurations
|
||||
if "tool" in config:
|
||||
for tool_id, tool_value in config["tool"].items():
|
||||
data = json.loads(tool_value)
|
||||
impl_id = data.get("type")
|
||||
name = data.get("name")
|
||||
|
||||
if impl_id == "knowledge-query":
|
||||
impl = functools.partial(
|
||||
KnowledgeQueryImpl,
|
||||
collection=data.get("collection"),
|
||||
)
|
||||
arguments = KnowledgeQueryImpl.get_arguments()
|
||||
elif impl_id == "text-completion":
|
||||
impl = TextCompletionImpl
|
||||
arguments = TextCompletionImpl.get_arguments()
|
||||
elif impl_id == "mcp-tool":
|
||||
config_args = data.get("arguments", [])
|
||||
arguments = [
|
||||
Argument(
|
||||
name=arg.get("name"),
|
||||
type=arg.get("type"),
|
||||
description=arg.get("description"),
|
||||
)
|
||||
for arg in config_args
|
||||
]
|
||||
impl = functools.partial(
|
||||
McpToolImpl,
|
||||
mcp_tool_id=data.get("mcp-tool"),
|
||||
arguments=arguments,
|
||||
)
|
||||
elif impl_id == "prompt":
|
||||
config_args = data.get("arguments", [])
|
||||
arguments = [
|
||||
Argument(
|
||||
name=arg.get("name"),
|
||||
type=arg.get("type"),
|
||||
description=arg.get("description"),
|
||||
)
|
||||
for arg in config_args
|
||||
]
|
||||
impl = functools.partial(
|
||||
PromptImpl,
|
||||
template_id=data.get("template"),
|
||||
arguments=arguments,
|
||||
)
|
||||
elif impl_id == "structured-query":
|
||||
impl = functools.partial(
|
||||
StructuredQueryImpl,
|
||||
collection=data.get("collection"),
|
||||
user=None,
|
||||
)
|
||||
arguments = StructuredQueryImpl.get_arguments()
|
||||
elif impl_id == "row-embeddings-query":
|
||||
impl = functools.partial(
|
||||
RowEmbeddingsQueryImpl,
|
||||
schema_name=data.get("schema-name"),
|
||||
collection=data.get("collection"),
|
||||
user=None,
|
||||
index_name=data.get("index-name"),
|
||||
limit=int(data.get("limit", 10)),
|
||||
)
|
||||
arguments = RowEmbeddingsQueryImpl.get_arguments()
|
||||
elif impl_id == "tool-service":
|
||||
service_ref = data.get("service")
|
||||
if not service_ref:
|
||||
raise RuntimeError(
|
||||
f"Tool {name} has type 'tool-service' "
|
||||
f"but no 'service' reference"
|
||||
)
|
||||
if service_ref not in tool_services:
|
||||
raise RuntimeError(
|
||||
f"Tool {name} references unknown "
|
||||
f"tool-service '{service_ref}'"
|
||||
)
|
||||
|
||||
service_config = tool_services[service_ref]
|
||||
request_queue = service_config.get("request-queue")
|
||||
response_queue = service_config.get("response-queue")
|
||||
if not request_queue or not response_queue:
|
||||
raise RuntimeError(
|
||||
f"Tool-service '{service_ref}' must define "
|
||||
f"'request-queue' and 'response-queue'"
|
||||
)
|
||||
|
||||
config_params = service_config.get("config-params", [])
|
||||
config_values = {}
|
||||
for param in config_params:
|
||||
param_name = (
|
||||
param.get("name")
|
||||
if isinstance(param, dict) else param
|
||||
)
|
||||
if param_name in data:
|
||||
config_values[param_name] = data[param_name]
|
||||
elif (
|
||||
isinstance(param, dict)
|
||||
and param.get("required", False)
|
||||
):
|
||||
raise RuntimeError(
|
||||
f"Tool {name} missing required config "
|
||||
f"param '{param_name}'"
|
||||
)
|
||||
|
||||
config_args = data.get("arguments", [])
|
||||
arguments = [
|
||||
Argument(
|
||||
name=arg.get("name"),
|
||||
type=arg.get("type"),
|
||||
description=arg.get("description"),
|
||||
)
|
||||
for arg in config_args
|
||||
]
|
||||
|
||||
impl = functools.partial(
|
||||
ToolServiceImpl,
|
||||
request_queue=request_queue,
|
||||
response_queue=response_queue,
|
||||
config_values=config_values,
|
||||
arguments=arguments,
|
||||
processor=self,
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
f"Tool type {impl_id} not known"
|
||||
)
|
||||
|
||||
validate_tool_config(data)
|
||||
|
||||
tools[name] = Tool(
|
||||
name=name,
|
||||
description=data.get("description"),
|
||||
implementation=impl,
|
||||
config=data,
|
||||
arguments=arguments,
|
||||
)
|
||||
|
||||
# Load additional context from agent config
|
||||
additional = None
|
||||
if self.config_key in config:
|
||||
agent_config = config[self.config_key]
|
||||
additional = agent_config.get("additional-context", None)
|
||||
|
||||
self.agent = AgentManager(
|
||||
tools=tools,
|
||||
additional_context=additional,
|
||||
)
|
||||
|
||||
# Re-initialise meta-router with config
|
||||
self.meta_router = MetaRouter(config=config)
|
||||
|
||||
logger.info(f"Loaded {len(tools)} tools")
|
||||
logger.info("Tool configuration reloaded.")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"on_tools_config Exception: {e}", exc_info=True
|
||||
)
|
||||
logger.error("Configuration reload failed")
|
||||
|
||||
async def agent_request(self, request, respond, next, flow):
|
||||
|
||||
try:
|
||||
pattern = getattr(request, 'pattern', '') or ''
|
||||
|
||||
# If no pattern set and this is the first iteration, route
|
||||
if not pattern and not request.history:
|
||||
context = UserAwareContext(flow, request.user)
|
||||
|
||||
if self.meta_router:
|
||||
pattern, task_type, framing = await self.meta_router.route(
|
||||
request.question, context,
|
||||
)
|
||||
else:
|
||||
pattern = "react"
|
||||
task_type = "general"
|
||||
framing = ""
|
||||
|
||||
# Update request with routing decision
|
||||
request.pattern = pattern
|
||||
request.task_type = task_type
|
||||
request.framing = framing
|
||||
|
||||
logger.info(
|
||||
f"Routed to pattern={pattern}, "
|
||||
f"task_type={task_type}"
|
||||
)
|
||||
|
||||
# Dispatch to the selected pattern
|
||||
if pattern == "plan-then-execute":
|
||||
await self.plan_pattern.iterate(
|
||||
request, respond, next, flow,
|
||||
)
|
||||
elif pattern == "supervisor":
|
||||
await self.supervisor_pattern.iterate(
|
||||
request, respond, next, flow,
|
||||
)
|
||||
else:
|
||||
# Default to react
|
||||
await self.react_pattern.iterate(
|
||||
request, respond, next, flow,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
logger.error(
|
||||
f"agent_request Exception: {e}", exc_info=True
|
||||
)
|
||||
|
||||
logger.debug("Send error response...")
|
||||
|
||||
error_obj = Error(
|
||||
type="agent-error",
|
||||
message=str(e),
|
||||
)
|
||||
|
||||
r = AgentResponse(
|
||||
chunk_type="error",
|
||||
content=str(e),
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
error=error_obj,
|
||||
)
|
||||
|
||||
await respond(r)
|
||||
|
||||
@staticmethod
|
||||
def add_args(parser):
|
||||
|
||||
AgentService.add_args(parser)
|
||||
|
||||
parser.add_argument(
|
||||
'--max-iterations',
|
||||
default=default_max_iterations,
|
||||
help=f'Maximum number of react iterations '
|
||||
f'(default: {default_max_iterations})',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--config-type',
|
||||
default="agent",
|
||||
help='Configuration key for prompts (default: agent)',
|
||||
)
|
||||
|
||||
|
||||
def run():
|
||||
Processor.launch(default_ident, __doc__)
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
"""
|
||||
SupervisorPattern — decomposes a query into subagent goals, fans out,
|
||||
then synthesises results when all subagents complete.
|
||||
|
||||
Phase 1 (decompose): LLM breaks the query into independent sub-goals.
|
||||
Fan-out: Each sub-goal is emitted as a new AgentRequest on the agent
|
||||
request topic, carrying a correlation_id and parent_session_id.
|
||||
Phase 2 (synthesise): Triggered when the aggregator detects all
|
||||
subagents have completed. The supervisor fetches results and
|
||||
produces the final answer.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
|
||||
from ... schema import AgentRequest, AgentResponse, AgentStep
|
||||
|
||||
from ..react.types import Action, Final
|
||||
|
||||
from . pattern_base import PatternBase
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_SUBAGENTS = 5
|
||||
|
||||
|
||||
class SupervisorPattern(PatternBase):
|
||||
"""
|
||||
Supervisor pattern: decompose, fan-out, synthesise.
|
||||
|
||||
History tracks phase via AgentStep.step_type:
|
||||
- "decompose": the decomposition step (subagent goals in arguments)
|
||||
- "synthesise": triggered by aggregator with results in subagent_results
|
||||
"""
|
||||
|
||||
async def iterate(self, request, respond, next, flow):
|
||||
|
||||
streaming = getattr(request, 'streaming', False)
|
||||
session_id = getattr(request, 'session_id', '') or str(uuid.uuid4())
|
||||
collection = getattr(request, 'collection', 'default')
|
||||
iteration_num = len(request.history) + 1
|
||||
session_uri = self.processor.provenance_session_uri(session_id)
|
||||
|
||||
# Emit session provenance on first iteration
|
||||
if iteration_num == 1:
|
||||
await self.emit_session_triples(
|
||||
flow, session_uri, request.question,
|
||||
request.user, collection, respond, streaming,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"SupervisorPattern iteration {iteration_num}: {request.question}"
|
||||
)
|
||||
|
||||
# Check if this is a synthesis request (has subagent_results)
|
||||
has_results = bool(
|
||||
request.history
|
||||
and any(
|
||||
getattr(h, 'step_type', '') == 'decompose'
|
||||
for h in request.history
|
||||
)
|
||||
and any(
|
||||
getattr(h, 'subagent_results', None)
|
||||
for h in request.history
|
||||
)
|
||||
)
|
||||
|
||||
if has_results:
|
||||
await self._synthesise(
|
||||
request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num,
|
||||
)
|
||||
else:
|
||||
await self._decompose_and_fanout(
|
||||
request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num,
|
||||
)
|
||||
|
||||
async def _decompose_and_fanout(self, request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num):
|
||||
"""Decompose the question into sub-goals and fan out subagents."""
|
||||
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
framing = getattr(request, 'framing', '')
|
||||
|
||||
tools = self.filter_tools(self.processor.agent.tools, request)
|
||||
|
||||
context = self.make_context(flow, request.user)
|
||||
client = context("prompt-request")
|
||||
|
||||
# Use the supervisor-decompose prompt template
|
||||
goals = await client.prompt(
|
||||
id="supervisor-decompose",
|
||||
variables={
|
||||
"question": request.question,
|
||||
"framing": framing,
|
||||
"max_subagents": MAX_SUBAGENTS,
|
||||
"tools": [
|
||||
{"name": t.name, "description": t.description}
|
||||
for t in tools.values()
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
# Validate result
|
||||
if not isinstance(goals, list):
|
||||
goals = []
|
||||
goals = [g for g in goals if isinstance(g, str)]
|
||||
goals = goals[:MAX_SUBAGENTS]
|
||||
|
||||
if not goals:
|
||||
goals = [request.question]
|
||||
|
||||
await think(
|
||||
f"Decomposed into {len(goals)} sub-goals: {goals}",
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
# Generate correlation ID for this fan-out
|
||||
correlation_id = str(uuid.uuid4())
|
||||
|
||||
# Emit decomposition provenance
|
||||
decompose_act = Action(
|
||||
thought=f"Decomposed into {len(goals)} sub-goals",
|
||||
name="decompose",
|
||||
arguments={"goals": json.dumps(goals), "correlation_id": correlation_id},
|
||||
observation=f"Fanning out {len(goals)} subagents",
|
||||
)
|
||||
await self.emit_iteration_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
decompose_act, request, respond, streaming,
|
||||
)
|
||||
|
||||
# Fan out: emit a subagent request for each goal
|
||||
for i, goal in enumerate(goals):
|
||||
subagent_session = str(uuid.uuid4())
|
||||
sub_request = AgentRequest(
|
||||
question=goal,
|
||||
state="",
|
||||
group=getattr(request, 'group', []),
|
||||
history=[],
|
||||
user=request.user,
|
||||
collection=collection,
|
||||
streaming=False, # Subagents don't stream
|
||||
session_id=subagent_session,
|
||||
conversation_id=getattr(request, 'conversation_id', ''),
|
||||
pattern="react", # Subagents use react by default
|
||||
task_type=getattr(request, 'task_type', ''),
|
||||
framing=getattr(request, 'framing', ''),
|
||||
correlation_id=correlation_id,
|
||||
parent_session_id=session_id,
|
||||
subagent_goal=goal,
|
||||
expected_siblings=len(goals),
|
||||
)
|
||||
await next(sub_request)
|
||||
logger.info(f"Fan-out: emitted subagent {i} for goal: {goal}")
|
||||
|
||||
# NOTE: The supervisor stops here. The aggregator will detect
|
||||
# when all subagents complete and emit a synthesis request
|
||||
# with the results populated.
|
||||
logger.info(
|
||||
f"Supervisor fan-out complete: {len(goals)} subagents, "
|
||||
f"correlation_id={correlation_id}"
|
||||
)
|
||||
|
||||
async def _synthesise(self, request, respond, next, flow,
|
||||
session_id, collection, streaming,
|
||||
session_uri, iteration_num):
|
||||
"""Synthesise final answer from subagent results."""
|
||||
|
||||
think = self.make_think_callback(respond, streaming)
|
||||
framing = getattr(request, 'framing', '')
|
||||
|
||||
# Collect subagent results from history
|
||||
subagent_results = {}
|
||||
for step in request.history:
|
||||
results = getattr(step, 'subagent_results', None)
|
||||
if results:
|
||||
subagent_results.update(results)
|
||||
|
||||
if not subagent_results:
|
||||
logger.warning("Synthesis called with no subagent results")
|
||||
subagent_results = {"(no results)": "No subagent results available"}
|
||||
|
||||
context = self.make_context(flow, request.user)
|
||||
client = context("prompt-request")
|
||||
|
||||
await think("Synthesising final answer from sub-agent results", is_final=True)
|
||||
|
||||
response_text = await self.prompt_as_answer(
|
||||
client, "supervisor-synthesise",
|
||||
variables={
|
||||
"question": request.question,
|
||||
"framing": framing,
|
||||
"results": [
|
||||
{"goal": goal, "result": result}
|
||||
for goal, result in subagent_results.items()
|
||||
],
|
||||
},
|
||||
respond=respond,
|
||||
streaming=streaming,
|
||||
)
|
||||
|
||||
await self.emit_final_triples(
|
||||
flow, session_id, iteration_num, session_uri,
|
||||
response_text, request, respond, streaming,
|
||||
)
|
||||
await self.send_final_response(
|
||||
respond, streaming, response_text, already_streamed=streaming,
|
||||
)
|
||||
|
|
@ -485,25 +485,16 @@ class Processor(AgentService):
|
|||
logger.debug(f"Think: {x} (is_final={is_final})")
|
||||
|
||||
if streaming:
|
||||
# Streaming format
|
||||
r = AgentResponse(
|
||||
chunk_type="thought",
|
||||
content=x,
|
||||
end_of_message=is_final,
|
||||
end_of_dialog=False,
|
||||
# Legacy fields for backward compatibility
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=x,
|
||||
observation=None,
|
||||
)
|
||||
else:
|
||||
# Non-streaming format
|
||||
r = AgentResponse(
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=x,
|
||||
observation=None,
|
||||
chunk_type="thought",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
|
@ -515,25 +506,16 @@ class Processor(AgentService):
|
|||
logger.debug(f"Observe: {x} (is_final={is_final})")
|
||||
|
||||
if streaming:
|
||||
# Streaming format
|
||||
r = AgentResponse(
|
||||
chunk_type="observation",
|
||||
content=x,
|
||||
end_of_message=is_final,
|
||||
end_of_dialog=False,
|
||||
# Legacy fields for backward compatibility
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=x,
|
||||
)
|
||||
else:
|
||||
# Non-streaming format
|
||||
r = AgentResponse(
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=x,
|
||||
chunk_type="observation",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
|
@ -545,25 +527,16 @@ class Processor(AgentService):
|
|||
logger.debug(f"Answer: {x}")
|
||||
|
||||
if streaming:
|
||||
# Streaming format
|
||||
r = AgentResponse(
|
||||
chunk_type="answer",
|
||||
content=x,
|
||||
end_of_message=False, # More chunks may follow
|
||||
end_of_message=False,
|
||||
end_of_dialog=False,
|
||||
# Legacy fields for backward compatibility
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None,
|
||||
)
|
||||
else:
|
||||
# Non-streaming format - shouldn't normally be called
|
||||
r = AgentResponse(
|
||||
answer=x,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None,
|
||||
chunk_type="answer",
|
||||
content=x,
|
||||
end_of_message=True,
|
||||
end_of_dialog=False,
|
||||
)
|
||||
|
|
@ -677,25 +650,17 @@ class Processor(AgentService):
|
|||
))
|
||||
|
||||
if streaming:
|
||||
# Streaming format - send end-of-dialog marker
|
||||
# Answer chunks were already sent via answer() callback during parsing
|
||||
# End-of-dialog marker — answer chunks already sent via callback
|
||||
r = AgentResponse(
|
||||
chunk_type="answer",
|
||||
content="", # Empty content, just marking end of dialog
|
||||
content="",
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
# Legacy fields set to None - answer already sent via streaming chunks
|
||||
answer=None,
|
||||
error=None,
|
||||
thought=None,
|
||||
)
|
||||
else:
|
||||
# Non-streaming format - send complete answer
|
||||
r = AgentResponse(
|
||||
answer=act.final,
|
||||
error=None,
|
||||
thought=None,
|
||||
observation=None,
|
||||
chunk_type="answer",
|
||||
content=f,
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
)
|
||||
|
|
@ -833,21 +798,13 @@ class Processor(AgentService):
|
|||
# Check if streaming was enabled (may not be set if error occurred early)
|
||||
streaming = getattr(request, 'streaming', False) if 'request' in locals() else False
|
||||
|
||||
if streaming:
|
||||
# Streaming format
|
||||
r = AgentResponse(
|
||||
chunk_type="error",
|
||||
content=str(e),
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
# Legacy fields for backward compatibility
|
||||
error=error_obj,
|
||||
)
|
||||
else:
|
||||
# Legacy format
|
||||
r = AgentResponse(
|
||||
error=error_obj,
|
||||
)
|
||||
r = AgentResponse(
|
||||
chunk_type="error",
|
||||
content=str(e),
|
||||
end_of_message=True,
|
||||
end_of_dialog=True,
|
||||
error=error_obj,
|
||||
)
|
||||
|
||||
await respond(r)
|
||||
|
||||
|
|
|
|||
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Reference in a new issue