atomr-agents-agent 0.16.2

//! Per-turn pipeline implementation.

use std::sync::Arc;
use std::time::Instant;

use async_trait::async_trait;
use atomr_agents_context::{ContextAssembler, ContextFragment};
use atomr_agents_core::{
    AgentContext, AgentError, AgentId, CallCtx, Event, IterationBudget, Json, MemoryItem, MemoryKind,
    MemoryNamespace, MoneyBudget, Result, TimeBudget, TokenBudget, ToolId, TurnInput,
};
use atomr_agents_instruction::InstructionStrategy;
use atomr_agents_observability::EventBus;
use atomr_agents_strategy::{MemoryStrategy, SkillStrategy, ToolStrategy};
use atomr_infer_core::batch::{ExecuteBatch, Message as InferMsg, MessageContent, Role, SamplingParams};

use crate::inference::{InferenceClient, TurnResult};
use crate::r#trait::AgentDispatch;

/// Generic agent. Strategy types are monomorphized for the hot
/// path; a `BoxedAgent` form (using `Box<dyn>` for each slot) is
/// produced from `AgentSpec`.
pub struct Agent<I, T, Ms, Sk>
where
    I: InstructionStrategy,
    T: ToolStrategy,
    Ms: MemoryStrategy,
    Sk: SkillStrategy,
{
    pub id: AgentId,
    pub model: String,
    pub instructions: I,
    pub tools: T,
    pub memory: Ms,
    pub skills: Sk,
    pub inference: Arc<dyn InferenceClient>,
    pub bus: EventBus,
    pub max_tool_iterations: u32,
}

impl<I, T, Ms, Sk> Agent<I, T, Ms, Sk>
where
    I: InstructionStrategy,
    T: ToolStrategy,
    Ms: MemoryStrategy,
    Sk: SkillStrategy,
{
    /// One full agent turn. Drives the per-turn pipeline (memory +
    /// skill + tool resolution → instruction render → context
    /// assembly → inference → tool-call loop → memory store).
    ///
    /// Thin wrapper around [`run_turn_impl`]; the typed `Agent<I,T,Ms,Sk>`
    /// stays monomorphic at the call site, only crossing into
    /// dyn-dispatch at the `run_turn_impl` boundary.
    pub async fn run_turn(&self, user: String, budgets: AgentBudgets) -> Result<TurnResult> {
        run_turn_impl(
            &self.id,
            &self.model,
            &self.instructions,
            &self.tools,
            &self.memory,
            &self.skills,
            &self.inference,
            &self.bus,
            self.max_tool_iterations,
            user,
            budgets,
        )
        .await
    }
}

/// Shared per-turn pipeline body. Both the typed [`Agent`] and the
/// fully-erased [`crate::BoxedAgent`] dispatch through this. The
/// `&dyn` references mean each strategy method becomes an indirect
/// call — fine here since strategies are invoked O(1) times per turn
/// (not in a hot loop).
pub(crate) async fn run_turn_impl(
    id: &AgentId,
    model: &str,
    instructions: &dyn InstructionStrategy,
    tools: &dyn ToolStrategy,
    memory: &dyn MemoryStrategy,
    skills: &dyn SkillStrategy,
    inference: &Arc<dyn InferenceClient>,
    bus: &EventBus,
    max_tool_iterations: u32,
    user: String,
    budgets: AgentBudgets,
) -> Result<TurnResult> {
    let start = Instant::now();
    let agent_ctx = AgentContext::for_agent(
        id.clone(),
        TurnInput {
            user: user.clone(),
            history: vec![],
        },
    );
    let AgentBudgets {
        mut tokens,
        time,
        money,
        mut iterations,
    } = budgets;

    // 1. Parallel strategy resolution.
    let mut subs = tokens.split(3);
    let (mut bm, mut bs, mut bt) = (subs.remove(0), subs.remove(0), subs.remove(0));
    let bm0 = bm.remaining;
    let bs0 = bs.remaining;
    let bt0 = bt.remaining;
    let (mem, sk_res, tool_refs) = tokio::join!(
        memory.retrieve(&agent_ctx, &mut bm),
        skills.applicable(&agent_ctx, &mut bs),
        tools.select(&agent_ctx, &mut bt),
    );
    let mem = mem?;
    let _skills = sk_res?;
    let tool_refs = tool_refs?;
    let consumed = bm0.saturating_sub(bm.remaining)
        + bs0.saturating_sub(bs.remaining)
        + bt0.saturating_sub(bt.remaining);
    tokens.consume(consumed.min(tokens.remaining)).ok();

    // 2. Render instructions.
    let mut instr_budget = tokens.split(2).remove(0);
    let r_instr = instructions.render(&agent_ctx, &mut instr_budget).await?;
    tokens
        .consume(r_instr.estimated_tokens.min(tokens.remaining))
        .ok();

    // 3. Assemble final context (system prompt + recalled memory).
    let mut frags = vec![ContextFragment {
        source: "system",
        priority: 9,
        estimated_tokens: r_instr.estimated_tokens,
        text: r_instr.system_prompt.clone(),
    }];
    for c in &mem {
        frags.push(ContextFragment {
            source: "memory",
            priority: 5,
            estimated_tokens: c.estimated_tokens,
            text: c.text.clone(),
        });
    }
    let assembled = ContextAssembler::assemble(frags, &mut tokens)?;

    // 4. Build initial messages.
    let mut messages: Vec<InferMsg> = Vec::new();
    messages.push(InferMsg {
        role: Role::System,
        content: MessageContent::Text(assembled.join("\n\n")),
    });
    messages.push(InferMsg {
        role: Role::User,
        content: MessageContent::Text(user.clone()),
    });

    // 5. Tool-call loop.
    let mut final_text = String::new();
    let mut final_usage = atomr_infer_core::tokens::TokenUsage::default();
    let mut final_finish = None;
    let mut all_tool_calls: Vec<atomr_agents_tool::ParsedToolCall> = Vec::new();
    for iter in 0..max_tool_iterations.max(1) {
        iterations.consume_one()?;
        let batch = ExecuteBatch {
            request_id: format!("turn-{}", uuid_str()),
            model: model.to_string(),
            messages: messages.clone(),
            sampling: SamplingParams::default(),
            stream: true,
            estimated_tokens: tokens.remaining,
        };
        let r = inference.run(batch).await?;
        final_text = r.text.clone();
        final_usage.add(r.usage);
        final_finish = r.finish_reason;
        // Surface every streamed tool call to observers before
        // dispatch — distinct from the post-call ToolInvoked event.
        for call in &r.tool_calls {
            let args = call.arguments().unwrap_or(Json::Value::Null);
            bus.emit(Event::ToolCallStreamed {
                agent_id: id.clone(),
                tool_name: call.name.clone(),
                arguments_hash: hash_value(&args),
                iteration: iter,
            });
        }
        all_tool_calls.extend(r.tool_calls.iter().cloned());
        // Stop conditions.
        if r.tool_calls.is_empty()
            || r.finish_reason != Some(atomr_infer_core::tokens::FinishReason::ToolCalls)
        {
            break;
        }
        // Append the assistant's tool-call turn (for provider
        // history coherence) and dispatch each tool — concurrently
        // when multiple are emitted, order-preserved on aggregation.
        messages.push(InferMsg {
            role: Role::Assistant,
            content: MessageContent::Text(r.text.clone()),
        });
        let mut handles: Vec<tokio::task::JoinHandle<Result<(usize, String, Json::Value, u64, u64)>>> =
            Vec::with_capacity(r.tool_calls.len());
        for (idx, call) in r.tool_calls.iter().enumerate() {
            let tool_ref = tool_refs
                .iter()
                .find(|t| t.name == call.name)
                .ok_or_else(|| AgentError::Tool(format!("unknown tool: {}", call.name)))?;
            let args = call.arguments().unwrap_or(Json::Value::Null);
            let invoke_ctx = CallCtx {
                agent_id: Some(id.clone()),
                tokens,
                time,
                money,
                iterations,
                trace: vec![format!("tool:{}", call.name)],
            };
            let handle = tool_ref.handle.clone();
            let name = call.name.clone();
            let args_for_task = args.clone();
            handles.push(tokio::spawn(async move {
                let t0 = Instant::now();
                let result = handle.call(args_for_task.clone(), invoke_ctx).await?;
                Ok::<_, AgentError>((
                    idx,
                    name,
                    result,
                    hash_value(&args_for_task),
                    t0.elapsed().as_millis() as u64,
                ))
            }));
        }
        let mut results: Vec<(usize, String, Json::Value, u64, u64)> = Vec::with_capacity(handles.len());
        for h in handles {
            let pair = h.await.map_err(|e| AgentError::Internal(e.to_string()))??;
            results.push(pair);
        }
        results.sort_by_key(|(i, _, _, _, _)| *i);
        for (_, name, result, args_hash, elapsed_ms) in results {
            bus.emit(Event::ToolInvoked {
                tool_id: ToolId::from(name.as_str()),
                args_hash,
                elapsed_ms,
                ok: true,
            });
            messages.push(InferMsg {
                role: Role::Tool,
                content: MessageContent::Text(serde_json::to_string(&result).unwrap_or_default()),
            });
        }
    }

    // 6. Memory store.
    let item = MemoryItem {
        id: format!("turn-{}", uuid_str()),
        kind: MemoryKind::Episodic,
        namespace: MemoryNamespace::Agent(id.clone()),
        payload: serde_json::json!({"user": user, "assistant": final_text}),
        timestamp_ms: chrono::Utc::now().timestamp_millis(),
        tags: vec![],
    };
    memory.store(item).await.ok();

    // 7. Emit AgentTurn event.
    bus.emit(Event::AgentTurn {
        agent_id: id.clone(),
        input_tokens: final_usage.input_tokens,
        output_tokens: final_usage.output_tokens,
        reasoning_tokens: final_usage.reasoning_tokens,
        cached_tokens: final_usage.cached_tokens,
        finish_reason: final_finish,
        elapsed_ms: start.elapsed().as_millis() as u64,
    });

    Ok(TurnResult {
        text: final_text,
        usage: final_usage,
        finish_reason: final_finish,
        tool_calls: all_tool_calls,
    })
}

#[async_trait]
impl<I, T, Ms, Sk> AgentDispatch for Agent<I, T, Ms, Sk>
where
    I: InstructionStrategy,
    T: ToolStrategy,
    Ms: MemoryStrategy,
    Sk: SkillStrategy,
{
    async fn dispatch(&self, user: String, ctx: CallCtx) -> Result<TurnResult> {
        self.run_turn(
            user,
            AgentBudgets {
                tokens: ctx.tokens,
                time: ctx.time,
                money: ctx.money,
                iterations: ctx.iterations,
            },
        )
        .await
    }
}

#[derive(Debug, Clone, Copy)]
pub struct AgentBudgets {
    pub tokens: TokenBudget,
    pub time: TimeBudget,
    pub money: MoneyBudget,
    pub iterations: IterationBudget,
}

fn uuid_str() -> String {
    use std::sync::atomic::{AtomicU64, Ordering};
    static N: AtomicU64 = AtomicU64::new(0);
    format!("{:016x}", N.fetch_add(1, Ordering::Relaxed))
}

fn hash_value(v: &Json::Value) -> u64 {
    use std::hash::{Hash, Hasher};
    let mut h = std::collections::hash_map::DefaultHasher::new();
    serde_json::to_string(v).unwrap_or_default().hash(&mut h);
    h.finish()
}

#[cfg(test)]
mod tests {
    use super::*;

    use std::sync::Arc;

    use async_trait::async_trait;
    use atomr_agents_core::{InvokeCtx, Value};
    use atomr_agents_instruction::{ComposedInstructionStrategy, StaticBehaviorStrategy, StaticTaskStrategy};
    use atomr_agents_memory::{InMemoryStore, RecencyMemoryStrategy};
    use atomr_agents_persona::StaticPersonaStrategy;
    use atomr_agents_skill::StaticSkillStrategy;
    use atomr_agents_tool::{DynTool, Provider, StaticToolStrategy, Tool, ToolDescriptor, ToolSchema};

    use crate::inference::LocalRunnerClient;
    use atomr_infer_testkit::{MockRunner, MockScript};

    struct CalculatorTool {
        d: ToolDescriptor,
    }
    impl CalculatorTool {
        fn new() -> Self {
            Self {
                d: ToolDescriptor {
                    id: ToolId::from("calculator"),
                    name: "calculator".into(),
                    description: "evaluate simple arithmetic".into(),
                    schema: ToolSchema::empty_object(),
                },
            }
        }
    }
    #[async_trait]
    impl Tool for CalculatorTool {
        fn descriptor(&self) -> &ToolDescriptor {
            &self.d
        }
        async fn invoke(&self, args: Value, _ctx: &InvokeCtx) -> Result<Value> {
            let a = args.get("a").and_then(|v| v.as_i64()).unwrap_or(0);
            let b = args.get("b").and_then(|v| v.as_i64()).unwrap_or(0);
            Ok(serde_json::json!({"sum": a + b}))
        }
    }

    fn build_agent(
        runner: MockRunner,
    ) -> Agent<
        ComposedInstructionStrategy<StaticPersonaStrategy, StaticTaskStrategy, StaticBehaviorStrategy>,
        StaticToolStrategy,
        RecencyMemoryStrategy,
        StaticSkillStrategy,
    > {
        let store = Arc::new(InMemoryStore::new());
        let mem = RecencyMemoryStrategy::new(store, 5, 30);
        let tools: Vec<DynTool> = vec![Arc::new(CalculatorTool::new())];
        let tool_strat = StaticToolStrategy::new(tools);
        let instr = ComposedInstructionStrategy::new(
            StaticPersonaStrategy::new("You are a calculator assistant."),
            StaticTaskStrategy("Use tools to answer arithmetic questions.".into()),
            StaticBehaviorStrategy("Reply tersely.".into()),
        );
        let skill_strat = StaticSkillStrategy::new(vec![]);
        let inference: Arc<dyn InferenceClient> = Arc::new(LocalRunnerClient::new(runner, Provider::OpenAi));
        Agent {
            id: AgentId::from("a-1"),
            model: "mock".into(),
            instructions: instr,
            tools: tool_strat,
            memory: mem,
            skills: skill_strat,
            inference,
            bus: EventBus::new(),
            max_tool_iterations: 3,
        }
    }

    #[tokio::test]
    async fn agent_runs_simple_text_turn() {
        let runner = MockRunner::new(MockScript::from_text(["the answer is ", "42"]));
        let agent = build_agent(runner);
        let r = agent
            .run_turn(
                "what's 1+2".into(),
                AgentBudgets {
                    tokens: TokenBudget::new(10_000),
                    time: TimeBudget::new(std::time::Duration::from_secs(30)),
                    money: MoneyBudget::from_usd(1.0),
                    iterations: IterationBudget::new(5),
                },
            )
            .await
            .unwrap();
        assert!(r.text.contains("42"));
        assert_eq!(r.usage.output_tokens, 2);
    }

    // ----- Tool-call loop test --------------------------------------

    use std::sync::Mutex as StdMutex;

    use atomr_infer_core::batch::ExecuteBatch as IBatch;
    use atomr_infer_core::error::{InferenceError, InferenceResult};
    use atomr_infer_core::runner::{ModelRunner, RunHandle, SessionRebuildCause};
    use atomr_infer_core::runtime::{RuntimeKind, TransportKind};
    use atomr_infer_core::tokens::{FinishReason, TokenChunk, TokenUsage as IUsage};
    use futures::stream::{self, BoxStream, StreamExt};

    /// Two-step mock: first call returns a tool-call asking calculator(2,3);
    /// second call returns the final text "answer: 5".
    struct ToolLoopMock {
        step: StdMutex<u32>,
    }
    impl ToolLoopMock {
        fn new() -> Self {
            Self {
                step: StdMutex::new(0),
            }
        }
    }
    #[async_trait]
    impl ModelRunner for ToolLoopMock {
        async fn execute(&mut self, batch: IBatch) -> InferenceResult<RunHandle> {
            let mut s = self.step.lock().unwrap();
            *s += 1;
            let request_id = batch.request_id.clone();
            let chunks: Vec<TokenChunk> = if *s == 1 {
                vec![TokenChunk {
                    request_id: request_id.clone(),
                    text_delta: String::new(),
                    tool_call_delta: Some(serde_json::json!({
                        "tool_calls": [{
                            "index": 0,
                            "id": "call_1",
                            "type": "function",
                            "function": {"name": "calculator", "arguments": "{\"a\":2,\"b\":3}"}
                        }]
                    })),
                    usage: Some(IUsage {
                        input_tokens: 5,
                        output_tokens: 0,
                        ..Default::default()
                    }),
                    finish_reason: Some(FinishReason::ToolCalls),
                }]
            } else {
                vec![TokenChunk {
                    request_id: request_id.clone(),
                    text_delta: "answer: 5".into(),
                    tool_call_delta: None,
                    usage: Some(IUsage {
                        input_tokens: 5,
                        output_tokens: 3,
                        ..Default::default()
                    }),
                    finish_reason: Some(FinishReason::Stop),
                }]
            };
            let stream: BoxStream<'static, InferenceResult<TokenChunk>> =
                stream::iter(chunks.into_iter().map(Ok::<_, InferenceError>)).boxed();
            Ok(RunHandle::streaming(stream))
        }
        async fn rebuild_session(&mut self, _: SessionRebuildCause) -> InferenceResult<()> {
            Ok(())
        }
        fn runtime_kind(&self) -> RuntimeKind {
            RuntimeKind::Custom("tool-loop-mock".into())
        }
        fn transport_kind(&self) -> TransportKind {
            TransportKind::LocalGpu
        }
    }

    #[tokio::test]
    async fn agent_drives_tool_call_loop() {
        let store = Arc::new(InMemoryStore::new());
        let mem = RecencyMemoryStrategy::new(store, 5, 30);
        let tools: Vec<DynTool> = vec![Arc::new(CalculatorTool::new())];
        let tool_strat = StaticToolStrategy::new(tools);
        let instr = ComposedInstructionStrategy::new(
            StaticPersonaStrategy::new("You are a calculator assistant."),
            StaticTaskStrategy("Use tools.".into()),
            StaticBehaviorStrategy("Reply tersely.".into()),
        );
        let skill_strat = StaticSkillStrategy::new(vec![]);
        let inference: Arc<dyn InferenceClient> =
            Arc::new(LocalRunnerClient::new(ToolLoopMock::new(), Provider::OpenAi));
        let agent: Agent<_, _, _, _> = Agent {
            id: AgentId::from("a-2"),
            model: "mock".into(),
            instructions: instr,
            tools: tool_strat,
            memory: mem,
            skills: skill_strat,
            inference,
            bus: EventBus::new(),
            max_tool_iterations: 3,
        };
        let r = agent
            .run_turn(
                "what is 2+3".into(),
                AgentBudgets {
                    tokens: TokenBudget::new(10_000),
                    time: TimeBudget::new(std::time::Duration::from_secs(30)),
                    money: MoneyBudget::from_usd(1.0),
                    iterations: IterationBudget::new(5),
                },
            )
            .await
            .unwrap();
        assert_eq!(r.text, "answer: 5");
        assert_eq!(r.finish_reason, Some(FinishReason::Stop));
    }
}