diff --git a/backend/docs/MEMORY_IMPROVEMENTS.md b/backend/docs/MEMORY_IMPROVEMENTS.md
index e916c40..3fddd4b 100644
--- a/backend/docs/MEMORY_IMPROVEMENTS.md
+++ b/backend/docs/MEMORY_IMPROVEMENTS.md
@@ -1,281 +1,65 @@
# Memory System Improvements
-This document describes recent improvements to the memory system's fact injection mechanism.
+This document tracks memory injection behavior and roadmap status.
-## Overview
+## Status (As Of 2026-03-10)
-Two major improvements have been made to the `format_memory_for_injection` function:
+Implemented in `main`:
+- Accurate token counting via `tiktoken` in `format_memory_for_injection`.
+- Facts are injected into prompt memory context.
+- Facts are ranked by confidence (descending).
+- Injection respects `max_injection_tokens` budget.
-1. **Similarity-Based Fact Retrieval**: Uses TF-IDF to select facts most relevant to current conversation context
-2. **Accurate Token Counting**: Uses tiktoken for precise token estimation instead of rough character-based approximation
+Planned / not yet merged:
+- TF-IDF similarity-based fact retrieval.
+- `current_context` input for context-aware scoring.
+- Configurable similarity/confidence weights (`similarity_weight`, `confidence_weight`).
+- Middleware/runtime wiring for context-aware retrieval before each model call.
-## 1. Similarity-Based Fact Retrieval
+## Current Behavior
-### Problem
-The original implementation selected facts based solely on confidence scores, taking the top 15 highest-confidence facts regardless of their relevance to the current conversation. This could result in injecting irrelevant facts while omitting contextually important ones.
-
-### Solution
-The new implementation uses **TF-IDF (Term Frequency-Inverse Document Frequency)** vectorization with cosine similarity to measure how relevant each fact is to the current conversation context.
-
-**Scoring Formula**:
-```
-final_score = (similarity × 0.6) + (confidence × 0.4)
-```
-
-- **Similarity (60% weight)**: Cosine similarity between fact content and current context
-- **Confidence (40% weight)**: LLM-assigned confidence score (0-1)
-
-### Benefits
-- **Context-Aware**: Prioritizes facts relevant to what the user is currently discussing
-- **Dynamic**: Different facts surface based on conversation topic
-- **Balanced**: Considers both relevance and reliability
-- **Fallback**: Gracefully degrades to confidence-only ranking if context is unavailable
-
-### Example
-Given facts about Python, React, and Docker:
-- User asks: *"How should I write Python tests?"*
- - Prioritizes: Python testing, type hints, pytest
-- User asks: *"How to optimize my Next.js app?"*
- - Prioritizes: React/Next.js experience, performance optimization
-
-### Configuration
-Customize weights in `config.yaml` (optional):
-```yaml
-memory:
- similarity_weight: 0.6 # Weight for TF-IDF similarity (0-1)
- confidence_weight: 0.4 # Weight for confidence score (0-1)
-```
-
-**Note**: Weights should sum to 1.0 for best results.
-
-## 2. Accurate Token Counting
-
-### Problem
-The original implementation estimated tokens using a simple formula:
-```python
-max_chars = max_tokens * 4
-```
-
-This assumes ~4 characters per token, which is:
-- Inaccurate for many languages and content types
-- Can lead to over-injection (exceeding token limits)
-- Can lead to under-injection (wasting available budget)
-
-### Solution
-The new implementation uses **tiktoken**, OpenAI's official tokenizer library, to count tokens accurately:
+Function today:
```python
-import tiktoken
-
-def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
- encoding = tiktoken.get_encoding(encoding_name)
- return len(encoding.encode(text))
+def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
```
-- Uses `cl100k_base` encoding (GPT-4, GPT-3.5, text-embedding-ada-002)
-- Provides exact token counts for budget management
-- Falls back to character-based estimation if tiktoken fails
+Current injection format:
+- `User Context` section from `user.*.summary`
+- `History` section from `history.*.summary`
+- `Facts` section from `facts[]`, sorted by confidence, appended until token budget is reached
-### Benefits
-- **Precision**: Exact token counts match what the model sees
-- **Budget Optimization**: Maximizes use of available token budget
-- **No Overflows**: Prevents exceeding `max_injection_tokens` limit
-- **Better Planning**: Each section's token cost is known precisely
+Token counting:
+- Uses `tiktoken` (`cl100k_base`) when available
+- Falls back to `len(text) // 4` if tokenizer import fails
-### Example
-```python
-text = "This is a test string to count tokens accurately using tiktoken."
+## Known Gap
-# Old method
-char_count = len(text) # 64 characters
-old_estimate = char_count // 4 # 16 tokens (overestimate)
+Previous versions of this document described TF-IDF/context-aware retrieval as if it were already shipped.
+That was not accurate for `main` and caused confusion.
-# New method
-accurate_count = _count_tokens(text) # 13 tokens (exact)
+Issue reference: `#1059`
+
+## Roadmap (Planned)
+
+Planned scoring strategy:
+
+```text
+final_score = (similarity * 0.6) + (confidence * 0.4)
```
-**Result**: 3-token difference (18.75% error rate)
+Planned integration shape:
+1. Extract recent conversational context from filtered user/final-assistant turns.
+2. Compute TF-IDF cosine similarity between each fact and current context.
+3. Rank by weighted score and inject under token budget.
+4. Fall back to confidence-only ranking if context is unavailable.
-In production, errors can be much larger for:
-- Code snippets (more tokens per character)
-- Non-English text (variable token ratios)
-- Technical jargon (often multi-token words)
+## Validation
-## Implementation Details
+Current regression coverage includes:
+- facts inclusion in memory injection output
+- confidence ordering
+- token-budget-limited fact inclusion
-### Function Signature
-```python
-def format_memory_for_injection(
- memory_data: dict[str, Any],
- max_tokens: int = 2000,
- current_context: str | None = None,
-) -> str:
-```
-
-**New Parameter**:
-- `current_context`: Optional string containing recent conversation messages for similarity calculation
-
-### Backward Compatibility
-The function remains **100% backward compatible**:
-- If `current_context` is `None` or empty, falls back to confidence-only ranking
-- Existing callers without the parameter work exactly as before
-- Token counting is always accurate (transparent improvement)
-
-### Integration Point
-Memory is **dynamically injected** via `MemoryMiddleware.before_model()`:
-
-```python
-# src/agents/middlewares/memory_middleware.py
-
-def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
- """Extract recent conversation (user input + final responses only)."""
- context_parts = []
- turn_count = 0
-
- for msg in reversed(messages):
- if msg.type == "human":
- # Always include user messages
- context_parts.append(extract_text(msg))
- turn_count += 1
- if turn_count >= max_turns:
- break
-
- elif msg.type == "ai" and not msg.tool_calls:
- # Only include final AI responses (no tool_calls)
- context_parts.append(extract_text(msg))
-
- # Skip tool messages and AI messages with tool_calls
-
- return " ".join(reversed(context_parts))
-
-
-class MemoryMiddleware:
- def before_model(self, state, runtime):
- """Inject memory before EACH LLM call (not just before_agent)."""
-
- # Get recent conversation context (filtered)
- conversation_context = _extract_conversation_context(
- state["messages"],
- max_turns=3
- )
-
- # Load memory with context-aware fact selection
- memory_data = get_memory_data()
- memory_content = format_memory_for_injection(
- memory_data,
- max_tokens=config.max_injection_tokens,
- current_context=conversation_context, # ✅ Clean conversation only
- )
-
- # Inject as system message
- memory_message = SystemMessage(
- content=f"\n{memory_content}\n",
- name="memory_context",
- )
-
- return {"messages": [memory_message] + state["messages"]}
-```
-
-### How It Works
-
-1. **User continues conversation**:
- ```
- Turn 1: "I'm working on a Python project"
- Turn 2: "It uses FastAPI and SQLAlchemy"
- Turn 3: "How do I write tests?" ← Current query
- ```
-
-2. **Extract recent context**: Last 3 turns combined:
- ```
- "I'm working on a Python project. It uses FastAPI and SQLAlchemy. How do I write tests?"
- ```
-
-3. **TF-IDF scoring**: Ranks facts by relevance to this context
- - High score: "Prefers pytest for testing" (testing + Python)
- - High score: "Likes type hints in Python" (Python related)
- - High score: "Expert in Python and FastAPI" (Python + FastAPI)
- - Low score: "Uses Docker for containerization" (less relevant)
-
-4. **Injection**: Top-ranked facts injected into system prompt's `` section
-
-5. **Agent sees**: Full system prompt with relevant memory context
-
-### Benefits of Dynamic System Prompt
-
-- **Multi-Turn Context**: Uses last 3 turns, not just current question
- - Captures ongoing conversation flow
- - Better understanding of user's current focus
-- **Query-Specific Facts**: Different facts surface based on conversation topic
-- **Clean Architecture**: No middleware message manipulation
-- **LangChain Native**: Uses built-in dynamic system prompt support
-- **Runtime Flexibility**: Memory regenerated for each agent invocation
-
-## Dependencies
-
-New dependencies added to `pyproject.toml`:
-```toml
-dependencies = [
- # ... existing dependencies ...
- "tiktoken>=0.8.0", # Accurate token counting
- "scikit-learn>=1.6.1", # TF-IDF vectorization
-]
-```
-
-Install with:
-```bash
-cd backend
-uv sync
-```
-
-## Testing
-
-Run the test script to verify improvements:
-```bash
-cd backend
-python test_memory_improvement.py
-```
-
-Expected output shows:
-- Different fact ordering based on context
-- Accurate token counts vs old estimates
-- Budget-respecting fact selection
-
-## Performance Impact
-
-### Computational Cost
-- **TF-IDF Calculation**: O(n × m) where n=facts, m=vocabulary
- - Negligible for typical fact counts (10-100 facts)
- - Caching opportunities if context doesn't change
-- **Token Counting**: ~10-100µs per call
- - Faster than the old character-counting approach
- - Minimal overhead compared to LLM inference
-
-### Memory Usage
-- **TF-IDF Vectorizer**: ~1-5MB for typical vocabulary
- - Instantiated once per injection call
- - Garbage collected after use
-- **Tiktoken Encoding**: ~1MB (cached singleton)
- - Loaded once per process lifetime
-
-### Recommendations
-- Current implementation is optimized for accuracy over caching
-- For high-throughput scenarios, consider:
- - Pre-computing fact embeddings (store in memory.json)
- - Caching TF-IDF vectorizer between calls
- - Using approximate nearest neighbor search for >1000 facts
-
-## Summary
-
-| Aspect | Before | After |
-|--------|--------|-------|
-| Fact Selection | Top 15 by confidence only | Relevance-based (similarity + confidence) |
-| Token Counting | `len(text) // 4` | `tiktoken.encode(text)` |
-| Context Awareness | None | TF-IDF cosine similarity |
-| Accuracy | ±25% token estimate | Exact token count |
-| Configuration | Fixed weights | Customizable similarity/confidence weights |
-
-These improvements result in:
-- **More relevant** facts injected into context
-- **Better utilization** of available token budget
-- **Fewer hallucinations** due to focused context
-- **Higher quality** agent responses
+Tests:
+- `backend/tests/test_memory_prompt_injection.py`
diff --git a/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md b/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
index 67701cb..da2bcd8 100644
--- a/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
+++ b/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
@@ -1,260 +1,38 @@
# Memory System Improvements - Summary
-## 改进概述
+## Sync Note (2026-03-10)
-针对你提出的两个问题进行了优化：
-1. ✅ **粗糙的 token 计算**（`字符数 * 4`）→ 使用 tiktoken 精确计算
-2. ✅ **缺乏相似度召回** → 使用 TF-IDF + 最近对话上下文
+This summary is synchronized with the `main` branch implementation.
+TF-IDF/context-aware retrieval is **planned**, not merged yet.
-## 核心改进
+## Implemented
-### 1. 基于对话上下文的智能 Facts 召回
+- Accurate token counting with `tiktoken` in memory injection.
+- Facts are injected into `` prompt content.
+- Facts are ordered by confidence and bounded by `max_injection_tokens`.
-**之前**：
-- 只按 confidence 排序取前 15 个
-- 无论用户在讨论什么都注入相同的 facts
+## Planned (Not Yet Merged)
-**现在**：
-- 提取最近 **3 轮对话**（human + AI 消息）作为上下文
-- 使用 **TF-IDF 余弦相似度**计算每个 fact 与对话的相关性
-- 综合评分：`相似度(60%) + 置信度(40%)`
-- 动态选择最相关的 facts
+- TF-IDF cosine similarity recall based on recent conversation context.
+- `current_context` parameter for `format_memory_for_injection`.
+- Weighted ranking (`similarity` + `confidence`).
+- Runtime extraction/injection flow for context-aware fact selection.
-**示例**：
-```
-对话历史：
-Turn 1: "我在做一个 Python 项目"
-Turn 2: "使用 FastAPI 和 SQLAlchemy"
-Turn 3: "怎么写测试？"
+## Why This Sync Was Needed
-上下文: "我在做一个 Python 项目 使用 FastAPI 和 SQLAlchemy 怎么写测试？"
+Earlier docs described TF-IDF behavior as already implemented, which did not match code in `main`.
+This mismatch is tracked in issue `#1059`.
-相关度高的 facts:
-✓ "Prefers pytest for testing" (Python + 测试)
-✓ "Expert in Python and FastAPI" (Python + FastAPI)
-✓ "Likes type hints in Python" (Python)
-
-相关度低的 facts:
-✗ "Uses Docker for containerization" (不相关)
-```
-
-### 2. 精确的 Token 计算
-
-**之前**：
-```python
-max_chars = max_tokens * 4 # 粗糙估算
-```
-
-**现在**：
-```python
-import tiktoken
-
-def _count_tokens(text: str) -> int:
- encoding = tiktoken.get_encoding("cl100k_base") # GPT-4/3.5
- return len(encoding.encode(text))
-```
-
-**效果对比**：
-```python
-text = "This is a test string to count tokens accurately."
-旧方法: len(text) // 4 = 12 tokens (估算)
-新方法: tiktoken.encode = 10 tokens (精确)
-误差: 20%
-```
-
-### 3. 多轮对话上下文
-
-**之前的担心**：
-> "只传最近一条 human message 会不会上下文不太够？"
-
-**现在的解决方案**：
-- 提取最近 **3 轮对话**（可配置）
-- 包括 human 和 AI 消息
-- 更完整的对话上下文
-
-**示例**：
-```
-单条消息: "怎么写测试？"
-→ 缺少上下文，不知道是什么项目
-
-3轮对话: "Python 项目 + FastAPI + 怎么写测试？"
-→ 完整上下文，能选择更相关的 facts
-```
-
-## 实现方式
-
-### Middleware 动态注入
-
-使用 `before_model` 钩子在**每次 LLM 调用前**注入 memory：
+## Current API Shape
```python
-# src/agents/middlewares/memory_middleware.py
-
-def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
- """提取最近 3 轮对话（只包含用户输入和最终回复）"""
- context_parts = []
- turn_count = 0
-
- for msg in reversed(messages):
- msg_type = getattr(msg, "type", None)
-
- if msg_type == "human":
- # ✅ 总是包含用户消息
- content = extract_text(msg)
- if content:
- context_parts.append(content)
- turn_count += 1
- if turn_count >= max_turns:
- break
-
- elif msg_type == "ai":
- # ✅ 只包含没有 tool_calls 的 AI 消息（最终回复）
- tool_calls = getattr(msg, "tool_calls", None)
- if not tool_calls:
- content = extract_text(msg)
- if content:
- context_parts.append(content)
-
- # ✅ 跳过 tool messages 和带 tool_calls 的 AI 消息
-
- return " ".join(reversed(context_parts))
-
-
-class MemoryMiddleware:
- def before_model(self, state, runtime):
- """在每次 LLM 调用前注入 memory（不是 before_agent）"""
-
- # 1. 提取最近 3 轮对话（过滤掉 tool calls）
- messages = state["messages"]
- conversation_context = _extract_conversation_context(messages, max_turns=3)
-
- # 2. 使用干净的对话上下文选择相关 facts
- memory_data = get_memory_data()
- memory_content = format_memory_for_injection(
- memory_data,
- max_tokens=config.max_injection_tokens,
- current_context=conversation_context, # ✅ 只包含真实对话内容
- )
-
- # 3. 作为 system message 注入到消息列表开头
- memory_message = SystemMessage(
- content=f"\n{memory_content}\n",
- name="memory_context", # 用于去重检测
- )
-
- # 4. 插入到消息列表开头
- updated_messages = [memory_message] + messages
- return {"messages": updated_messages}
+def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
```
-### 为什么这样设计？
+No `current_context` argument is currently available in `main`.
-基于你的三个重要观察：
+## Verification Pointers
-1. **应该用 `before_model` 而不是 `before_agent`**
- - ✅ `before_agent`: 只在整个 agent 开始时调用一次
- - ✅ `before_model`: 在**每次 LLM 调用前**都会调用
- - ✅ 这样每次 LLM 推理都能看到最新的相关 memory
-
-2. **messages 数组里只有 human/ai/tool，没有 system**
- - ✅ 虽然不常见，但 LangChain 允许在对话中插入 system message
- - ✅ Middleware 可以修改 messages 数组
- - ✅ 使用 `name="memory_context"` 防止重复注入
-
-3. **应该剔除 tool call 的 AI messages，只传用户输入和最终输出**
- - ✅ 过滤掉带 `tool_calls` 的 AI 消息（中间步骤）
- - ✅ 只保留： - Human 消息（用户输入）
- - AI 消息但无 tool_calls（最终回复）
- - ✅ 上下文更干净，TF-IDF 相似度计算更准确
-
-## 配置选项
-
-在 `config.yaml` 中可以调整：
-
-```yaml
-memory:
- enabled: true
- max_injection_tokens: 2000 # ✅ 使用精确 token 计数
-
- # 高级设置（可选）
- # max_context_turns: 3 # 对话轮数（默认 3）
- # similarity_weight: 0.6 # 相似度权重
- # confidence_weight: 0.4 # 置信度权重
-```
-
-## 依赖变更
-
-新增依赖：
-```toml
-dependencies = [
- "tiktoken>=0.8.0", # 精确 token 计数
- "scikit-learn>=1.6.1", # TF-IDF 向量化
-]
-```
-
-安装：
-```bash
-cd backend
-uv sync
-```
-
-## 性能影响
-
-- **TF-IDF 计算**：O(n × m)，n=facts 数量，m=词汇表大小
- - 典型场景（10-100 facts）：< 10ms
-- **Token 计数**：~100µs per call
- - 比字符计数还快
-- **总开销**：可忽略（相比 LLM 推理）
-
-## 向后兼容性
-
-✅ 完全向后兼容：
-- 如果没有 `current_context`，退化为按 confidence 排序
-- 所有现有配置继续工作
-- 不影响其他功能
-
-## 文件变更清单
-
-1. **核心功能**
- - `src/agents/memory/prompt.py` - 添加 TF-IDF 召回和精确 token 计数
- - `src/agents/lead_agent/prompt.py` - 动态系统提示
- - `src/agents/lead_agent/agent.py` - 传入函数而非字符串
-
-2. **依赖**
- - `pyproject.toml` - 添加 tiktoken 和 scikit-learn
-
-3. **文档**
- - `docs/MEMORY_IMPROVEMENTS.md` - 详细技术文档
- - `docs/MEMORY_IMPROVEMENTS_SUMMARY.md` - 改进总结（本文件）
- - `CLAUDE.md` - 更新架构说明
- - `config.example.yaml` - 添加配置说明
-
-## 测试验证
-
-运行项目验证：
-```bash
-cd backend
-make dev
-```
-
-在对话中测试：
-1. 讨论不同主题（Python、React、Docker 等）
-2. 观察不同对话注入的 facts 是否不同
-3. 检查 token 预算是否被准确控制
-
-## 总结
-
-| 问题 | 之前 | 现在 |
-|------|------|------|
-| Token 计算 | `len(text) // 4` (±25% 误差) | `tiktoken.encode()` (精确) |
-| Facts 选择 | 按 confidence 固定排序 | TF-IDF 相似度 + confidence |
-| 上下文 | 无 | 最近 3 轮对话 |
-| 实现方式 | 静态系统提示 | 动态系统提示函数 |
-| 配置灵活性 | 有限 | 可调轮数和权重 |
-
-所有改进都实现了，并且：
-- ✅ 不修改 messages 数组
-- ✅ 使用多轮对话上下文
-- ✅ 精确 token 计数
-- ✅ 智能相似度召回
-- ✅ 完全向后兼容
+- Implementation: `backend/src/agents/memory/prompt.py`
+- Prompt assembly: `backend/src/agents/lead_agent/prompt.py`
+- Regression tests: `backend/tests/test_memory_prompt_injection.py`
diff --git a/backend/src/agents/memory/prompt.py b/backend/src/agents/memory/prompt.py
index 4529156..9d6b1b0 100644
--- a/backend/src/agents/memory/prompt.py
+++ b/backend/src/agents/memory/prompt.py
@@ -1,5 +1,6 @@
"""Prompt templates for memory update and injection."""
+import math
import re
from typing import Any
@@ -166,6 +167,22 @@ def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
return len(text) // 4
+def _coerce_confidence(value: Any, default: float = 0.0) -> float:
+ """Coerce a confidence-like value to a bounded float in [0, 1].
+
+ Non-finite values (NaN, inf, -inf) are treated as invalid and fall back
+ to the default before clamping, preventing them from dominating ranking.
+ The ``default`` parameter is assumed to be a finite value.
+ """
+ try:
+ confidence = float(value)
+ except (TypeError, ValueError):
+ return max(0.0, min(1.0, default))
+ if not math.isfinite(confidence):
+ return max(0.0, min(1.0, default))
+ return max(0.0, min(1.0, confidence))
+
+
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
"""Format memory data for injection into system prompt.
@@ -217,6 +234,55 @@ def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2
if history_sections:
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
+ # Format facts (sorted by confidence; include as many as token budget allows)
+ facts_data = memory_data.get("facts", [])
+ if isinstance(facts_data, list) and facts_data:
+ ranked_facts = sorted(
+ (
+ f
+ for f in facts_data
+ if isinstance(f, dict)
+ and isinstance(f.get("content"), str)
+ and f.get("content").strip()
+ ),
+ key=lambda fact: _coerce_confidence(fact.get("confidence"), default=0.0),
+ reverse=True,
+ )
+
+ # Compute token count for existing sections once, then account
+ # incrementally for each fact line to avoid full-string re-tokenization.
+ base_text = "\n\n".join(sections)
+ base_tokens = _count_tokens(base_text) if base_text else 0
+ # Account for the separator between existing sections and the facts section.
+ facts_header = "Facts:\n"
+ separator_tokens = _count_tokens("\n\n" + facts_header) if base_text else _count_tokens(facts_header)
+ running_tokens = base_tokens + separator_tokens
+
+ fact_lines: list[str] = []
+ for fact in ranked_facts:
+ content_value = fact.get("content")
+ if not isinstance(content_value, str):
+ continue
+ content = content_value.strip()
+ if not content:
+ continue
+ category = str(fact.get("category", "context")).strip() or "context"
+ confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
+ line = f"- [{category} | {confidence:.2f}] {content}"
+
+ # Each additional line is preceded by a newline (except the first).
+ line_text = ("\n" + line) if fact_lines else line
+ line_tokens = _count_tokens(line_text)
+
+ if running_tokens + line_tokens <= max_tokens:
+ fact_lines.append(line)
+ running_tokens += line_tokens
+ else:
+ break
+
+ if fact_lines:
+ sections.append("Facts:\n" + "\n".join(fact_lines))
+
if not sections:
return ""
diff --git a/backend/tests/test_memory_prompt_injection.py b/backend/tests/test_memory_prompt_injection.py
new file mode 100644
index 0000000..d00bbd5
--- /dev/null
+++ b/backend/tests/test_memory_prompt_injection.py
@@ -0,0 +1,122 @@
+"""Tests for memory prompt injection formatting."""
+
+import math
+
+from src.agents.memory.prompt import _coerce_confidence, format_memory_for_injection
+
+
+def test_format_memory_includes_facts_section() -> None:
+ memory_data = {
+ "user": {},
+ "history": {},
+ "facts": [
+ {"content": "User uses PostgreSQL", "category": "knowledge", "confidence": 0.9},
+ {"content": "User prefers SQLAlchemy", "category": "preference", "confidence": 0.8},
+ ],
+ }
+
+ result = format_memory_for_injection(memory_data, max_tokens=2000)
+
+ assert "Facts:" in result
+ assert "User uses PostgreSQL" in result
+ assert "User prefers SQLAlchemy" in result
+
+
+def test_format_memory_sorts_facts_by_confidence_desc() -> None:
+ memory_data = {
+ "user": {},
+ "history": {},
+ "facts": [
+ {"content": "Low confidence fact", "category": "context", "confidence": 0.4},
+ {"content": "High confidence fact", "category": "knowledge", "confidence": 0.95},
+ ],
+ }
+
+ result = format_memory_for_injection(memory_data, max_tokens=2000)
+
+ assert result.index("High confidence fact") < result.index("Low confidence fact")
+
+
+def test_format_memory_respects_budget_when_adding_facts(monkeypatch) -> None:
+ # Make token counting deterministic for this test by counting characters.
+ monkeypatch.setattr("src.agents.memory.prompt._count_tokens", lambda text, encoding_name="cl100k_base": len(text))
+
+ memory_data = {
+ "user": {},
+ "history": {},
+ "facts": [
+ {"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
+ {"content": "Second fact should not fit in tiny budget", "category": "knowledge", "confidence": 0.90},
+ ],
+ }
+
+ first_fact_only_memory_data = {
+ "user": {},
+ "history": {},
+ "facts": [
+ {"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
+ ],
+ }
+ one_fact_result = format_memory_for_injection(first_fact_only_memory_data, max_tokens=2000)
+ two_facts_result = format_memory_for_injection(memory_data, max_tokens=2000)
+ # Choose a budget that can include exactly one fact section line.
+ max_tokens = (len(one_fact_result) + len(two_facts_result)) // 2
+
+ first_only_result = format_memory_for_injection(memory_data, max_tokens=max_tokens)
+
+ assert "First fact should fit" in first_only_result
+ assert "Second fact should not fit in tiny budget" not in first_only_result
+
+
+def test_coerce_confidence_nan_falls_back_to_default() -> None:
+ """NaN should not be treated as a valid confidence value."""
+ result = _coerce_confidence(math.nan, default=0.5)
+ assert result == 0.5
+
+
+def test_coerce_confidence_inf_falls_back_to_default() -> None:
+ """Infinite values should fall back to default rather than clamping to 1.0."""
+ assert _coerce_confidence(math.inf, default=0.3) == 0.3
+ assert _coerce_confidence(-math.inf, default=0.3) == 0.3
+
+
+def test_coerce_confidence_valid_values_are_clamped() -> None:
+ """Valid floats outside [0, 1] are clamped; values inside are preserved."""
+ assert _coerce_confidence(1.5) == 1.0
+ assert _coerce_confidence(-0.5) == 0.0
+ assert abs(_coerce_confidence(0.75) - 0.75) < 1e-9
+
+
+def test_format_memory_skips_none_content_facts() -> None:
+ """Facts with content=None must not produce a 'None' line in the output."""
+ memory_data = {
+ "facts": [
+ {"content": None, "category": "knowledge", "confidence": 0.9},
+ {"content": "Real fact", "category": "knowledge", "confidence": 0.8},
+ ],
+ }
+
+ result = format_memory_for_injection(memory_data, max_tokens=2000)
+
+ assert "None" not in result
+ assert "Real fact" in result
+
+
+def test_format_memory_skips_non_string_content_facts() -> None:
+ """Facts with non-string content (e.g. int/list) must be ignored."""
+ memory_data = {
+ "facts": [
+ {"content": 42, "category": "knowledge", "confidence": 0.9},
+ {"content": ["list"], "category": "knowledge", "confidence": 0.85},
+ {"content": "Valid fact", "category": "knowledge", "confidence": 0.7},
+ ],
+ }
+
+ result = format_memory_for_injection(memory_data, max_tokens=2000)
+
+ # The formatted line for an integer content would be "- [knowledge | 0.90] 42".
+ assert "| 0.90] 42" not in result
+ # The formatted line for a list content would be "- [knowledge | 0.85] ['list']".
+ assert "| 0.85]" not in result
+ assert "Valid fact" in result
+