Memory Module

Memory module.

class atloop.memory.AgentState(step: int = 0, phase: str = 'DISCOVER', last_error: ~atloop.memory.state.LastError = <factory>, memory: ~atloop.memory.state.Memory = <factory>, artifacts: ~atloop.memory.state.Artifacts = <factory>, budget_used: ~atloop.memory.state.BudgetUsed = <factory>)

Bases: object

Agent execution state.

__init__(step: int = 0, phase: str = 'DISCOVER', last_error: ~atloop.memory.state.LastError = <factory>, memory: ~atloop.memory.state.Memory = <factory>, artifacts: ~atloop.memory.state.Artifacts = <factory>, budget_used: ~atloop.memory.state.BudgetUsed = <factory>) → None

classmethod from_dict(data: Dict[str, Any]) → AgentState

Create from dictionary.

phase: str = 'DISCOVER'

step: int = 0

to_dict() → Dict[str, Any]

Convert to dictionary.

last_error: LastError

memory: Memory

artifacts: Artifacts

budget_used: BudgetUsed

class atloop.memory.LastError(summary: str = '', repro_cmd: str = '', raw_stderr_tail: str = '', error_signature: str = '')

Bases: object

Last error information.

__init__(summary: str = '', repro_cmd: str = '', raw_stderr_tail: str = '', error_signature: str = '') → None

error_signature: str = ''

raw_stderr_tail: str = ''

repro_cmd: str = ''

summary: str = ''

class atloop.memory.Memory(decisions: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, attempts: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, key_files: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, notes: ~typing.List[str] = <factory>, created_files: ~typing.List[str] = <factory>, plan: str | ~typing.List[~typing.Any] = <factory>, task_summary: str = '', important_decisions: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, milestones: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, learnings: ~typing.List[str] = <factory>, llm_responses: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, tool_results_history: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, modified_files_content: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>)

Bases: object

Memory for tracking decisions and attempts.

__init__(decisions: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, attempts: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, key_files: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, notes: ~typing.List[str] = <factory>, created_files: ~typing.List[str] = <factory>, plan: str | ~typing.List[~typing.Any] = <factory>, task_summary: str = '', important_decisions: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, milestones: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, learnings: ~typing.List[str] = <factory>, llm_responses: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, tool_results_history: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, modified_files_content: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>) → None

task_summary: str = ''

decisions: List[Dict[str, Any]]

attempts: List[Dict[str, Any]]

key_files: List[Dict[str, Any]]

notes: List[str]

created_files: List[str]

plan: str | List[Any]

important_decisions: List[Dict[str, Any]]

milestones: List[Dict[str, Any]]

learnings: List[str]

llm_responses: List[Dict[str, Any]]

tool_results_history: List[Dict[str, Any]]

modified_files_content: List[Dict[str, Any]]

class atloop.memory.Artifacts(current_diff: str = '', test_results: str = '', verification_success: bool | None = None, dod_result: Any | None = None)

Bases: object

Artifacts produced during execution.

__init__(current_diff: str = '', test_results: str = '', verification_success: bool | None = None, dod_result: Any | None = None) → None

current_diff: str = ''

dod_result: Any | None = None

test_results: str = ''

verification_success: bool | None = None

class atloop.memory.BudgetUsed(llm_calls: int = 0, tool_calls: int = 0, wall_time_sec: int = 0)

Bases: object

Budget usage tracking.

__init__(llm_calls: int = 0, tool_calls: int = 0, wall_time_sec: int = 0) → None

llm_calls: int = 0

tool_calls: int = 0

wall_time_sec: int = 0

class atloop.memory.MemorySummarizer

Bases: object

Summarize agent memory for LLM input.

static get_memory_overview(state: AgentState) → str

Get a brief overview of memory for terminal output.

Parameters:

state – Agent state

Returns:

Brief overview string (single line, compact format)

static summarize(state: AgentState, max_length: int = 64000, task_goal: str | None = None) → str

Summarize agent state memory.

Parameters:

• state – Agent state

• max_length – Maximum summary length

• task_goal – Optional task goal for completion detection

Returns:

Summary string

MemoryManager

class atloop.memory.memory_manager.MemoryManager

Bases: object

Manager for dynamic memory updates.

static update_plan(state: AgentState, plan: str, reason: str | None = None) → None

Update the execution plan in long-term memory.

Parameters:

• state – Agent state

• plan – New plan text

• reason – Optional reason for the update

static add_important_decision(state: AgentState, content: str, step: int | None = None, context: Dict[str, Any] | None = None) → None

Add an important decision to long-term memory.

Parameters:

• state – Agent state

• content – Decision content

• step – Step number (defaults to current step)

• context – Optional context information

static add_milestone(state: AgentState, content: str, step: int | None = None, context: Dict[str, Any] | None = None) → None

Add a milestone to long-term memory.

Parameters:

• state – Agent state

• content – Milestone content

• step – Step number (defaults to current step)

• context – Optional context information

static add_learning(state: AgentState, learning: str, step: int | None = None) → None

Add a learning to long-term memory.

Parameters:

• state – Agent state

• learning – Learning content

• step – Step number (defaults to current step)

static update_task_summary(state: AgentState, summary: str) → None

Update task summary in long-term memory.

Parameters:

• state – Agent state

• summary – Task summary text

static get_long_term_memory_summary(state: AgentState) → str

Get a summary of long-term memory.

Parameters:

state – Agent state

Returns:

Summary string

MemorySummarizer

class atloop.memory.summarizer.MemorySummarizer

Bases: object

Summarize agent memory for LLM input.

static get_memory_overview(state: AgentState) → str

Get a brief overview of memory for terminal output.

Parameters:

state – Agent state

Returns:

Brief overview string (single line, compact format)

static summarize(state: AgentState, max_length: int = 64000, task_goal: str | None = None) → str

Summarize agent state memory.

Parameters:

• state – Agent state

• max_length – Maximum summary length

• task_goal – Optional task goal for completion detection

Returns:

Summary string

MemoryCompressor

class atloop.memory.compressor.MemoryCompressor

Bases: object

Compress old memory to prevent unbounded growth.

ATTEMPTS_KEEP_RECENT = 10

DECISIONS_KEEP_RECENT = 5

IMPORTANT_DECISIONS_KEEP = 20

MILESTONES_KEEP = 20

LEARNINGS_KEEP = 10

static compress_if_needed(state: AgentState, memory_config=None, llm_client=None) → bool

Compress memory if it exceeds limits.

Phase 4: Enhanced with LLM compression and deduplication.

Parameters:

• state – Agent state

• memory_config – Optional MemoryConfig instance (if None, uses defaults)

• llm_client – Optional LLMClient for LLM compression (if None, LLM compression is skipped)

Returns:

True if compression was performed, False otherwise

AgentState

class atloop.memory.state.AgentState(step: int = 0, phase: str = 'DISCOVER', last_error: ~atloop.memory.state.LastError = <factory>, memory: ~atloop.memory.state.Memory = <factory>, artifacts: ~atloop.memory.state.Artifacts = <factory>, budget_used: ~atloop.memory.state.BudgetUsed = <factory>)

Bases: object

Agent execution state.

step: int = 0

phase: str = 'DISCOVER'

last_error: LastError

memory: Memory

artifacts: Artifacts

budget_used: BudgetUsed

to_dict() → Dict[str, Any]

Convert to dictionary.

__init__(step: int = 0, phase: str = 'DISCOVER', last_error: ~atloop.memory.state.LastError = <factory>, memory: ~atloop.memory.state.Memory = <factory>, artifacts: ~atloop.memory.state.Artifacts = <factory>, budget_used: ~atloop.memory.state.BudgetUsed = <factory>) → None

classmethod from_dict(data: Dict[str, Any]) → AgentState

Create from dictionary.