recollect 0.10.0

Human-like memory for AI applications

recollect

Persistent memory SDK for LLM agents. See the project README for architecture and scoring details.

Install

pip install recollect    # or: uv add recollect

Quick Start

import asyncio
from recollect import CognitiveMemory

async def main():
    memory = CognitiveMemory()
    await memory.connect()

    await memory.experience(
        "The team decided to migrate from Redis to PostgreSQL for persistence."
    )

    thoughts = await memory.think_about("database decisions", token_budget=500)
    for thought in thoughts:
        print(f"[{thought.relevance:.2f}] {thought.reconstruction}")

    await memory.close()

asyncio.run(main())

API

Method	Description
connect(db_url=None)	Connect to PostgreSQL. Uses DATABASE_URL env var if no argument.
experience(content)	Store a memory trace. LLM extracts entities, concepts, significance.
think_about(query, token_budget)	Retrieve memories that fit within a token limit. Returns list[Thought].
consolidate(threshold=None)	Decay, consolidate, or archive traces past their grace period.
forget(trace_id, force=False)	Archive a trace and its derived facts. Safety-critical and pinned facts are retained unless force=True. Returns ForgetResult with per-fact dispositions.
erase(trace_id)	Hard-delete a trace and its derived rows. The escape hatch; forget() is the normal path.
reinforce(trace_id, factor=1.1)	Strengthen a trace.
pin(trace_id)	Promote a trace's extracted relations to permanent persona facts. Returns the promoted facts.
unpin(fact_id)	Archive a persona fact. It stops surfacing but is retained.
facts(subject=None)	List persona facts.
start_session(user_id)	Begin a scoped session.
close()	Disconnect and release resources.

Memory lifecycle

Forgetting is reversible. Weak traces past their grace period are archived, not deleted -- the trace and everything derived from it (facts, concept embeddings, situational tokens) survive as substrate. When an archived trace becomes relevant to a query again, it revives automatically: status flips back to active, strength resets to its significance, and the normal reinforcement loop takes over. Memories fade when unused and return when they matter. erase() is the only true deletion.

Environment Variables

Variable	Required	Default	Description
DATABASE_URL	Yes	postgresql://localhost:5432/memory_sdk	PostgreSQL connection string.
PYDANTIC_AI_MODEL	No	--	pydantic-ai model string in provider:model format (e.g., ollama:ministral-3, anthropic:claude-haiku-4-5-20251001).
ANTHROPIC_API_KEY	For Anthropic models	--	Anthropic API key. Read by pydantic-ai's Anthropic backend.
OPENAI_API_KEY	For OpenAI models	--	OpenAI API key. Read by pydantic-ai's OpenAI backend.
OLLAMA_BASE_URL	No	http://localhost:11434/v1	Ollama API endpoint.
MEMORY_EXTRACTION_MAX_TOKENS	No	8192	Max tokens for LLM extraction. Reasoning models consume thinking tokens before output; 8192 covers most cases.
MEMORY_CONFIG	No	--	Path to custom TOML config file.
MEMORY_EXTRACTION_INSTRUCTIONS	No	--	Override extraction prompt instructions (inline string).
MEMORY_EXTRACTION_TEMPLATE_PATH	No	--	Path to override extraction prompt (markdown with version / applies-to / placeholders header schema).
MEMORY_RECALL_TOKENS_ENABLED	No	true	Enable write-time token stamping and query-time activation.
MEMORY_RECALL_TOKENS_TOP_K	No	5	Max related traces to consider for token assessment.
MEMORY_RECALL_TOKENS_THRESHOLD	No	0.42	Min cosine similarity to consider a trace as related at write time.
MEMORY_RECALL_TOKENS_STRENGTH_THRESHOLD	No	0.1	Min token strength to activate at query time.
MEMORY_RECALL_TOKENS_REINFORCE_BOOST	No	0.1	Strength increment on token activation (capped at 1.0).
MEMORY_RECALL_TOKENS_DECAY_FACTOR	No	0.9	Multiply inactive token strength by this during consolidation.
MEMORY_RECALL_TOKENS_HOP_DECAY	No	0.85	Signal attenuation per token hop during query-time propagation.
MEMORY_RECALL_TOKENS_PROPAGATION_BLEND	No	0.5	Weight of propagated signal in the additive blend.
MEMORY_RECALL_TOKENS_MAX_ROUNDS	No	3	Max re-seeding iterations at query time.
MEMORY_RECALL_TOKENS_STABILITY_THRESHOLD	No	0.95	Top-K overlap fraction to stop re-seeding early.
MEMORY_RECALL_TOKENS_TOP_SEEDS	No	3	Token-discovered traces used as seeds per re-seeding round.
MEMORY_RECALL_TOKENS_SYSTEM_PROMPT	No	--	Override situational-assessment system prompt (inline string).
MEMORY_RECALL_TOKENS_USER_PROMPT	No	--	Override situational-assessment user prompt (inline string).

Configuration

[memory]
decay_rate = 0.05

[retrieval]
max_retrievals = 10

[extraction]
pydantic_ai_model = "ollama:ministral-3"   # pydantic-ai provider:model format

Config sections

Section	Controls	Key parameters
[database]	PostgreSQL connection	url
[memory]	Core memory model	initial_strength, consolidation_threshold, decay_rate
[working_memory]	Working memory capacity	capacity (default 7, range 5-9)
[retrieval]	Retrieval pipeline tuning	max_retrievals, search_limit, selection_threshold, reactivation_floor
[extraction]	LLM extraction	max_tokens, max_concepts, max_relations, pydantic_ai_model, template_path, embed_relation_tags
[extraction.model_settings]	Provider-specific settings forwarded to pydantic-ai	openrouter_reasoning, anthropic_thinking_budget, thinking, top_p
[embedding]	Local embedding model	model, dimensions
[persona]	Persona fact management	auto_extract, confidence_threshold, ranking_strategy, max_facts_per_query
[recall_tokens]	Situational grouping at write + propagation at read	enabled, assessment_max_tokens, assessment_template_path, plus strength / decay / propagation knobs (env-var-exposed above)
[session]	Session summaries	summary_strength, summary_max_tokens

Full defaults: config.toml

Config is layered: the packaged defaults always load first, then your TOML file overrides individual keys (explicit config_path wins over MEMORY_CONFIG, which wins over ./memory.toml in the working directory), then environment variables override everything. Your file only needs the keys you change.

from recollect.config import MemoryConfig

config = MemoryConfig(config_path=Path("./my-config.toml"))
memory = CognitiveMemory(config=config)

LLM Provider

from recollect.llm.pydantic_ai import PydanticAIProvider

# Model configured via PYDANTIC_AI_MODEL env var, or pass explicitly:
provider = PydanticAIProvider()  # uses PYDANTIC_AI_MODEL
provider = PydanticAIProvider(model="anthropic:claude-sonnet-4-6")
provider = PydanticAIProvider(model="ollama:llama3")

Reasoning models

Models that use internal chain-of-thought (OpenAI o1/o3, Qwen3, DeepSeek-R1) consume thinking tokens from the max_tokens budget. If extraction returns empty responses, increase the token budget:

# memory.toml
[extraction]
max_tokens = 8192

The default is 8192 to accommodate thinking tokens. Non-reasoning models work fine at this budget; no need to reduce it.

Requirements

• Python 3.12+
• PostgreSQL 17 with pgvector
• DATABASE_URL environment variable

License

MIT