* fix: resolve all Dependabot security vulnerabilities
npm (package-lock.json):
- fast-xml-parser: 4.5.3 → 4.5.4 (critical entity encoding bypass + DoS)
- serialize-javascript: 6.0.2 → 7.0.4 (high RCE via RegExp/Date)
- minimatch: 3.1.2 → 3.1.5, 5.1.6 → 5.1.9, 9.0.5 → 9.0.9 (high ReDoS)
- ajv: 6.12.6 → 6.14.0, 8.17.1 → 8.18.0 (medium ReDoS with $data option)
- qs: 6.14.1 → 6.15.0 (low arrayLimit bypass DoS)
- rollup: 4.57.x → 4.59.0 in ai-sdk and openclaw integrations (high path traversal)
Python (uv.lock / pyproject.toml):
- cryptography: 46.0.3 → 46.0.5 (high subgroup attack on SECT curves)
- pillow: 12.0.0 → 12.1.1 (high out-of-bounds write in PSD loading)
- langchain-core: 1.2.7 → 1.2.17 (low SSRF in ChatOpenAI token counting)
- langsmith: 0.4.42 → 0.7.11 (medium SSRF via tracing header injection)
- protobuf: 6.33.1 → 6.33.5 (high JSON recursion depth bypass)
Rust (Cargo.lock):
- bytes: 1.11.0 → 1.11.1 in hindsight-clients/rust (medium integer overflow)
Remaining unfixable: diskcache <= 5.6.3 (no patched version available)
* fix: remove over-broad schema-utils ajv override that broke docs build
The 'schema-utils': {'ajv': '^8.18.0'} override was forcing schema-utils@3.x
(used by url-loader/file-loader with ajv-keywords@3.x) to use ajv@8.18.0.
In 8.18.0, internal property _formats was renamed to formats, breaking
ajv-keywords@3.x's _formatLimit.js which accesses ajv._formats.date.
Removing the broad override: schema-utils@4.3.3 (root level) already has
ajv@8.18.0 in its nested install from the prior npm update, while
schema-utils@3.x correctly falls back to the hoisted root ajv@6.14.0.
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| .. | ||
| hindsight_crewai | ||
| tests | ||
| pyproject.toml | ||
| README.md | ||
| test_manual.py | ||
| uv.lock | ||
hindsight-crewai
Persistent memory for AI agent crews via Hindsight. Give your CrewAI crews long-term memory with fact extraction, entity tracking, and temporal awareness.
Features
- Drop-in Storage Backend - Implements CrewAI's
Storageinterface forExternalMemory - Automatic Memory Flow - CrewAI automatically stores task outputs and retrieves relevant memories
- Per-Agent Banks - Optionally give each agent its own isolated memory bank
- Reflect Tool - Agents can explicitly reason over memories with disposition-aware synthesis
- Simple Configuration - Configure once, use everywhere
Installation
pip install hindsight-crewai
Quick Start
from hindsight_crewai import configure, HindsightStorage
from crewai.memory.external.external_memory import ExternalMemory
from crewai import Agent, Crew, Task
# Step 1: Configure connection
configure(hindsight_api_url="http://localhost:8888")
# Step 2: Create crew with Hindsight-backed memory
crew = Crew(
agents=[
Agent(role="Researcher", goal="Find information", backstory="..."),
Agent(role="Writer", goal="Write reports", backstory="..."),
],
tasks=[
Task(description="Research AI trends", expected_output="Report"),
],
external_memory=ExternalMemory(
storage=HindsightStorage(bank_id="my-crew")
),
)
crew.kickoff()
That's it. CrewAI will automatically:
- Query memories at the start of each task
- Store task outputs to Hindsight after each task completes
Memories persist across crew runs, so your crew learns over time.
Per-Agent Memory Banks
Give each agent its own isolated memory bank:
storage = HindsightStorage(
bank_id="my-crew",
per_agent_banks=True, # Researcher -> "my-crew-researcher", Writer -> "my-crew-writer"
)
Or use a custom bank resolver for full control:
storage = HindsightStorage(
bank_id="my-crew",
bank_resolver=lambda base, agent: f"{base}-{agent.lower()}" if agent else base,
)
Reflect Tool
CrewAI's storage interface only supports save/search/reset. To give agents access to Hindsight's reflect (disposition-aware memory synthesis), add it as a tool:
from hindsight_crewai import HindsightReflectTool
reflect_tool = HindsightReflectTool(
bank_id="my-crew",
budget="mid",
reflect_context="You are helping a software team track decisions.",
)
agent = Agent(
role="Analyst",
goal="Analyze project history",
backstory="...",
tools=[reflect_tool],
)
When the agent calls this tool, it gets a synthesized, contextual answer based on all relevant memories — not just raw facts.
Bank Missions
Set a mission to guide how Hindsight processes and organizes memories:
storage = HindsightStorage(
bank_id="my-crew",
mission="Track software architecture decisions, technical debt, and team preferences.",
)
Configuration
Global Configuration
from hindsight_crewai import configure
configure(
hindsight_api_url="http://localhost:8888", # Default: production API
api_key="your-api-key", # Or set HINDSIGHT_API_KEY env var
budget="mid", # Recall budget: low/mid/high
max_tokens=4096, # Max tokens for recall results
tags=["env:prod"], # Tags for stored memories
recall_tags=["scope:global"], # Tags to filter recall
recall_tags_match="any", # Tag match mode: any/all/any_strict/all_strict
verbose=True, # Enable logging
)
Per-Storage Overrides
Constructor arguments override global configuration:
storage = HindsightStorage(
bank_id="my-crew",
budget="high", # Override global budget
max_tokens=8192, # Override global max_tokens
tags=["team:alpha"], # Override global tags
)
Examples
See the CrewAI memory example in the Hindsight Cookbook for a complete working demo with a Researcher + Writer crew.
Configuration Reference
| Parameter | Default | Description |
|---|---|---|
hindsight_api_url |
Production API | Hindsight API URL |
api_key |
HINDSIGHT_API_KEY env |
API key for authentication |
budget |
"mid" |
Recall budget level (low/mid/high) |
max_tokens |
4096 |
Maximum tokens for recall results |
tags |
None |
Tags applied when storing memories |
recall_tags |
None |
Tags to filter when searching |
recall_tags_match |
"any" |
Tag matching mode |
per_agent_banks |
False |
Give each agent its own bank |
bank_resolver |
None |
Custom (bank_id, agent) -> bank_id function |
mission |
None |
Bank mission for memory organization |
verbose |
False |
Enable verbose logging |