Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Hindsight

Long-term memory for AI agents.

Why Hindsight?

AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.

The problem is harder than it looks:

Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
Context matters — The same information means different things to different memory banks with different personalities

Hindsight solves these problems with a memory system designed specifically for AI memory banks.

Quick Start

Option 1: Docker (recommended)

Get the full experience with the API and Control Plane UI:

export OPENAI_API_KEY=your-key
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight

API: http://localhost:8888
Control Plane UI: http://localhost:9999

Then use the Python client:

pip install hindsight-client

fromhindsightimportHindsightClientclient=HindsightClient(base_url="http://localhost:8888")
# Store memoriesclient.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
client.retain(bank_id="my-agent", content="Alice mentioned she loves hiking in the mountains")
# Query with temporal reasoningresults=client.recall(bank_id="my-agent", query="What does Alice do for work?")
# Get a synthesized perspectiveresponse=client.reflect(bank_id="my-agent", query="Tell me about Alice")
print(response.text)

Option 2: Embedded (no docker/server required)

For quick prototyping, run everything in-process:

pip install hindsight-all
export OPENAI_API_KEY=your-key

importosfromhindsightimportHindsightServer, HindsightClientwithHindsightServer(llm_provider="openai", llm_model="gpt-4o-mini", llm_api_key=os.environ["OPENAI_API_KEY"]) asserver:
client=HindsightClient(base_url=server.url)
client.retain(bank_id="my-user", content="User prefers functional programming")
response=client.reflect(bank_id="my-user", query="What coding style should I use?")
print(response.text)

Documentation

Full documentation: vectorize-io.github.io/hindsight

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cookbook		cookbook
docker/standalone		docker/standalone
helm		helm
hindsight-api		hindsight-api
hindsight-cli		hindsight-cli
hindsight-clients		hindsight-clients
hindsight-control-plane		hindsight-control-plane
hindsight-dev		hindsight-dev
hindsight-docs		hindsight-docs
hindsight-integrations		hindsight-integrations
hindsight		hindsight
scripts		scripts
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.sesskey		.sesskey
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
openapi.json		openapi.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cookbook		cookbook
docker/standalone		docker/standalone
helm		helm
hindsight-api		hindsight-api
hindsight-cli		hindsight-cli
hindsight-clients		hindsight-clients
hindsight-control-plane		hindsight-control-plane
hindsight-dev		hindsight-dev
hindsight-docs		hindsight-docs
hindsight-integrations		hindsight-integrations
hindsight		hindsight
scripts		scripts
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.sesskey		.sesskey
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
openapi.json		openapi.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cookbook		cookbook
docker/standalone		docker/standalone
helm		helm
hindsight-api		hindsight-api
hindsight-cli		hindsight-cli
hindsight-clients		hindsight-clients
hindsight-control-plane		hindsight-control-plane
hindsight-dev		hindsight-dev
hindsight-docs		hindsight-docs
hindsight-integrations		hindsight-integrations
hindsight		hindsight
scripts		scripts
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.sesskey		.sesskey
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
openapi.json		openapi.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cookbook		cookbook
docker/standalone		docker/standalone
helm		helm
hindsight-api		hindsight-api
hindsight-cli		hindsight-cli
hindsight-clients		hindsight-clients
hindsight-control-plane		hindsight-control-plane
hindsight-dev		hindsight-dev
hindsight-docs		hindsight-docs
hindsight-integrations		hindsight-integrations
hindsight		hindsight
scripts		scripts
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.sesskey		.sesskey
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
openapi.json		openapi.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cookbook		cookbook
docker/standalone		docker/standalone
helm		helm
hindsight-api		hindsight-api
hindsight-cli		hindsight-cli
hindsight-clients		hindsight-clients
hindsight-control-plane		hindsight-control-plane
hindsight-dev		hindsight-dev
hindsight-docs		hindsight-docs
hindsight-integrations		hindsight-integrations
hindsight		hindsight
scripts		scripts
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
.sesskey		.sesskey
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
openapi.json		openapi.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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Option 1: Docker (recommended)

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Hindsight

Why Hindsight?

Quick Start

Option 1: Docker (recommended)

Option 2: Embedded (no docker/server required)

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Hindsight

Why Hindsight?

Quick Start

Option 1: Docker (recommended)

Option 2: Embedded (no docker/server required)

Documentation

Contributing

License

About

Resources

Code of conduct

Contributing

Security policy

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Hindsight

Why Hindsight?

Quick Start

Option 1: Docker (recommended)