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agent-observability
Reproduce any agent failure without paying for it again
A LangGraph agent fails after 8 calls to the model, and reproducing it costs 15 cents each time. Agent Observability records every network call once, then replays it in under a millisecond, no API calls needed. Free and open source under Apache 2.0.
A LangGraph agent fails on step 8. The trace shows exactly what broke. To reproduce it, you have to run the whole thing again: eight more calls to the model, about 30 seconds, and another 15 cents in API costs. If the failure came from the model giving an unusual response, you may not be able to reproduce it at all. You end up debugging something that will not hold still.
We built Agent Observability to fix that. It is a Python library and command-line tool that records every network request your agent makes to a small local file, then replays those exact bytes offline, in about 0.93 milliseconds on average, compared to roughly 8,500 milliseconds for a real run against GPT-4o across 10 steps. Recording adds about 0.011% overhead, roughly 0.09 milliseconds per call to the model. The replayed result is identical, byte for byte, to what was recorded. You record once, and you can replay as many times as you want, for free.
pip install agent-observability-trace-cli
Record a run with @tracer.instrument(record=True) around the call you want captured. Replay it with the replay() context manager, same code path, zero network, identical result. In CI, set AGENT_TRACE_NETWORK_GUARD=1 so any test that tries to make a real network call fails loudly right away, instead of quietly using up your API budget.
On how it compares to other tools: LangSmith is a strong choice if your team already uses LangChain and needs dataset management, prompt versioning, and human feedback in one place, and its LANGSMITH_TEST_CACHE feature gives you partial offline replay. The gap is that it only works with LangChain, requires a LangSmith account, and only captures calls to api.openai.com, not other HTTP clients or the full request and response data. Langfuse is a genuinely good choice if you want a fully open-source, self-hostable observability tool with strong database storage. Its gap is that it has no offline replay at all.
Agent Observability is not meant to replace dashboards or evaluation tools. It solves one specific, earlier problem: reproducing a failed run without spending any more money on the model API, for any agent built on any Python HTTP client.
The project is currently in beta, so treat these limitations as real and current. It only sees traffic from your own program, so it cannot observe calls made by a separate hosted service you do not run yourself. Our support for gRPC is only partial: the common request-and-response pattern that Gemini and Vertex AI use is fully captured, but client-streaming, bidirectional-streaming, and the newer async streaming style are not. Recording only starts once an actual request exists, so an error raised earlier, during SDK setup for example, produces no recording at all.
Agent Observability is part of a small set of open-source tools we are building to cover observability, testing, and benchmarking for production AI agents: agent-eval for statistical regression testing of agent behavior, memtrust as an independent benchmark for agent memory backends, and agentmemory for persistent memory in coding agents. We are building this piece by piece, because we think this whole layer is currently missing for teams running agents in production.
Star the repo: https://github.com/RudrenduPaul/... Install: pip install agent-observability-trace-cli Built by Rudrendu Paul and Sourav Nandy.
About agent-observability on Product Hunt
“Reproduce any agent failure without paying for it again”
agent-observability was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #28 on the daily leaderboard. A LangGraph agent fails after 8 calls to the model, and reproducing it costs 15 cents each time. Agent Observability records every network call once, then replays it in under a millisecond, no API calls needed. Free and open source under Apache 2.0.
On the analytics side, agent-observability competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how agent-observability performed against the three products that launched closest to it on the same day.
Who hunted agent-observability?
agent-observability was hunted by Sourav Nandy. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of agent-observability including community comment highlights and product details, visit the product overview.
Sourav Nandy
A LangGraph agent fails on step 8. The trace shows exactly what broke. To reproduce it, you have to run the whole thing again: eight more calls to the model, about 30 seconds, and another 15 cents in API costs. If the failure came from the model giving an unusual response, you may not be able to reproduce it at all. You end up debugging something that will not hold still.
We built Agent Observability to fix that. It is a Python library and command-line tool that records every network request your agent makes to a small local file, then replays those exact bytes offline, in about 0.93 milliseconds on average, compared to roughly 8,500 milliseconds for a real run against GPT-4o across 10 steps. Recording adds about 0.011% overhead, roughly 0.09 milliseconds per call to the model. The replayed result is identical, byte for byte, to what was recorded. You record once, and you can replay as many times as you want, for free.
pip install agent-observability-trace-cli
Record a run with @tracer.instrument(record=True) around the call you want captured. Replay it with the replay() context manager, same code path, zero network, identical result. In CI, set AGENT_TRACE_NETWORK_GUARD=1 so any test that tries to make a real network call fails loudly right away, instead of quietly using up your API budget.
On how it compares to other tools: LangSmith is a strong choice if your team already uses LangChain and needs dataset management, prompt versioning, and human feedback in one place, and its LANGSMITH_TEST_CACHE feature gives you partial offline replay. The gap is that it only works with LangChain, requires a LangSmith account, and only captures calls to api.openai.com, not other HTTP clients or the full request and response data. Langfuse is a genuinely good choice if you want a fully open-source, self-hostable observability tool with strong database storage. Its gap is that it has no offline replay at all.
Agent Observability is not meant to replace dashboards or evaluation tools. It solves one specific, earlier problem: reproducing a failed run without spending any more money on the model API, for any agent built on any Python HTTP client.
The project is currently in beta, so treat these limitations as real and current. It only sees traffic from your own program, so it cannot observe calls made by a separate hosted service you do not run yourself. Our support for gRPC is only partial: the common request-and-response pattern that Gemini and Vertex AI use is fully captured, but client-streaming, bidirectional-streaming, and the newer async streaming style are not. Recording only starts once an actual request exists, so an error raised earlier, during SDK setup for example, produces no recording at all.
Agent Observability is part of a small set of open-source tools we are building to cover observability, testing, and benchmarking for production AI agents: agent-eval for statistical regression testing of agent behavior, memtrust as an independent benchmark for agent memory backends, and agentmemory for persistent memory in coding agents. We are building this piece by piece, because we think this whole layer is currently missing for teams running agents in production.
Star the repo: https://github.com/RudrenduPaul/...
Install: pip install agent-observability-trace-cli
Built by Rudrendu Paul and Sourav Nandy.